diff --git a/.clang-format b/.clang-format new file mode 100644 index 000000000..45232b80e --- /dev/null +++ b/.clang-format @@ -0,0 +1,161 @@ +--- +Language: Cpp +AlignAfterOpenBracket: Align +AlignArrayOfStructures: Left +AlignConsecutiveAssignments: AcrossComments +AlignConsecutiveBitFields: AcrossComments +AlignConsecutiveDeclarations: AcrossComments +AlignConsecutiveMacros: AcrossComments +# AlignConsecutiveShortCaseStatements: AcrossComments +AlignEscapedNewlines: Left # LeftWithLastLine +AlignOperands: Align +AlignTrailingComments: + Kind: Always + OverEmptyLines: 1 +AllowAllArgumentsOnNextLine: true +AllowAllParametersOfDeclarationOnNextLine: false +# AllowBreakBeforeNoexceptSpecifier: OnlyWithParen +AllowShortBlocksOnASingleLine: Never +AllowShortCaseLabelsOnASingleLine: false +AllowShortFunctionsOnASingleLine: Inline +AllowShortIfStatementsOnASingleLine: Never +AllowShortLambdasOnASingleLine: Inline +AllowShortLoopsOnASingleLine: false +AlwaysBreakBeforeMultilineStrings: true +BinPackArguments: true +BinPackParameters: true # OnePerLine +BitFieldColonSpacing: Both +BreakBeforeBraces: Custom # Attach +BraceWrapping: + AfterCaseLabel: true + AfterClass: false + AfterControlStatement: false + AfterEnum: false + AfterFunction: false + AfterNamespace: false + AfterObjCDeclaration: false + AfterStruct: false + AfterUnion: false + AfterExternBlock: false + BeforeCatch: false + BeforeElse: false + BeforeLambdaBody: false + BeforeWhile: false + IndentBraces: false + SplitEmptyFunction: false + SplitEmptyRecord: false + SplitEmptyNamespace: false +# BreakAdjacentStringLiterals: true +BreakAfterAttributes: Never +BreakBeforeBinaryOperators: None +BreakBeforeInlineASMColon: OnlyMultiline +BreakBeforeTernaryOperators: false +# BreakBinaryOperations: Never +BreakConstructorInitializers: AfterColon +# BreakFunctionDefinitionParameters: false +BreakInheritanceList: AfterComma +BreakStringLiterals: true +# BreakTemplateDeclarations: Yes +ColumnLimit: 120 +CommentPragmas: '^ IWYU pragma:' +CompactNamespaces: false +ConstructorInitializerIndentWidth: 4 +ContinuationIndentWidth: 4 +Cpp11BracedListStyle: false +DerivePointerAlignment: false +DisableFormat: false +EmptyLineBeforeAccessModifier: Leave +EmptyLineAfterAccessModifier: Never +ExperimentalAutoDetectBinPacking: false +FixNamespaceComments: true +IncludeBlocks: Regroup +IncludeCategories: + - Regex: '^<.*\.h>' + Priority: 1 + SortPriority: 0 + - Regex: '^<.*' + Priority: 2 + SortPriority: 0 + - Regex: '.*' + Priority: 3 + SortPriority: 0 +IncludeIsMainRegex: '([-_](test|unittest))?$' +IncludeIsMainSourceRegex: '' +IndentAccessModifiers: false +IndentCaseBlocks: true +IndentCaseLabels: true +IndentExternBlock: NoIndent +IndentGotoLabels: false +IndentPPDirectives: AfterHash +IndentWidth: 4 +IndentWrappedFunctionNames: false +InsertBraces: true # NOTE: may lead to incorrect formatting +InsertNewlineAtEOF: true +JavaScriptQuotes: Leave +JavaScriptWrapImports: true +KeepEmptyLinesAtTheStartOfBlocks: false +LambdaBodyIndentation: Signature +LineEnding: LF +MacroBlockBegin: '' +MacroBlockEnd: '' +MaxEmptyLinesToKeep: 1 +NamespaceIndentation: None +ObjCBinPackProtocolList: Auto +ObjCBlockIndentWidth: 4 +ObjCSpaceAfterProperty: true +ObjCSpaceBeforeProtocolList: true +PPIndentWidth: -1 +PackConstructorInitializers: CurrentLine +PenaltyBreakAssignment: 2 +PenaltyBreakBeforeFirstCallParameter: 1 +PenaltyBreakComment: 300 +PenaltyBreakFirstLessLess: 120 +PenaltyBreakString: 1000 +PenaltyBreakTemplateDeclaration: 10 +PenaltyExcessCharacter: 1000000 +PenaltyReturnTypeOnItsOwnLine: 200 +PointerAlignment: Middle +QualifierAlignment: Left +#QualifierOrder: ['static', 'inline', 'friend', 'constexpr', 'const', 'volatile', 'type', 'restrict'] +RawStringFormats: + - Language: Cpp + Delimiters: + - cc + - CC + - cpp + - Cpp + - CPP + - 'c++' + - 'C++' + CanonicalDelimiter: '' +ReferenceAlignment: Middle +ReflowComments: false # IndentOnly +SeparateDefinitionBlocks: Always +SortIncludes: CaseInsensitive +SortUsingDeclarations: LexicographicNumeric +SpaceAfterCStyleCast: true +SpaceAfterLogicalNot: false +SpaceAfterTemplateKeyword: true +SpaceBeforeAssignmentOperators: true +SpaceBeforeCpp11BracedList: false +SpaceBeforeCtorInitializerColon: true +SpaceBeforeInheritanceColon: true +SpaceBeforeParens: ControlStatements +SpaceBeforeRangeBasedForLoopColon: true +SpaceInEmptyBlock: false +SpaceInEmptyParentheses: false +SpacesBeforeTrailingComments: 2 +SpacesInAngles: Never +SpacesInContainerLiterals: true +SpacesInLineCommentPrefix: + Minimum: 1 + Maximum: -1 +SpacesInParentheses: false +SpacesInSquareBrackets: false +SpaceBeforeSquareBrackets: false +Standard: c++17 +TabWidth: 4 +UseTab: Never +WhitespaceSensitiveMacros: ['STRINGIZE'] +... + diff --git a/.clang-tidy b/.clang-tidy index 952c0cca8..310c3d182 100644 --- a/.clang-tidy +++ b/.clang-tidy @@ -17,8 +17,10 @@ Checks: > -clang-analyzer-security.insecureAPI.DeprecatedOrUnsafeBufferHandling, performance-*, portability-*, + -portability-simd-intrinsics, misc-*, -misc-const-correctness, -misc-non-private-member-variables-in-classes, -misc-no-recursion, + -misc-use-anonymous-namespace, FormatStyle: none diff --git a/.devops/full-cuda.Dockerfile b/.devops/full-cuda.Dockerfile index d5acd35e2..05bff1bdf 100644 --- a/.devops/full-cuda.Dockerfile +++ b/.devops/full-cuda.Dockerfile @@ -26,7 +26,7 @@ COPY . . RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \ export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \ fi && \ - cmake -B build -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ cmake --build build --config Release -j$(nproc) && \ cp build/bin/* . diff --git a/.devops/full-musa.Dockerfile b/.devops/full-musa.Dockerfile index 34ba856d3..3193fea1e 100644 --- a/.devops/full-musa.Dockerfile +++ b/.devops/full-musa.Dockerfile @@ -6,6 +6,9 @@ ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_V FROM ${BASE_MUSA_DEV_CONTAINER} AS build +# MUSA architecture to build for (defaults to all supported archs) +ARG MUSA_DOCKER_ARCH=default + RUN apt-get update && \ apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-dev libgomp1 @@ -19,7 +22,11 @@ WORKDIR /app COPY . . -RUN cmake -B build -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ +# Use the default MUSA archs if not specified +RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \ + export CMAKE_ARGS="-DMUSA_ARCHITECTURES=${MUSA_DOCKER_ARCH}"; \ + fi && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ cmake --build build --config Release -j$(nproc) && \ cp build/bin/* . diff --git a/.devops/full.Dockerfile b/.devops/full.Dockerfile index 2a06f82b7..d93c0be6a 100644 --- a/.devops/full.Dockerfile +++ b/.devops/full.Dockerfile @@ -3,23 +3,36 @@ ARG UBUNTU_VERSION=22.04 FROM ubuntu:$UBUNTU_VERSION AS build RUN apt-get update && \ - apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev libgomp1 - -COPY requirements.txt requirements.txt -COPY requirements requirements - -RUN pip install --upgrade pip setuptools wheel \ - && pip install -r requirements.txt + apt-get install -y build-essential git cmake libcurl4-openssl-dev WORKDIR /app COPY . . -ENV LLAMA_CURL=1 +RUN cmake -S . -B build -DGGML_BACKEND_DL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_CURL=ON -DCMAKE_BUILD_TYPE=Release && \ + cmake --build build -j $(nproc) && \ + mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib/ \; +FROM ubuntu:$UBUNTU_VERSION as runtime -RUN make -j$(nproc) +WORKDIR /app + +RUN apt-get update && \ + apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev libgomp1 + +COPY requirements.txt /app/requirements.txt +COPY requirements /app/requirements +COPY .devops/tools.sh /app/tools.sh + +RUN pip install --upgrade pip setuptools wheel && \ + pip install -r /app/requirements.txt + +COPY --from=build /app/build/bin/ /app/ +COPY --from=build /app/lib/ /app/ +COPY --from=build /app/convert_hf_to_gguf.py /app/ +COPY --from=build /app/gguf-py /app/gguf-py ENV LC_ALL=C.utf8 -ENTRYPOINT ["/app/.devops/tools.sh"] +ENTRYPOINT ["/app/tools.sh"] diff --git a/.devops/llama-cli-cann.Dockerfile b/.devops/llama-cli-cann.Dockerfile index db5ba2f25..02dce501c 100644 --- a/.devops/llama-cli-cann.Dockerfile +++ b/.devops/llama-cli-cann.Dockerfile @@ -1,6 +1,6 @@ ARG ASCEND_VERSION=8.0.rc2.alpha003-910b-openeuler22.03-py3.8 -FROM cosdt/cann:$ASCEND_VERSION AS build +FROM ascendai/cann:$ASCEND_VERSION AS build WORKDIR /app @@ -22,11 +22,11 @@ ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/runtime/lib64/stub:$LD_LIBRARY_PATH RUN echo "Building with static libs" && \ source /usr/local/Ascend/ascend-toolkit/set_env.sh --force && \ - cmake -B build -DGGML_CANN=ON -DBUILD_SHARED_LIBS=OFF && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_CANN=ON -DBUILD_SHARED_LIBS=OFF && \ cmake --build build --config Release --target llama-cli # TODO: use image with NNRT -FROM cosdt/cann:$ASCEND_VERSION AS runtime +FROM ascendai/cann:$ASCEND_VERSION AS runtime COPY --from=build /app/build/bin/llama-cli /llama-cli ENV LC_ALL=C.utf8 diff --git a/.devops/llama-cli-cuda.Dockerfile b/.devops/llama-cli-cuda.Dockerfile index b75163b94..7796891d5 100644 --- a/.devops/llama-cli-cuda.Dockerfile +++ b/.devops/llama-cli-cuda.Dockerfile @@ -22,16 +22,17 @@ COPY . . RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \ export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \ fi && \ - cmake -B build -DGGML_CUDA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ - cmake --build build --config Release --target llama-cli -j$(nproc) + cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ + cmake --build build --config Release --target llama-cli -j$(nproc) && \ + mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; FROM ${BASE_CUDA_RUN_CONTAINER} AS runtime RUN apt-get update && \ apt-get install -y libgomp1 -COPY --from=build /app/build/ggml/src/libggml.so /libggml.so -COPY --from=build /app/build/src/libllama.so /libllama.so -COPY --from=build /app/build/bin/llama-cli /llama-cli +COPY --from=build /app/lib/ / +COPY --from=build /app/build/bin/llama-cli / ENTRYPOINT [ "/llama-cli" ] diff --git a/.devops/llama-cli-intel.Dockerfile b/.devops/llama-cli-intel.Dockerfile index 79dba06a7..0706f732a 100644 --- a/.devops/llama-cli-intel.Dockerfile +++ b/.devops/llama-cli-intel.Dockerfile @@ -1,4 +1,4 @@ -ARG ONEAPI_VERSION=2024.1.1-devel-ubuntu22.04 +ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04 FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build @@ -15,7 +15,7 @@ RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \ export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \ fi && \ echo "Building with static libs" && \ - cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx \ ${OPT_SYCL_F16} -DBUILD_SHARED_LIBS=OFF && \ cmake --build build --config Release --target llama-cli diff --git a/.devops/llama-cli-musa.Dockerfile b/.devops/llama-cli-musa.Dockerfile index b5696794f..e7c75af20 100644 --- a/.devops/llama-cli-musa.Dockerfile +++ b/.devops/llama-cli-musa.Dockerfile @@ -8,6 +8,9 @@ ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU FROM ${BASE_MUSA_DEV_CONTAINER} AS build +# MUSA architecture to build for (defaults to all supported archs) +ARG MUSA_DOCKER_ARCH=default + RUN apt-get update && \ apt-get install -y build-essential git cmake @@ -15,16 +18,21 @@ WORKDIR /app COPY . . -RUN cmake -B build -DGGML_MUSA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ - cmake --build build --config Release --target llama-cli -j$(nproc) +# Use the default MUSA archs if not specified +RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \ + export CMAKE_ARGS="-DMUSA_ARCHITECTURES=${MUSA_DOCKER_ARCH}"; \ + fi && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ + cmake --build build --config Release --target llama-cli -j$(nproc) && \ + mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; FROM ${BASE_MUSA_RUN_CONTAINER} AS runtime RUN apt-get update && \ apt-get install -y libgomp1 -COPY --from=build /app/build/ggml/src/libggml.so /libggml.so -COPY --from=build /app/build/src/libllama.so /libllama.so +COPY --from=build /app/lib/ / COPY --from=build /app/build/bin/llama-cli /llama-cli ENTRYPOINT [ "/llama-cli" ] diff --git a/.devops/llama-cli-vulkan.Dockerfile b/.devops/llama-cli-vulkan.Dockerfile index 9b0dad8bf..92a6e0479 100644 --- a/.devops/llama-cli-vulkan.Dockerfile +++ b/.devops/llama-cli-vulkan.Dockerfile @@ -14,7 +14,7 @@ RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key # Build it WORKDIR /app COPY . . -RUN cmake -B build -DGGML_VULKAN=1 && \ +RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 && \ cmake --build build --config Release --target llama-cli # Clean up diff --git a/.devops/llama-cli.Dockerfile b/.devops/llama-cli.Dockerfile index 7f741aa46..be234d55d 100644 --- a/.devops/llama-cli.Dockerfile +++ b/.devops/llama-cli.Dockerfile @@ -3,21 +3,27 @@ ARG UBUNTU_VERSION=22.04 FROM ubuntu:$UBUNTU_VERSION AS build RUN apt-get update && \ - apt-get install -y build-essential git + apt-get install -y build-essential git cmake libcurl4-openssl-dev WORKDIR /app COPY . . -RUN make -j$(nproc) llama-cli +RUN cmake -S . -B build -DGGML_BACKEND_DL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_CURL=ON -DCMAKE_BUILD_TYPE=Release && \ + cmake --build build -j $(nproc) && \ + mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib/ \; FROM ubuntu:$UBUNTU_VERSION AS runtime -RUN apt-get update && \ - apt-get install -y libgomp1 +WORKDIR /app -COPY --from=build /app/llama-cli /llama-cli +RUN apt-get update && \ + apt-get install -y libcurl4-openssl-dev libgomp1 curl + +COPY --from=build /app/build/bin/llama-cli /app/ +COPY --from=build /app/lib/ /app/ ENV LC_ALL=C.utf8 -ENTRYPOINT [ "/llama-cli" ] +ENTRYPOINT [ "/app/llama-cli" ] diff --git a/.devops/llama-server-cuda.Dockerfile b/.devops/llama-server-cuda.Dockerfile index a40e24205..bf8a198f9 100644 --- a/.devops/llama-server-cuda.Dockerfile +++ b/.devops/llama-server-cuda.Dockerfile @@ -22,16 +22,17 @@ COPY . . RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \ export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \ fi && \ - cmake -B build -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ - cmake --build build --config Release --target llama-server -j$(nproc) + cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ + cmake --build build --config Release --target llama-server -j$(nproc) && \ + mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; FROM ${BASE_CUDA_RUN_CONTAINER} AS runtime RUN apt-get update && \ apt-get install -y libcurl4-openssl-dev libgomp1 curl -COPY --from=build /app/build/ggml/src/libggml.so /libggml.so -COPY --from=build /app/build/src/libllama.so /libllama.so +COPY --from=build /app/lib/ / COPY --from=build /app/build/bin/llama-server /llama-server # Must be set to 0.0.0.0 so it can listen to requests from host machine diff --git a/.devops/llama-server-intel.Dockerfile b/.devops/llama-server-intel.Dockerfile index 9c355b664..b503b8cfe 100644 --- a/.devops/llama-server-intel.Dockerfile +++ b/.devops/llama-server-intel.Dockerfile @@ -1,4 +1,4 @@ -ARG ONEAPI_VERSION=2024.1.1-devel-ubuntu22.04 +ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04 FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build @@ -15,7 +15,7 @@ RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \ export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \ fi && \ echo "Building with dynamic libs" && \ - cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \ cmake --build build --config Release --target llama-server FROM intel/oneapi-basekit:$ONEAPI_VERSION AS runtime diff --git a/.devops/llama-server-musa.Dockerfile b/.devops/llama-server-musa.Dockerfile index 193a6d77c..cebe51d42 100644 --- a/.devops/llama-server-musa.Dockerfile +++ b/.devops/llama-server-musa.Dockerfile @@ -8,6 +8,9 @@ ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU FROM ${BASE_MUSA_DEV_CONTAINER} AS build +# MUSA architecture to build for (defaults to all supported archs) +ARG MUSA_DOCKER_ARCH=default + RUN apt-get update && \ apt-get install -y build-essential git cmake libcurl4-openssl-dev @@ -15,16 +18,21 @@ WORKDIR /app COPY . . -RUN cmake -B build -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ - cmake --build build --config Release --target llama-server -j$(nproc) +# Use the default MUSA archs if not specified +RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \ + export CMAKE_ARGS="-DMUSA_ARCHITECTURES=${MUSA_DOCKER_ARCH}"; \ + fi && \ + cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ + cmake --build build --config Release --target llama-server -j$(nproc) && \ + mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib \; FROM ${BASE_MUSA_RUN_CONTAINER} AS runtime RUN apt-get update && \ apt-get install -y libcurl4-openssl-dev libgomp1 curl -COPY --from=build /app/build/ggml/src/libggml.so /libggml.so -COPY --from=build /app/build/src/libllama.so /libllama.so +COPY --from=build /app/lib/ / COPY --from=build /app/build/bin/llama-server /llama-server # Must be set to 0.0.0.0 so it can listen to requests from host machine diff --git a/.devops/llama-server-vulkan.Dockerfile b/.devops/llama-server-vulkan.Dockerfile index 93c5e0c26..6aa786779 100644 --- a/.devops/llama-server-vulkan.Dockerfile +++ b/.devops/llama-server-vulkan.Dockerfile @@ -14,7 +14,7 @@ RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key # Build it WORKDIR /app COPY . . -RUN cmake -B build -DGGML_VULKAN=1 -DLLAMA_CURL=1 && \ +RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 -DLLAMA_CURL=1 && \ cmake --build build --config Release --target llama-server # Clean up diff --git a/.devops/llama-server.Dockerfile b/.devops/llama-server.Dockerfile index 02accc85e..72ccde2fe 100644 --- a/.devops/llama-server.Dockerfile +++ b/.devops/llama-server.Dockerfile @@ -3,22 +3,26 @@ ARG UBUNTU_VERSION=22.04 FROM ubuntu:$UBUNTU_VERSION AS build RUN apt-get update && \ - apt-get install -y build-essential git libcurl4-openssl-dev + apt-get install -y build-essential git cmake libcurl4-openssl-dev WORKDIR /app COPY . . -ENV LLAMA_CURL=1 - -RUN make -j$(nproc) llama-server +RUN cmake -S . -B build -DGGML_BACKEND_DL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_CURL=ON -DCMAKE_BUILD_TYPE=Release && \ + cmake --build build -j $(nproc) && \ + mkdir -p /app/lib && \ + find build -name "*.so" -exec cp {} /app/lib/ \; FROM ubuntu:$UBUNTU_VERSION AS runtime +WORKDIR /app + RUN apt-get update && \ apt-get install -y libcurl4-openssl-dev libgomp1 curl -COPY --from=build /app/llama-server /llama-server +COPY --from=build /app/build/bin/llama-server /app/ +COPY --from=build /app/lib/ /app/ ENV LC_ALL=C.utf8 # Must be set to 0.0.0.0 so it can listen to requests from host machine @@ -26,4 +30,4 @@ ENV LLAMA_ARG_HOST=0.0.0.0 HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ] -ENTRYPOINT [ "/llama-server" ] +ENTRYPOINT [ "/app/llama-server" ] diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix index 5d7d7ea5a..b88e6ca80 100644 --- a/.devops/nix/package.nix +++ b/.devops/nix/package.nix @@ -126,9 +126,9 @@ effectiveStdenv.mkDerivation (finalAttrs: { }; postPatch = '' - substituteInPlace ./ggml/src/ggml-metal.m \ + substituteInPlace ./ggml/src/ggml-metal/ggml-metal.m \ --replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/bin/ggml-metal.metal\";" - substituteInPlace ./ggml/src/ggml-metal.m \ + substituteInPlace ./ggml/src/ggml-metal/ggml-metal.m \ --replace '[bundle pathForResource:@"default" ofType:@"metallib"];' "@\"$out/bin/default.metallib\";" ''; @@ -173,7 +173,7 @@ effectiveStdenv.mkDerivation (finalAttrs: { (cmakeBool "GGML_NATIVE" false) (cmakeBool "GGML_BLAS" useBlas) (cmakeBool "GGML_CUDA" useCuda) - (cmakeBool "GGML_HIPBLAS" useRocm) + (cmakeBool "GGML_HIP" useRocm) (cmakeBool "GGML_METAL" useMetalKit) (cmakeBool "GGML_VULKAN" useVulkan) (cmakeBool "GGML_STATIC" enableStatic) diff --git a/.devops/nix/python-scripts.nix b/.devops/nix/python-scripts.nix index 392e9ffe4..56ea18278 100644 --- a/.devops/nix/python-scripts.nix +++ b/.devops/nix/python-scripts.nix @@ -34,7 +34,7 @@ let # server tests openai - behave + pytest prometheus-client ]; in diff --git a/.editorconfig b/.editorconfig index 19eb50434..fa84cb064 100644 --- a/.editorconfig +++ b/.editorconfig @@ -24,6 +24,16 @@ insert_final_newline = unset [examples/server/public/*] indent_size = 2 +[examples/server/public/deps_*] +trim_trailing_whitespace = unset +indent_style = unset +indent_size = unset + +[examples/server/deps_*] +trim_trailing_whitespace = unset +indent_style = unset +indent_size = unset + [examples/llama.swiftui/llama.swiftui.xcodeproj/*] indent_style = tab diff --git a/.github/ISSUE_TEMPLATE/01-bug-low.yml b/.github/ISSUE_TEMPLATE/01-bug-low.yml deleted file mode 100644 index 54785854f..000000000 --- a/.github/ISSUE_TEMPLATE/01-bug-low.yml +++ /dev/null @@ -1,50 +0,0 @@ -name: Low Severity Bugs -description: Used to report low severity bugs in llama.cpp (e.g. cosmetic issues, non critical UI glitches) -title: "Bug: " -labels: ["bug-unconfirmed", "low severity"] -body: - - type: markdown - attributes: - value: | - Thanks for taking the time to fill out this bug report! - Please include information about your system, the steps to reproduce the bug, - and the version of llama.cpp that you are using. - If possible, please provide a minimal code example that reproduces the bug. - - type: textarea - id: what-happened - attributes: - label: What happened? - description: Also tell us, what did you expect to happen? - placeholder: Tell us what you see! - validations: - required: true - - type: textarea - id: version - attributes: - label: Name and Version - description: Which executable and which version of our software are you running? (use `--version` to get a version string) - placeholder: | - $./llama-cli --version - version: 2999 (42b4109e) - built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu - validations: - required: true - - type: dropdown - id: operating-system - attributes: - label: What operating system are you seeing the problem on? - multiple: true - options: - - Linux - - Mac - - Windows - - BSD - - Other? (Please let us know in description) - validations: - required: false - - type: textarea - id: logs - attributes: - label: Relevant log output - description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. - render: shell diff --git a/.github/ISSUE_TEMPLATE/010-bug-compilation.yml b/.github/ISSUE_TEMPLATE/010-bug-compilation.yml new file mode 100644 index 000000000..f10b3a2b2 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/010-bug-compilation.yml @@ -0,0 +1,77 @@ +name: Bug (compilation) +description: Something goes wrong when trying to compile llama.cpp. +title: "Compile bug: " +labels: ["bug-unconfirmed", "compilation"] +body: + - type: markdown + attributes: + value: > + Thanks for taking the time to fill out this bug report! + This issue template is intended for bug reports where the compilation of llama.cpp fails. + Before opening an issue, please confirm that the compilation still fails with `-DGGML_CCACHE=OFF`. + If the compilation succeeds with ccache disabled you should be able to permanently fix the issue + by clearing `~/.cache/ccache` (on Linux). + - type: textarea + id: commit + attributes: + label: Git commit + description: Which commit are you trying to compile? + placeholder: | + $git rev-parse HEAD + 84a07a17b1b08cf2b9747c633a2372782848a27f + validations: + required: true + - type: dropdown + id: operating-system + attributes: + label: Operating systems + description: Which operating systems do you know to be affected? + multiple: true + options: + - Linux + - Mac + - Windows + - BSD + - Other? (Please let us know in description) + validations: + required: true + - type: dropdown + id: backends + attributes: + label: GGML backends + description: Which GGML backends do you know to be affected? + options: [AMX, BLAS, CPU, CUDA, HIP, Kompute, Metal, Musa, RPC, SYCL, Vulkan] + multiple: true + validations: + required: true + - type: textarea + id: info + attributes: + label: Problem description & steps to reproduce + description: > + Please give us a summary of the problem and tell us how to reproduce it. + If you can narrow down the bug to specific compile flags, that information would be very much appreciated by us. + placeholder: > + I'm trying to compile llama.cpp with CUDA support on a fresh install of Ubuntu and get error XY. + Here are the exact commands that I used: ... + validations: + required: true + - type: textarea + id: first_bad_commit + attributes: + label: First Bad Commit + description: > + If the bug was not present on an earlier version: when did it start appearing? + If possible, please do a git bisect and identify the exact commit that introduced the bug. + validations: + required: false + - type: textarea + id: logs + attributes: + label: Relevant log output + description: > + Please copy and paste any relevant log output, including the command that you entered and any generated text. + This will be automatically formatted into code, so no need for backticks. + render: shell + validations: + required: true diff --git a/.github/ISSUE_TEMPLATE/011-bug-results.yml b/.github/ISSUE_TEMPLATE/011-bug-results.yml new file mode 100644 index 000000000..1ccef0793 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/011-bug-results.yml @@ -0,0 +1,101 @@ +name: Bug (model use) +description: Something goes wrong when using a model (in general, not specific to a single llama.cpp module). +title: "Eval bug: " +labels: ["bug-unconfirmed", "model evaluation"] +body: + - type: markdown + attributes: + value: > + Thanks for taking the time to fill out this bug report! + This issue template is intended for bug reports where the model evaluation results + (i.e. the generated text) are incorrect or llama.cpp crashes during model evaluation. + If you encountered the issue while using an external UI (e.g. ollama), + please reproduce your issue using one of the examples/binaries in this repository. + The `llama-cli` binary can be used for simple and reproducible model inference. + - type: textarea + id: version + attributes: + label: Name and Version + description: Which version of our software are you running? (use `--version` to get a version string) + placeholder: | + $./llama-cli --version + version: 2999 (42b4109e) + built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu + validations: + required: true + - type: dropdown + id: operating-system + attributes: + label: Operating systems + description: Which operating systems do you know to be affected? + multiple: true + options: + - Linux + - Mac + - Windows + - BSD + - Other? (Please let us know in description) + validations: + required: true + - type: dropdown + id: backends + attributes: + label: GGML backends + description: Which GGML backends do you know to be affected? + options: [AMX, BLAS, CPU, CUDA, HIP, Kompute, Metal, Musa, RPC, SYCL, Vulkan] + multiple: true + validations: + required: true + - type: textarea + id: hardware + attributes: + label: Hardware + description: Which CPUs/GPUs are you using? + placeholder: > + e.g. Ryzen 5950X + 2x RTX 4090 + validations: + required: true + - type: textarea + id: model + attributes: + label: Models + description: > + Which model(s) at which quantization were you using when encountering the bug? + If you downloaded a GGUF file off of Huggingface, please provide a link. + placeholder: > + e.g. Meta LLaMA 3.1 Instruct 8b q4_K_M + validations: + required: false + - type: textarea + id: info + attributes: + label: Problem description & steps to reproduce + description: > + Please give us a summary of the problem and tell us how to reproduce it. + If you can narrow down the bug to specific hardware, compile flags, or command line arguments, + that information would be very much appreciated by us. + placeholder: > + e.g. when I run llama-cli with -ngl 99 I get garbled outputs. + When I use -ngl 0 it works correctly. + Here are the exact commands that I used: ... + validations: + required: true + - type: textarea + id: first_bad_commit + attributes: + label: First Bad Commit + description: > + If the bug was not present on an earlier version: when did it start appearing? + If possible, please do a git bisect and identify the exact commit that introduced the bug. + validations: + required: false + - type: textarea + id: logs + attributes: + label: Relevant log output + description: > + Please copy and paste any relevant log output, including the command that you entered and any generated text. + This will be automatically formatted into code, so no need for backticks. + render: shell + validations: + required: true diff --git a/.github/ISSUE_TEMPLATE/019-bug-misc.yml b/.github/ISSUE_TEMPLATE/019-bug-misc.yml new file mode 100644 index 000000000..d157ea307 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/019-bug-misc.yml @@ -0,0 +1,81 @@ +name: Bug (misc.) +description: Something is not working the way it should (and it's not covered by any of the above cases). +title: "Misc. bug: " +labels: ["bug-unconfirmed"] +body: + - type: markdown + attributes: + value: > + Thanks for taking the time to fill out this bug report! + This issue template is intended for miscellaneous bugs that don't fit into any other category. + If you encountered the issue while using an external UI (e.g. ollama), + please reproduce your issue using one of the examples/binaries in this repository. + - type: textarea + id: version + attributes: + label: Name and Version + description: Which version of our software is affected? (You can use `--version` to get a version string.) + placeholder: | + $./llama-cli --version + version: 2999 (42b4109e) + built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu + validations: + required: true + - type: dropdown + id: operating-system + attributes: + label: Operating systems + description: Which operating systems do you know to be affected? + multiple: true + options: + - Linux + - Mac + - Windows + - BSD + - Other? (Please let us know in description) + validations: + required: false + - type: dropdown + id: module + attributes: + label: Which llama.cpp modules do you know to be affected? + multiple: true + options: + - Documentation/Github + - libllama (core library) + - llama-cli + - llama-server + - llama-bench + - llama-quantize + - Python/Bash scripts + - Test code + - Other (Please specify in the next section) + validations: + required: false + - type: textarea + id: info + attributes: + label: Problem description & steps to reproduce + description: > + Please give us a summary of the problem and tell us how to reproduce it (if applicable). + validations: + required: true + - type: textarea + id: first_bad_commit + attributes: + label: First Bad Commit + description: > + If the bug was not present on an earlier version and it's not trivial to track down: when did it start appearing? + If possible, please do a git bisect and identify the exact commit that introduced the bug. + validations: + required: false + - type: textarea + id: logs + attributes: + label: Relevant log output + description: > + If applicable, please copy and paste any relevant log output, including the command that you entered and any generated text. + This will be automatically formatted into code, so no need for backticks. + render: shell + validations: + required: false diff --git a/.github/ISSUE_TEMPLATE/02-bug-medium.yml b/.github/ISSUE_TEMPLATE/02-bug-medium.yml deleted file mode 100644 index a6285c6f0..000000000 --- a/.github/ISSUE_TEMPLATE/02-bug-medium.yml +++ /dev/null @@ -1,50 +0,0 @@ -name: Medium Severity Bug -description: Used to report medium severity bugs in llama.cpp (e.g. Malfunctioning Features but generally still useable) -title: "Bug: " -labels: ["bug-unconfirmed", "medium severity"] -body: - - type: markdown - attributes: - value: | - Thanks for taking the time to fill out this bug report! - Please include information about your system, the steps to reproduce the bug, - and the version of llama.cpp that you are using. - If possible, please provide a minimal code example that reproduces the bug. - - type: textarea - id: what-happened - attributes: - label: What happened? - description: Also tell us, what did you expect to happen? - placeholder: Tell us what you see! - validations: - required: true - - type: textarea - id: version - attributes: - label: Name and Version - description: Which executable and which version of our software are you running? (use `--version` to get a version string) - placeholder: | - $./llama-cli --version - version: 2999 (42b4109e) - built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu - validations: - required: true - - type: dropdown - id: operating-system - attributes: - label: What operating system are you seeing the problem on? - multiple: true - options: - - Linux - - Mac - - Windows - - BSD - - Other? (Please let us know in description) - validations: - required: false - - type: textarea - id: logs - attributes: - label: Relevant log output - description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. - render: shell diff --git a/.github/ISSUE_TEMPLATE/05-enhancement.yml b/.github/ISSUE_TEMPLATE/020-enhancement.yml similarity index 97% rename from .github/ISSUE_TEMPLATE/05-enhancement.yml rename to .github/ISSUE_TEMPLATE/020-enhancement.yml index 58fca7318..02dd4f575 100644 --- a/.github/ISSUE_TEMPLATE/05-enhancement.yml +++ b/.github/ISSUE_TEMPLATE/020-enhancement.yml @@ -1,5 +1,5 @@ name: Enhancement -description: Used to request enhancements for llama.cpp +description: Used to request enhancements for llama.cpp. title: "Feature Request: " labels: ["enhancement"] body: diff --git a/.github/ISSUE_TEMPLATE/03-bug-high.yml b/.github/ISSUE_TEMPLATE/03-bug-high.yml deleted file mode 100644 index ff816b937..000000000 --- a/.github/ISSUE_TEMPLATE/03-bug-high.yml +++ /dev/null @@ -1,50 +0,0 @@ -name: High Severity Bug -description: Used to report high severity bugs in llama.cpp (e.g. Malfunctioning features hindering important common workflow) -title: "Bug: " -labels: ["bug-unconfirmed", "high severity"] -body: - - type: markdown - attributes: - value: | - Thanks for taking the time to fill out this bug report! - Please include information about your system, the steps to reproduce the bug, - and the version of llama.cpp that you are using. - If possible, please provide a minimal code example that reproduces the bug. - - type: textarea - id: what-happened - attributes: - label: What happened? - description: Also tell us, what did you expect to happen? - placeholder: Tell us what you see! - validations: - required: true - - type: textarea - id: version - attributes: - label: Name and Version - description: Which executable and which version of our software are you running? (use `--version` to get a version string) - placeholder: | - $./llama-cli --version - version: 2999 (42b4109e) - built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu - validations: - required: true - - type: dropdown - id: operating-system - attributes: - label: What operating system are you seeing the problem on? - multiple: true - options: - - Linux - - Mac - - Windows - - BSD - - Other? (Please let us know in description) - validations: - required: false - - type: textarea - id: logs - attributes: - label: Relevant log output - description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. - render: shell diff --git a/.github/ISSUE_TEMPLATE/06-research.yml b/.github/ISSUE_TEMPLATE/030-research.yml similarity index 97% rename from .github/ISSUE_TEMPLATE/06-research.yml rename to .github/ISSUE_TEMPLATE/030-research.yml index 3ae4e9f8c..18975dbbf 100644 --- a/.github/ISSUE_TEMPLATE/06-research.yml +++ b/.github/ISSUE_TEMPLATE/030-research.yml @@ -1,5 +1,5 @@ name: Research -description: Track new technical research area +description: Track new technical research area. title: "Research: " labels: ["research 🔬"] body: diff --git a/.github/ISSUE_TEMPLATE/04-bug-critical.yml b/.github/ISSUE_TEMPLATE/04-bug-critical.yml deleted file mode 100644 index 7af42a80b..000000000 --- a/.github/ISSUE_TEMPLATE/04-bug-critical.yml +++ /dev/null @@ -1,50 +0,0 @@ -name: Critical Severity Bug -description: Used to report critical severity bugs in llama.cpp (e.g. Crashing, Corrupted, Dataloss) -title: "Bug: " -labels: ["bug-unconfirmed", "critical severity"] -body: - - type: markdown - attributes: - value: | - Thanks for taking the time to fill out this bug report! - Please include information about your system, the steps to reproduce the bug, - and the version of llama.cpp that you are using. - If possible, please provide a minimal code example that reproduces the bug. - - type: textarea - id: what-happened - attributes: - label: What happened? - description: Also tell us, what did you expect to happen? - placeholder: Tell us what you see! - validations: - required: true - - type: textarea - id: version - attributes: - label: Name and Version - description: Which executable and which version of our software are you running? (use `--version` to get a version string) - placeholder: | - $./llama-cli --version - version: 2999 (42b4109e) - built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu - validations: - required: true - - type: dropdown - id: operating-system - attributes: - label: What operating system are you seeing the problem on? - multiple: true - options: - - Linux - - Mac - - Windows - - BSD - - Other? (Please let us know in description) - validations: - required: false - - type: textarea - id: logs - attributes: - label: Relevant log output - description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. - render: shell diff --git a/.github/ISSUE_TEMPLATE/07-refactor.yml b/.github/ISSUE_TEMPLATE/040-refactor.yml similarity index 95% rename from .github/ISSUE_TEMPLATE/07-refactor.yml rename to .github/ISSUE_TEMPLATE/040-refactor.yml index 3a68d3d53..b6e6ab36d 100644 --- a/.github/ISSUE_TEMPLATE/07-refactor.yml +++ b/.github/ISSUE_TEMPLATE/040-refactor.yml @@ -1,5 +1,5 @@ name: Refactor (Maintainers) -description: Used to track refactoring opportunities +description: Used to track refactoring opportunities. title: "Refactor: " labels: ["refactor"] body: diff --git a/.github/labeler.yml b/.github/labeler.yml index 89436740d..1b47bc968 100644 --- a/.github/labeler.yml +++ b/.github/labeler.yml @@ -3,19 +3,18 @@ Kompute: - changed-files: - any-glob-to-any-file: - ggml/include/ggml-kompute.h - - ggml/src/ggml-kompute.cpp + - ggml/src/ggml-kompute/** - README-kompute.md Apple Metal: - changed-files: - any-glob-to-any-file: - ggml/include/ggml-metal.h - - ggml/src/ggml-metal.cpp + - ggml/src/ggml-metal/** - README-metal.md SYCL: - changed-files: - any-glob-to-any-file: - ggml/include/ggml-sycl.h - - ggml/src/ggml-sycl.cpp - ggml/src/ggml-sycl/** - docs/backend/SYCL.md - examples/sycl/** @@ -27,8 +26,8 @@ Nvidia GPU: Vulkan: - changed-files: - any-glob-to-any-file: - - ggml/ggml_vk_generate_shaders.py - - ggml/src/ggml-vulkan* + - ggml/include/ggml-vulkan.h + - ggml/src/ggml-vulkan/** documentation: - changed-files: - any-glob-to-any-file: @@ -75,11 +74,7 @@ server: ggml: - changed-files: - any-glob-to-any-file: - - ggml/include/ggml*.h - - ggml/src/ggml*.c - - ggml/src/ggml*.cpp - - ggml/src/ggml*.h - - ggml-cuda/** + - ggml/** nix: - changed-files: - any-glob-to-any-file: diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index 997c6d9d0..d9f5bdc23 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -1,7 +1 @@ - - -- [x] I have read the [contributing guidelines](https://github.com/ggerganov/llama.cpp/blob/master/CONTRIBUTING.md) -- Self-reported review complexity: - - [ ] Low - - [ ] Medium - - [ ] High +*Make sure to read the [contributing guidelines](https://github.com/ggerganov/llama.cpp/blob/master/CONTRIBUTING.md) before submitting a PR* diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 423173b97..f3326a5fb 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -55,7 +55,13 @@ jobs: sysctl -a mkdir build cd build - cmake -DLLAMA_FATAL_WARNINGS=ON -DGGML_METAL_EMBED_LIBRARY=ON -DLLAMA_CURL=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=OFF .. + cmake .. \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DLLAMA_CURL=ON \ + -DGGML_METAL_USE_BF16=ON \ + -DGGML_METAL_EMBED_LIBRARY=ON \ + -DGGML_RPC=ON \ + -DBUILD_SHARED_LIBS=OFF cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) - name: Test @@ -92,7 +98,7 @@ jobs: name: llama-bin-macos-arm64.zip macOS-latest-cmake-x64: - runs-on: macos-12 + runs-on: macos-13 steps: - name: Clone @@ -113,7 +119,12 @@ jobs: sysctl -a # Metal is disabled due to intermittent failures with Github runners not having a GPU: # https://github.com/ggerganov/llama.cpp/actions/runs/8635935781/job/23674807267#step:5:2313 - cmake -B build -DLLAMA_FATAL_WARNINGS=ON -DGGML_METAL=OFF -DLLAMA_CURL=ON -DGGML_RPC=ON -DBUILD_SHARED_LIBS=OFF + cmake -B build \ + -DLLAMA_FATAL_WARNINGS=ON \ + -DLLAMA_CURL=ON \ + -DGGML_METAL=OFF \ + -DGGML_RPC=ON \ + -DBUILD_SHARED_LIBS=OFF cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) - name: Test @@ -149,66 +160,6 @@ jobs: path: llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip name: llama-bin-macos-x64.zip - ubuntu-focal-make: - runs-on: ubuntu-20.04 - env: - LLAMA_NODE_AVAILABLE: true - LLAMA_PYTHON_AVAILABLE: true - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v4 - - - name: Dependencies - id: depends - run: | - sudo apt-get update - sudo apt-get install build-essential gcc-8 - - - uses: actions/setup-node@v4 - with: - node-version: "20" - - - uses: actions/setup-python@v5 - with: - python-version: "3.11" - - - name: Build - id: make_build - env: - LLAMA_FATAL_WARNINGS: 1 - run: | - CC=gcc-8 make -j $(nproc) - - - name: Test - id: make_test - run: | - CC=gcc-8 make tests -j $(nproc) - make test -j $(nproc) - - ubuntu-focal-make-curl: - runs-on: ubuntu-20.04 - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v4 - - - name: Dependencies - id: depends - run: | - sudo apt-get update - sudo apt-get install build-essential gcc-8 libcurl4-openssl-dev - - - name: Build - id: make_build - env: - LLAMA_FATAL_WARNINGS: 1 - LLAMA_CURL: 1 - run: | - CC=gcc-8 make -j $(nproc) - ubuntu-latest-cmake: runs-on: ubuntu-latest @@ -394,15 +345,36 @@ jobs: - name: Build with native CMake HIP support id: cmake_build run: | - cmake -B build -S . -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" -DGGML_HIPBLAS=ON + cmake -B build -S . -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" -DGGML_HIP=ON cmake --build build --config Release -j $(nproc) - name: Build with legacy HIP support id: cmake_build_legacy_hip run: | - cmake -B build2 -S . -DCMAKE_C_COMPILER=hipcc -DCMAKE_CXX_COMPILER=hipcc -DGGML_HIPBLAS=ON + cmake -B build2 -S . -DCMAKE_C_COMPILER=hipcc -DCMAKE_CXX_COMPILER=hipcc -DGGML_HIP=ON cmake --build build2 --config Release -j $(nproc) + ubuntu-22-cmake-musa: + runs-on: ubuntu-22.04 + container: mthreads/musa:rc3.1.0-devel-ubuntu22.04 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: Dependencies + id: depends + run: | + apt-get update + apt-get install -y build-essential git cmake libcurl4-openssl-dev + + - name: Build with native CMake MUSA support + id: cmake_build + run: | + cmake -B build -S . -DGGML_MUSA=ON + cmake --build build --config Release -j $(nproc) + ubuntu-22-cmake-sycl: runs-on: ubuntu-22.04 @@ -485,36 +457,6 @@ jobs: cmake -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON .. cmake --build . --config Release -j $(nproc) - # TODO: build with GGML_NO_METAL because test-backend-ops fail on "Apple Paravirtual device" and I don't know - # how to debug it. - # ref: https://github.com/ggerganov/llama.cpp/actions/runs/7131777249/job/19420981052#step:5:1124 - macOS-latest-make: - runs-on: macos-latest - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v4 - - - name: Dependencies - id: depends - continue-on-error: true - run: | - brew update - - - name: Build - id: make_build - env: - LLAMA_FATAL_WARNINGS: 1 - run: | - GGML_NO_METAL=1 make -j $(sysctl -n hw.logicalcpu) - - - name: Test - id: make_test - run: | - GGML_NO_METAL=1 make tests -j $(sysctl -n hw.logicalcpu) - GGML_NO_METAL=1 make test -j $(sysctl -n hw.logicalcpu) - # TODO: build with GGML_METAL=OFF because test-backend-ops fail on "Apple Paravirtual device" and I don't know # how to debug it. # ref: https://github.com/ggerganov/llama.cpp/actions/runs/7132125951/job/19422043567?pr=4359#step:5:6584 @@ -569,6 +511,7 @@ jobs: mkdir build cd build cmake -G Xcode .. \ + -DGGML_METAL_USE_BF16=ON \ -DGGML_METAL_EMBED_LIBRARY=ON \ -DLLAMA_BUILD_EXAMPLES=OFF \ -DLLAMA_BUILD_TESTS=OFF \ @@ -599,6 +542,7 @@ jobs: mkdir build cd build cmake -G Xcode .. \ + -DGGML_METAL_USE_BF16=ON \ -DGGML_METAL_EMBED_LIBRARY=ON \ -DLLAMA_BUILD_EXAMPLES=OFF \ -DLLAMA_BUILD_TESTS=OFF \ @@ -608,33 +552,35 @@ jobs: -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO - macOS-latest-swift: - runs-on: macos-latest - - strategy: - matrix: - destination: ['generic/platform=macOS', 'generic/platform=iOS', 'generic/platform=tvOS'] - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v4 - - - name: Dependencies - id: depends - continue-on-error: true - run: | - brew update - - - name: xcodebuild for swift package - id: xcodebuild - run: | - xcodebuild -scheme llama -destination "${{ matrix.destination }}" - - - name: Build Swift Example - id: make_build_swift_example - run: | - make swift +# TODO: tmp disabled. see for possible re-enable: +# https://github.com/ggerganov/llama.cpp/pull/10525 +# macOS-latest-swift: +# runs-on: macos-latest +# +# strategy: +# matrix: +# destination: ['generic/platform=macOS', 'generic/platform=iOS', 'generic/platform=tvOS'] +# +# steps: +# - name: Clone +# id: checkout +# uses: actions/checkout@v4 +# +# - name: Dependencies +# id: depends +# continue-on-error: true +# run: | +# brew update +# +# - name: xcodebuild for swift package +# id: xcodebuild +# run: | +# xcodebuild -scheme llama -destination "${{ matrix.destination }}" +# +# - name: Build Swift Example +# id: make_build_swift_example +# run: | +# make swift windows-msys2: runs-on: windows-latest @@ -661,21 +607,6 @@ jobs: mingw-w64-${{matrix.env}}-cmake mingw-w64-${{matrix.env}}-openblas - - name: Build using make - shell: msys2 {0} - run: | - make -j $(nproc) - - - name: Clean after building using make - shell: msys2 {0} - run: | - make clean - - - name: Build using make w/ OpenBLAS - shell: msys2 {0} - run: | - make GGML_OPENBLAS=1 -j $(nproc) - - name: Build using CMake shell: msys2 {0} run: | @@ -694,7 +625,7 @@ jobs: cmake --build build --config ${{ matrix.build }} -j $(nproc) windows-latest-cmake: - runs-on: windows-2019 + runs-on: windows-latest env: OPENBLAS_VERSION: 0.3.23 @@ -734,7 +665,7 @@ jobs: id: clone_kompute if: ${{ matrix.build == 'kompute-x64' }} run: | - git submodule update --init ggml/src/kompute + git submodule update --init ggml/src/ggml-kompute/kompute - name: Download OpenBLAS id: get_openblas @@ -837,12 +768,33 @@ jobs: path: llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}.zip name: llama-bin-win-${{ matrix.build }}.zip - windows-latest-cmake-cuda: + ubuntu-latest-cmake-cuda: + runs-on: ubuntu-latest + container: nvidia/cuda:12.6.2-devel-ubuntu24.04 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: Install dependencies + env: + DEBIAN_FRONTEND: noninteractive + run: | + apt update + apt install -y cmake build-essential ninja-build libgomp1 git + + - name: Build with CMake + run: | + cmake -S . -B build -G Ninja -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=89-real -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined -DLLAMA_FATAL_WARNINGS=ON + cmake --build build + + windows-2019-cmake-cuda: runs-on: windows-2019 strategy: matrix: - cuda: ['12.2.0', '11.7.1'] + cuda: ['12.4', '11.7'] build: ['cuda'] steps: @@ -850,24 +802,83 @@ jobs: id: checkout uses: actions/checkout@v4 with: - fetch-depth: 0 + fetch-depth: 0 - - name: Install CUDA toolkit - id: cuda-toolkit - uses: Jimver/cuda-toolkit@v0.2.15 + - name: Install Cuda Toolkit 11.7 + if: ${{ matrix.cuda == '11.7' }} + run: | + mkdir -p "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" + choco install unzip -y + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_cudart/windows-x86_64/cuda_cudart-windows-x86_64-11.7.99-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvcc/windows-x86_64/cuda_nvcc-windows-x86_64-11.7.99-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvrtc/windows-x86_64/cuda_nvrtc-windows-x86_64-11.7.99-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/libcublas/windows-x86_64/libcublas-windows-x86_64-11.7.4.6-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvtx/windows-x86_64/cuda_nvtx-windows-x86_64-11.7.91-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/visual_studio_integration/windows-x86_64/visual_studio_integration-windows-x86_64-11.7.91-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvprof/windows-x86_64/cuda_nvprof-windows-x86_64-11.7.101-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_cccl/windows-x86_64/cuda_cccl-windows-x86_64-11.7.91-archive.zip" + unzip '*.zip' -d "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\cuda_cudart-windows-x86_64-11.7.99-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\cuda_nvcc-windows-x86_64-11.7.99-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\cuda_nvrtc-windows-x86_64-11.7.99-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\libcublas-windows-x86_64-11.7.4.6-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\cuda_nvtx-windows-x86_64-11.7.91-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\visual_studio_integration-windows-x86_64-11.7.91-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\cuda_nvprof-windows-x86_64-11.7.101-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\cuda_cccl-windows-x86_64-11.7.91-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y + echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append + echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\libnvvp" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append + echo "CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8 + echo "CUDA_PATH_V11_7=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8 + + - name: Install Cuda Toolkit 12.4 + if: ${{ matrix.cuda == '12.4' }} + run: | + mkdir -p "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" + choco install unzip -y + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_cudart/windows-x86_64/cuda_cudart-windows-x86_64-12.4.127-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvcc/windows-x86_64/cuda_nvcc-windows-x86_64-12.4.131-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvrtc/windows-x86_64/cuda_nvrtc-windows-x86_64-12.4.127-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/libcublas/windows-x86_64/libcublas-windows-x86_64-12.4.5.8-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvtx/windows-x86_64/cuda_nvtx-windows-x86_64-12.4.127-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_profiler_api/windows-x86_64/cuda_profiler_api-windows-x86_64-12.4.127-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/visual_studio_integration/windows-x86_64/visual_studio_integration-windows-x86_64-12.4.127-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvprof/windows-x86_64/cuda_nvprof-windows-x86_64-12.4.127-archive.zip" + curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_cccl/windows-x86_64/cuda_cccl-windows-x86_64-12.4.127-archive.zip" + unzip '*.zip' -d "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_cudart-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_nvcc-windows-x86_64-12.4.131-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_nvrtc-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\libcublas-windows-x86_64-12.4.5.8-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_nvtx-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_profiler_api-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\visual_studio_integration-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_nvprof-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_cccl-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y + echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append + echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\libnvvp" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append + echo "CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8 + echo "CUDA_PATH_V12_4=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8 + + - name: Install ccache + uses: hendrikmuhs/ccache-action@v1.2 with: - cuda: ${{ matrix.cuda }} - method: 'network' - sub-packages: '["nvcc", "cudart", "cublas", "cublas_dev", "thrust", "visual_studio_integration"]' + key: ${{ github.job }}-${{ matrix.cuda }}-${{ matrix.build }} + + - name: Install Ninja + id: install_ninja + run: | + choco install ninja - name: Build id: cmake_build + shell: cmd run: | - mkdir build - cd build - cmake .. -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_CUDA=ON -DBUILD_SHARED_LIBS=ON -DGGML_RPC=ON - cmake --build . --config Release -j $((${env:NUMBER_OF_PROCESSORS} - 1)) -t ggml - cmake --build . --config Release -j ${env:NUMBER_OF_PROCESSORS} + call "C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\VC\Auxiliary\Build\vcvars64.bat" + cmake -S . -B build -G "Ninja Multi-Config" -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_CUDA=ON -DBUILD_SHARED_LIBS=ON -DGGML_RPC=ON + set /A NINJA_JOBS=%NUMBER_OF_PROCESSORS%-1 + cmake --build build --config Release -j %NINJA_JOBS% -t ggml + cmake --build build --config Release - name: Determine tag name id: tag @@ -896,10 +907,12 @@ jobs: name: llama-bin-win-cu${{ matrix.cuda }}-x64.zip - name: Copy and pack Cuda runtime + if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }} run: | - echo "Cuda install location: ${{steps.cuda-toolkit.outputs.CUDA_PATH}}" + echo "Cuda install location: ${{ env.CUDA_PATH }}" $dst='.\build\bin\cudart\' - robocopy "${{steps.cuda-toolkit.outputs.CUDA_PATH}}\bin" $dst cudart64_*.dll cublas64_*.dll cublasLt64_*.dll + robocopy "${{env.CUDA_PATH}}\bin" $dst cudart64_*.dll cublas64_*.dll cublasLt64_*.dll + robocopy "${{env.CUDA_PATH}}\lib" $dst cudart64_*.dll cublas64_*.dll cublasLt64_*.dll 7z a cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip $dst\* - name: Upload Cuda runtime @@ -917,8 +930,8 @@ jobs: shell: bash env: - WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/7dff44ba-e3af-4448-841c-0d616c8da6e7/w_BaseKit_p_2024.1.0.595_offline.exe - WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel + WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/b380d914-366b-4b77-a74a-05e3c38b3514/intel-oneapi-base-toolkit-2025.0.0.882_offline.exe + WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel:intel.oneapi.win.dnnl:intel.oneapi.win.tbb.devel ONEAPI_ROOT: "C:/Program Files (x86)/Intel/oneAPI" steps: - name: Clone @@ -928,7 +941,8 @@ jobs: fetch-depth: 0 - name: Install - run: scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL + run: | + scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL - name: Build id: cmake_build @@ -947,25 +961,33 @@ jobs: echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT fi - - name: Pack artifacts + - name: Build the release package id: pack_artifacts if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} run: | echo "cp oneAPI running time dll files in ${{ env.ONEAPI_ROOT }} to ./build/bin" - cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_sycl_blas.4.dll" ./build/bin + + cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_sycl_blas.5.dll" ./build/bin cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_core.2.dll" ./build/bin cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_tbb_thread.2.dll" ./build/bin - cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/pi_win_proxy_loader.dll" ./build/bin - cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/pi_level_zero.dll" ./build/bin - cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/sycl7.dll" ./build/bin + cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_adapter_level_zero.dll" ./build/bin + cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_adapter_opencl.dll" ./build/bin + cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_loader.dll" ./build/bin + cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_win_proxy_loader.dll" ./build/bin + + cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/sycl8.dll" ./build/bin cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/svml_dispmd.dll" ./build/bin cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libmmd.dll" ./build/bin cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libiomp5md.dll" ./build/bin + + cp "${{ env.ONEAPI_ROOT }}/dnnl/latest/bin/dnnl.dll" ./build/bin + cp "${{ env.ONEAPI_ROOT }}/tbb/latest/bin/tbb12.dll" ./build/bin + echo "cp oneAPI running time dll files to ./build/bin done" 7z a llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip ./build/bin/* - - name: Upload artifacts + - name: Upload the release package if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} uses: actions/upload-artifact@v4 with: @@ -996,12 +1018,17 @@ jobs: run: | & 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' --version + - name: Install ccache + uses: hendrikmuhs/ccache-action@v1.2 + with: + key: ${{ github.job }} + - name: Build id: cmake_build run: | $env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path) $env:CMAKE_PREFIX_PATH="${env:HIP_PATH}" - cmake -G "Unix Makefiles" -B build -S . -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" -DGGML_HIPBLAS=ON -DCMAKE_BUILD_TYPE=Release -DGGML_RPC=ON + cmake -G "Unix Makefiles" -B build -S . -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" -DGGML_HIP=ON -DCMAKE_BUILD_TYPE=Release -DGGML_RPC=ON cmake --build build -j ${env:NUMBER_OF_PROCESSORS} windows-latest-cmake-hip-release: @@ -1016,6 +1043,8 @@ jobs: - name: Clone id: checkout uses: actions/checkout@v4 + with: + fetch-depth: 0 - name: Install id: depends @@ -1037,7 +1066,7 @@ jobs: run: | $env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path) $env:CMAKE_PREFIX_PATH="${env:HIP_PATH}" - cmake -G "Unix Makefiles" -B build -S . -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" -DGGML_HIPBLAS=ON -DCMAKE_BUILD_TYPE=Release -DAMDGPU_TARGETS=${{ matrix.gpu_target }} -DGGML_RPC=ON + cmake -G "Unix Makefiles" -B build -S . -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" -DGGML_HIP=ON -DCMAKE_BUILD_TYPE=Release -DAMDGPU_TARGETS=${{ matrix.gpu_target }} -DGGML_RPC=ON cmake --build build -j ${env:NUMBER_OF_PROCESSORS} md "build\bin\rocblas\library\" cp "${env:HIP_PATH}\bin\hipblas.dll" "build\bin\" @@ -1125,12 +1154,10 @@ jobs: runs-on: ubuntu-latest needs: - - ubuntu-focal-make - ubuntu-latest-cmake - - macOS-latest-make - macOS-latest-cmake - windows-latest-cmake - - windows-latest-cmake-cuda + - windows-2019-cmake-cuda - windows-latest-cmake-hip-release - macOS-latest-cmake-arm64 - macOS-latest-cmake-x64 diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index a953cdac9..bc2e5020d 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -10,12 +10,10 @@ name: Publish Docker image on: - #pull_request: - push: - branches: - - master - paths: ['.github/workflows/docker.yml', '.devops/*.Dockerfile', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal'] - workflow_dispatch: # allows manual triggering, useful for debugging + workflow_dispatch: # allows manual triggering + schedule: + # Rebuild daily rather than on every push because it is expensive + - cron: '12 4 * * *' concurrency: group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} @@ -29,7 +27,6 @@ permissions: jobs: push_to_registry: name: Push Docker image to Docker Hub - #if: github.event.pull_request.draft == false runs-on: ubuntu-latest env: @@ -117,7 +114,7 @@ jobs: swap-storage: true - name: Build and push Docker image (tagged + versioned) - if: github.event_name == 'push' + if: ${{ github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch' }} uses: docker/build-push-action@v6 with: context: . diff --git a/.github/workflows/nix-ci-aarch64.yml b/.github/workflows/nix-ci-aarch64.yml deleted file mode 100644 index 0da6acdf1..000000000 --- a/.github/workflows/nix-ci-aarch64.yml +++ /dev/null @@ -1,72 +0,0 @@ -name: Nix aarch64 builds - -on: - workflow_dispatch: # allows manual triggering - schedule: - # Rebuild daily rather than on every push because QEMU is expensive (e.g. - # 1.5h instead of minutes with the cold cache). - # - # randint(0, 59), randint(0, 23) - - cron: '26 12 * * *' - # But also rebuild if we touched any of the Nix expressions: - push: - branches: - - master - paths: ['**/*.nix', 'flake.lock'] - pull_request: - types: [opened, synchronize, reopened] - paths: ['**/*.nix', 'flake.lock'] - -concurrency: - group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} - cancel-in-progress: true - -# Fine-grant permission -# https://docs.github.com/en/actions/security-for-github-actions/security-guides/automatic-token-authentication#modifying-the-permissions-for-the-github_token -permissions: - # https://github.com/DeterminateSystems/nix-installer-action?tab=readme-ov-file#with-flakehub - id-token: write - contents: read - -jobs: - nix-build-aarch64: - runs-on: ubuntu-latest - steps: - - name: Checkout repository - uses: actions/checkout@v4 - - name: Install QEMU - # Copy-paste from https://github.com/orgs/community/discussions/8305#discussioncomment-5888654 - run: | - sudo apt-get update - sudo apt-get install -y qemu-user-static qemu-system-aarch64 - sudo usermod -a -G kvm $USER - - name: Install Nix - uses: DeterminateSystems/nix-installer-action@v9 - with: - github-token: ${{ secrets.GITHUB_TOKEN }} - extra-conf: | - extra-platforms = aarch64-linux - extra-system-features = nixos-test kvm - extra-substituters = https://llama-cpp.cachix.org https://cuda-maintainers.cachix.org - extra-trusted-public-keys = llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc= cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E= - - uses: DeterminateSystems/magic-nix-cache-action@v2 - with: - upstream-cache: https://${{ matrix.cachixName }}.cachix.org - - name: Set-up cachix to push the results to - uses: cachix/cachix-action@v13 - with: - authToken: '${{ secrets.CACHIX_AUTH_TOKEN }}' - name: llama-cpp - - name: Show all output paths - run: > - nix run github:nix-community/nix-eval-jobs - -- --gc-roots-dir gcroot - --flake - ".#packages.aarch64-linux" - - name: Build - run: > - nix run github:Mic92/nix-fast-build - -- --skip-cached --no-nom - --systems aarch64-linux - --flake - ".#checks.aarch64-linux" diff --git a/.github/workflows/nix-ci.yml b/.github/workflows/nix-ci.yml deleted file mode 100644 index 8ecbbe53b..000000000 --- a/.github/workflows/nix-ci.yml +++ /dev/null @@ -1,79 +0,0 @@ -name: Nix CI - -on: - workflow_dispatch: # allows manual triggering - push: - branches: - - master - pull_request: - types: [opened, synchronize, reopened] - -concurrency: - group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} - cancel-in-progress: true - -# Fine-grant permission -# https://docs.github.com/en/actions/security-for-github-actions/security-guides/automatic-token-authentication#modifying-the-permissions-for-the-github_token -permissions: - # https://github.com/DeterminateSystems/nix-installer-action?tab=readme-ov-file#with-flakehub - id-token: write - contents: read - -jobs: - nix-eval: - strategy: - fail-fast: false - matrix: - os: [ ubuntu-latest, macos-latest ] - runs-on: ${{ matrix.os }} - steps: - - name: Checkout repository - uses: actions/checkout@v4 - - name: Install Nix - uses: DeterminateSystems/nix-installer-action@v9 - with: - github-token: ${{ secrets.GITHUB_TOKEN }} - extra-conf: | - extra-substituters = https://llama-cpp.cachix.org https://cuda-maintainers.cachix.org - extra-trusted-public-keys = llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc= cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E= - - uses: DeterminateSystems/magic-nix-cache-action@v2 - with: - upstream-cache: https://${{ matrix.cachixName }}.cachix.org - - name: List all flake outputs - run: nix flake show --all-systems - - name: Show all output paths - run: > - nix run github:nix-community/nix-eval-jobs - -- --gc-roots-dir gcroot - --flake - ".#packages.$(nix eval --raw --impure --expr builtins.currentSystem)" - nix-build: - strategy: - fail-fast: false - matrix: - os: [ ubuntu-latest, macos-latest ] - runs-on: ${{ matrix.os }} - steps: - - name: Checkout repository - uses: actions/checkout@v4 - - name: Install Nix - uses: DeterminateSystems/nix-installer-action@v9 - with: - github-token: ${{ secrets.GITHUB_TOKEN }} - extra-conf: | - extra-substituters = https://llama-cpp.cachix.org https://cuda-maintainers.cachix.org - extra-trusted-public-keys = llama-cpp.cachix.org-1:H75X+w83wUKTIPSO1KWy9ADUrzThyGs8P5tmAbkWhQc= cuda-maintainers.cachix.org-1:0dq3bujKpuEPMCX6U4WylrUDZ9JyUG0VpVZa7CNfq5E= - - uses: DeterminateSystems/magic-nix-cache-action@v2 - with: - upstream-cache: https://${{ matrix.cachixName }}.cachix.org - - name: Set-up cachix to push the results to - uses: cachix/cachix-action@v13 - with: - authToken: '${{ secrets.CACHIX_AUTH_TOKEN }}' - name: llama-cpp - - name: Build - run: > - nix run github:Mic92/nix-fast-build - -- --skip-cached --no-nom - --flake - ".#checks.$(nix eval --raw --impure --expr builtins.currentSystem)" diff --git a/.github/workflows/nix-flake-update.yml b/.github/workflows/nix-flake-update.yml deleted file mode 100644 index 3a6a96e26..000000000 --- a/.github/workflows/nix-flake-update.yml +++ /dev/null @@ -1,22 +0,0 @@ -name: update-flake-lock -on: - workflow_dispatch: - schedule: - - cron: '0 0 * * 0' # runs weekly on Sunday at 00:00 - -jobs: - lockfile: - runs-on: ubuntu-latest - steps: - - name: Checkout repository - uses: actions/checkout@v4 - - name: Install Nix - uses: DeterminateSystems/nix-installer-action@main - - name: Update flake.lock - uses: DeterminateSystems/update-flake-lock@main - with: - pr-title: "nix: update flake.lock" - pr-labels: | - nix - pr-reviewers: philiptaron,SomeoneSerge - token: ${{ secrets.FLAKE_TOKEN }} diff --git a/.github/workflows/nix-publish-flake.yml b/.github/workflows/nix-publish-flake.yml deleted file mode 100644 index 2c3c1ebda..000000000 --- a/.github/workflows/nix-publish-flake.yml +++ /dev/null @@ -1,36 +0,0 @@ -# Make the flake discoverable on https://flakestry.dev and https://flakehub.com/flakes -name: "Publish a flake to flakestry & flakehub" -on: - push: - tags: - - "*" - workflow_dispatch: - inputs: - tag: - description: "The existing tag to publish" - type: "string" - required: true -jobs: - flakestry-publish: - runs-on: ubuntu-latest - permissions: - id-token: "write" - contents: "read" - steps: - - uses: flakestry/flakestry-publish@main - with: - version: "${{ inputs.tag || github.ref_name }}" - flakehub-publish: - runs-on: "ubuntu-latest" - permissions: - id-token: "write" - contents: "read" - steps: - - uses: "actions/checkout@v4" - with: - ref: "${{ (inputs.tag != null) && format('refs/tags/{0}', inputs.tag) || '' }}" - - uses: "DeterminateSystems/nix-installer-action@main" - - uses: "DeterminateSystems/flakehub-push@main" - with: - visibility: "public" - tag: "${{ inputs.tag }}" diff --git a/.github/workflows/python-lint.yml b/.github/workflows/python-lint.yml index a8d46f31d..ddfdf73b8 100644 --- a/.github/workflows/python-lint.yml +++ b/.github/workflows/python-lint.yml @@ -1,6 +1,13 @@ name: flake8 Lint -on: [push, pull_request] +on: + push: + branches: + - master + paths: ['.github/workflows/python-lint.yml', '**/*.py'] + pull_request: + types: [opened, synchronize, reopened] + paths: ['.github/workflows/python-lint.yml', '**/*.py'] concurrency: group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index 699ac095d..9e66fb68c 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -76,20 +76,26 @@ jobs: run: | pip install -r examples/server/tests/requirements.txt - - name: Verify server deps - id: verify_server_deps + # Setup nodejs (to be used for verifying bundled index.html) + - uses: actions/setup-node@v4 + with: + node-version: 22 + + - name: Verify bundled index.html + id: verify_server_index_html run: | git config --global --add safe.directory $(realpath .) - cd examples/server - git ls-files --others --modified + cd examples/server/webui git status - ./deps.sh + npm ci + npm run build git status - not_ignored_files="$(git ls-files --others --modified)" - echo "Modified files: ${not_ignored_files}" - if [ -n "${not_ignored_files}" ]; then - echo "Repository is dirty or server deps are not built as expected" - echo "${not_ignored_files}" + modified_files="$(git status -s)" + echo "Modified files: ${modified_files}" + if [ -n "${modified_files}" ]; then + echo "Repository is dirty or server/webui is not built as expected" + echo "Hint: You may need to follow Web UI build guide in server/README.md" + echo "${modified_files}" exit 1 fi @@ -122,14 +128,14 @@ jobs: id: server_integration_tests run: | cd examples/server/tests - PORT=8888 ./tests.sh + ./tests.sh - name: Slow tests id: server_integration_tests_slow if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }} run: | cd examples/server/tests - PORT=8888 ./tests.sh --stop --no-skipped --no-capture --tags slow + SLOW_TESTS=1 ./tests.sh server-windows: @@ -180,11 +186,12 @@ jobs: run: | cd examples/server/tests $env:PYTHONIOENCODING = ":replace" - behave.exe --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp + pytest -v -x - name: Slow tests id: server_integration_tests_slow if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }} run: | cd examples/server/tests - behave.exe --stop --no-skipped --no-capture --tags slow + $env:SLOW_TESTS = "1" + pytest -v -x diff --git a/.gitignore b/.gitignore index 1092d097a..1df7cf4a1 100644 --- a/.gitignore +++ b/.gitignore @@ -3,6 +3,7 @@ *.a *.bat *.bin +*.d *.dll *.dot *.etag @@ -103,6 +104,10 @@ examples/server/*.mjs.hpp !examples/sycl/*.bat !examples/sycl/*.sh +# Server Web UI temporary files +node_modules +examples/server/webui/dist + # Python /.venv @@ -133,3 +138,7 @@ poetry.toml # Test models for lora adapters /lora-tests + +# Local scripts +/run-vim.sh +/run-chat.sh diff --git a/.gitmodules b/.gitmodules index 5861d59cb..23ce5ff05 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,3 +1,3 @@ [submodule "kompute"] - path = ggml/src/kompute + path = ggml/src/ggml-kompute/kompute url = https://github.com/nomic-ai/kompute.git diff --git a/AUTHORS b/AUTHORS index 1bd36158a..2eb60806a 100644 --- a/AUTHORS +++ b/AUTHORS @@ -1,4 +1,4 @@ -# date: Wed Jun 26 19:36:34 EEST 2024 +# date: Thu Nov 28 20:46:15 EET 2024 # this file is auto-generated by scripts/gen-authors.sh 0cc4m @@ -7,6 +7,7 @@ 2f38b454 3ooabkhxtn <31479382+3ooabkhxtn@users.noreply.github.com> 44670 <44670@users.noreply.github.com> +65a <10104049+65a@users.noreply.github.com> AN Long AT Aarni Koskela @@ -19,20 +20,28 @@ Adithya Balaji AdithyanI Adrian Adrian Hesketh +Ahmad Tameem <113388789+Tameem-10xE@users.noreply.github.com> Ahmet Zeer AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> +AidanBeltonS Aisuko +Akarshan Biswas Akarshan Biswas +Al Mochkin <14274697+amochkin@users.noreply.github.com> Albert Jin Alberto <57916483+albbus-stack@users.noreply.github.com> +Alberto Cabrera Pérez +Alberto Cabrera Pérez Alex Alex Azarov Alex Azarov Alex Klinkhamer Alex Klinkhamer Alex Nguyen +Alex O'Connell <35843486+acon96@users.noreply.github.com> Alex Petenchea Alex Renda +Alex Tuddenham <61622354+AlexsCode@users.noreply.github.com> Alex von Gluck IV Alexey Parfenov Ali Chraghi <63465728+alichraghi@users.noreply.github.com> @@ -45,18 +54,25 @@ AmirAli Mirian <37371367+amiralimi@users.noreply.github.com> Ananta Bastola Anas Ahouzi <112881240+aahouzi@users.noreply.github.com> András Salamon +Andreas (Andi) Kunar Andrei Andrew Canis Andrew Downing Andrew Duffy Andrew Godfrey +Andrew Minh Nguyen <40281306+amqdn@users.noreply.github.com> +Andy Salerno Andy Tai +Anthony Van de Gejuchte +Antonis Makropoulos Arik Poznanski +Armen Kaleshian Artem Artem Zinnatullin Artyom Lebedev Asbjørn Olling Ásgeir Bjarni Ingvarsson +Asghar Ghorbani Ashish <1856117+ashishdatta@users.noreply.github.com> Ashok Gelal <401055+ashokgelal@users.noreply.github.com> Ashraful Islam @@ -76,12 +92,16 @@ Ben Williams Benjamin Findley <39356821+Kartoffelsaft@users.noreply.github.com> Benjamin Lecaillon <84293038+blecaillon@users.noreply.github.com> Bernat Vadell +Bert Wagner Bingan <70050083+binganao@users.noreply.github.com> +Bjarke Viksøe <164612031+bviksoe@users.noreply.github.com> Bodo Graumann Bono Lv Borislav Stanimirov Branden Butler +Brandon Squizzato <35474886+bsquizz@users.noreply.github.com> Brian +Brian Cunnie Bruce MacDonald Bryan Honof CJ Pais @@ -90,32 +110,47 @@ Calvin Laurenson Cameron Cameron Kaiser Carolinabanana <140120812+Carolinabanana@users.noreply.github.com> +CarryFun <76023481+CarryFun@users.noreply.github.com> +Carsten Kragelund Jørgensen +CarterLi999 <664681047@qq.com> Casey Primozic Casey Primozic CausalLM <148736309+CausalLM@users.noreply.github.com> Cebtenzzre Chad Brewbaker +Changyeon Kim Chao Jiang +Charles Xu <63788048+chaxu01@users.noreply.github.com> +Charles Xu +Chen Xi +Chen Xi Cheng Shao +Chenguang Li <87689256+noemotiovon@users.noreply.github.com> Chris Elrod Chris Kuehl Christian Demsar Christian Demsar Christian Falch <875252+chrfalch@users.noreply.github.com> Christian Kögler +Christian Köhnenkamp Christian Zhou-Zheng <59622928+christianazinn@users.noreply.github.com> Clark Saben <76020733+csaben@users.noreply.github.com> Clint Herron +Conrad Kramer CrispStrobe <154636388+CrispStrobe@users.noreply.github.com> +Csaba Kecskemeti Cuong Trinh Manh DAN™ Damian Stewart +Dan Johansson <164997844+eddnjjn@users.noreply.github.com> +Dan Johansson Dane Madsen DaniAndTheWeb <57776841+DaniAndTheWeb@users.noreply.github.com> Daniel Bevenius Daniel Drake Daniel Hiltgen Daniel Illescas Romero +Daniel Kleine <53251018+d-kleine@users.noreply.github.com> Daniele <57776841+daniandtheweb@users.noreply.github.com> DannyDaemonic Dat Quoc Nguyen <2412555+datquocnguyen@users.noreply.github.com> @@ -129,19 +164,28 @@ David Pflug David Renshaw David Sommers <12738+databyte@users.noreply.github.com> David Yang +DavidKorczynski Dawid Potocki Dawid Wysocki <62249621+TortillaZHawaii@users.noreply.github.com> Dean Deins +Denis Spasyuk <34203011+dspasyuk@users.noreply.github.com> +Derrick T. Woolworth Deven Mistry <31466137+deven367@users.noreply.github.com> +Dibakar Gope Didzis Gosko +Diego Devesa +Diogo Teles Sant'Anna Djip007 Don Mahurin DooWoong Lee (David) Doomsdayrs <38189170+Doomsdayrs@users.noreply.github.com> +Dou Xinpeng <15529241576@163.com> +Dou Xinpeng <81913537+Dou-Git@users.noreply.github.com> Douglas Hanley Dr. Tom Murphy VII Ph.D <499244+tom7@users.noreply.github.com> Ebey Abraham +Echo Nolan Ed Lee Ed Lepedus Eddie-Wang @@ -151,10 +195,13 @@ Elbios <141279586+Elbios@users.noreply.github.com> Elton Kola Engininja2 <139037756+Engininja2@users.noreply.github.com> Equim +Eric Curtin +Eric Curtin Eric Sommerlade Eric Zhang <34133756+EZForever@users.noreply.github.com> Erik Garrison Erik Scholz +Esko Toivonen Ettore Di Giacinto Evan Jones Evan Miller @@ -166,19 +213,26 @@ FK Fabian Fabio R. Sluzala Faez Shakil +Faisal Zaghloul +Faisal Zaghloul +Fan Shupei FantasyGmm <16450052+FantasyGmm@users.noreply.github.com> +Farbod Bijary <110523279+farbodbj@users.noreply.github.com> Fattire <528174+fat-tire@users.noreply.github.com> Felix Finn Voorhees Firat +FirstTimeEZ <179362031+FirstTimeEZ@users.noreply.github.com> Folko-Ven <71110216+Folko-Ven@users.noreply.github.com> Foul-Tarnished <107711110+Foul-Tarnished@users.noreply.github.com> Francisco Melo <43780565+francis2tm@users.noreply.github.com> Frank Mai FrankHB +Frankie Robertson Fred Douglas <43351173+fredlas@users.noreply.github.com> Frederik Vogel Gabe Goodhart +Gabe Goodhart GainLee Galunid Gary Linscott @@ -187,11 +241,13 @@ Gavin Zhao Genkagaku.GPT Georgi Gerganov Gilad S +Gilad S. <7817232+giladgd@users.noreply.github.com> Giuseppe Scrivano GiviMAD Govlzkoy Guillaume "Vermeille" Sanchez Guillaume Wenzek +Guoliang Hua <32868157+nbcsm@users.noreply.github.com> Guoteng <32697156+SolenoidWGT@users.noreply.github.com> Gustavo Rocha Dias <91472747+gustrd@users.noreply.github.com> Haggai Nuchi @@ -213,11 +269,14 @@ Hong Bo PENG Hongyu Ouyang <96765450+casavaca@users.noreply.github.com> Howard Su Hua Jiang +Huang Qi Huawei Lin Hugo Roussel +Huifeng Ou <79071290+ho2103@users.noreply.github.com> Ian Bull Ian Bull Ian Scrivener +Icecream95 Ido S IgnacioFDM Igor Okulist @@ -226,11 +285,15 @@ Ilya Kurdyukov <59548320+ilyakurdyukov@users.noreply.github.com> Ionoclast Laboratories Isaac McFadyen IsaacDynamo <61521674+IsaacDynamo@users.noreply.github.com> +Ivan +Ivan Filipov <159561759+vanaka11@users.noreply.github.com> Ivan Komarov Ivan Stepanov JH23X <165871467+JH23X@users.noreply.github.com> +Jack Mousseau Jack Mousseau JackJollimore <130917767+JackJollimore@users.noreply.github.com> +Jaeden Amero Jaemin Son Jag Chadha Jakub N @@ -243,10 +306,14 @@ Jannis Schönleber Jared Van Bortel Jared Van Bortel Jason McCartney +Jason Stillerman Jean-Christophe Hoelt Jean-Michaël Celerier Jed Fox +Jeff Bolz +Jeffrey Morgan Jeffrey Quesnelle +Jeroen Mostert Jesse Jojo Johnson Jeximo Jhen-Jie Hong @@ -258,6 +325,9 @@ Jiří Podivín <66251151+jpodivin@users.noreply.github.com> Jiří Sejkora Joan Fontanals Joan Fontanals +João Dinis Ferreira +Joe Eli McIlvain +Joe Todd Johan Johannes Gäßler Johannes Rudolph @@ -274,7 +344,9 @@ Joyce Juan Calderon-Perez <835733+gaby@users.noreply.github.com> Judd Julius Arkenberg +Jun Hee Yoo Jun Jie <71215065+junnjiee16@users.noreply.github.com> +Junil Kim Junyang Lin Juraj Bednar Justin Parker @@ -292,12 +364,14 @@ Karthik Sethuraman Kasumi <90275229+kasumi-1@users.noreply.github.com> Kawrakow <48489457+ikawrakow@users.noreply.github.com> Keiichi Tabata +Keke Han Kenvix ⭐ Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Kevin Gibbons Kevin Ji <1146876+kevinji@users.noreply.github.com> Kevin Kwok Kevin Lo +Kevin Wang Kolen Cheung Konstantin Herud Konstantin Zhuravlyov @@ -315,22 +389,29 @@ LeonEricsson <70749762+LeonEricsson@users.noreply.github.com> Leonardo Neumann Li Tan Linwei Wang +Liu Jia <109258120+Septa2112@users.noreply.github.com> +Liu Jia LoganDark +Loïc Carrère LostRuins <39025047+LostRuins@users.noreply.github.com> Luciano Luo Tian Lyle Dean +M-A M. Yusuf Sarıgöz +Ma Mingfei Maarten ter Huurne Mack Straight Maël Kerbiriou MaggotHATE +Mahesh Madhav <67384846+heshpdx@users.noreply.github.com> Manuel <44313466+makuche@users.noreply.github.com> Marc Köhlbrugge Marco Matthies <71844+marcom@users.noreply.github.com> Marcus Dunn <51931484+MarcusDunn@users.noreply.github.com> Marian Cepok Mark Fairbairn +Mark Zhuang Marko Tasic Markus Tavenrath Martin Delille @@ -342,11 +423,15 @@ MasterYi1024 <39848311+MasterYi1024@users.noreply.github.com> Mateusz Charytoniuk Matheus C. França Matheus Gabriel Alves Silva +Mathieu Geli Mathieu Nayrolles +Mathijs Henquet Mathijs de Bruin Matt Clayton <156335168+mattjcly@users.noreply.github.com> Matt Pulver +Matt Stephenson Matteo Boschini <12133566+mbosc@users.noreply.github.com> +Matteo Mortari Mattheus Chediak Matthew Tejo Matvey Soloviev @@ -356,8 +441,10 @@ Maxime <672982+maximegmd@users.noreply.github.com> Maximilian Winter Meng Zhang Meng, Hengyu +Mengqing Cao Merrick Christensen Michael Coppola +Michael Francis Michael Hueschen Michael Kesper Michael Klimenko @@ -365,41 +452,57 @@ Michael Podvitskiy Michael Potter Michael de Gans Michaël de Vries +Michał Tuszyński Mihai Mike Mikko Juola Minsoo Cheong <54794500+mscheong01@users.noreply.github.com> +Minsoo Cheong Mirko185 Mirror Azure <54669636+MirrorAzure@users.noreply.github.com> +MistApproach <98988043+MistApproach@users.noreply.github.com> Miwa / Ensan <63481257+ensan-hcl@users.noreply.github.com> Mohammadreza Hendiani Mohammadreza Hendiani +Molly Sophia +MorganRO8 <47795945+MorganRO8@users.noreply.github.com> Murilo Santana Musab Gultekin Nam D. Tran <42194884+namtranase@users.noreply.github.com> Nathan Epstein +Natsu NawafAlansari <72708095+NawafAlansari@users.noreply.github.com> Nebula Neo Zhang <14088817+arthw@users.noreply.github.com> Neo Zhang Neo Zhang Jianyu Neuman Vong +Nexes the Old <124105151+Nexesenex@users.noreply.github.com> Nexesenex <124105151+Nexesenex@users.noreply.github.com> Niall Coates <1349685+Niall-@users.noreply.github.com> +Nicholai Tukanov +Nico Bosshard Nicolai Weitkemper Nicolás Pérez Nigel Bosch Niklas Korz +NikolaiLyssogor <59844691+NikolaiLyssogor@users.noreply.github.com> Nikolas <127742645+nneubacher@users.noreply.github.com> Nindaleth +OSecret <135510162+OLSecret@users.noreply.github.com> Oleksandr Nikitin Oleksii Maryshchenko Olivier Chafik Ondřej Čertík Ouadie EL FAROUKI +PAB +Pablo Duboue +Pascal Patry Patrice Ferlet Paul Tsochantaris +Pavel Zloi Pavol Rusnak +Paweł Wodnicki <151604+32bitmicro@users.noreply.github.com> Pedro Cuenca Peter Sugihara Phil H <5756783+phiharri@users.noreply.github.com> @@ -407,10 +510,15 @@ Philip Taron Phillip Kravtsov Pierre Alexandre SCHEMBRI Pierrick Hymbert +Pieter Ouwerkerk +Plamen Minev +Prashant Vithule <119530321+Vithulep@users.noreply.github.com> Przemysław Pawełczyk Qin Yue Chen <71813199+chenqiny@users.noreply.github.com> Qingyou Meng Qu Zongfu <43257352+yancaoweidaode@users.noreply.github.com> +R0CKSTAR +R0CKSTAR RJ Adriaansen Radoslav Gerganov Radosław Gryta @@ -419,11 +527,13 @@ Raj Hammeer Singh Hada Ralph Soika Rand Xie Randall Fitzgerald +Random Fly Reinforce-II Ren Xuancheng Rene Leonhardt <65483435+reneleonhardt@users.noreply.github.com> RhinoDevel Riceball LEE +Rich Dougherty Richard Kiss Richard Roberson Rick G <26732651+TheFlipbook@users.noreply.github.com> @@ -439,21 +549,30 @@ Robey Holderith Robyn Roger Meier Roland <14355895+rbur0425@users.noreply.github.com> +Romain Biessy Romain D <90720+Artefact2@users.noreply.github.com> Romain Neutron Roman Parykin Ron Evans Ron Jailall +Roni Ronny Brendel Ronsor Rowan Hart +Ruchira Hasaranga +Ruixin Huang <18860020911@163.com> Rune <43761327+Rune-AI@users.noreply.github.com> +RunningLeon +RunningLeon Ryan Landay Ryder Wishart Ryuei Rőczey Barnabás <31726601+An0nie@users.noreply.github.com> +SRHMorris <69468379+SRHMorris@users.noreply.github.com> +SXX SakuraUmi Salvador E. Tropea +Salvatore Mesoraca Sam Spilsbury Sami Farin <3876865+Safari77@users.noreply.github.com> Samuel Maynard @@ -463,23 +582,29 @@ Sebastián A SebastianApel <13675545+SebastianApel@users.noreply.github.com> Senemu <10880819+Senemu@users.noreply.github.com> Sergey Alirzaev +Sergio López Sergio López Sertaç Özercan <852750+sozercan@users.noreply.github.com> SeungWon Jeong <65549245+redlion0929@users.noreply.github.com> ShadovvBeast Shakhar Dasgupta +Shane A Shangning Xu <32517059+xushangning@users.noreply.github.com> +Shankar +Shanshan Shen <467638484@qq.com> Shijie <821898965@qq.com> Shintarou Okada Shouzheng Liu <61452103+lshzh-ww@users.noreply.github.com> Shouzheng Liu Shuichi Tsutsumi +Shupei Fan Sigbjørn Skjæret Simon Willison Siwen Yu Sky Yan Slaren <2141330+slaren@users.noreply.github.com> Slava Primenko +Small Grass Forest SoftwareRenderer <138734813+SoftwareRenderer@users.noreply.github.com> Someone Someone Serge @@ -491,12 +616,15 @@ Stefan Sydow Steffen Röcker Stephan Walter Stephen Nichols +Steve Bonds Steve Grubb Steven Prichard Steven Roussey Steward Garcia <57494570+FSSRepo@users.noreply.github.com> +StrangeBytesDev <141275258+StrangeBytesDev@users.noreply.github.com> Suaj Carrot <72162667+SuajCarrot@users.noreply.github.com> SuperUserNameMan +Sutou Kouhei Tai Duc Nguyen Taikono-Himazin Tameem <113388789+AhmadTameem@users.noreply.github.com> @@ -507,7 +635,9 @@ Theia Vogel Thérence <13496987+Royalphax@users.noreply.github.com> Thibault Terrasson Thomas Klausner +Thorsten Sommer Tim Miller +Tim Wang Timmy Knight Timothy Cronin <40186632+4imothy@users.noreply.github.com> Ting Lou @@ -517,24 +647,31 @@ Tom C Tom Jobbins <784313+TheBloke@users.noreply.github.com> Tomas Tomáš Pazdiora +Tony Wasserka <4840017+neobrain@users.noreply.github.com> Tristan Druyen Tristan Ross +Trivikram Kamat <16024985+trivikr@users.noreply.github.com> Tungsten842 <886724vf@anonaddy.me> Tungsten842 Tushar UEXTM.com <84163508+uextm@users.noreply.github.com> +Ujjawal Panchal <31011628+Ujjawal-K-Panchal@users.noreply.github.com> Ulrich Drepper Uzo Nweke Vaibhav Srivastav Val Kharitonov Valentin Konovalov Valentyn Bezshapkin <61702053+valentynbez@users.noreply.github.com> +Vali Malinoiu <0x4139@gmail.com> Victor Nogueira Victor Z. Peng +Viet-Anh NGUYEN (Andrew) +Vinesh Janarthanan <36610342+VJHack@users.noreply.github.com> Vlad Vladimir Vladimir Malyutin Vladimir Zorin +VoidIsVoid <343750470@qq.com> Volodymyr Vitvitskyi <72226+signalpillar@users.noreply.github.com> WangHaoranRobin <56047610+WangHaoranRobin@users.noreply.github.com> Weird Constructor @@ -551,15 +688,22 @@ Xiang (Kevin) Li Xiao-Yong Jin XiaotaoChen Xiaoyi Chen +Xie Yanbo Xingchen Song(宋星辰) +Xinpeng Dou <81913537+Dou-Git@users.noreply.github.com> Xuan Son Nguyen +Yaiko Yann Follet <131855179+YannFollet@users.noreply.github.com> Yaroslav Yazan Agha-Schrader Yiming Cui Yishuo Wang +Yoshi Suhara +Yoshi Suhara +Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Yueh-Po Peng <94939112+y10ab1@users.noreply.github.com> Yui +Yuri Khrustalev Yusuf Kağan Hanoğlu Yuval Peled <31162840+Yuval-Peled@users.noreply.github.com> ZHAOKAI WANG @@ -568,6 +712,8 @@ Zay <95888118+isaiahbjork@users.noreply.github.com> Zenix Zhang Peiyuan Zheng.Deng <32841220+dengzheng-cloud@users.noreply.github.com> +Zhenwei Jin <109658203+kylo5aby@users.noreply.github.com> +Zhiyuan Li ZhouYuChen Ziad Ben Hadj-Alouane Ziang Wu <97337387+ZiangWu-77@users.noreply.github.com> @@ -581,6 +727,7 @@ alexpinel <93524949+alexpinel@users.noreply.github.com> alonfaraj alwqx amd-lalithnc +amritahs-ibm andrijdavid anon998 <131767832+anon998@users.noreply.github.com> anzz1 @@ -588,14 +735,18 @@ apaz apcameron <37645737+apcameron@users.noreply.github.com> arch-btw <57669023+arch-btw@users.noreply.github.com> arcrank +ardfork <134447697+ardfork@users.noreply.github.com> arlo-phoenix <140345165+arlo-phoenix@users.noreply.github.com> at8u <129688334+at8u@users.noreply.github.com> automaticcat +awatuna <23447591+awatuna@users.noreply.github.com> +b4b4o bandoti <141645996+bandoti@users.noreply.github.com> beiller bhubbb <79117352+bhubbb@users.noreply.github.com> bmwl bobqianic <129547291+bobqianic@users.noreply.github.com> +brucepro bryanSwk <93190252+bryanSwk@users.noreply.github.com> bsilvereagle bssrdf @@ -614,10 +765,14 @@ cpumaxx <163466046+cpumaxx@users.noreply.github.com> crasm crasm daboe01 +daghanerdonmez <44506702+daghanerdonmez@users.noreply.github.com> +daminho <37615795+daminho@users.noreply.github.com> david raistrick ddh0 ddpasa <112642920+ddpasa@users.noreply.github.com> deepdiffuser <112834445+deepdiffuser@users.noreply.github.com> +devojony <61173062+devojony@users.noreply.github.com> +ditsuke divinity76 dm4 dotpy314 <33351922+dotpy314@users.noreply.github.com> @@ -629,14 +784,18 @@ ebraminio eiery <19350831+eiery@users.noreply.github.com> eric8607242 fairydreaming <166155368+fairydreaming@users.noreply.github.com> +fengerhu1 <2748250768@qq.com> fraxy-v <65565042+fraxy-v@users.noreply.github.com> github-actions[bot] gliptic goerch grahameth <96447521+grahameth@users.noreply.github.com> +gtygo gwjr <502526+gwjr@users.noreply.github.com> h-h-h-h <13482553+h-h-h-h@users.noreply.github.com> hankcs +haopeng <657407891@qq.com> +hipudding hoangmit hongbo.mo <352280764@qq.com> hopkins385 <98618192+hopkins385@users.noreply.github.com> @@ -649,12 +808,14 @@ hxer7963 hydai iSma iacore <74560659+iacore@users.noreply.github.com> +icppWorld <124377669+icppWorld@users.noreply.github.com> igarnier intelmatt <61025942+intelmatt@users.noreply.github.com> iohub jacobi petrucciani <8117202+jpetrucciani@users.noreply.github.com> jaime-m-p <167997752+jaime-m-p@users.noreply.github.com> jameswu2014 <545426914@qq.com> +jdomke <28772296+jdomke@users.noreply.github.com> jiez <373447296@qq.com> jneem joecryptotoo <80373433+joecryptotoo@users.noreply.github.com> @@ -677,28 +838,35 @@ klosax <131523366+klosax@users.noreply.github.com> kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com> kunnis kuronekosaiko +kustaaya <58045274+kustaaya@users.noreply.github.com> kuvaus <22169537+kuvaus@users.noreply.github.com> kwin1412 <42286931+kwin1412@users.noreply.github.com> l3utterfly +laik ldwang le.chang leejet +leo-pony limitedAtonement liuwei-git <14815172+liuwei-git@users.noreply.github.com> lon <114724657+longregen@users.noreply.github.com> loonerin <132926317+loonerin@users.noreply.github.com> +ltoniazzi <61414566+ltoniazzi@users.noreply.github.com> luoyu-intel m3ndax maddes8cht <55592906+maddes8cht@users.noreply.github.com> makomk manikbhandari maor-ps <154728172+maor-ps@users.noreply.github.com> +matiaslin <45382001+matiaslin@users.noreply.github.com> +matteo mdrokz mgroeber9110 <45620825+mgroeber9110@users.noreply.github.com> minarchist mj-shifu <77107165+mj-shifu@users.noreply.github.com> mmyjona momonga <115213907+mmnga@users.noreply.github.com> +momonga <146910567+mmngays@users.noreply.github.com> moritzbrantner <31051084+moritzbrantner@users.noreply.github.com> mzcu nanahi <130121847+na-na-hi@users.noreply.github.com> @@ -716,8 +884,10 @@ omahs <73983677+omahs@users.noreply.github.com> oobabooga <112222186+oobabooga@users.noreply.github.com> opparco ostix360 <55257054+ostix360@users.noreply.github.com> +pculliton pengxin99 perserk +piDack <104877312+piDack@users.noreply.github.com> pmysl postmasters pudepiedj @@ -733,6 +903,7 @@ runfuture sandyiscool sasha0552 semidark +serhii-nakon <57632032+serhii-nakon@users.noreply.github.com> sharpHL <132747147+sharpHL@users.noreply.github.com> shibe2 singularity <12184989+singularity-s0@users.noreply.github.com> @@ -741,42 +912,55 @@ sjxx <63994076+ylsdamxssjxxdd@users.noreply.github.com> slaren <2141330+slaren@users.noreply.github.com> slaren snadampal <87143774+snadampal@users.noreply.github.com> +standby24x7 staviq stduhpf strawberrymelonpanda <152940198+strawberrymelonpanda@users.noreply.github.com> swittk takov751 <40316768+takov751@users.noreply.github.com> tarcey +tc-mb <157115220+tc-mb@users.noreply.github.com> texmex76 <40733439+texmex76@users.noreply.github.com> thement <40525767+thement@users.noreply.github.com> +thewh1teagle <61390950+thewh1teagle@users.noreply.github.com> tjohnman +toyer <2042519524@qq.com> tslmy ubik2 uint256_t uint256_t unbounded +uvos valiray <133289098+valiray@users.noreply.github.com> +vb vik viric vodkaslime <646329483@qq.com> vvhg1 <94630311+vvhg1@users.noreply.github.com> vxiiduu <73044267+vxiiduu@users.noreply.github.com> +wangshuai09 <391746016@qq.com> wbpxre150 <100937007+wbpxre150@users.noreply.github.com> whoreson <139810751+whoreson@users.noreply.github.com> woachk <24752637+woachk@users.noreply.github.com> wonjun Jang woodx <124784234+woodx9@users.noreply.github.com> +wwoodsTM <104587230+wwoodsTM@users.noreply.github.com> wzy <32936898+Freed-Wu@users.noreply.github.com> xaedes xaedes +xctan xloem <0xloem@gmail.com> yangli2 yuiseki +yuri@FreeBSD zakkor zhangkaihuo +zhentaoyu zhouwg <6889919+zhouwg@users.noreply.github.com> zhouwg zrm Ștefan-Gabriel Muscalu +杨朱 · Kiki 源文雨 <41315874+fumiama@users.noreply.github.com> +蕭澧邦 <45505768+shou692199@users.noreply.github.com> Нияз Гарифзянов <112617865+garrnizon@users.noreply.github.com> diff --git a/CMakeLists.txt b/CMakeLists.txt index ef0932a7b..f84fff9e6 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -46,6 +46,13 @@ if (WIN32) add_compile_definitions(_CRT_SECURE_NO_WARNINGS) endif() +if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "MSVC") + add_compile_options("$<$:/source-charset:utf-8>") + add_compile_options("$<$:/source-charset:utf-8>") + add_compile_options("$<$:/execution-charset:utf-8>") + add_compile_options("$<$:/execution-charset:utf-8>") +endif() + # # option list # @@ -75,6 +82,7 @@ option(LLAMA_CURL "llama: use libcurl to download model from an URL" OFF) # Required for relocatable CMake package include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info.cmake) +include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/common.cmake) # override ggml options set(GGML_SANITIZE_THREAD ${LLAMA_SANITIZE_THREAD}) @@ -88,10 +96,6 @@ if (NOT DEFINED GGML_LLAMAFILE) set(GGML_LLAMAFILE_DEFAULT ON) endif() -if (NOT DEFINED GGML_AMX) - set(GGML_AMX ON) -endif() - if (NOT DEFINED GGML_CUDA_GRAPHS) set(GGML_CUDA_GRAPHS_DEFAULT ON) endif() @@ -140,7 +144,6 @@ set(LLAMA_INCLUDE_INSTALL_DIR ${CMAKE_INSTALL_INCLUDEDIR} CACHE PATH "Location o set(LLAMA_LIB_INSTALL_DIR ${CMAKE_INSTALL_LIBDIR} CACHE PATH "Location of library files") set(LLAMA_BIN_INSTALL_DIR ${CMAKE_INSTALL_BINDIR} CACHE PATH "Location of binary files") - # At the moment some compile definitions are placed within the ggml/src # directory but not exported on the `ggml` target. This could be improved by # determining _precisely_ which defines are necessary for the llama-config @@ -157,8 +160,11 @@ if (GGML_TARGET_DEFINES) list(APPEND GGML_TRANSIENT_DEFINES ${GGML_TARGET_DEFINES}) endif() get_target_property(GGML_LINK_LIBRARIES ggml LINK_LIBRARIES) - -set_target_properties(llama PROPERTIES PUBLIC_HEADER ${CMAKE_CURRENT_SOURCE_DIR}/include/llama.h) +# all public headers +set(LLAMA_PUBLIC_HEADERS + ${CMAKE_CURRENT_SOURCE_DIR}/include/llama.h + ${CMAKE_CURRENT_SOURCE_DIR}/include/llama-cpp.h) +set_target_properties(llama PROPERTIES PUBLIC_HEADER "${LLAMA_PUBLIC_HEADERS}") install(TARGETS llama LIBRARY PUBLIC_HEADER) configure_package_config_file( diff --git a/CMakePresets.json b/CMakePresets.json index d22ffa490..436448967 100644 --- a/CMakePresets.json +++ b/CMakePresets.json @@ -24,11 +24,12 @@ "CMAKE_INSTALL_RPATH": "$ORIGIN;$ORIGIN/.." } }, - { "name": "debug", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Debug" } }, - { "name": "release", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Release" } }, - { "name": "reldbg", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "RelWithDebInfo" } }, - { "name": "static", "hidden": true, "cacheVariables": { "GGML_STATIC": "ON" } }, - { "name": "sycl_f16", "hidden": true, "cacheVariables": { "GGML_SYCL_F16": "ON" } }, + { "name": "debug", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Debug" } }, + { "name": "release", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Release" } }, + { "name": "reldbg", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "RelWithDebInfo" } }, + { "name": "static", "hidden": true, "cacheVariables": { "GGML_STATIC": "ON" } }, + { "name": "sycl_f16", "hidden": true, "cacheVariables": { "GGML_SYCL_F16": "ON" } }, + { "name": "vulkan", "hidden": true, "cacheVariables": { "GGML_VULKAN": "ON" } }, { "name": "arm64-windows-msvc", "hidden": true, @@ -48,21 +49,37 @@ } }, - { "name": "arm64-windows-llvm-debug" , "inherits": [ "base", "arm64-windows-llvm", "debug" ] }, - { "name": "arm64-windows-llvm-release", "inherits": [ "base", "arm64-windows-llvm", "reldbg" ] }, - { "name": "arm64-windows-llvm+static-release", "inherits": [ "base", "arm64-windows-llvm", "reldbg", "static" ] }, + { + "name": "arm64-apple-clang", "hidden": true, + "architecture": { "value": "arm64", "strategy": "external" }, + "toolset": { "value": "host=x64", "strategy": "external" }, + "cacheVariables": { + "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/arm64-apple-clang.cmake" + } + }, - { "name": "arm64-windows-msvc-debug" , "inherits": [ "base", "arm64-windows-msvc", "debug" ] }, + { "name": "arm64-windows-llvm-debug", "inherits": [ "base", "arm64-windows-llvm", "debug" ] }, + { "name": "arm64-windows-llvm-release", "inherits": [ "base", "arm64-windows-llvm", "reldbg" ] }, + { "name": "arm64-windows-llvm+static-release", "inherits": [ "base", "arm64-windows-llvm", "reldbg", "static" ] }, + + { "name": "arm64-apple-clang-debug", "inherits": [ "base", "arm64-apple-clang", "debug" ] }, + { "name": "arm64-apple-clang-release", "inherits": [ "base", "arm64-apple-clang", "reldbg" ] }, + { "name": "arm64-apple-clang+static-release", "inherits": [ "base", "arm64-apple-clang", "reldbg", "static" ] }, + + { "name": "arm64-windows-msvc-debug", "inherits": [ "base", "arm64-windows-msvc", "debug" ] }, { "name": "arm64-windows-msvc-release", "inherits": [ "base", "arm64-windows-msvc", "reldbg" ] }, { "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc", "reldbg", "static" ] }, - { "name": "x64-windows-msvc-debug" , "inherits": [ "base", "debug" ] }, + { "name": "x64-windows-msvc-debug", "inherits": [ "base", "debug" ] }, { "name": "x64-windows-msvc-release", "inherits": [ "base", "reldbg" ] }, { "name": "x64-windows-msvc+static-release", "inherits": [ "base", "reldbg", "static" ] }, - { "name": "x64-windows-sycl-debug" , "inherits": [ "sycl-base", "debug" ] }, + { "name": "x64-windows-sycl-debug", "inherits": [ "sycl-base", "debug" ] }, { "name": "x64-windows-sycl-debug-f16", "inherits": [ "sycl-base", "debug", "sycl_f16" ] }, { "name": "x64-windows-sycl-release", "inherits": [ "sycl-base", "release" ] }, - { "name": "x64-windows-sycl-release-f16", "inherits": [ "sycl-base", "release", "sycl_f16" ] } + { "name": "x64-windows-sycl-release-f16", "inherits": [ "sycl-base", "release", "sycl_f16" ] }, + + { "name": "x64-windows-vulkan-debug", "inherits": [ "base", "vulkan", "debug" ] }, + { "name": "x64-windows-vulkan-release", "inherits": [ "base", "vulkan", "release" ] } ] } diff --git a/CODEOWNERS b/CODEOWNERS new file mode 100644 index 000000000..88ab6de4f --- /dev/null +++ b/CODEOWNERS @@ -0,0 +1,3 @@ +# collaborators can optionally add themselves here to indicate their availability for reviewing related PRs + +ci/ @ggerganov diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 4c882c254..5a85ec5d2 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,9 +1,10 @@ # Pull requests (for contributors) - Test your changes: - - Using the commands in the [`tests`](tests) folder. For instance, running the `./tests/test-backend-ops` command tests different backend implementations of the `ggml` library - Execute [the full CI locally on your machine](ci/README.md) before publishing -- Optionally rate the complexity of your PR (i.e. `Review Complexity : Low`, `Review Complexity : Medium`, `Review Complexity : High`). This makes it easier for maintainers to triage the PRs + - Verify that the perplexity and the performance are not affected negatively by your changes (use `llama-perplexity` and `llama-bench`) + - If you modified the `ggml` source, run the `test-backend-ops` tool to check whether different backend implementations of the `ggml` operators produce consistent results (this requires access to at least two different `ggml` backends) + - If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops` - Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly - If your PR becomes stale, don't hesitate to ping the maintainers in the comments @@ -12,6 +13,7 @@ - Squash-merge PRs - Use the following format for the squashed commit title: ` : (#)`. For example: `utils : fix typo in utils.py (#1234)` - Optionally pick a `` from here: https://github.com/ggerganov/llama.cpp/wiki/Modules +- Consider adding yourself to [CODEOWNERS](CODEOWNERS) # Coding guidelines diff --git a/Makefile b/Makefile index 764a1cbd3..8c6ef2440 100644 --- a/Makefile +++ b/Makefile @@ -1,7 +1,10 @@ +ifndef LLAMA_MAKEFILE +$(error The Makefile build is deprecated. Use the CMake build instead. For more details, see https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md) +endif + # Define the default target now so that it is always the first target BUILD_TARGETS = \ libllava.a \ - llama-baby-llama \ llama-batched \ llama-batched-bench \ llama-bench \ @@ -34,6 +37,8 @@ BUILD_TARGETS = \ llama-save-load-state \ llama-server \ llama-simple \ + llama-simple-chat \ + llama-run \ llama-speculative \ llama-tokenize \ llama-vdot \ @@ -49,7 +54,6 @@ TEST_TARGETS = \ tests/test-backend-ops \ tests/test-chat-template \ tests/test-double-float \ - tests/test-grad0 \ tests/test-grammar-integration \ tests/test-grammar-parser \ tests/test-json-schema-to-grammar \ @@ -57,7 +61,6 @@ TEST_TARGETS = \ tests/test-llama-grammar \ tests/test-log \ tests/test-model-load-cancel \ - tests/test-opt \ tests/test-quantize-fns \ tests/test-quantize-perf \ tests/test-rope \ @@ -66,6 +69,7 @@ TEST_TARGETS = \ tests/test-tokenizer-0 \ tests/test-tokenizer-1-bpe \ tests/test-tokenizer-1-spm +# tests/test-opt \ # Legacy build targets that were renamed in #7809, but should still be removed when the project is cleaned LEGACY_TARGETS_CLEAN = main quantize quantize-stats perplexity imatrix embedding vdot q8dot convert-llama2c-to-ggml \ @@ -254,11 +258,11 @@ endif # Compile flags # -# keep standard at C11 and C++11 -MK_CPPFLAGS = -Iggml/include -Iggml/src -Iinclude -Isrc -Icommon +# keep standard at C11 and C++17 +MK_CPPFLAGS = -Iggml/include -Iggml/src -Iinclude -Isrc -Icommon -DGGML_USE_CPU MK_CFLAGS = -std=c11 -fPIC -MK_CXXFLAGS = -std=c++11 -fPIC -MK_NVCCFLAGS = -std=c++11 +MK_CXXFLAGS = -std=c++17 -fPIC +MK_NVCCFLAGS = -std=c++17 ifdef LLAMA_NO_CCACHE GGML_NO_CCACHE := 1 @@ -294,6 +298,7 @@ endif # some memory allocation are available on Linux through GNU extensions in libc ifeq ($(UNAME_S),Linux) MK_CPPFLAGS += -D_GNU_SOURCE + MK_LDFLAGS += -ldl endif # RLIMIT_MEMLOCK came in BSD, is not specified in POSIX.1, @@ -362,6 +367,10 @@ ifdef LLAMA_SERVER_SSL MK_LDFLAGS += -lssl -lcrypto endif +ifndef GGML_NO_CPU_AARCH64 + MK_CPPFLAGS += -DGGML_USE_CPU_AARCH64 +endif + # warnings WARN_FLAGS = \ -Wall \ @@ -526,70 +535,62 @@ ifndef GGML_NO_ACCELERATE # Mac OS - include Accelerate framework. # `-framework Accelerate` works both with Apple Silicon and Mac Intel ifeq ($(UNAME_S),Darwin) - MK_CPPFLAGS += -DGGML_USE_ACCELERATE -DGGML_USE_BLAS - MK_CPPFLAGS += -DACCELERATE_NEW_LAPACK - MK_CPPFLAGS += -DACCELERATE_LAPACK_ILP64 - MK_LDFLAGS += -framework Accelerate - OBJ_GGML += ggml/src/ggml-blas.o + MK_CPPFLAGS += -DGGML_USE_ACCELERATE -DGGML_USE_BLAS -DGGML_BLAS_USE_ACCELERATE + MK_CPPFLAGS += -DACCELERATE_NEW_LAPACK + MK_CPPFLAGS += -DACCELERATE_LAPACK_ILP64 + MK_LDFLAGS += -framework Accelerate + OBJ_GGML_EXT += ggml/src/ggml-blas/ggml-blas.o endif endif # GGML_NO_ACCELERATE -ifdef GGML_MUSA - CC := clang - CXX := clang++ - GGML_CUDA := 1 - MK_CPPFLAGS += -DGGML_USE_MUSA -endif - ifndef GGML_NO_OPENMP MK_CPPFLAGS += -DGGML_USE_OPENMP MK_CFLAGS += -fopenmp MK_CXXFLAGS += -fopenmp - ifdef GGML_MUSA - MK_CPPFLAGS += -I/usr/lib/llvm-10/include/openmp - MK_LDFLAGS += -L/usr/lib/llvm-10/lib - endif # GGML_MUSA endif # GGML_NO_OPENMP ifdef GGML_OPENBLAS - MK_CPPFLAGS += -DGGML_USE_BLAS $(shell pkg-config --cflags-only-I openblas) - MK_CFLAGS += $(shell pkg-config --cflags-only-other openblas) - MK_LDFLAGS += $(shell pkg-config --libs openblas) - OBJ_GGML += ggml/src/ggml-blas.o + MK_CPPFLAGS += -DGGML_USE_BLAS $(shell pkg-config --cflags-only-I openblas) + MK_CFLAGS += $(shell pkg-config --cflags-only-other openblas) + MK_LDFLAGS += $(shell pkg-config --libs openblas) + OBJ_GGML_EXT += ggml/src/ggml-blas/ggml-blas.o endif # GGML_OPENBLAS ifdef GGML_OPENBLAS64 - MK_CPPFLAGS += -DGGML_USE_BLAS $(shell pkg-config --cflags-only-I openblas64) - MK_CFLAGS += $(shell pkg-config --cflags-only-other openblas64) - MK_LDFLAGS += $(shell pkg-config --libs openblas64) - OBJ_GGML += ggml/src/ggml-blas.o + MK_CPPFLAGS += -DGGML_USE_BLAS $(shell pkg-config --cflags-only-I openblas64) + MK_CFLAGS += $(shell pkg-config --cflags-only-other openblas64) + MK_LDFLAGS += $(shell pkg-config --libs openblas64) + OBJ_GGML_EXT += ggml/src/ggml-blas/ggml-blas.o endif # GGML_OPENBLAS64 ifdef GGML_BLIS - MK_CPPFLAGS += -DGGML_USE_BLAS -DGGML_BLAS_USE_BLIS -I/usr/local/include/blis -I/usr/include/blis - MK_LDFLAGS += -lblis -L/usr/local/lib - OBJ_GGML += ggml/src/ggml-blas.o + MK_CPPFLAGS += -DGGML_USE_BLAS -DGGML_BLAS_USE_BLIS -I/usr/local/include/blis -I/usr/include/blis + MK_LDFLAGS += -lblis -L/usr/local/lib + OBJ_GGML_EXT += ggml/src/ggml-blas/ggml-blas.o endif # GGML_BLIS ifdef GGML_NVPL - MK_CPPFLAGS += -DGGML_USE_BLAS -DGGML_BLAS_USE_NVPL -DNVPL_ILP64 -I/usr/local/include/nvpl_blas -I/usr/include/nvpl_blas - MK_LDFLAGS += -L/usr/local/lib -lnvpl_blas_core -lnvpl_blas_ilp64_gomp - OBJ_GGML += ggml/src/ggml-blas.o + MK_CPPFLAGS += -DGGML_USE_BLAS -DGGML_BLAS_USE_NVPL -DNVPL_ILP64 -I/usr/local/include/nvpl_blas -I/usr/include/nvpl_blas + MK_LDFLAGS += -L/usr/local/lib -lnvpl_blas_core -lnvpl_blas_ilp64_gomp + OBJ_GGML_EXT += ggml/src/ggml-blas/ggml-blas.o endif # GGML_NVPL ifndef GGML_NO_LLAMAFILE - MK_CPPFLAGS += -DGGML_USE_LLAMAFILE - OBJ_GGML += ggml/src/llamafile/sgemm.o + MK_CPPFLAGS += -DGGML_USE_LLAMAFILE + OBJ_GGML_EXT += ggml/src/ggml-cpu/llamafile/sgemm.o endif ifndef GGML_NO_AMX MK_CPPFLAGS += -DGGML_USE_AMX - OBJ_GGML += ggml/src/ggml-amx.o ggml/src/ggml-amx/mmq.o + OBJ_GGML_EXT += ggml/src/ggml-cpu/amx/amx.o ggml/src/ggml-cpu/amx/mmq.o endif +# only necessary for the CPU backend files +MK_CPPFLAGS += -Iggml/src/ggml-cpu + ifdef GGML_RPC - MK_CPPFLAGS += -DGGML_USE_RPC - OBJ_GGML += ggml/src/ggml-rpc.o + MK_CPPFLAGS += -DGGML_USE_RPC + OBJ_GGML_EXT += ggml/src/ggml-rpc.o endif # GGML_RPC OBJ_CUDA_TMPL = $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-wmma*.cu)) @@ -604,41 +605,27 @@ else endif # GGML_CUDA_FA_ALL_QUANTS ifdef GGML_CUDA - ifdef GGML_MUSA - ifneq ('', '$(wildcard /opt/musa)') - CUDA_PATH ?= /opt/musa - else - CUDA_PATH ?= /usr/local/musa - endif - - MK_CPPFLAGS += -DGGML_USE_CUDA -I$(CUDA_PATH)/include - MK_LDFLAGS += -lmusa -lmublas -lmusart -lpthread -ldl -lrt -L$(CUDA_PATH)/lib -L/usr/lib64 - MK_NVCCFLAGS += -x musa -mtgpu --cuda-gpu-arch=mp_21 --cuda-gpu-arch=mp_22 + ifneq ('', '$(wildcard /opt/cuda)') + CUDA_PATH ?= /opt/cuda else - ifneq ('', '$(wildcard /opt/cuda)') - CUDA_PATH ?= /opt/cuda - else - CUDA_PATH ?= /usr/local/cuda - endif + CUDA_PATH ?= /usr/local/cuda + endif - MK_CPPFLAGS += -DGGML_USE_CUDA -DGGML_CUDA_USE_GRAPHS -I$(CUDA_PATH)/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include - MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(CUDA_PATH)/lib64 -L/usr/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L$(CUDA_PATH)/lib64/stubs -L/usr/lib/wsl/lib - MK_NVCCFLAGS += -use_fast_math - endif # GGML_MUSA + MK_CPPFLAGS += -DGGML_USE_CUDA -DGGML_CUDA_USE_GRAPHS -I$(CUDA_PATH)/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include + MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(CUDA_PATH)/lib64 -L/usr/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L$(CUDA_PATH)/lib64/stubs -L/usr/lib/wsl/lib + MK_NVCCFLAGS += -use_fast_math - OBJ_GGML += ggml/src/ggml-cuda.o - OBJ_GGML += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/*.cu)) - OBJ_GGML += $(OBJ_CUDA_TMPL) + OBJ_GGML_EXT += ggml/src/ggml-cuda/ggml-cuda.o + OBJ_GGML_EXT += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/*.cu)) + OBJ_GGML_EXT += $(OBJ_CUDA_TMPL) ifdef LLAMA_FATAL_WARNINGS MK_NVCCFLAGS += -Werror all-warnings endif # LLAMA_FATAL_WARNINGS -ifndef GGML_MUSA ifndef JETSON_EOL_MODULE_DETECT MK_NVCCFLAGS += --forward-unknown-to-host-compiler endif # JETSON_EOL_MODULE_DETECT -endif # GGML_MUSA ifdef LLAMA_DEBUG MK_NVCCFLAGS += -lineinfo @@ -651,11 +638,7 @@ endif # GGML_CUDA_DEBUG ifdef GGML_CUDA_NVCC NVCC = $(CCACHE) $(GGML_CUDA_NVCC) else - ifdef GGML_MUSA - NVCC = $(CCACHE) mcc - else - NVCC = $(CCACHE) nvcc - endif # GGML_MUSA + NVCC = $(CCACHE) nvcc endif # GGML_CUDA_NVCC ifdef CUDA_DOCKER_ARCH @@ -664,10 +647,6 @@ else ifndef CUDA_POWER_ARCH MK_NVCCFLAGS += -arch=native endif # CUDA_DOCKER_ARCH -ifdef GGML_CUDA_FORCE_DMMV - MK_NVCCFLAGS += -DGGML_CUDA_FORCE_DMMV -endif # GGML_CUDA_FORCE_DMMV - ifdef GGML_CUDA_FORCE_MMQ MK_NVCCFLAGS += -DGGML_CUDA_FORCE_MMQ endif # GGML_CUDA_FORCE_MMQ @@ -676,20 +655,6 @@ ifdef GGML_CUDA_FORCE_CUBLAS MK_NVCCFLAGS += -DGGML_CUDA_FORCE_CUBLAS endif # GGML_CUDA_FORCE_CUBLAS -ifdef GGML_CUDA_DMMV_X - MK_NVCCFLAGS += -DGGML_CUDA_DMMV_X=$(GGML_CUDA_DMMV_X) -else - MK_NVCCFLAGS += -DGGML_CUDA_DMMV_X=32 -endif # GGML_CUDA_DMMV_X - -ifdef GGML_CUDA_MMV_Y - MK_NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(GGML_CUDA_MMV_Y) -else ifdef GGML_CUDA_DMMV_Y - MK_NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(GGML_CUDA_DMMV_Y) # for backwards compatibility -else - MK_NVCCFLAGS += -DGGML_CUDA_MMV_Y=1 -endif # GGML_CUDA_MMV_Y - ifdef GGML_CUDA_F16 MK_NVCCFLAGS += -DGGML_CUDA_F16 endif # GGML_CUDA_F16 @@ -698,12 +663,6 @@ ifdef GGML_CUDA_DMMV_F16 MK_NVCCFLAGS += -DGGML_CUDA_F16 endif # GGML_CUDA_DMMV_F16 -ifdef GGML_CUDA_KQUANTS_ITER - MK_NVCCFLAGS += -DK_QUANTS_PER_ITERATION=$(GGML_CUDA_KQUANTS_ITER) -else - MK_NVCCFLAGS += -DK_QUANTS_PER_ITERATION=2 -endif - ifdef GGML_CUDA_PEER_MAX_BATCH_SIZE MK_NVCCFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=$(GGML_CUDA_PEER_MAX_BATCH_SIZE) else @@ -727,15 +686,9 @@ define NVCC_COMPILE $(NVCC) -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_CUDA -I/usr/local/cuda/include -I/opt/cuda/include -I/usr/local/cuda/targets/aarch64-linux/include -std=c++11 -O3 $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@ endef # NVCC_COMPILE else - ifdef GGML_MUSA -define NVCC_COMPILE - $(NVCC) $(NVCCFLAGS) $(CPPFLAGS) -c $< -o $@ -endef # NVCC_COMPILE - else define NVCC_COMPILE $(NVCC) $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@ endef # NVCC_COMPILE - endif # GGML_MUSA endif # JETSON_EOL_MODULE_DETECT ggml/src/ggml-cuda/%.o: \ @@ -745,8 +698,8 @@ ggml/src/ggml-cuda/%.o: \ ggml/src/ggml-cuda/common.cuh $(NVCC_COMPILE) -ggml/src/ggml-cuda.o: \ - ggml/src/ggml-cuda.cu \ +ggml/src/ggml-cuda/ggml-cuda.o: \ + ggml/src/ggml-cuda/ggml-cuda.cu \ ggml/include/ggml-cuda.h \ ggml/include/ggml.h \ ggml/include/ggml-backend.h \ @@ -757,9 +710,9 @@ ggml/src/ggml-cuda.o: \ endif # GGML_CUDA ifdef GGML_VULKAN - MK_CPPFLAGS += -DGGML_USE_VULKAN - MK_LDFLAGS += $(shell pkg-config --libs vulkan) - OBJ_GGML += ggml/src/ggml-vulkan.o ggml/src/ggml-vulkan-shaders.o + MK_CPPFLAGS += -DGGML_USE_VULKAN + MK_LDFLAGS += $(shell pkg-config --libs vulkan) + OBJ_GGML_EXT += ggml/src/ggml-vulkan.o ggml/src/ggml-vulkan-shaders.o ifdef GGML_VULKAN_CHECK_RESULTS MK_CPPFLAGS += -DGGML_VULKAN_CHECK_RESULTS @@ -789,10 +742,10 @@ GLSLC_CMD = glslc _ggml_vk_genshaders_cmd = $(shell pwd)/vulkan-shaders-gen _ggml_vk_header = ggml/src/ggml-vulkan-shaders.hpp _ggml_vk_source = ggml/src/ggml-vulkan-shaders.cpp -_ggml_vk_input_dir = ggml/src/vulkan-shaders +_ggml_vk_input_dir = ggml/src/ggml-vulkan/vulkan-shaders _ggml_vk_shader_deps = $(echo $(_ggml_vk_input_dir)/*.comp) -ggml/src/ggml-vulkan.o: ggml/src/ggml-vulkan.cpp ggml/include/ggml-vulkan.h $(_ggml_vk_header) $(_ggml_vk_source) +ggml/src/ggml-vulkan.o: ggml/src/ggml-vulkan/ggml-vulkan.cpp ggml/include/ggml-vulkan.h $(_ggml_vk_header) $(_ggml_vk_source) $(CXX) $(CXXFLAGS) $(shell pkg-config --cflags vulkan) -c $< -o $@ $(_ggml_vk_header): $(_ggml_vk_source) @@ -804,12 +757,12 @@ $(_ggml_vk_source): $(_ggml_vk_shader_deps) vulkan-shaders-gen --target-hpp $(_ggml_vk_header) \ --target-cpp $(_ggml_vk_source) -vulkan-shaders-gen: ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp - $(CXX) $(CXXFLAGS) -o $@ $(LDFLAGS) ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp +vulkan-shaders-gen: ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp + $(CXX) $(CXXFLAGS) -o $@ $(LDFLAGS) ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp endif # GGML_VULKAN -ifdef GGML_HIPBLAS +ifdef GGML_HIP ifeq ($(wildcard /opt/rocm),) ROCM_PATH ?= /usr AMDGPU_TARGETS ?= $(shell $(shell which amdgpu-arch)) @@ -818,11 +771,7 @@ ifdef GGML_HIPBLAS AMDGPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch) endif - GGML_CUDA_DMMV_X ?= 32 - GGML_CUDA_MMV_Y ?= 1 - GGML_CUDA_KQUANTS_ITER ?= 2 - - MK_CPPFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUDA + MK_CPPFLAGS += -DGGML_USE_HIP -DGGML_USE_CUDA ifdef GGML_HIP_UMA MK_CPPFLAGS += -DGGML_HIP_UMA @@ -835,13 +784,6 @@ endif # GGML_HIP_UMA HIPCC ?= $(CCACHE) $(ROCM_PATH)/bin/hipcc HIPFLAGS += $(addprefix --offload-arch=,$(AMDGPU_TARGETS)) - HIPFLAGS += -DGGML_CUDA_DMMV_X=$(GGML_CUDA_DMMV_X) - HIPFLAGS += -DGGML_CUDA_MMV_Y=$(GGML_CUDA_MMV_Y) - HIPFLAGS += -DK_QUANTS_PER_ITERATION=$(GGML_CUDA_KQUANTS_ITER) - -ifdef GGML_CUDA_FORCE_DMMV - HIPFLAGS += -DGGML_CUDA_FORCE_DMMV -endif # GGML_CUDA_FORCE_DMMV ifdef GGML_CUDA_FORCE_MMQ HIPFLAGS += -DGGML_CUDA_FORCE_MMQ @@ -855,12 +797,12 @@ ifdef GGML_CUDA_NO_PEER_COPY HIPFLAGS += -DGGML_CUDA_NO_PEER_COPY endif # GGML_CUDA_NO_PEER_COPY - OBJ_GGML += ggml/src/ggml-cuda.o - OBJ_GGML += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/*.cu)) - OBJ_GGML += $(OBJ_CUDA_TMPL) + OBJ_GGML_EXT += ggml/src/ggml-cuda/ggml-cuda.o + OBJ_GGML_EXT += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/*.cu)) + OBJ_GGML_EXT += $(OBJ_CUDA_TMPL) -ggml/src/ggml-cuda.o: \ - ggml/src/ggml-cuda.cu \ +ggml/src/ggml-cuda/ggml-cuda.o: \ + ggml/src/ggml-cuda/ggml-cuda.cu \ ggml/include/ggml-cuda.h \ ggml/include/ggml.h \ ggml/include/ggml-backend.h \ @@ -875,73 +817,172 @@ ggml/src/ggml-cuda/%.o: \ ggml/src/ggml-common.h \ ggml/src/ggml-cuda/common.cuh $(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $< -endif # GGML_HIPBLAS +endif # GGML_HIP + +ifdef GGML_MUSA + ifeq ($(wildcard /opt/musa),) + MUSA_PATH ?= /usr/local/musa + else + MUSA_PATH ?= /opt/musa + endif + MUSA_ARCHITECTURES ?= 21;22 + + MK_CPPFLAGS += -DGGML_USE_MUSA -DGGML_USE_CUDA + MK_LDFLAGS += -L$(MUSA_PATH)/lib -Wl,-rpath=$(MUSA_PATH)/lib + MK_LDFLAGS += -lmusa -lmusart -lmublas + + ifndef GGML_NO_OPENMP + # For Ubuntu Focal + MK_CPPFLAGS += -I/usr/lib/llvm-10/include/openmp + MK_LDFLAGS += -L/usr/lib/llvm-10/lib + # For Ubuntu Jammy + MK_CPPFLAGS += -I/usr/lib/llvm-14/lib/clang/14.0.0/include + MK_LDFLAGS += -L/usr/lib/llvm-14/lib + endif # GGML_NO_OPENMP + + CC := $(MUSA_PATH)/bin/clang + CXX := $(MUSA_PATH)/bin/clang++ + MCC := $(CCACHE) $(MUSA_PATH)/bin/mcc + + MUSAFLAGS = -x musa -mtgpu + MUSAFLAGS += $(foreach arch,$(subst ;, ,$(MUSA_ARCHITECTURES)),--cuda-gpu-arch=mp_$(arch)) + +ifdef GGML_CUDA_FORCE_MMQ + MUSAFLAGS += -DGGML_CUDA_FORCE_MMQ +endif # GGML_CUDA_FORCE_MMQ + +ifdef GGML_CUDA_FORCE_CUBLAS + MUSAFLAGS += -DGGML_CUDA_FORCE_CUBLAS +endif # GGML_CUDA_FORCE_CUBLAS + +ifdef GGML_CUDA_F16 + MUSAFLAGS += -DGGML_CUDA_F16 +endif # GGML_CUDA_F16 + +ifdef GGML_CUDA_DMMV_F16 + MUSAFLAGS += -DGGML_CUDA_F16 +endif # GGML_CUDA_DMMV_F16 + +ifdef GGML_CUDA_PEER_MAX_BATCH_SIZE + MUSAFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=$(GGML_CUDA_PEER_MAX_BATCH_SIZE) +else + MUSAFLAGS += -DGGML_CUDA_PEER_MAX_BATCH_SIZE=128 +endif # GGML_CUDA_PEER_MAX_BATCH_SIZE + +ifdef GGML_CUDA_NO_PEER_COPY + MUSAFLAGS += -DGGML_CUDA_NO_PEER_COPY +endif # GGML_CUDA_NO_PEER_COPY + +ifdef GGML_CUDA_FA_ALL_QUANTS + MUSAFLAGS += -DGGML_CUDA_FA_ALL_QUANTS +endif # GGML_CUDA_FA_ALL_QUANTS + + OBJ_GGML_EXT += ggml/src/ggml-cuda/ggml-cuda.o + OBJ_GGML_EXT += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/*.cu)) + OBJ_GGML_EXT += $(OBJ_CUDA_TMPL) + +ggml/src/ggml-cuda/ggml-cuda.o: \ + ggml/src/ggml-cuda/ggml-cuda.cu \ + ggml/include/ggml-cuda.h \ + ggml/include/ggml.h \ + ggml/include/ggml-backend.h \ + ggml/src/ggml-backend-impl.h \ + ggml/src/ggml-common.h \ + $(wildcard ggml/src/ggml-cuda/*.cuh) + $(MCC) $(CXXFLAGS) $(MUSAFLAGS) -c -o $@ $< + +ggml/src/ggml-cuda/%.o: \ + ggml/src/ggml-cuda/%.cu \ + ggml/include/ggml.h \ + ggml/src/ggml-common.h \ + ggml/src/ggml-cuda/common.cuh + $(MCC) $(CXXFLAGS) $(MUSAFLAGS) -c -o $@ $< +endif # GGML_MUSA ifdef GGML_METAL - MK_CPPFLAGS += -DGGML_USE_METAL - MK_LDFLAGS += -framework Foundation -framework Metal -framework MetalKit - OBJ_GGML += ggml/src/ggml-metal.o + MK_CPPFLAGS += -DGGML_USE_METAL + MK_LDFLAGS += -framework Foundation -framework Metal -framework MetalKit + OBJ_GGML_EXT += ggml/src/ggml-metal/ggml-metal.o + +ifdef GGML_METAL_USE_BF16 + MK_CPPFLAGS += -DGGML_METAL_USE_BF16 +endif # GGML_METAL_USE_BF16 ifdef GGML_METAL_NDEBUG MK_CPPFLAGS += -DGGML_METAL_NDEBUG endif ifdef GGML_METAL_EMBED_LIBRARY - MK_CPPFLAGS += -DGGML_METAL_EMBED_LIBRARY - OBJ_GGML += ggml/src/ggml-metal-embed.o + MK_CPPFLAGS += -DGGML_METAL_EMBED_LIBRARY + OBJ_GGML_EXT += ggml/src/ggml-metal-embed.o endif endif # GGML_METAL ifdef GGML_METAL -ggml/src/ggml-metal.o: \ - ggml/src/ggml-metal.m \ +ggml/src/ggml-metal/ggml-metal.o: \ + ggml/src/ggml-metal/ggml-metal.m \ + ggml/src/ggml-metal/ggml-metal-impl.h \ ggml/include/ggml-metal.h \ ggml/include/ggml.h $(CC) $(CFLAGS) -c $< -o $@ ifdef GGML_METAL_EMBED_LIBRARY ggml/src/ggml-metal-embed.o: \ - ggml/src/ggml-metal.metal \ + ggml/src/ggml-metal/ggml-metal.metal \ + ggml/src/ggml-metal/ggml-metal-impl.h \ ggml/src/ggml-common.h @echo "Embedding Metal library" - @sed -e '/#include "ggml-common.h"/r ggml/src/ggml-common.h' -e '/#include "ggml-common.h"/d' < ggml/src/ggml-metal.metal > ggml/src/ggml-metal-embed.metal + @sed -e '/__embed_ggml-common.h__/r ggml/src/ggml-common.h' -e '/__embed_ggml-common.h__/d' < ggml/src/ggml-metal/ggml-metal.metal > ggml/src/ggml-metal/ggml-metal-embed.metal.tmp + @sed -e '/#include "ggml-metal-impl.h"/r ggml/src/ggml-metal/ggml-metal-impl.h' -e '/#include "ggml-metal-impl.h"/d' < ggml/src/ggml-metal/ggml-metal-embed.metal.tmp > ggml/src/ggml-metal/ggml-metal-embed.metal $(eval TEMP_ASSEMBLY=$(shell mktemp -d)) - @echo ".section __DATA, __ggml_metallib" > $(TEMP_ASSEMBLY)/ggml-metal-embed.s - @echo ".globl _ggml_metallib_start" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s - @echo "_ggml_metallib_start:" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s - @echo ".incbin \"ggml/src/ggml-metal-embed.metal\"" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s - @echo ".globl _ggml_metallib_end" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s - @echo "_ggml_metallib_end:" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s + @echo ".section __DATA, __ggml_metallib" > $(TEMP_ASSEMBLY)/ggml-metal-embed.s + @echo ".globl _ggml_metallib_start" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s + @echo "_ggml_metallib_start:" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s + @echo ".incbin \"ggml/src/ggml-metal/ggml-metal-embed.metal\"" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s + @echo ".globl _ggml_metallib_end" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s + @echo "_ggml_metallib_end:" >> $(TEMP_ASSEMBLY)/ggml-metal-embed.s $(CC) $(CFLAGS) -c $(TEMP_ASSEMBLY)/ggml-metal-embed.s -o $@ @rm -f ${TEMP_ASSEMBLY}/ggml-metal-embed.s @rmdir ${TEMP_ASSEMBLY} endif endif # GGML_METAL -OBJ_GGML += \ - ggml/src/ggml.o \ - ggml/src/ggml-alloc.o \ - ggml/src/ggml-backend.o \ - ggml/src/ggml-quants.o \ - ggml/src/ggml-aarch64.o +DIR_GGML = ggml +DIR_LLAMA = src +DIR_COMMON = common + +OBJ_GGML = \ + $(DIR_GGML)/src/ggml.o \ + $(DIR_GGML)/src/ggml-aarch64.o \ + $(DIR_GGML)/src/ggml-alloc.o \ + $(DIR_GGML)/src/ggml-backend.o \ + $(DIR_GGML)/src/ggml-backend-reg.o \ + $(DIR_GGML)/src/ggml-opt.o \ + $(DIR_GGML)/src/ggml-quants.o \ + $(DIR_GGML)/src/ggml-threading.o \ + $(DIR_GGML)/src/ggml-cpu/ggml-cpu.o \ + $(DIR_GGML)/src/ggml-cpu/ggml-cpu-cpp.o \ + $(DIR_GGML)/src/ggml-cpu/ggml-cpu-aarch64.o \ + $(DIR_GGML)/src/ggml-cpu/ggml-cpu-quants.o \ + $(OBJ_GGML_EXT) OBJ_LLAMA = \ - src/llama.o \ - src/llama-vocab.o \ - src/llama-grammar.o \ - src/llama-sampling.o \ - src/unicode.o \ - src/unicode-data.o + $(DIR_LLAMA)/llama.o \ + $(DIR_LLAMA)/llama-vocab.o \ + $(DIR_LLAMA)/llama-grammar.o \ + $(DIR_LLAMA)/llama-sampling.o \ + $(DIR_LLAMA)/unicode.o \ + $(DIR_LLAMA)/unicode-data.o OBJ_COMMON = \ - common/common.o \ - common/arg.o \ - common/log.o \ - common/console.o \ - common/ngram-cache.o \ - common/sampling.o \ - common/train.o \ - common/build-info.o \ - common/json-schema-to-grammar.o \ - common/tool-call.o + $(DIR_COMMON)/common.o \ + $(DIR_COMMON)/arg.o \ + $(DIR_COMMON)/log.o \ + $(DIR_COMMON)/console.o \ + $(DIR_COMMON)/ngram-cache.o \ + $(DIR_COMMON)/sampling.o \ + $(DIR_COMMON)/speculative.o \ + $(DIR_COMMON)/build-info.o \ + $(DIR_COMMON)/tool-call.o \ + $(DIR_COMMON)/json-schema-to-grammar.o OBJ_ALL = $(OBJ_GGML) $(OBJ_LLAMA) $(OBJ_COMMON) @@ -997,7 +1038,6 @@ $(info I CXX: $(shell $(CXX) --version | head -n 1)) ifdef GGML_CUDA $(info I NVCC: $(shell $(NVCC) --version | tail -n 1)) CUDA_VERSION := $(shell $(NVCC) --version | grep -oP 'release (\K[0-9]+\.[0-9])') -ifndef GGML_MUSA ifeq ($(shell awk -v "v=$(CUDA_VERSION)" 'BEGIN { print (v < 11.7) }'),1) ifndef CUDA_DOCKER_ARCH @@ -1007,7 +1047,6 @@ endif # CUDA_POWER_ARCH endif # CUDA_DOCKER_ARCH endif # eq ($(shell echo "$(CUDA_VERSION) < 11.7" | bc),1) -endif # GGML_MUSA endif # GGML_CUDA $(info ) @@ -1044,232 +1083,85 @@ endif # Build libraries # -# ggml +# Libraries +LIB_GGML = libggml.so +LIB_GGML_S = libggml.a -ggml/src/ggml.o: \ - ggml/src/ggml.c \ - ggml/include/ggml.h - $(CC) $(CFLAGS) -c $< -o $@ +LIB_LLAMA = libllama.so +LIB_LLAMA_S = libllama.a -ggml/src/ggml-alloc.o: \ - ggml/src/ggml-alloc.c \ - ggml/include/ggml.h \ - ggml/include/ggml-alloc.h - $(CC) $(CFLAGS) -c $< -o $@ +LIB_COMMON = libcommon.so +LIB_COMMON_S = libcommon.a -ggml/src/ggml-backend.o: \ - ggml/src/ggml-backend.cpp \ - ggml/src/ggml-backend-impl.h \ - ggml/include/ggml.h \ - ggml/include/ggml-backend.h - $(CXX) $(CXXFLAGS) -c $< -o $@ +# Targets +BUILD_TARGETS += $(LIB_GGML) $(LIB_GGML_S) $(LIB_LLAMA) $(LIB_LLAMA_S) $(LIB_COMMON) $(LIB_COMMON_S) -ggml/src/ggml-quants.o: \ - ggml/src/ggml-quants.c \ - ggml/include/ggml.h \ - ggml/src/ggml-quants.h \ - ggml/src/ggml-common.h - $(CC) $(CFLAGS) -c $< -o $@ +# Dependency files +DEP_FILES = $(OBJ_GGML:.o=.d) $(OBJ_LLAMA:.o=.d) $(OBJ_COMMON:.o=.d) -ggml/src/ggml-aarch64.o: \ - ggml/src/ggml-aarch64.c \ - ggml/include/ggml.h \ - ggml/src/ggml-aarch64.h \ - ggml/src/ggml-common.h - $(CC) $(CFLAGS) -c $< -o $@ +# Default target +all: $(BUILD_TARGETS) -ggml/src/ggml-blas.o: \ - ggml/src/ggml-blas.cpp \ - ggml/include/ggml-blas.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - -ifndef GGML_NO_LLAMAFILE -ggml/src/llamafile/sgemm.o: \ - ggml/src/llamafile/sgemm.cpp \ - ggml/src/llamafile/sgemm.h \ - ggml/include/ggml.h - $(CXX) $(CXXFLAGS) -c $< -o $@ -endif # GGML_NO_LLAMAFILE - -ifndef GGML_NO_AMX -ggml/src/ggml-amx.o: \ - ggml/src/ggml-amx.cpp \ - ggml/include/ggml-amx.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - -ggml/src/ggml-amx/mmq.o: \ - ggml/src/ggml-amx/mmq.cpp \ - ggml/src/ggml-amx/mmq.h \ - ggml/include/ggml.h - $(CXX) $(CXXFLAGS) -c $< -o $@ -endif - -ifdef GGML_RPC -ggml/src/ggml-rpc.o: \ - ggml/src/ggml-rpc.cpp \ - ggml/include/ggml-rpc.h - $(CXX) $(CXXFLAGS) -c $< -o $@ -endif # GGML_RPC - -$(LIB_GGML): \ - $(OBJ_GGML) - $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) - -$(LIB_GGML_S): \ - $(OBJ_GGML) - ar rcs $(LIB_GGML_S) $^ - -# llama - -src/unicode.o: \ - src/unicode.cpp \ - src/unicode.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - -src/unicode-data.o: \ - src/unicode-data.cpp \ - src/unicode-data.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - -src/llama.o: \ - src/llama.cpp \ - src/llama-impl.h \ - src/llama-vocab.h \ - src/llama-grammar.h \ - src/llama-sampling.h \ - src/unicode.h \ - include/llama.h \ - ggml/include/ggml-cuda.h \ - ggml/include/ggml-metal.h \ +# Note: need this exception because `ggml-cpu.c` and `ggml-cpu.cpp` both produce the same obj/dep files +# g++ -M -I ./ggml/include/ -I ./ggml/src ggml/src/ggml-cpu/ggml-cpu.cpp | grep ggml +$(DIR_GGML)/src/ggml-cpu/ggml-cpu-cpp.o: \ + ggml/src/ggml-cpu/ggml-cpu.cpp \ + ggml/include/ggml-backend.h \ ggml/include/ggml.h \ ggml/include/ggml-alloc.h \ - ggml/include/ggml-backend.h - $(CXX) $(CXXFLAGS) -c $< -o $@ + ggml/src/ggml-backend-impl.h \ + ggml/include/ggml-cpu.h \ + ggml/src/ggml-impl.h + $(CXX) $(CXXFLAGS) -c $< -o $@ -src/llama-vocab.o: \ - src/llama-vocab.cpp \ - src/llama-vocab.h \ - src/llama-impl.h \ - include/llama.h - $(CXX) $(CXXFLAGS) -c $< -o $@ +# Rules for building object files +$(DIR_GGML)/%.o: $(DIR_GGML)/%.c + $(CC) $(CFLAGS) -MMD -c $< -o $@ -src/llama-grammar.o: \ - src/llama-grammar.cpp \ - src/llama-grammar.h \ - src/llama-impl.h \ - src/llama-vocab.h \ - src/llama-sampling.h \ - include/llama.h - $(CXX) $(CXXFLAGS) -c $< -o $@ +$(DIR_GGML)/%.o: $(DIR_GGML)/%.cpp + $(CXX) $(CXXFLAGS) -MMD -c $< -o $@ -src/llama-sampling.o: \ - src/llama-sampling.cpp \ - src/llama-sampling.h \ - src/llama-impl.h \ - include/llama.h - $(CXX) $(CXXFLAGS) -c $< -o $@ +$(DIR_LLAMA)/%.o: $(DIR_LLAMA)/%.cpp + $(CXX) $(CXXFLAGS) -MMD -c $< -o $@ -$(LIB_LLAMA): \ - $(OBJ_LLAMA) \ - $(LIB_GGML) +$(DIR_COMMON)/%.o: $(DIR_COMMON)/%.cpp + $(CXX) $(CXXFLAGS) -MMD -c $< -o $@ + +# Rules for building libraries +$(LIB_GGML): $(OBJ_GGML) $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) -$(LIB_LLAMA_S): \ - $(OBJ_LLAMA) +$(LIB_GGML_S): $(OBJ_GGML) + ar rcs $(LIB_GGML_S) $^ + +$(LIB_LLAMA): $(OBJ_LLAMA) $(LIB_GGML) + $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) + +$(LIB_LLAMA_S): $(OBJ_LLAMA) ar rcs $(LIB_LLAMA_S) $^ -# common - -common/common.o: \ - common/common.cpp \ - common/common.h \ - common/chat-template.hpp \ - common/console.h \ - common/sampling.h \ - common/json.hpp \ - common/json-schema-to-grammar.h \ - common/minja.hpp \ - common/tool-call.cpp \ - common/tool-call.h \ - include/llama.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - -common/arg.o: \ - common/arg.cpp \ - common/arg.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - -common/log.o: \ - common/log.cpp \ - common/log.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - -common/sampling.o: \ - common/sampling.cpp \ - common/sampling.h \ - include/llama.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - -common/console.o: \ - common/console.cpp \ - common/console.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - -common/json-schema-to-grammar.o: \ - common/json-schema-to-grammar.cpp \ - common/json-schema-to-grammar.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - -common/tool-call.o: \ - common/tool-call.cpp \ - common/tool-call.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - -common/train.o: \ - common/train.cpp \ - common/train.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - -common/ngram-cache.o: \ - common/ngram-cache.cpp \ - common/ngram-cache.h - $(CXX) $(CXXFLAGS) -c $< -o $@ - -$(LIB_COMMON): \ - $(OBJ_COMMON) \ - $(LIB_LLAMA) \ - $(LIB_GGML) +$(LIB_COMMON): $(OBJ_COMMON) $(LIB_LLAMA) $(LIB_GGML) $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) -$(LIB_COMMON_S): \ - $(OBJ_COMMON) +$(LIB_COMMON_S): $(OBJ_COMMON) ar rcs $(LIB_COMMON_S) $^ -clean: - rm -vrf *.dot $(BUILD_TARGETS) $(TEST_TARGETS) - rm -rvf src/*.o - rm -rvf tests/*.o - rm -rvf examples/*.o - rm -rvf common/*.o - rm -rvf *.a - rm -rvf *.dll - rm -rvf *.so - rm -rvf *.dot - rm -rvf ggml/*.a - rm -rvf ggml/*.dll - rm -rvf ggml/*.so - rm -vrf ggml/src/*.o - rm -rvf ggml/src/llamafile/*.o - rm -rvf common/build-info.cpp - rm -vrf ggml/src/ggml-metal-embed.metal - rm -vrf ggml/src/ggml-cuda/*.o - rm -vrf ggml/src/ggml-cuda/template-instances/*.o - rm -vrf ggml/src/ggml-amx/*.o - rm -rvf $(BUILD_TARGETS) - rm -rvf $(TEST_TARGETS) - rm -f vulkan-shaders-gen ggml/src/ggml-vulkan-shaders.hpp ggml/src/ggml-vulkan-shaders.cpp - rm -rvf $(LEGACY_TARGETS_CLEAN) - find examples pocs -type f -name "*.o" -delete +# Include dependency files +-include $(DEP_FILES) + +# Clean generated server assets +clean-server-assets: + find examples/server -type f -name "*.js.hpp" -delete + find examples/server -type f -name "*.mjs.hpp" -delete + find examples/server -type f -name "*.css.hpp" -delete + find examples/server -type f -name "*.html.hpp" -delete + +# Clean rule +clean: clean-server-assets + rm -vrf $(BUILD_TARGETS) $(TEST_TARGETS) + rm -rvf *.a *.dll *.so *.dot + find ggml src common tests examples pocs -type f -name "*.o" -delete + find ggml src common tests examples pocs -type f -name "*.d" -delete # # Examples @@ -1295,11 +1187,21 @@ llama-infill: examples/infill/infill.cpp \ $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) +llama-run: examples/run/run.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + llama-simple: examples/simple/simple.cpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) +llama-simple-chat: examples/simple-chat/simple-chat.cpp \ + $(OBJ_ALL) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + llama-tokenize: examples/tokenize/tokenize.cpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) @@ -1397,11 +1299,6 @@ llama-bench: examples/llama-bench/llama-bench.cpp \ $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -llama-baby-llama: examples/baby-llama/baby-llama.cpp \ - $(OBJ_ALL) - $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) - $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) - llama-export-lora: examples/export-lora/export-lora.cpp \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) @@ -1467,33 +1364,18 @@ llama-server: \ examples/server/server.cpp \ examples/server/utils.hpp \ examples/server/httplib.h \ - examples/server/colorthemes.css.hpp \ - examples/server/style.css.hpp \ - examples/server/theme-beeninorder.css.hpp \ - examples/server/theme-ketivah.css.hpp \ - examples/server/theme-mangotango.css.hpp \ - examples/server/theme-playground.css.hpp \ - examples/server/theme-polarnight.css.hpp \ - examples/server/theme-snowstorm.css.hpp \ examples/server/index.html.hpp \ - examples/server/index-new.html.hpp \ - examples/server/index.js.hpp \ - examples/server/completion.js.hpp \ - examples/server/system-prompts.js.hpp \ - examples/server/prompt-formats.js.hpp \ - examples/server/json-schema-to-grammar.mjs.hpp \ examples/server/loading.html.hpp \ common/chat-template.hpp \ common/json.hpp \ common/minja.hpp \ - common/stb_image.h \ common/tool-call.h \ $(OBJ_ALL) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2) # Portable equivalent of `cd examples/server/public && xxd -i $(notdir $<) ../$(notdir $<).hpp`: -examples/server/%.hpp: examples/server/public/% Makefile +examples/server/%.hpp: examples/server/public/% FORCE Makefile @( export NAME=$(subst .,_,$(subst -,_,$(notdir $<))) && \ echo "unsigned char $${NAME}[] = {" && \ cat $< | od -v -t x1 -An | sed -E 's/([0-9a-fA-F]+)/0x\1, /g' && \ @@ -1602,11 +1484,6 @@ tests/test-minja: tests/test-minja.cpp \ $(CXX) $(CXXFLAGS) -Iexamples/server -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -tests/test-grad0: tests/test-grad0.cpp \ - $(OBJ_GGML) - $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) - $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) - tests/test-opt: tests/test-opt.cpp \ $(OBJ_GGML) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) @@ -1688,7 +1565,7 @@ llama-q8dot: pocs/vdot/q8dot.cpp ggml/src/ggml.o \ # Deprecated binaries that we want to keep around long enough for people to migrate to the new filenames, then these can be removed. # # Mark legacy binary targets as .PHONY so that they are always checked. -.PHONY: main quantize perplexity embedding server +.PHONY: FORCE main quantize perplexity embedding server # Define the object file target examples/deprecation-warning/deprecation-warning.o: examples/deprecation-warning/deprecation-warning.cpp diff --git a/Package.swift b/Package.swift index 3a17e6c34..d32b74a63 100644 --- a/Package.swift +++ b/Package.swift @@ -10,10 +10,16 @@ var sources = [ "src/unicode.cpp", "src/unicode-data.cpp", "ggml/src/ggml.c", + "ggml/src/ggml-aarch64.c", "ggml/src/ggml-alloc.c", "ggml/src/ggml-backend.cpp", + "ggml/src/ggml-backend-reg.cpp", + "ggml/src/ggml-cpu/ggml-cpu.c", + "ggml/src/ggml-cpu/ggml-cpu.cpp", + "ggml/src/ggml-cpu/ggml-cpu-aarch64.c", + "ggml/src/ggml-cpu/ggml-cpu-quants.c", + "ggml/src/ggml-threading.cpp", "ggml/src/ggml-quants.c", - "ggml/src/ggml-aarch64.c", ] var resources: [Resource] = [] @@ -21,21 +27,26 @@ var linkerSettings: [LinkerSetting] = [] var cSettings: [CSetting] = [ .unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]), .unsafeFlags(["-fno-objc-arc"]), + .headerSearchPath("ggml/src"), + .headerSearchPath("ggml/src/ggml-cpu"), // NOTE: NEW_LAPACK will required iOS version 16.4+ // We should consider add this in the future when we drop support for iOS 14 // (ref: ref: https://developer.apple.com/documentation/accelerate/1513264-cblas_sgemm?language=objc) // .define("ACCELERATE_NEW_LAPACK"), // .define("ACCELERATE_LAPACK_ILP64") + .define("GGML_USE_CPU"), ] + #if canImport(Darwin) -sources.append("ggml/src/ggml-metal.m") -resources.append(.process("ggml/src/ggml-metal.metal")) +sources.append("ggml/src/ggml-common.h") +sources.append("ggml/src/ggml-metal/ggml-metal.m") +resources.append(.process("ggml/src/ggml-metal/ggml-metal.metal")) linkerSettings.append(.linkedFramework("Accelerate")) cSettings.append( contentsOf: [ .define("GGML_USE_ACCELERATE"), - .define("GGML_USE_METAL") + .define("GGML_USE_METAL"), ] ) #endif @@ -60,13 +71,15 @@ let package = Package( name: "llama", path: ".", exclude: [ + "build", "cmake", "examples", "scripts", "models", "tests", "CMakeLists.txt", - "Makefile" + "Makefile", + "ggml/src/ggml-metal-embed.metal" ], sources: sources, resources: resources, @@ -75,5 +88,5 @@ let package = Package( linkerSettings: linkerSettings ) ], - cxxLanguageStandard: .cxx11 + cxxLanguageStandard: .cxx17 ) diff --git a/README.md b/README.md index 8fe1f4b4b..6fdd8d9ee 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,6 @@ [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) [![Server](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml/badge.svg)](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml) -[![Conan Center](https://shields.io/conan/v/llama-cpp)](https://conan.io/center/llama-cpp) [Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggerganov/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml) @@ -17,7 +16,8 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ## Hot topics -- **Hugging Face Inference Endpoints now support GGUF out of the box! https://github.com/ggerganov/llama.cpp/discussions/9669** +- **Introducing GGUF-my-LoRA** https://github.com/ggerganov/llama.cpp/discussions/10123 +- Hugging Face Inference Endpoints now support GGUF out of the box! https://github.com/ggerganov/llama.cpp/discussions/9669 - Hugging Face GGUF editor: [discussion](https://github.com/ggerganov/llama.cpp/discussions/9268) | [tool](https://huggingface.co/spaces/CISCai/gguf-editor) ---- @@ -25,7 +25,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ## Description The main goal of `llama.cpp` is to enable LLM inference with minimal setup and state-of-the-art performance on a wide -variety of hardware - locally and in the cloud. +range of hardware - locally and in the cloud. - Plain C/C++ implementation without any dependencies - Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks @@ -35,14 +35,17 @@ variety of hardware - locally and in the cloud. - Vulkan and SYCL backend support - CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity -Since its [inception](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022), the project has -improved significantly thanks to many contributions. It is the main playground for developing new features for the -[ggml](https://github.com/ggerganov/ggml) library. +The `llama.cpp` project is the main playground for developing new features for the [ggml](https://github.com/ggerganov/ggml) library. -**Supported models:** +
+Models Typically finetunes of the base models below are supported as well. +Instructions for adding support for new models: [HOWTO-add-model.md](docs/development/HOWTO-add-model.md) + +#### Text-only + - [X] LLaMA 🦙 - [x] LLaMA 2 🦙🦙 - [x] LLaMA 3 🦙🦙🦙 @@ -78,6 +81,7 @@ Typically finetunes of the base models below are supported as well. - [x] [SEA-LION](https://huggingface.co/models?search=sea-lion) - [x] [GritLM-7B](https://huggingface.co/GritLM/GritLM-7B) + [GritLM-8x7B](https://huggingface.co/GritLM/GritLM-8x7B) - [x] [OLMo](https://allenai.org/olmo) +- [x] [OLMo 2](https://allenai.org/olmo) - [x] [OLMoE](https://huggingface.co/allenai/OLMoE-1B-7B-0924) - [x] [Granite models](https://huggingface.co/collections/ibm-granite/granite-code-models-6624c5cec322e4c148c8b330) - [x] [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) + [Pythia](https://github.com/EleutherAI/pythia) @@ -95,9 +99,7 @@ Typically finetunes of the base models below are supported as well. - [x] [Bielik-11B-v2.3](https://huggingface.co/collections/speakleash/bielik-11b-v23-66ee813238d9b526a072408a) - [x] [RWKV-6](https://github.com/BlinkDL/RWKV-LM) -(instructions for supporting more models: [HOWTO-add-model.md](./docs/development/HOWTO-add-model.md)) - -**Multimodal models:** +#### Multimodal - [x] [LLaVA 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e), [LLaVA 1.6 models](https://huggingface.co/collections/liuhaotian/llava-16-65b9e40155f60fd046a5ccf2) - [x] [BakLLaVA](https://huggingface.co/models?search=SkunkworksAI/Bakllava) @@ -109,7 +111,10 @@ Typically finetunes of the base models below are supported as well. - [x] [Moondream](https://huggingface.co/vikhyatk/moondream2) - [x] [Bunny](https://github.com/BAAI-DCAI/Bunny) -**Bindings:** +
+ +
+Bindings - Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python) - Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp) @@ -130,321 +135,320 @@ Typically finetunes of the base models below are supported as well. - Java: [kherud/java-llama.cpp](https://github.com/kherud/java-llama.cpp) - Zig: [deins/llama.cpp.zig](https://github.com/Deins/llama.cpp.zig) - Flutter/Dart: [netdur/llama_cpp_dart](https://github.com/netdur/llama_cpp_dart) +- Flutter: [xuegao-tzx/Fllama](https://github.com/xuegao-tzx/Fllama) - PHP (API bindings and features built on top of llama.cpp): [distantmagic/resonance](https://github.com/distantmagic/resonance) [(more info)](https://github.com/ggerganov/llama.cpp/pull/6326) - Guile Scheme: [guile_llama_cpp](https://savannah.nongnu.org/projects/guile-llama-cpp) - Swift [srgtuszy/llama-cpp-swift](https://github.com/srgtuszy/llama-cpp-swift) - Swift [ShenghaiWang/SwiftLlama](https://github.com/ShenghaiWang/SwiftLlama) -**UI:** +
-Unless otherwise noted these projects are open-source with permissive licensing: - -- [MindWorkAI/AI-Studio](https://github.com/MindWorkAI/AI-Studio) (FSL-1.1-MIT) -- [iohub/collama](https://github.com/iohub/coLLaMA) -- [janhq/jan](https://github.com/janhq/jan) (AGPL) -- [nat/openplayground](https://github.com/nat/openplayground) -- [Faraday](https://faraday.dev/) (proprietary) -- [LMStudio](https://lmstudio.ai/) (proprietary) -- [Layla](https://play.google.com/store/apps/details?id=com.laylalite) (proprietary) -- [ramalama](https://github.com/containers/ramalama) (MIT) -- [LocalAI](https://github.com/mudler/LocalAI) (MIT) -- [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL) -- [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile) -- [nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all) -- [ollama/ollama](https://github.com/ollama/ollama) -- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) (AGPL) -- [psugihara/FreeChat](https://github.com/psugihara/FreeChat) -- [cztomsik/ava](https://github.com/cztomsik/ava) (MIT) -- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) -- [pythops/tenere](https://github.com/pythops/tenere) (AGPL) -- [RAGNA Desktop](https://ragna.app/) (proprietary) -- [RecurseChat](https://recurse.chat/) (proprietary) -- [semperai/amica](https://github.com/semperai/amica) -- [withcatai/catai](https://github.com/withcatai/catai) -- [Mobile-Artificial-Intelligence/maid](https://github.com/Mobile-Artificial-Intelligence/maid) (MIT) -- [Msty](https://msty.app) (proprietary) -- [LLMFarm](https://github.com/guinmoon/LLMFarm?tab=readme-ov-file) (MIT) -- [KanTV](https://github.com/zhouwg/kantv?tab=readme-ov-file)(Apachev2.0 or later) -- [Dot](https://github.com/alexpinel/Dot) (GPL) -- [MindMac](https://mindmac.app) (proprietary) -- [KodiBot](https://github.com/firatkiral/kodibot) (GPL) -- [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT) -- [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT) -- [AIKit](https://github.com/sozercan/aikit) (MIT) -- [LARS - The LLM & Advanced Referencing Solution](https://github.com/abgulati/LARS) (AGPL) -- [LLMUnity](https://github.com/undreamai/LLMUnity) (MIT) -- [Llama Assistant](https://github.com/vietanhdev/llama-assistant) (GPL) -- [PocketPal AI - An iOS and Android App](https://github.com/a-ghorbani/pocketpal-ai) (MIT) +
+UIs *(to have a project listed here, it should clearly state that it depends on `llama.cpp`)* -**Tools:** +- [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT) +- [cztomsik/ava](https://github.com/cztomsik/ava) (MIT) +- [Dot](https://github.com/alexpinel/Dot) (GPL) +- [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT) +- [iohub/collama](https://github.com/iohub/coLLaMA) (Apache-2.0) +- [janhq/jan](https://github.com/janhq/jan) (AGPL) +- [KanTV](https://github.com/zhouwg/kantv?tab=readme-ov-file) (Apache-2.0) +- [KodiBot](https://github.com/firatkiral/kodibot) (GPL) +- [llama.vim](https://github.com/ggml-org/llama.vim) (MIT) +- [LARS](https://github.com/abgulati/LARS) (AGPL) +- [Llama Assistant](https://github.com/vietanhdev/llama-assistant) (GPL) +- [LLMFarm](https://github.com/guinmoon/LLMFarm?tab=readme-ov-file) (MIT) +- [LLMUnity](https://github.com/undreamai/LLMUnity) (MIT) +- [LMStudio](https://lmstudio.ai/) (proprietary) +- [LocalAI](https://github.com/mudler/LocalAI) (MIT) +- [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL) +- [MindMac](https://mindmac.app) (proprietary) +- [MindWorkAI/AI-Studio](https://github.com/MindWorkAI/AI-Studio) (FSL-1.1-MIT) +- [Mobile-Artificial-Intelligence/maid](https://github.com/Mobile-Artificial-Intelligence/maid) (MIT) +- [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile) (Apache-2.0) +- [nat/openplayground](https://github.com/nat/openplayground) (MIT) +- [nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all) (MIT) +- [ollama/ollama](https://github.com/ollama/ollama) (MIT) +- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) (AGPL) +- [PocketPal AI](https://github.com/a-ghorbani/pocketpal-ai) (MIT) +- [psugihara/FreeChat](https://github.com/psugihara/FreeChat) (MIT) +- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) (MIT) +- [pythops/tenere](https://github.com/pythops/tenere) (AGPL) +- [ramalama](https://github.com/containers/ramalama) (MIT) +- [semperai/amica](https://github.com/semperai/amica) (MIT) +- [withcatai/catai](https://github.com/withcatai/catai) (MIT) + +
+ +
+Tools - [akx/ggify](https://github.com/akx/ggify) – download PyTorch models from HuggingFace Hub and convert them to GGML - [akx/ollama-dl](https://github.com/akx/ollama-dl) – download models from the Ollama library to be used directly with llama.cpp - [crashr/gppm](https://github.com/crashr/gppm) – launch llama.cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption - [gpustack/gguf-parser](https://github.com/gpustack/gguf-parser-go/tree/main/cmd/gguf-parser) - review/check the GGUF file and estimate the memory usage -- [Styled Lines](https://marketplace.unity.com/packages/tools/generative-ai/styled-lines-llama-cpp-model-292902) (proprietary licensed, async wrapper of inference part for game development in Unity3d with prebuild Mobile and Web platform wrappers and a model example) +- [Styled Lines](https://marketplace.unity.com/packages/tools/generative-ai/styled-lines-llama-cpp-model-292902) (proprietary licensed, async wrapper of inference part for game development in Unity3d with pre-built Mobile and Web platform wrappers and a model example) -**Infrastructure:** +
+ +
+Infrastructure - [Paddler](https://github.com/distantmagic/paddler) - Stateful load balancer custom-tailored for llama.cpp - [GPUStack](https://github.com/gpustack/gpustack) - Manage GPU clusters for running LLMs - [llama_cpp_canister](https://github.com/onicai/llama_cpp_canister) - llama.cpp as a smart contract on the Internet Computer, using WebAssembly -**Games:** +
+ +
+Games + - [Lucy's Labyrinth](https://github.com/MorganRO8/Lucys_Labyrinth) - A simple maze game where agents controlled by an AI model will try to trick you. -## Demo - -
-Typical run using LLaMA v2 13B on M2 Ultra - -``` -$ make -j && ./llama-cli -m models/llama-13b-v2/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -I llama.cpp build info: -I UNAME_S: Darwin -I UNAME_P: arm -I UNAME_M: arm64 -I CFLAGS: -I. -O3 -std=c11 -fPIC -DNDEBUG -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes -pthread -DGGML_USE_K_QUANTS -DGGML_USE_ACCELERATE -I CXXFLAGS: -I. -I./common -O3 -std=c++11 -fPIC -DNDEBUG -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar -pthread -DGGML_USE_K_QUANTS -I LDFLAGS: -framework Accelerate -I CC: Apple clang version 14.0.3 (clang-1403.0.22.14.1) -I CXX: Apple clang version 14.0.3 (clang-1403.0.22.14.1) - -make: Nothing to be done for `default'. -main: build = 1041 (cf658ad) -main: seed = 1692823051 -llama_model_loader: loaded meta data with 16 key-value pairs and 363 tensors from models/llama-13b-v2/ggml-model-q4_0.gguf (version GGUF V1 (latest)) -llama_model_loader: - type f32: 81 tensors -llama_model_loader: - type q4_0: 281 tensors -llama_model_loader: - type q6_K: 1 tensors -llm_load_print_meta: format = GGUF V1 (latest) -llm_load_print_meta: arch = llama -llm_load_print_meta: vocab type = SPM -llm_load_print_meta: n_vocab = 32000 -llm_load_print_meta: n_merges = 0 -llm_load_print_meta: n_ctx_train = 4096 -llm_load_print_meta: n_ctx = 512 -llm_load_print_meta: n_embd = 5120 -llm_load_print_meta: n_head = 40 -llm_load_print_meta: n_head_kv = 40 -llm_load_print_meta: n_layer = 40 -llm_load_print_meta: n_rot = 128 -llm_load_print_meta: n_gqa = 1 -llm_load_print_meta: f_norm_eps = 1.0e-05 -llm_load_print_meta: f_norm_rms_eps = 1.0e-05 -llm_load_print_meta: n_ff = 13824 -llm_load_print_meta: freq_base = 10000.0 -llm_load_print_meta: freq_scale = 1 -llm_load_print_meta: model type = 13B -llm_load_print_meta: model ftype = mostly Q4_0 -llm_load_print_meta: model size = 13.02 B -llm_load_print_meta: general.name = LLaMA v2 -llm_load_print_meta: BOS token = 1 '' -llm_load_print_meta: EOS token = 2 '' -llm_load_print_meta: UNK token = 0 '' -llm_load_print_meta: LF token = 13 '<0x0A>' -llm_load_tensors: ggml ctx size = 0.11 MB -llm_load_tensors: mem required = 7024.01 MB (+ 400.00 MB per state) -................................................................................................... -llama_new_context_with_model: kv self size = 400.00 MB -llama_new_context_with_model: compute buffer total size = 75.41 MB - -system_info: n_threads = 16 / 24 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 | -sampling: repeat_last_n = 64, repeat_penalty = 1.100000, presence_penalty = 0.000000, frequency_penalty = 0.000000, top_k = 40, tfs_z = 1.000000, top_p = 0.950000, typical_p = 1.000000, temp = 0.800000, mirostat = 0, mirostat_lr = 0.100000, mirostat_ent = 5.000000 -generate: n_ctx = 512, n_batch = 512, n_predict = 400, n_keep = 0 - - - Building a website can be done in 10 simple steps: -Step 1: Find the right website platform. -Step 2: Choose your domain name and hosting plan. -Step 3: Design your website layout. -Step 4: Write your website content and add images. -Step 5: Install security features to protect your site from hackers or spammers -Step 6: Test your website on multiple browsers, mobile devices, operating systems etc… -Step 7: Test it again with people who are not related to you personally – friends or family members will work just fine! -Step 8: Start marketing and promoting the website via social media channels or paid ads -Step 9: Analyze how many visitors have come to your site so far, what type of people visit more often than others (e.g., men vs women) etc… -Step 10: Continue to improve upon all aspects mentioned above by following trends in web design and staying up-to-date on new technologies that can enhance user experience even further! -How does a Website Work? -A website works by having pages, which are made of HTML code. This code tells your computer how to display the content on each page you visit – whether it’s an image or text file (like PDFs). In order for someone else’s browser not only be able but also want those same results when accessing any given URL; some additional steps need taken by way of programming scripts that will add functionality such as making links clickable! -The most common type is called static HTML pages because they remain unchanged over time unless modified manually (either through editing files directly or using an interface such as WordPress). They are usually served up via HTTP protocols – this means anyone can access them without having any special privileges like being part of a group who is allowed into restricted areas online; however, there may still exist some limitations depending upon where one lives geographically speaking. -How to -llama_print_timings: load time = 576.45 ms -llama_print_timings: sample time = 283.10 ms / 400 runs ( 0.71 ms per token, 1412.91 tokens per second) -llama_print_timings: prompt eval time = 599.83 ms / 19 tokens ( 31.57 ms per token, 31.68 tokens per second) -llama_print_timings: eval time = 24513.59 ms / 399 runs ( 61.44 ms per token, 16.28 tokens per second) -llama_print_timings: total time = 25431.49 ms -``` -
-
-Demo of running both LLaMA-7B and whisper.cpp on a single M1 Pro MacBook - -And here is another demo of running both LLaMA-7B and [whisper.cpp](https://github.com/ggerganov/whisper.cpp) on a single M1 Pro MacBook: - -https://user-images.githubusercontent.com/1991296/224442907-7693d4be-acaa-4e01-8b4f-add84093ffff.mp4 - -
- -## Usage - -Here are the end-to-end binary build and model conversion steps for most supported models. - -### Basic usage - -Firstly, you need to get the binary. There are different methods that you can follow: -- Method 1: Clone this repository and build locally, see [how to build](./docs/build.md) -- Method 2: If you are using MacOS or Linux, you can install llama.cpp via [brew, flox or nix](./docs/install.md) -- Method 3: Use a Docker image, see [documentation for Docker](./docs/docker.md) -- Method 4: Download pre-built binary from [releases](https://github.com/ggerganov/llama.cpp/releases) - -You can run a basic completion using this command: - -```bash -llama-cli -m your_model.gguf -p "I believe the meaning of life is" -n 128 - -# Output: -# I believe the meaning of life is to find your own truth and to live in accordance with it. For me, this means being true to myself and following my passions, even if they don't align with societal expectations. I think that's what I love about yoga – it's not just a physical practice, but a spiritual one too. It's about connecting with yourself, listening to your inner voice, and honoring your own unique journey. -``` - -See [this page](./examples/main/README.md) for a full list of parameters. - -### Conversation mode - -If you want a more ChatGPT-like experience, you can run in conversation mode by passing `-cnv` as a parameter: - -```bash -llama-cli -m your_model.gguf -p "You are a helpful assistant" -cnv - -# Output: -# > hi, who are you? -# Hi there! I'm your helpful assistant! I'm an AI-powered chatbot designed to assist and provide information to users like you. I'm here to help answer your questions, provide guidance, and offer support on a wide range of topics. I'm a friendly and knowledgeable AI, and I'm always happy to help with anything you need. What's on your mind, and how can I assist you today? -# -# > what is 1+1? -# Easy peasy! The answer to 1+1 is... 2! -``` - -By default, the chat template will be taken from the input model. If you want to use another chat template, pass `--chat-template NAME` as a parameter. See the list of [supported templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) - -```bash -./llama-cli -m your_model.gguf -p "You are a helpful assistant" -cnv --chat-template chatml -``` - -You can also use your own template via in-prefix, in-suffix and reverse-prompt parameters: - -```bash -./llama-cli -m your_model.gguf -p "You are a helpful assistant" -cnv --in-prefix 'User: ' --reverse-prompt 'User:' -``` - -### Web server - -[llama.cpp web server](./examples/server/README.md) is a lightweight [OpenAI API](https://github.com/openai/openai-openapi) compatible HTTP server that can be used to serve local models and easily connect them to existing clients. - -Example usage: - -```bash -./llama-server -m your_model.gguf --port 8080 - -# Basic web UI can be accessed via browser: http://localhost:8080 -# Chat completion endpoint: http://localhost:8080/v1/chat/completions -``` - -### Interactive mode - -> [!NOTE] -> If you prefer basic usage, please consider using conversation mode instead of interactive mode - -In this mode, you can always interrupt generation by pressing Ctrl+C and entering one or more lines of text, which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt that makes LLaMA emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`. - -Here is an example of a few-shot interaction, invoked with the command - -```bash -# default arguments using a 7B model -./examples/chat.sh - -# advanced chat with a 13B model -./examples/chat-13B.sh - -# custom arguments using a 13B model -./llama-cli -m ./models/13B/ggml-model-q4_0.gguf -n 256 --repeat_penalty 1.0 --color -i -r "User:" -f prompts/chat-with-bob.txt -``` - -Note the use of `--color` to distinguish between user input and generated text. Other parameters are explained in more detail in the [README](examples/main/README.md) for the `llama-cli` example program. - -![image](https://user-images.githubusercontent.com/1991296/224575029-2af3c7dc-5a65-4f64-a6bb-517a532aea38.png) - -### Persistent Interaction - -The prompt, user inputs, and model generations can be saved and resumed across calls to `./llama-cli` by leveraging `--prompt-cache` and `--prompt-cache-all`. The `./examples/chat-persistent.sh` script demonstrates this with support for long-running, resumable chat sessions. To use this example, you must provide a file to cache the initial chat prompt and a directory to save the chat session, and may optionally provide the same variables as `chat-13B.sh`. The same prompt cache can be reused for new chat sessions. Note that both prompt cache and chat directory are tied to the initial prompt (`PROMPT_TEMPLATE`) and the model file. - -```bash -# Start a new chat -PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/default ./examples/chat-persistent.sh - -# Resume that chat -PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/default ./examples/chat-persistent.sh - -# Start a different chat with the same prompt/model -PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/another ./examples/chat-persistent.sh - -# Different prompt cache for different prompt/model -PROMPT_TEMPLATE=./prompts/chat-with-bob.txt PROMPT_CACHE_FILE=bob.prompt.bin \ - CHAT_SAVE_DIR=./chat/bob ./examples/chat-persistent.sh -``` - -### Constrained output with grammars - -`llama.cpp` supports grammars to constrain model output. For example, you can force the model to output JSON only: - -```bash -./llama-cli -m ./models/13B/ggml-model-q4_0.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:' -``` - -The `grammars/` folder contains a handful of sample grammars. To write your own, check out the [GBNF Guide](./grammars/README.md). - -For authoring more complex JSON grammars, you can also check out https://grammar.intrinsiclabs.ai/, a browser app that lets you write TypeScript interfaces which it compiles to GBNF grammars that you can save for local use. Note that the app is built and maintained by members of the community, please file any issues or FRs on [its repo](http://github.com/intrinsiclabsai/gbnfgen) and not this one. - -## Build - -Please refer to [Build llama.cpp locally](./docs/build.md) - ## Supported backends | Backend | Target devices | | --- | --- | -| [Metal](./docs/build.md#metal-build) | Apple Silicon | -| [BLAS](./docs/build.md#blas-build) | All | -| [BLIS](./docs/backend/BLIS.md) | All | -| [SYCL](./docs/backend/SYCL.md) | Intel and Nvidia GPU | -| [MUSA](./docs/build.md#musa) | Moore Threads MTT GPU | -| [CUDA](./docs/build.md#cuda) | Nvidia GPU | -| [hipBLAS](./docs/build.md#hipblas) | AMD GPU | -| [Vulkan](./docs/build.md#vulkan) | GPU | -| [CANN](./docs/build.md#cann) | Ascend NPU | +| [Metal](docs/build.md#metal-build) | Apple Silicon | +| [BLAS](docs/build.md#blas-build) | All | +| [BLIS](docs/backend/BLIS.md) | All | +| [SYCL](docs/backend/SYCL.md) | Intel and Nvidia GPU | +| [MUSA](docs/build.md#musa) | Moore Threads MTT GPU | +| [CUDA](docs/build.md#cuda) | Nvidia GPU | +| [hipBLAS](docs/build.md#hipblas) | AMD GPU | +| [Vulkan](docs/build.md#vulkan) | GPU | +| [CANN](docs/build.md#cann) | Ascend NPU | -## Tools +## Building the project -### Prepare and Quantize +The main product of this project is the `llama` library. Its C-style interface can be found in [include/llama.h](include/llama.h). +The project also includes many example programs and tools using the `llama` library. The examples range from simple, minimal code snippets to sophisticated sub-projects such as an OpenAI-compatible HTTP server. Possible methods for obtaining the binaries: -> [!NOTE] -> You can use the [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space on Hugging Face to quantise your model weights without any setup too. It is synced from `llama.cpp` main every 6 hours. +- Clone this repository and build locally, see [how to build](docs/build.md) +- On MacOS or Linux, install `llama.cpp` via [brew, flox or nix](docs/install.md) +- Use a Docker image, see [documentation for Docker](docs/docker.md) +- Download pre-built binaries from [releases](https://github.com/ggerganov/llama.cpp/releases) -To obtain the official LLaMA 2 weights please see the Obtaining and using the Facebook LLaMA 2 model section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face. +## Obtaining and quantizing models -Note: `convert.py` has been moved to `examples/convert_legacy_llama.py` and shouldn't be used for anything other than `Llama/Llama2/Mistral` models and their derivatives. -It does not support LLaMA 3, you can use `convert_hf_to_gguf.py` with LLaMA 3 downloaded from Hugging Face. +The [Hugging Face](https://huggingface.co) platform hosts a [number of LLMs](https://huggingface.co/models?library=gguf&sort=trending) compatible with `llama.cpp`: -To learn more about quantizing model, [read this documentation](./examples/quantize/README.md) +- [Trending](https://huggingface.co/models?library=gguf&sort=trending) +- [LLaMA](https://huggingface.co/models?sort=trending&search=llama+gguf) -### Perplexity (measuring model quality) +After downloading a model, use the CLI tools to run it locally - see below. -You can use the `perplexity` example to measure perplexity over a given prompt (lower perplexity is better). -For more information, see [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity). +`llama.cpp` requires the model to be stored in the [GGUF](https://github.com/ggerganov/ggml/blob/master/docs/gguf.md) file format. Models in other data formats can be converted to GGUF using the `convert_*.py` Python scripts in this repo. + +The Hugging Face platform provides a variety of online tools for converting, quantizing and hosting models with `llama.cpp`: + +- Use the [GGUF-my-repo space](https://huggingface.co/spaces/ggml-org/gguf-my-repo) to convert to GGUF format and quantize model weights to smaller sizes +- Use the [GGUF-my-LoRA space](https://huggingface.co/spaces/ggml-org/gguf-my-lora) to convert LoRA adapters to GGUF format (more info: https://github.com/ggerganov/llama.cpp/discussions/10123) +- Use the [GGUF-editor space](https://huggingface.co/spaces/CISCai/gguf-editor) to edit GGUF meta data in the browser (more info: https://github.com/ggerganov/llama.cpp/discussions/9268) +- Use the [Inference Endpoints](https://ui.endpoints.huggingface.co/) to directly host `llama.cpp` in the cloud (more info: https://github.com/ggerganov/llama.cpp/discussions/9669) + +To learn more about model quantization, [read this documentation](examples/quantize/README.md) + +## [`llama-cli`](examples/main) + +#### A CLI tool for accessing and experimenting with most of `llama.cpp`'s functionality. + +-
+ Run simple text completion + + ```bash + llama-cli -m model.gguf -p "I believe the meaning of life is" -n 128 + + # I believe the meaning of life is to find your own truth and to live in accordance with it. For me, this means being true to myself and following my passions, even if they don't align with societal expectations. I think that's what I love about yoga – it's not just a physical practice, but a spiritual one too. It's about connecting with yourself, listening to your inner voice, and honoring your own unique journey. + ``` + +
+ +-
+ Run in conversation mode + + ```bash + llama-cli -m model.gguf -p "You are a helpful assistant" -cnv + + # > hi, who are you? + # Hi there! I'm your helpful assistant! I'm an AI-powered chatbot designed to assist and provide information to users like you. I'm here to help answer your questions, provide guidance, and offer support on a wide range of topics. I'm a friendly and knowledgeable AI, and I'm always happy to help with anything you need. What's on your mind, and how can I assist you today? + # + # > what is 1+1? + # Easy peasy! The answer to 1+1 is... 2! + ``` + +
+ +-
+ Run with custom chat template + + ```bash + # use the "chatml" template + llama-cli -m model.gguf -p "You are a helpful assistant" -cnv --chat-template chatml + + # use a custom template + llama-cli -m model.gguf -p "You are a helpful assistant" -cnv --in-prefix 'User: ' --reverse-prompt 'User:' + ``` + + [Supported templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) + +
+ +-
+ Constrain the output with a custom grammar + + ```bash + llama-cli -m model.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:' + + # {"appointmentTime": "8pm", "appointmentDetails": "schedule a a call"} + ``` + + The [grammars/](grammars/) folder contains a handful of sample grammars. To write your own, check out the [GBNF Guide](grammars/README.md). + + For authoring more complex JSON grammars, check out https://grammar.intrinsiclabs.ai/ + +
+ + +## [`llama-server`](examples/server) + +#### A lightweight, [OpenAI API](https://github.com/openai/openai-openapi) compatible, HTTP server for serving LLMs. + +-
+ Start a local HTTP server with default configuration on port 8080 + + ```bash + llama-server -m model.gguf --port 8080 + + # Basic web UI can be accessed via browser: http://localhost:8080 + # Chat completion endpoint: http://localhost:8080/v1/chat/completions + ``` + +
+ +-
+ Support multiple-users and parallel decoding + + ```bash + # up to 4 concurrent requests, each with 4096 max context + llama-server -m model.gguf -c 16384 -np 4 + ``` + +
+ +-
+ Enable speculative decoding + + ```bash + # the draft.gguf model should be a small variant of the target model.gguf + llama-server -m model.gguf -md draft.gguf + ``` + +
+ +-
+ Serve an embedding model + + ```bash + # use the /embedding endpoint + llama-server -m model.gguf --embedding --pooling cls -ub 8192 + ``` + +
+ +-
+ Serve a reranking model + + ```bash + # use the /reranking endpoint + llama-server -m model.gguf --reranking + ``` + +
+ +-
+ Constrain all outputs with a grammar + + ```bash + # custom grammar + llama-server -m model.gguf --grammar-file grammar.gbnf + + # JSON + llama-server -m model.gguf --grammar-file grammars/json.gbnf + ``` + +
+ + +## [`llama-perplexity`](examples/perplexity) + +#### A tool for measuring the perplexity [^1][^2] (and other quality metrics) of a model over a given text. + +-
+ Measure the perplexity over a text file + + ```bash + llama-perplexity -m model.gguf -f file.txt + + # [1]15.2701,[2]5.4007,[3]5.3073,[4]6.2965,[5]5.8940,[6]5.6096,[7]5.7942,[8]4.9297, ... + # Final estimate: PPL = 5.4007 +/- 0.67339 + ``` + +
+ +-
+ Measure KL divergence + + ```bash + # TODO + ``` + +
+ +[^1]: [examples/perplexity/README.md](examples/perplexity/README.md) +[^2]: [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity) + +## [`llama-bench`](example/bench) + +#### Benchmark the performance of the inference for various parameters. + +-
+ Run default benchmark + + ```bash + llama-bench -m model.gguf + + # Output: + # | model | size | params | backend | threads | test | t/s | + # | ------------------- | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: | + # | qwen2 1.5B Q4_0 | 885.97 MiB | 1.54 B | Metal,BLAS | 16 | pp512 | 5765.41 ± 20.55 | + # | qwen2 1.5B Q4_0 | 885.97 MiB | 1.54 B | Metal,BLAS | 16 | tg128 | 197.71 ± 0.81 | + # + # build: 3e0ba0e60 (4229) + ``` + +
+ + +## [`llama-simple`](examples/simple) + +#### A minimal example for implementing apps with `llama.cpp`. Useful for developers. + +-
+ Basic text completion + + ```bash + llama-simple -m model.gguf + + # Hello my name is Kaitlyn and I am a 16 year old girl. I am a junior in high school and I am currently taking a class called "The Art of + ``` + +
-To learn more how to measure perplexity using llama.cpp, [read this documentation](./examples/perplexity/README.md) ## Contributing @@ -457,22 +461,21 @@ To learn more how to measure perplexity using llama.cpp, [read this documentatio - Make sure to read this: [Inference at the edge](https://github.com/ggerganov/llama.cpp/discussions/205) - A bit of backstory for those who are interested: [Changelog podcast](https://changelog.com/podcast/532) -## Other documentations +## Other documentation -- [main (cli)](./examples/main/README.md) -- [server](./examples/server/README.md) -- [jeopardy](./examples/jeopardy/README.md) -- [GBNF grammars](./grammars/README.md) +- [main (cli)](examples/main/README.md) +- [server](examples/server/README.md) +- [GBNF grammars](grammars/README.md) -**Development documentations** +#### Development documentation -- [How to build](./docs/build.md) -- [Running on Docker](./docs/docker.md) -- [Build on Android](./docs/android.md) -- [Performance troubleshooting](./docs/development/token_generation_performance_tips.md) +- [How to build](docs/build.md) +- [Running on Docker](docs/docker.md) +- [Build on Android](docs/android.md) +- [Performance troubleshooting](docs/development/token_generation_performance_tips.md) - [GGML tips & tricks](https://github.com/ggerganov/llama.cpp/wiki/GGML-Tips-&-Tricks) -**Seminal papers and background on the models** +#### Seminal papers and background on the models If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT: - LLaMA: @@ -483,3 +486,6 @@ If your issue is with model generation quality, then please at least scan the fo - GPT-3.5 / InstructGPT / ChatGPT: - [Aligning language models to follow instructions](https://openai.com/research/instruction-following) - [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155) + +#### References + diff --git a/ci/run.sh b/ci/run.sh index dc26d94ee..abf08a4ff 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -39,7 +39,7 @@ SRC=`pwd` CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=ON" if [ ! -z ${GG_BUILD_METAL} ]; then - CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON" + CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON -DGGML_METAL_USE_BF16=ON" fi if [ ! -z ${GG_BUILD_CUDA} ]; then @@ -326,36 +326,36 @@ function gg_run_open_llama_7b_v2 { ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k - (time ./bin/llama-cli --model ${model_f16} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/llama-cli --model ${model_q8_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/llama-cli --model ${model_q4_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/llama-cli --model ${model_q4_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/llama-cli --model ${model_q5_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/llama-cli --model ${model_q5_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/llama-cli --model ${model_q2_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/llama-cli --model ${model_q3_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/llama-cli --model ${model_q4_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/llama-cli --model ${model_q5_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/llama-cli --model ${model_q6_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + (time ./bin/llama-cli --model ${model_f16} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-cli --model ${model_q8_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-cli --model ${model_q4_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-cli --model ${model_q4_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-cli --model ${model_q5_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-cli --model ${model_q5_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-cli --model ${model_q2_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-cli --model ${model_q3_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-cli --model ${model_q4_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-cli --model ${model_q5_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-cli --model ${model_q6_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log - (time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + (time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log - (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log + (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log - (time ./bin/llama-save-load-state -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log - (time ./bin/llama-save-load-state -fa -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log - (time ./bin/llama-save-load-state -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log - (time ./bin/llama-save-load-state -fa -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state--model ${model_q4_0} -ngl 10 -c 0 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state--model ${model_q4_0} -ngl 10 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state--model ${model_q4_0} -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state--model ${model_q4_0} -ngl 99 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log function check_ppl { qnt="$1" @@ -460,34 +460,34 @@ function gg_run_pythia_1_4b { ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k - (time ./bin/llama-cli --model ${model_f16} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/llama-cli --model ${model_q8_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/llama-cli --model ${model_q4_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/llama-cli --model ${model_q4_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/llama-cli --model ${model_q5_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/llama-cli --model ${model_q5_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/llama-cli --model ${model_q2_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/llama-cli --model ${model_q3_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/llama-cli --model ${model_q4_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/llama-cli --model ${model_q5_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/llama-cli --model ${model_q6_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + (time ./bin/llama-cli --model ${model_f16} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-cli --model ${model_q8_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-cli --model ${model_q4_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-cli --model ${model_q4_1} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-cli --model ${model_q5_0} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-cli --model ${model_q5_1} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-cli --model ${model_q2_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-cli --model ${model_q3_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-cli --model ${model_q4_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-cli --model ${model_q5_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-cli --model ${model_q6_k} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log - (time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + (time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log - (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log + (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log - (time ./bin/llama-save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log - (time ./bin/llama-save-load-state -fa --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log function check_ppl { qnt="$1" @@ -591,36 +591,36 @@ function gg_run_pythia_2_8b { ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k - (time ./bin/llama-cli --model ${model_f16} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/llama-cli --model ${model_q8_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/llama-cli --model ${model_q4_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/llama-cli --model ${model_q4_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/llama-cli --model ${model_q5_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/llama-cli --model ${model_q5_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/llama-cli --model ${model_q2_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/llama-cli --model ${model_q3_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/llama-cli --model ${model_q4_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/llama-cli --model ${model_q5_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/llama-cli --model ${model_q6_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + (time ./bin/llama-cli --model ${model_f16} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-cli --model ${model_q8_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-cli --model ${model_q4_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-cli --model ${model_q4_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-cli --model ${model_q5_0} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-cli --model ${model_q5_1} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-cli --model ${model_q2_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-cli --model ${model_q3_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-cli --model ${model_q4_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-cli --model ${model_q5_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-cli --model ${model_q6_k} -t 1 -ngl 99 -c 0 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log - (time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + (time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log - (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log + (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log - (time ./bin/llama-save-load-state -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log - (time ./bin/llama-save-load-state -fa -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log - (time ./bin/llama-save-load-state -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log - (time ./bin/llama-save-load-state -fa -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log function check_ppl { qnt="$1" @@ -706,8 +706,8 @@ function gg_run_embd_bge_small { ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0 - (time ./bin/llama-embedding --model ${model_f16} -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/llama-embedding --model ${model_q8_0} -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-embedding --model ${model_f16} -p "I believe the meaning of life is" -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-embedding --model ${model_q8_0} -p "I believe the meaning of life is" -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log set +e } @@ -752,7 +752,7 @@ function gg_run_rerank_tiny { model_f16="${path_models}/ggml-model-f16.gguf" # for this model, the SEP token is "" - (time ./bin/llama-embedding --model ${model_f16} -p "what is panda?hi\nwhat is panda?it's a bear\nwhat is panda?The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." --pooling rank --embd-normalize -1 --verbose-prompt) 2>&1 | tee -a $OUT/${ci}-rk-f16.log + (time ./bin/llama-embedding --model ${model_f16} -p "what is panda?hi\nwhat is panda?it's a bear\nwhat is panda?The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." -ngl 99 -c 0 --pooling rank --embd-normalize -1 --verbose-prompt) 2>&1 | tee -a $OUT/${ci}-rk-f16.log # sample output # rerank score 0: 0.029 @@ -815,7 +815,10 @@ if [ -z ${GG_BUILD_LOW_PERF} ]; then ln -sfn ${mnt_models} ${SRC}/models-mnt # Create a fresh python3 venv and enter it - python3 -m venv "$MNT/venv" + if ! python3 -m venv "$MNT/venv"; then + echo "Error: Failed to create Python virtual environment at $MNT/venv." + exit 1 + fi source "$MNT/venv/bin/activate" pip install -r ${SRC}/requirements.txt --disable-pip-version-check diff --git a/cmake/arm64-apple-clang.cmake b/cmake/arm64-apple-clang.cmake new file mode 100644 index 000000000..5fcd2882a --- /dev/null +++ b/cmake/arm64-apple-clang.cmake @@ -0,0 +1,16 @@ +set( CMAKE_SYSTEM_NAME Darwin ) +set( CMAKE_SYSTEM_PROCESSOR arm64 ) + +set( target arm64-apple-darwin-macho ) + +set( CMAKE_C_COMPILER clang ) +set( CMAKE_CXX_COMPILER clang++ ) + +set( CMAKE_C_COMPILER_TARGET ${target} ) +set( CMAKE_CXX_COMPILER_TARGET ${target} ) + +set( arch_c_flags "-march=armv8.4-a -fvectorize -ffp-model=fast -fno-finite-math-only" ) +set( warn_c_flags "-Wno-format -Wno-unused-variable -Wno-unused-function" ) + +set( CMAKE_C_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" ) +set( CMAKE_CXX_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" ) diff --git a/cmake/common.cmake b/cmake/common.cmake new file mode 100644 index 000000000..0f54871e4 --- /dev/null +++ b/cmake/common.cmake @@ -0,0 +1,33 @@ +function(llama_add_compile_flags) + if (LLAMA_FATAL_WARNINGS) + if (CMAKE_CXX_COMPILER_ID MATCHES "GNU" OR CMAKE_CXX_COMPILER_ID MATCHES "Clang") + list(APPEND C_FLAGS -Werror) + list(APPEND CXX_FLAGS -Werror) + elseif (CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") + add_compile_options(/WX) + endif() + endif() + + if (LLAMA_ALL_WARNINGS) + if (NOT MSVC) + list(APPEND C_FLAGS -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes + -Werror=implicit-int -Werror=implicit-function-declaration) + + list(APPEND CXX_FLAGS -Wmissing-declarations -Wmissing-noreturn) + + list(APPEND WARNING_FLAGS -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function) + + list(APPEND C_FLAGS ${WARNING_FLAGS}) + list(APPEND CXX_FLAGS ${WARNING_FLAGS}) + + ggml_get_flags(${CMAKE_CXX_COMPILER_ID} ${CMAKE_CXX_COMPILER_VERSION}) + + add_compile_options("$<$:${C_FLAGS};${GF_C_FLAGS}>" + "$<$:${CXX_FLAGS};${GF_CXX_FLAGS}>") + else() + # todo : msvc + set(C_FLAGS "" PARENT_SCOPE) + set(CXX_FLAGS "" PARENT_SCOPE) + endif() + endif() +endfunction() diff --git a/cmake/llama-config.cmake.in b/cmake/llama-config.cmake.in index f072b76a3..5c55bc6b8 100644 --- a/cmake/llama-config.cmake.in +++ b/cmake/llama-config.cmake.in @@ -3,18 +3,60 @@ set(LLAMA_BUILD_COMMIT @LLAMA_BUILD_COMMIT@) set(LLAMA_BUILD_NUMBER @LLAMA_BUILD_NUMBER@) set(LLAMA_SHARED_LIB @BUILD_SHARED_LIBS@) -set(GGML_BLAS @GGML_BLAS@) -set(GGML_CUDA @GGML_CUDA@) -set(GGML_METAL @GGML_METAL@) -set(GGML_HIPBLAS @GGML_HIPBLAS@) +set(GGML_STATIC @GGML_STATIC@) +set(GGML_NATIVE @GGML_NATIVE@) +set(GGML_LTO @GGML_LTO@) +set(GGML_CCACHE @GGML_CCACHE@) +set(GGML_AVX @GGML_AVX@) +set(GGML_AVX2 @GGML_AVX2@) +set(GGML_AVX512 @GGML_AVX512@) +set(GGML_AVX512_VBMI @GGML_AVX512_VBMI@) +set(GGML_AVX512_VNNI @GGML_AVX512_VNNI@) +set(GGML_AVX512_BF16 @GGML_AVX512_BF16@) +set(GGML_AMX_TILE @GGML_AMX_TILE@) +set(GGML_AMX_INT8 @GGML_AMX_INT8@) +set(GGML_AMX_BF16 @GGML_AMX_BF16@) +set(GGML_FMA @GGML_FMA@) +set(GGML_LASX @GGML_LASX@) +set(GGML_LSX @GGML_LSX@) +set(GGML_RVV @GGML_RVV@) +set(GGML_SVE @GGML_SVE@) + set(GGML_ACCELERATE @GGML_ACCELERATE@) -set(GGML_VULKAN @GGML_VULKAN@) +set(GGML_OPENMP @GGML_OPENMP@) +set(GGML_CPU_HBM @GGML_CPU_HBM@) +set(GGML_BLAS_VENDOR @GGML_BLAS_VENDOR@) + +set(GGML_CUDA_FORCE_MMQ @GGML_CUDA_FORCE_MMQ@) +set(GGML_CUDA_FORCE_CUBLAS @GGML_CUDA_FORCE_CUBLAS@) +set(GGML_CUDA_F16 @GGML_CUDA_F16@) +set(GGML_CUDA_PEER_MAX_BATCH_SIZE @GGML_CUDA_PEER_MAX_BATCH_SIZE@) +set(GGML_CUDA_NO_PEER_COPY @GGML_CUDA_NO_PEER_COPY@) +set(GGML_CUDA_NO_VMM @GGML_CUDA_NO_VMM@) +set(GGML_CUDA_FA_ALL_QUANTS @GGML_CUDA_FA_ALL_QUANTS@) +set(GGML_CUDA_GRAPHS @GGML_CUDA_GRAPHS@) + +set(GGML_HIP_UMA @GGML_HIP_UMA@) + set(GGML_VULKAN_CHECK_RESULTS @GGML_VULKAN_CHECK_RESULTS@) -set(GGML_VULKAN_DEBUG @GGML_VULKAN_DEBUG@) -set(GGML_VULKAN_MEMORY_DEBUG @GGML_VULKAN_MEMORY_DEBUG@) -set(GGML_VULKAN_VALIDATE @GGML_VULKAN_VALIDATE@) -set(GGML_SYCL @GGML_SYCL@) -set(GGML_OPENMP @GGML_OPENMP@) +set(GGML_VULKAN_DEBUG @GGML_VULKAN_DEBUG@) +set(GGML_VULKAN_MEMORY_DEBUG @GGML_VULKAN_MEMORY_DEBUG@) +set(GGML_VULKAN_SHADER_DEBUG_INFO @GGML_VULKAN_SHADER_DEBUG_INFO@) +set(GGML_VULKAN_PERF @GGML_VULKAN_PERF@) +set(GGML_VULKAN_VALIDATE @GGML_VULKAN_VALIDATE@) +set(GGML_VULKAN_RUN_TESTS @GGML_VULKAN_RUN_TESTS@) + +set(GGML_METAL_USE_BF16 @GGML_METAL_USE_BF16@) +set(GGML_METAL_NDEBUG @GGML_METAL_NDEBUG@) +set(GGML_METAL_SHADER_DEBUG @GGML_METAL_SHADER_DEBUG@) +set(GGML_METAL_EMBED_LIBRARY @GGML_METAL_EMBED_LIBRARY@) +set(GGML_METAL_MACOSX_VERSION_MIN @GGML_METAL_MACOSX_VERSION_MIN@) +set(GGML_METAL_STD @GGML_METAL_STD@) + +set(GGML_SYCL_F16 @GGML_SYCL_F16@) +set(GGML_SYCL_TARGET @GGML_SYCL_TARGET@) +set(GGML_SYCL_DEVICE_ARCH @GGML_SYCL_DEVICE_ARCH@) + @PACKAGE_INIT@ @@ -22,65 +64,111 @@ set_and_check(LLAMA_INCLUDE_DIR "@PACKAGE_LLAMA_INCLUDE_INSTALL_DIR@") set_and_check(LLAMA_LIB_DIR "@PACKAGE_LLAMA_LIB_INSTALL_DIR@") set_and_check(LLAMA_BIN_DIR "@PACKAGE_LLAMA_BIN_INSTALL_DIR@") -# Ensure transient dependencies satisfied - find_package(Threads REQUIRED) -if (APPLE AND GGML_ACCELERATE) - find_library(ACCELERATE_FRAMEWORK Accelerate REQUIRED) +set(_llama_transient_defines "@GGML_TRANSIENT_DEFINES@") +set(_llama_link_deps "") +set(_llama_link_opts "") +foreach(_ggml_lib ggml ggml-base) + string(REPLACE "-" "_" _ggml_lib_var "${_ggml_lib}_LIBRARY") + find_library(${_ggml_lib_var} ${_ggml_lib} + REQUIRED + HINTS ${LLAMA_LIB_DIR} + NO_CMAKE_FIND_ROOT_PATH + ) + list(APPEND _llama_link_deps "${${_ggml_lib_var}}") + message(STATUS "Found ${${_ggml_lib_var}}") +endforeach() + +foreach(backend amx blas cann cpu cuda hip kompute metal musa rpc sycl vulkan) + string(TOUPPER "GGML_${backend}" backend_id) + set(_ggml_lib "ggml-${backend}") + string(REPLACE "-" "_" _ggml_lib_var "${_ggml_lib}_LIBRARY") + + find_library(${_ggml_lib_var} ${_ggml_lib} + HINTS ${LLAMA_LIB_DIR} + NO_CMAKE_FIND_ROOT_PATH + ) + if(${_ggml_lib_var}) + list(APPEND _llama_link_deps "${${_ggml_lib_var}}") + set(${backend_id} ON) + message(STATUS "Found backend ${${_ggml_lib_var}}") + else() + set(${backend_id} OFF) + endif() +endforeach() + +if (NOT LLAMA_SHARED_LIB) + if (APPLE AND GGML_ACCELERATE) + find_library(ACCELERATE_FRAMEWORK Accelerate REQUIRED) + list(APPEND _llama_link_deps ${ACCELERATE_FRAMEWORK}) + endif() + + if (GGML_OPENMP) + find_package(OpenMP REQUIRED) + list(APPEND _llama_link_deps OpenMP::OpenMP_C OpenMP::OpenMP_CXX) + endif() + + if (GGML_CPU_HBM) + find_library(memkind memkind REQUIRED) + list(APPEND _llama_link_deps memkind) + endif() + + if (GGML_BLAS) + find_package(BLAS REQUIRED) + list(APPEND _llama_link_deps ${BLAS_LIBRARIES}) + list(APPEND _llama_link_opts ${BLAS_LINKER_FLAGS}) + endif() + + if (GGML_CUDA) + find_package(CUDAToolkit REQUIRED) + endif() + + if (GGML_METAL) + find_library(FOUNDATION_LIBRARY Foundation REQUIRED) + find_library(METAL_FRAMEWORK Metal REQUIRED) + find_library(METALKIT_FRAMEWORK MetalKit REQUIRED) + list(APPEND _llama_link_deps ${FOUNDATION_LIBRARY} + ${METAL_FRAMEWORK} ${METALKIT_FRAMEWORK}) + endif() + + if (GGML_VULKAN) + find_package(Vulkan REQUIRED) + list(APPEND _llama_link_deps Vulkan::Vulkan) + endif() + + if (GGML_HIP) + find_package(hip REQUIRED) + find_package(hipblas REQUIRED) + find_package(rocblas REQUIRED) + list(APPEND _llama_link_deps hip::host roc::rocblas roc::hipblas) + endif() + + if (GGML_SYCL) + find_package(DNNL) + if (${DNNL_FOUND} AND GGML_SYCL_TARGET STREQUAL "INTEL") + list(APPEND _llama_link_deps DNNL::dnnl) + endif() + if (WIN32) + find_package(IntelSYCL REQUIRED) + find_package(MKL REQUIRED) + list(APPEND _llama_link_deps IntelSYCL::SYCL_CXX MKL::MKL MKL::MKL_SYCL) + endif() + endif() endif() -if (GGML_BLAS) - find_package(BLAS REQUIRED) -endif() - -if (GGML_CUDA) - find_package(CUDAToolkit REQUIRED) -endif() - -if (GGML_METAL) - find_library(FOUNDATION_LIBRARY Foundation REQUIRED) - find_library(METAL_FRAMEWORK Metal REQUIRED) - find_library(METALKIT_FRAMEWORK MetalKit REQUIRED) -endif() - -if (GGML_VULKAN) - find_package(Vulkan REQUIRED) -endif() - -if (GGML_HIPBLAS) - find_package(hip REQUIRED) - find_package(hipblas REQUIRED) - find_package(rocblas REQUIRED) -endif() - -if (GGML_SYCL) - find_package(IntelSYCL REQUIRED) - find_package(MKL REQUIRED) -endif() - -if (GGML_OPENMP) - find_package(OpenMP REQUIRED) -endif() - - -find_library(ggml_LIBRARY ggml - REQUIRED - HINTS ${LLAMA_LIB_DIR}) - find_library(llama_LIBRARY llama REQUIRED - HINTS ${LLAMA_LIB_DIR}) - -set(_llama_link_deps "${ggml_LIBRARY}" "@GGML_LINK_LIBRARIES@") -set(_llama_transient_defines "@GGML_TRANSIENT_DEFINES@") + HINTS ${LLAMA_LIB_DIR} + NO_CMAKE_FIND_ROOT_PATH +) add_library(llama UNKNOWN IMPORTED) - set_target_properties(llama PROPERTIES INTERFACE_INCLUDE_DIRECTORIES "${LLAMA_INCLUDE_DIR}" INTERFACE_LINK_LIBRARIES "${_llama_link_deps}" + INTERFACE_LINK_OPTIONS "${_llama_link_opts}" INTERFACE_COMPILE_DEFINITIONS "${_llama_transient_defines}" IMPORTED_LINK_INTERFACE_LANGUAGES "CXX" IMPORTED_LOCATION "${llama_LIBRARY}" diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index fe8fff2af..3bd75a975 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -2,6 +2,8 @@ find_package(Threads REQUIRED) +llama_add_compile_flags() + # Build info header # @@ -68,9 +70,9 @@ add_library(${TARGET} STATIC ngram-cache.h sampling.cpp sampling.h + speculative.cpp + speculative.h tool-call.cpp - train.cpp - train.h ) if (BUILD_SHARED_LIBS) @@ -89,5 +91,5 @@ if (LLAMA_CURL) endif () target_include_directories(${TARGET} PUBLIC .) -target_compile_features (${TARGET} PUBLIC cxx_std_11) +target_compile_features (${TARGET} PUBLIC cxx_std_17) target_link_libraries (${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama Threads::Threads) diff --git a/common/arg.cpp b/common/arg.cpp index 36277cafa..0e4bc6549 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -128,7 +128,11 @@ static void common_params_handle_model_default(common_params & params) { } params.hf_file = params.model; } else if (params.model.empty()) { - params.model = fs_get_cache_file(string_split(params.hf_file, '/').back()); + // this is to avoid different repo having same file name, or same file name in different subdirs + std::string filename = params.hf_repo + "_" + params.hf_file; + // to make sure we don't have any slashes in the filename + string_replace_all(filename, "/", "_"); + params.model = fs_get_cache_file(filename); } } else if (!params.model_url.empty()) { if (params.model.empty()) { @@ -233,10 +237,11 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context } } - postprocess_cpu_params(params.cpuparams, nullptr); + postprocess_cpu_params(params.cpuparams, nullptr); postprocess_cpu_params(params.cpuparams_batch, ¶ms.cpuparams); - postprocess_cpu_params(params.draft_cpuparams, ¶ms.cpuparams); - postprocess_cpu_params(params.draft_cpuparams_batch, ¶ms.cpuparams_batch); + + postprocess_cpu_params(params.speculative.cpuparams, ¶ms.cpuparams); + postprocess_cpu_params(params.speculative.cpuparams_batch, ¶ms.cpuparams_batch); if (params.prompt_cache_all && (params.interactive || params.interactive_first)) { throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n"); @@ -251,7 +256,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context for (auto & antiprompt : params.antiprompt) { string_process_escapes(antiprompt); } - for (auto & seq_breaker : params.sparams.dry_sequence_breakers) { + for (auto & seq_breaker : params.sampling.dry_sequence_breakers) { string_process_escapes(seq_breaker); } } @@ -297,6 +302,27 @@ static void common_params_print_usage(common_params_context & ctx_arg) { print_options(specific_options); } +static std::vector parse_device_list(const std::string & value) { + std::vector devices; + auto dev_names = string_split(value, ','); + if (dev_names.empty()) { + throw std::invalid_argument("no devices specified"); + } + if (dev_names.size() == 1 && dev_names[0] == "none") { + devices.push_back(nullptr); + } else { + for (const auto & device : dev_names) { + auto * dev = ggml_backend_dev_by_name(device.c_str()); + if (!dev || ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_GPU) { + throw std::invalid_argument(string_format("invalid device: %s", device.c_str())); + } + devices.push_back(dev); + } + devices.push_back(nullptr); + } + return devices; +} + bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **)) { auto ctx_arg = common_params_parser_init(params, ex, print_usage); const common_params params_org = ctx_arg.params; // the example can modify the default params @@ -322,14 +348,29 @@ bool common_params_parse(int argc, char ** argv, common_params & params, llama_e return true; } +static std::string list_builtin_chat_templates() { + std::vector supported_tmpl; + int32_t res = llama_chat_builtin_templates(nullptr, 0); + supported_tmpl.resize(res); + res = llama_chat_builtin_templates(supported_tmpl.data(), supported_tmpl.size()); + std::ostringstream msg; + for (auto & tmpl : supported_tmpl) { + msg << tmpl << (&tmpl == &supported_tmpl.back() ? "" : ", "); + } + return msg.str(); +} + common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **)) { + // load dynamic backends + ggml_backend_load_all(); + common_params_context ctx_arg(params); ctx_arg.print_usage = print_usage; ctx_arg.ex = ex; std::string sampler_type_chars; std::string sampler_type_names; - for (const auto & sampler : params.sparams.samplers) { + for (const auto & sampler : params.sampling.samplers) { sampler_type_chars += common_sampler_type_to_chr(sampler); sampler_type_names += common_sampler_type_to_str(sampler) + ";"; } @@ -407,26 +448,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } } )); - add_opt(common_arg( - {"-td", "--threads-draft"}, "N", - "number of threads to use during generation (default: same as --threads)", - [](common_params & params, int value) { - params.draft_cpuparams.n_threads = value; - if (params.draft_cpuparams.n_threads <= 0) { - params.draft_cpuparams.n_threads = std::thread::hardware_concurrency(); - } - } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); - add_opt(common_arg( - {"-tbd", "--threads-batch-draft"}, "N", - "number of threads to use during batch and prompt processing (default: same as --threads-draft)", - [](common_params & params, int value) { - params.draft_cpuparams_batch.n_threads = value; - if (params.draft_cpuparams_batch.n_threads <= 0) { - params.draft_cpuparams_batch.n_threads = std::thread::hardware_concurrency(); - } - } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); add_opt(common_arg( {"-C", "--cpu-mask"}, "M", "CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: \"\")", @@ -515,108 +536,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.cpuparams_batch.poll = value; } )); - add_opt(common_arg( - {"-Cd", "--cpu-mask-draft"}, "M", - "Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)", - [](common_params & params, const std::string & mask) { - params.draft_cpuparams.mask_valid = true; - if (!parse_cpu_mask(mask, params.draft_cpuparams.cpumask)) { - throw std::invalid_argument("invalid cpumask"); - } - } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); - add_opt(common_arg( - {"-Crd", "--cpu-range-draft"}, "lo-hi", - "Ranges of CPUs for affinity. Complements --cpu-mask-draft", - [](common_params & params, const std::string & range) { - params.draft_cpuparams.mask_valid = true; - if (!parse_cpu_range(range, params.draft_cpuparams.cpumask)) { - throw std::invalid_argument("invalid range"); - } - } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); - add_opt(common_arg( - {"--cpu-strict-draft"}, "<0|1>", - "Use strict CPU placement for draft model (default: same as --cpu-strict)", - [](common_params & params, int value) { - params.draft_cpuparams.strict_cpu = value; - } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); - add_opt(common_arg( - {"--prio-draft"}, "N", - string_format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.draft_cpuparams.priority), - [](common_params & params, int prio) { - if (prio < 0 || prio > 3) { - throw std::invalid_argument("invalid value"); - } - params.draft_cpuparams.priority = (enum ggml_sched_priority) prio; - } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); - add_opt(common_arg( - {"--poll-draft"}, "<0|1>", - "Use polling to wait for draft model work (default: same as --poll])", - [](common_params & params, int value) { - params.draft_cpuparams.poll = value; - } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); - add_opt(common_arg( - {"-Cbd", "--cpu-mask-batch-draft"}, "M", - "Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)", - [](common_params & params, const std::string & mask) { - params.draft_cpuparams_batch.mask_valid = true; - if (!parse_cpu_mask(mask, params.draft_cpuparams_batch.cpumask)) { - throw std::invalid_argument("invalid cpumask"); - } - } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); - add_opt(common_arg( - {"-Crbd", "--cpu-range-batch-draft"}, "lo-hi", - "Ranges of CPUs for affinity. Complements --cpu-mask-draft-batch)", - [](common_params & params, const std::string & range) { - params.draft_cpuparams_batch.mask_valid = true; - if (!parse_cpu_range(range, params.draft_cpuparams_batch.cpumask)) { - throw std::invalid_argument("invalid cpumask"); - } - } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); - add_opt(common_arg( - {"--cpu-strict-batch-draft"}, "<0|1>", - "Use strict CPU placement for draft model (default: --cpu-strict-draft)", - [](common_params & params, int value) { - params.draft_cpuparams_batch.strict_cpu = value; - } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); - add_opt(common_arg( - {"--prio-batch-draft"}, "N", - string_format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.draft_cpuparams_batch.priority), - [](common_params & params, int prio) { - if (prio < 0 || prio > 3) { - throw std::invalid_argument("invalid value"); - } - params.draft_cpuparams_batch.priority = (enum ggml_sched_priority) prio; - } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); - add_opt(common_arg( - {"--poll-batch-draft"}, "<0|1>", - "Use polling to wait for draft model work (default: --poll-draft)", - [](common_params & params, int value) { - params.draft_cpuparams_batch.poll = value; - } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); - add_opt(common_arg( - {"--draft"}, "N", - string_format("number of tokens to draft for speculative decoding (default: %d)", params.n_draft), - [](common_params & params, int value) { - params.n_draft = value; - } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP})); - add_opt(common_arg( - {"-ps", "--p-split"}, "N", - string_format("speculative decoding split probability (default: %.1f)", (double)params.p_split), - [](common_params & params, const std::string & value) { - params.p_split = std::stof(value); - } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); add_opt(common_arg( {"-lcs", "--lookup-cache-static"}, "FNAME", "path to static lookup cache to use for lookup decoding (not updated by generation)", @@ -701,7 +620,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex string_format("disable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"), [](common_params & params) { params.no_perf = true; - params.sparams.no_perf = true; + params.sampling.no_perf = true; } ).set_env("LLAMA_ARG_NO_PERF")); add_opt(common_arg( @@ -883,155 +802,155 @@ common_params_context common_params_parser_init(common_params & params, llama_ex string_format("samplers that will be used for generation in the order, separated by \';\'\n(default: %s)", sampler_type_names.c_str()), [](common_params & params, const std::string & value) { const auto sampler_names = string_split(value, ';'); - params.sparams.samplers = common_sampler_types_from_names(sampler_names, true); + params.sampling.samplers = common_sampler_types_from_names(sampler_names, true); } ).set_sparam()); add_opt(common_arg( {"-s", "--seed"}, "SEED", - string_format("RNG seed (default: %d, use random seed for %d)", params.sparams.seed, LLAMA_DEFAULT_SEED), + string_format("RNG seed (default: %d, use random seed for %d)", params.sampling.seed, LLAMA_DEFAULT_SEED), [](common_params & params, const std::string & value) { - params.sparams.seed = std::stoul(value); + params.sampling.seed = std::stoul(value); } ).set_sparam()); add_opt(common_arg( {"--sampling-seq"}, "SEQUENCE", string_format("simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str()), [](common_params & params, const std::string & value) { - params.sparams.samplers = common_sampler_types_from_chars(value); + params.sampling.samplers = common_sampler_types_from_chars(value); } ).set_sparam()); add_opt(common_arg( {"--ignore-eos"}, "ignore end of stream token and continue generating (implies --logit-bias EOS-inf)", [](common_params & params) { - params.sparams.ignore_eos = true; + params.sampling.ignore_eos = true; } ).set_sparam()); add_opt(common_arg( {"--penalize-nl"}, - string_format("penalize newline tokens (default: %s)", params.sparams.penalize_nl ? "true" : "false"), + string_format("penalize newline tokens (default: %s)", params.sampling.penalize_nl ? "true" : "false"), [](common_params & params) { - params.sparams.penalize_nl = true; + params.sampling.penalize_nl = true; } ).set_sparam()); add_opt(common_arg( {"--temp"}, "N", - string_format("temperature (default: %.1f)", (double)params.sparams.temp), + string_format("temperature (default: %.1f)", (double)params.sampling.temp), [](common_params & params, const std::string & value) { - params.sparams.temp = std::stof(value); - params.sparams.temp = std::max(params.sparams.temp, 0.0f); + params.sampling.temp = std::stof(value); + params.sampling.temp = std::max(params.sampling.temp, 0.0f); } ).set_sparam()); add_opt(common_arg( {"--top-k"}, "N", - string_format("top-k sampling (default: %d, 0 = disabled)", params.sparams.top_k), + string_format("top-k sampling (default: %d, 0 = disabled)", params.sampling.top_k), [](common_params & params, int value) { - params.sparams.top_k = value; + params.sampling.top_k = value; } ).set_sparam()); add_opt(common_arg( {"--top-p"}, "N", - string_format("top-p sampling (default: %.1f, 1.0 = disabled)", (double)params.sparams.top_p), + string_format("top-p sampling (default: %.1f, 1.0 = disabled)", (double)params.sampling.top_p), [](common_params & params, const std::string & value) { - params.sparams.top_p = std::stof(value); + params.sampling.top_p = std::stof(value); } ).set_sparam()); add_opt(common_arg( {"--min-p"}, "N", - string_format("min-p sampling (default: %.1f, 0.0 = disabled)", (double)params.sparams.min_p), + string_format("min-p sampling (default: %.1f, 0.0 = disabled)", (double)params.sampling.min_p), [](common_params & params, const std::string & value) { - params.sparams.min_p = std::stof(value); + params.sampling.min_p = std::stof(value); } ).set_sparam()); add_opt(common_arg( {"--xtc-probability"}, "N", - string_format("xtc probability (default: %.1f, 0.0 = disabled)", (double)params.sparams.xtc_probability), + string_format("xtc probability (default: %.1f, 0.0 = disabled)", (double)params.sampling.xtc_probability), [](common_params & params, const std::string & value) { - params.sparams.xtc_probability = std::stof(value); + params.sampling.xtc_probability = std::stof(value); } ).set_sparam()); add_opt(common_arg( {"--xtc-threshold"}, "N", - string_format("xtc threshold (default: %.1f, 1.0 = disabled)", (double)params.sparams.xtc_threshold), + string_format("xtc threshold (default: %.1f, 1.0 = disabled)", (double)params.sampling.xtc_threshold), [](common_params & params, const std::string & value) { - params.sparams.xtc_threshold = std::stof(value); + params.sampling.xtc_threshold = std::stof(value); } ).set_sparam()); add_opt(common_arg( {"--typical"}, "N", - string_format("locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)params.sparams.typ_p), + string_format("locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)params.sampling.typ_p), [](common_params & params, const std::string & value) { - params.sparams.typ_p = std::stof(value); + params.sampling.typ_p = std::stof(value); } ).set_sparam()); add_opt(common_arg( {"--repeat-last-n"}, "N", - string_format("last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", params.sparams.penalty_last_n), + string_format("last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", params.sampling.penalty_last_n), [](common_params & params, int value) { - params.sparams.penalty_last_n = value; - params.sparams.n_prev = std::max(params.sparams.n_prev, params.sparams.penalty_last_n); + params.sampling.penalty_last_n = value; + params.sampling.n_prev = std::max(params.sampling.n_prev, params.sampling.penalty_last_n); } ).set_sparam()); add_opt(common_arg( {"--repeat-penalty"}, "N", - string_format("penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)params.sparams.penalty_repeat), + string_format("penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)params.sampling.penalty_repeat), [](common_params & params, const std::string & value) { - params.sparams.penalty_repeat = std::stof(value); + params.sampling.penalty_repeat = std::stof(value); } ).set_sparam()); add_opt(common_arg( {"--presence-penalty"}, "N", - string_format("repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)params.sparams.penalty_present), + string_format("repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)params.sampling.penalty_present), [](common_params & params, const std::string & value) { - params.sparams.penalty_present = std::stof(value); + params.sampling.penalty_present = std::stof(value); } ).set_sparam()); add_opt(common_arg( {"--frequency-penalty"}, "N", - string_format("repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)params.sparams.penalty_freq), + string_format("repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)params.sampling.penalty_freq), [](common_params & params, const std::string & value) { - params.sparams.penalty_freq = std::stof(value); + params.sampling.penalty_freq = std::stof(value); } ).set_sparam()); add_opt(common_arg( {"--dry-multiplier"}, "N", - string_format("set DRY sampling multiplier (default: %.1f, 0.0 = disabled)", (double)params.sparams.dry_multiplier), + string_format("set DRY sampling multiplier (default: %.1f, 0.0 = disabled)", (double)params.sampling.dry_multiplier), [](common_params & params, const std::string & value) { - params.sparams.dry_multiplier = std::stof(value); + params.sampling.dry_multiplier = std::stof(value); } ).set_sparam()); add_opt(common_arg( {"--dry-base"}, "N", - string_format("set DRY sampling base value (default: %.2f)", (double)params.sparams.dry_base), + string_format("set DRY sampling base value (default: %.2f)", (double)params.sampling.dry_base), [](common_params & params, const std::string & value) { float potential_base = std::stof(value); if (potential_base >= 1.0f) { - params.sparams.dry_base = potential_base; + params.sampling.dry_base = potential_base; } } ).set_sparam()); add_opt(common_arg( {"--dry-allowed-length"}, "N", - string_format("set allowed length for DRY sampling (default: %d)", params.sparams.dry_allowed_length), + string_format("set allowed length for DRY sampling (default: %d)", params.sampling.dry_allowed_length), [](common_params & params, int value) { - params.sparams.dry_allowed_length = value; + params.sampling.dry_allowed_length = value; } ).set_sparam()); add_opt(common_arg( {"--dry-penalty-last-n"}, "N", - string_format("set DRY penalty for the last n tokens (default: %d, 0 = disable, -1 = context size)", params.sparams.dry_penalty_last_n), + string_format("set DRY penalty for the last n tokens (default: %d, 0 = disable, -1 = context size)", params.sampling.dry_penalty_last_n), [](common_params & params, int value) { - params.sparams.dry_penalty_last_n = value; + params.sampling.dry_penalty_last_n = value; } ).set_sparam()); add_opt(common_arg( {"--dry-sequence-breaker"}, "STRING", string_format("add sequence breaker for DRY sampling, clearing out default breakers (%s) in the process; use \"none\" to not use any sequence breakers\n", - params.sparams.dry_sequence_breakers.empty() ? "none" : - std::accumulate(std::next(params.sparams.dry_sequence_breakers.begin()), - params.sparams.dry_sequence_breakers.end(), - std::string("'") + (params.sparams.dry_sequence_breakers[0] == "\n" ? "\\n" : params.sparams.dry_sequence_breakers[0]) + "'", + params.sampling.dry_sequence_breakers.empty() ? "none" : + std::accumulate(std::next(params.sampling.dry_sequence_breakers.begin()), + params.sampling.dry_sequence_breakers.end(), + std::string("'") + (params.sampling.dry_sequence_breakers[0] == "\n" ? "\\n" : params.sampling.dry_sequence_breakers[0]) + "'", [](const std::string& a, const std::string& b) { std::string formatted_b = (b == "\n") ? "\\n" : b; return a + ", '" + formatted_b + "'"; @@ -1040,51 +959,51 @@ common_params_context common_params_parser_init(common_params & params, llama_ex static bool defaults_cleared = false; if (!defaults_cleared) { - params.sparams.dry_sequence_breakers.clear(); + params.sampling.dry_sequence_breakers.clear(); defaults_cleared = true; } if (value == "none") { - params.sparams.dry_sequence_breakers.clear(); + params.sampling.dry_sequence_breakers.clear(); } else { - params.sparams.dry_sequence_breakers.emplace_back(value); + params.sampling.dry_sequence_breakers.emplace_back(value); } } ).set_sparam()); add_opt(common_arg( {"--dynatemp-range"}, "N", - string_format("dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)params.sparams.dynatemp_range), + string_format("dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)params.sampling.dynatemp_range), [](common_params & params, const std::string & value) { - params.sparams.dynatemp_range = std::stof(value); + params.sampling.dynatemp_range = std::stof(value); } ).set_sparam()); add_opt(common_arg( {"--dynatemp-exp"}, "N", - string_format("dynamic temperature exponent (default: %.1f)", (double)params.sparams.dynatemp_exponent), + string_format("dynamic temperature exponent (default: %.1f)", (double)params.sampling.dynatemp_exponent), [](common_params & params, const std::string & value) { - params.sparams.dynatemp_exponent = std::stof(value); + params.sampling.dynatemp_exponent = std::stof(value); } ).set_sparam()); add_opt(common_arg( {"--mirostat"}, "N", string_format("use Mirostat sampling.\nTop K, Nucleus and Locally Typical samplers are ignored if used.\n" - "(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", params.sparams.mirostat), + "(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", params.sampling.mirostat), [](common_params & params, int value) { - params.sparams.mirostat = value; + params.sampling.mirostat = value; } ).set_sparam()); add_opt(common_arg( {"--mirostat-lr"}, "N", - string_format("Mirostat learning rate, parameter eta (default: %.1f)", (double)params.sparams.mirostat_eta), + string_format("Mirostat learning rate, parameter eta (default: %.1f)", (double)params.sampling.mirostat_eta), [](common_params & params, const std::string & value) { - params.sparams.mirostat_eta = std::stof(value); + params.sampling.mirostat_eta = std::stof(value); } ).set_sparam()); add_opt(common_arg( {"--mirostat-ent"}, "N", - string_format("Mirostat target entropy, parameter tau (default: %.1f)", (double)params.sparams.mirostat_tau), + string_format("Mirostat target entropy, parameter tau (default: %.1f)", (double)params.sampling.mirostat_tau), [](common_params & params, const std::string & value) { - params.sparams.mirostat_tau = std::stof(value); + params.sampling.mirostat_tau = std::stof(value); } ).set_sparam()); add_opt(common_arg( @@ -1100,7 +1019,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex try { if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) { const float bias = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f); - params.sparams.logit_bias.push_back({key, bias}); + params.sampling.logit_bias.push_back({key, bias}); } else { throw std::invalid_argument("invalid input format"); } @@ -1111,9 +1030,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_sparam()); add_opt(common_arg( {"--grammar"}, "GRAMMAR", - string_format("BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", params.sparams.grammar.c_str()), + string_format("BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", params.sampling.grammar.c_str()), [](common_params & params, const std::string & value) { - params.sparams.grammar = value; + params.sampling.grammar = value; } ).set_sparam()); add_opt(common_arg( @@ -1127,7 +1046,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex std::copy( std::istreambuf_iterator(file), std::istreambuf_iterator(), - std::back_inserter(params.sparams.grammar) + std::back_inserter(params.sampling.grammar) ); } ).set_sparam()); @@ -1135,7 +1054,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex {"-j", "--json-schema"}, "SCHEMA", "JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\nFor schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead", [](common_params & params, const std::string & value) { - params.sparams.grammar = json_schema_to_grammar(json::parse(value)); + params.sampling.grammar = json_schema_to_grammar(json::parse(value)); } ).set_sparam()); add_opt(common_arg( @@ -1433,28 +1352,42 @@ common_params_context common_params_parser_init(common_params & params, llama_ex else { throw std::invalid_argument("invalid value"); } } ).set_env("LLAMA_ARG_NUMA")); + add_opt(common_arg( + {"-dev", "--device"}, "", + "comma-separated list of devices to use for offloading (none = don't offload)\n" + "use --list-devices to see a list of available devices", + [](common_params & params, const std::string & value) { + params.devices = parse_device_list(value); + } + ).set_env("LLAMA_ARG_DEVICE")); + add_opt(common_arg( + {"--list-devices"}, + "print list of available devices and exit", + [](common_params &) { + printf("Available devices:\n"); + for (size_t i = 0; i < ggml_backend_dev_count(); ++i) { + auto * dev = ggml_backend_dev_get(i); + if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_GPU) { + size_t free, total; + ggml_backend_dev_memory(dev, &free, &total); + printf(" %s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024); + } + } + exit(0); + } + )); add_opt(common_arg( {"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N", "number of layers to store in VRAM", [](common_params & params, int value) { params.n_gpu_layers = value; if (!llama_supports_gpu_offload()) { - fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers option will be ignored\n"); - fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); + fprintf(stderr, "warning: no usable GPU found, --gpu-layers option will be ignored\n"); + fprintf(stderr, "warning: one possible reason is that llama.cpp was compiled without GPU support\n"); + fprintf(stderr, "warning: consult docs/build.md for compilation instructions\n"); } } ).set_env("LLAMA_ARG_N_GPU_LAYERS")); - add_opt(common_arg( - {"-ngld", "--gpu-layers-draft", "--n-gpu-layers-draft"}, "N", - "number of layers to store in VRAM for the draft model", - [](common_params & params, int value) { - params.n_gpu_layers_draft = value; - if (!llama_supports_gpu_offload()) { - fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers-draft option will be ignored\n"); - fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); - } - } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); add_opt(common_arg( {"-sm", "--split-mode"}, "{none,layer,row}", "how to split the model across multiple GPUs, one of:\n" @@ -1468,10 +1401,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } else if (arg_next == "layer") { params.split_mode = LLAMA_SPLIT_MODE_LAYER; } else if (arg_next == "row") { -#ifdef GGML_USE_SYCL - fprintf(stderr, "warning: The split mode value:[row] is not supported by llama.cpp with SYCL. It's developing.\nExit!\n"); - exit(1); -#endif // GGML_USE_SYCL params.split_mode = LLAMA_SPLIT_MODE_ROW; } else { throw std::invalid_argument("invalid value"); @@ -1593,13 +1522,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.model = value; } ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}).set_env("LLAMA_ARG_MODEL")); - add_opt(common_arg( - {"-md", "--model-draft"}, "FNAME", - "draft model for speculative decoding (default: unused)", - [](common_params & params, const std::string & value) { - params.model_draft = value; - } - ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); add_opt(common_arg( {"-mu", "--model-url"}, "MODEL_URL", "model download url (default: unused)", @@ -1911,10 +1833,12 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({LLAMA_EXAMPLE_SERVER})); add_opt(common_arg( {"--chat-template"}, "JINJA_TEMPLATE", - "set custom jinja chat template (default: template taken from model's metadata)\n" - "if suffix/prefix are specified, template will be disabled\n" - "only commonly used templates are accepted (unless --jinja is set before this flag):\n" - "https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template", + string_format( + "set custom jinja chat template (default: template taken from model's metadata)\n" + "if suffix/prefix are specified, template will be disabled\n" + "only commonly used templates are accepted (unless --jinja is set before this flag):\n" + "list of built-in templates:\n%s", list_builtin_chat_templates().c_str() + ), [](common_params & params, const std::string & value) { if (!common_chat_verify_template(value, params.use_jinja)) { throw std::runtime_error(string_format( @@ -1974,17 +1898,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.simple_io = true; } ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL})); - add_opt(common_arg( - {"-ld", "--logdir"}, "LOGDIR", - "path under which to save YAML logs (no logging if unset)", - [](common_params & params, const std::string & value) { - params.logdir = value; - - if (params.logdir.back() != DIRECTORY_SEPARATOR) { - params.logdir += DIRECTORY_SEPARATOR; - } - } - )); add_opt(common_arg( {"--positive-file"}, "FNAME", string_format("positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str()), @@ -2083,5 +1996,177 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_env("LLAMA_LOG_TIMESTAMPS")); + // speculative parameters + add_opt(common_arg( + {"-td", "--threads-draft"}, "N", + "number of threads to use during generation (default: same as --threads)", + [](common_params & params, int value) { + params.speculative.cpuparams.n_threads = value; + if (params.speculative.cpuparams.n_threads <= 0) { + params.speculative.cpuparams.n_threads = std::thread::hardware_concurrency(); + } + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"-tbd", "--threads-batch-draft"}, "N", + "number of threads to use during batch and prompt processing (default: same as --threads-draft)", + [](common_params & params, int value) { + params.speculative.cpuparams_batch.n_threads = value; + if (params.speculative.cpuparams_batch.n_threads <= 0) { + params.speculative.cpuparams_batch.n_threads = std::thread::hardware_concurrency(); + } + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"-Cd", "--cpu-mask-draft"}, "M", + "Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)", + [](common_params & params, const std::string & mask) { + params.speculative.cpuparams.mask_valid = true; + if (!parse_cpu_mask(mask, params.speculative.cpuparams.cpumask)) { + throw std::invalid_argument("invalid cpumask"); + } + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"-Crd", "--cpu-range-draft"}, "lo-hi", + "Ranges of CPUs for affinity. Complements --cpu-mask-draft", + [](common_params & params, const std::string & range) { + params.speculative.cpuparams.mask_valid = true; + if (!parse_cpu_range(range, params.speculative.cpuparams.cpumask)) { + throw std::invalid_argument("invalid range"); + } + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"--cpu-strict-draft"}, "<0|1>", + "Use strict CPU placement for draft model (default: same as --cpu-strict)", + [](common_params & params, int value) { + params.speculative.cpuparams.strict_cpu = value; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"--prio-draft"}, "N", + string_format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.speculative.cpuparams.priority), + [](common_params & params, int prio) { + if (prio < 0 || prio > 3) { + throw std::invalid_argument("invalid value"); + } + params.speculative.cpuparams.priority = (enum ggml_sched_priority) prio; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"--poll-draft"}, "<0|1>", + "Use polling to wait for draft model work (default: same as --poll])", + [](common_params & params, int value) { + params.speculative.cpuparams.poll = value; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"-Cbd", "--cpu-mask-batch-draft"}, "M", + "Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)", + [](common_params & params, const std::string & mask) { + params.speculative.cpuparams_batch.mask_valid = true; + if (!parse_cpu_mask(mask, params.speculative.cpuparams_batch.cpumask)) { + throw std::invalid_argument("invalid cpumask"); + } + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"-Crbd", "--cpu-range-batch-draft"}, "lo-hi", + "Ranges of CPUs for affinity. Complements --cpu-mask-draft-batch)", + [](common_params & params, const std::string & range) { + params.speculative.cpuparams_batch.mask_valid = true; + if (!parse_cpu_range(range, params.speculative.cpuparams_batch.cpumask)) { + throw std::invalid_argument("invalid cpumask"); + } + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"--cpu-strict-batch-draft"}, "<0|1>", + "Use strict CPU placement for draft model (default: --cpu-strict-draft)", + [](common_params & params, int value) { + params.speculative.cpuparams_batch.strict_cpu = value; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"--prio-batch-draft"}, "N", + string_format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.speculative.cpuparams_batch.priority), + [](common_params & params, int prio) { + if (prio < 0 || prio > 3) { + throw std::invalid_argument("invalid value"); + } + params.speculative.cpuparams_batch.priority = (enum ggml_sched_priority) prio; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"--poll-batch-draft"}, "<0|1>", + "Use polling to wait for draft model work (default: --poll-draft)", + [](common_params & params, int value) { + params.speculative.cpuparams_batch.poll = value; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"--draft-max", "--draft", "--draft-n"}, "N", + string_format("number of tokens to draft for speculative decoding (default: %d)", params.speculative.n_max), + [](common_params & params, int value) { + params.speculative.n_max = value; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--draft-min", "--draft-n-min"}, "N", + string_format("minimum number of draft tokens to use for speculative decoding (default: %d)", params.speculative.n_min), + [](common_params & params, int value) { + params.speculative.n_min = value; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--draft-p-split"}, "P", + string_format("speculative decoding split probability (default: %.1f)", (double)params.speculative.p_split), + [](common_params & params, const std::string & value) { + params.speculative.p_split = std::stof(value); + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"--draft-p-min"}, "P", + string_format("minimum speculative decoding probability (greedy) (default: %.1f)", (double)params.speculative.p_min), + [](common_params & params, const std::string & value) { + params.speculative.p_min = std::stof(value); + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"-cd", "--ctx-size-draft"}, "N", + string_format("size of the prompt context for the draft model (default: %d, 0 = loaded from model)", params.speculative.n_ctx), + [](common_params & params, int value) { + params.speculative.n_ctx = value; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"-devd", "--device-draft"}, "", + "comma-separated list of devices to use for offloading the draft model (none = don't offload)\n" + "use --list-devices to see a list of available devices", + [](common_params & params, const std::string & value) { + params.speculative.devices = parse_device_list(value); + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"-ngld", "--gpu-layers-draft", "--n-gpu-layers-draft"}, "N", + "number of layers to store in VRAM for the draft model", + [](common_params & params, int value) { + params.speculative.n_gpu_layers = value; + if (!llama_supports_gpu_offload()) { + fprintf(stderr, "warning: no usable GPU found, --gpu-layers-draft option will be ignored\n"); + fprintf(stderr, "warning: one possible reason is that llama.cpp was compiled without GPU support\n"); + fprintf(stderr, "warning: consult docs/build.md for compilation instructions\n"); + } + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"-md", "--model-draft"}, "FNAME", + "draft model for speculative decoding (default: unused)", + [](common_params & params, const std::string & value) { + params.speculative.model = value; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER})); + return ctx_arg; } diff --git a/common/common.cpp b/common/common.cpp index 9508aee6c..0fb19e79e 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -538,12 +538,12 @@ std::string string_from(const struct llama_context * ctx, const struct llama_bat [](const unsigned char c) { return !std::isprint(c); }), detokenized.end()); - buf << "\n" << std::to_string(i) - << ":token '" << detokenized << "'" - << ":pos " << std::to_string(batch.pos[i]) - << ":n_seq_id " << std::to_string(batch.n_seq_id[i]) - << ":seq_id " << std::to_string(batch.seq_id[i][0]) - << ":logits " << std::to_string(batch.logits[i]); + buf << "\n" << std::to_string(i) + << ", token '" << detokenized << "'" + << ", pos " << std::to_string(batch.pos[i]) + << ", n_seq_id " << std::to_string(batch.n_seq_id[i]) + << ", seq_id " << std::to_string(batch.seq_id[i][0]) + << ", logits " << std::to_string(batch.logits[i]); } buf << " ]"; @@ -654,7 +654,17 @@ bool fs_validate_filename(const std::string & filename) { std::u32string filename_utf32; try { +#if defined(__clang__) + // disable C++17 deprecation warning for std::codecvt_utf8 +# pragma clang diagnostic push +# pragma clang diagnostic ignored "-Wdeprecated-declarations" +#endif std::wstring_convert, char32_t> converter; + +#if defined(__clang__) +# pragma clang diagnostic pop +#endif + filename_utf32 = converter.from_bytes(filename); // If the reverse conversion mismatches, it means overlong UTF-8 sequences were used, @@ -868,9 +878,9 @@ struct common_init_result common_init_from_params(common_params & params) { llama_model * model = nullptr; if (!params.hf_repo.empty() && !params.hf_file.empty()) { - model = common_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams); + model = common_load_model_from_hf(params.hf_repo, params.hf_file, params.model, params.hf_token, mparams); } else if (!params.model_url.empty()) { - model = common_load_model_from_url(params.model_url.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams); + model = common_load_model_from_url(params.model_url, params.model, params.hf_token, mparams); } else { model = llama_load_model_from_file(params.model.c_str(), mparams); } @@ -914,6 +924,12 @@ struct common_init_result common_init_from_params(common_params & params) { return iparams; } + if (params.ctx_shift && !llama_kv_cache_can_shift(lctx)) { + LOG_ERR("%s: KV cache shifting is not supported for this model (--no-context-shift to disable)'\n", __func__); + llama_free_model(model); + return iparams; + } + if (!params.control_vectors.empty()) { if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1; if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model); @@ -958,9 +974,9 @@ struct common_init_result common_init_from_params(common_params & params) { common_lora_adapters_apply(lctx, iparams.lora_adapters); } - if (params.sparams.ignore_eos && llama_token_eos(model) == LLAMA_TOKEN_NULL) { + if (params.sampling.ignore_eos && llama_token_eos(model) == LLAMA_TOKEN_NULL) { LOG_WRN("%s: warning: model does not have an EOS token, ignoring --ignore-eos\n", __func__); - params.sparams.ignore_eos = false; + params.sampling.ignore_eos = false; } if (params.warmup) { @@ -1012,9 +1028,12 @@ void common_lora_adapters_apply(struct llama_context * ctx, std::vector prev_row(b_len + 1, 0); + std::vector curr_row(b_len + 1, 0); + + // iterate through the elements of a + for (size_t i = 1; i <= a_len; i++) { + // iterate through the elements of b + for (size_t j = 1; j <= b_len; j++) { + // if elements at the current positions match + if (a[i - 1] == b[j - 1]) { + // if it's the first element of either sequences, set LCS length to 1 + if (i == 1 || j == 1) { + curr_row[j] = 1; + } else { + // increment LCS length by 1 compared to the previous element + curr_row[j] = prev_row[j - 1] + 1; + } + + // update max_length if necessary + if (curr_row[j] > max_length) { + max_length = curr_row[j]; + } + } else { + // reset LCS length if elements don't match + curr_row[j] = 0; + } + } + + // update the previous row for the next iteration + prev_row = curr_row; + } + + // return the maximum length of the LCS + return max_length; +} + // // Vocab utils // @@ -1984,216 +2066,3 @@ common_control_vector_data common_control_vector_load(const std::vector & data) { - if (data.empty()) { - fprintf(stream, "%s:\n", prop_name); - return; - } - - fprintf(stream, "%s: [", prop_name); - for (size_t i = 0; i < data.size() - 1; ++i) { - fprintf(stream, "%e, ", data[i]); - } - fprintf(stream, "%e]\n", data.back()); -} - -void yaml_dump_vector_int(FILE * stream, const char * prop_name, const std::vector & data) { - if (data.empty()) { - fprintf(stream, "%s:\n", prop_name); - return; - } - - fprintf(stream, "%s: [", prop_name); - for (size_t i = 0; i < data.size() - 1; ++i) { - fprintf(stream, "%d, ", data[i]); - } - fprintf(stream, "%d]\n", data.back()); -} - -void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const char * data) { - std::string data_str(data == NULL ? "" : data); - - if (data_str.empty()) { - fprintf(stream, "%s:\n", prop_name); - return; - } - - size_t pos_start = 0; - size_t pos_found = 0; - - if (std::isspace(data_str[0]) || std::isspace(data_str.back())) { - data_str = std::regex_replace(data_str, std::regex("\n"), "\\n"); - data_str = std::regex_replace(data_str, std::regex("\""), "\\\""); - data_str = std::regex_replace(data_str, std::regex(R"(\\[^n"])"), R"(\$&)"); - data_str = "\"" + data_str + "\""; - fprintf(stream, "%s: %s\n", prop_name, data_str.c_str()); - return; - } - - if (data_str.find('\n') == std::string::npos) { - fprintf(stream, "%s: %s\n", prop_name, data_str.c_str()); - return; - } - - fprintf(stream, "%s: |\n", prop_name); - while ((pos_found = data_str.find('\n', pos_start)) != std::string::npos) { - fprintf(stream, " %s\n", data_str.substr(pos_start, pos_found-pos_start).c_str()); - pos_start = pos_found + 1; - } -} - -void yaml_dump_non_result_info(FILE * stream, const common_params & params, const llama_context * lctx, - const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc) { - const auto & sparams = params.sparams; - - fprintf(stream, "build_commit: %s\n", LLAMA_COMMIT); - fprintf(stream, "build_number: %d\n", LLAMA_BUILD_NUMBER); - fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false"); - fprintf(stream, "cpu_has_avx: %s\n", ggml_cpu_has_avx() ? "true" : "false"); - fprintf(stream, "cpu_has_avx_vnni: %s\n", ggml_cpu_has_avx_vnni() ? "true" : "false"); - fprintf(stream, "cpu_has_avx2: %s\n", ggml_cpu_has_avx2() ? "true" : "false"); - fprintf(stream, "cpu_has_avx512: %s\n", ggml_cpu_has_avx512() ? "true" : "false"); - fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false"); - fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false"); - fprintf(stream, "cpu_has_cuda: %s\n", ggml_cpu_has_cuda() ? "true" : "false"); - fprintf(stream, "cpu_has_vulkan: %s\n", ggml_cpu_has_vulkan() ? "true" : "false"); - fprintf(stream, "cpu_has_kompute: %s\n", ggml_cpu_has_kompute() ? "true" : "false"); - fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false"); - fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false"); - fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false"); - fprintf(stream, "cpu_has_sve: %s\n", ggml_cpu_has_sve() ? "true" : "false"); - fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false"); - fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false"); - fprintf(stream, "cpu_has_riscv_v: %s\n", ggml_cpu_has_riscv_v() ? "true" : "false"); - fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false"); - fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false"); - fprintf(stream, "cpu_has_sse3: %s\n", ggml_cpu_has_sse3() ? "true" : "false"); - fprintf(stream, "cpu_has_vsx: %s\n", ggml_cpu_has_vsx() ? "true" : "false"); - fprintf(stream, "cpu_has_matmul_int8: %s\n", ggml_cpu_has_matmul_int8() ? "true" : "false"); - -#ifdef NDEBUG - fprintf(stream, "debug: false\n"); -#else - fprintf(stream, "debug: true\n"); -#endif // NDEBUG - - fprintf(stream, "model_desc: %s\n", model_desc); - fprintf(stream, "n_vocab: %d # output size of the final layer, 32001 for some models\n", llama_n_vocab(llama_get_model(lctx))); - -#ifdef __OPTIMIZE__ - fprintf(stream, "optimize: true\n"); -#else - fprintf(stream, "optimize: false\n"); -#endif // __OPTIMIZE__ - - fprintf(stream, "time: %s\n", timestamp.c_str()); - - fprintf(stream, "\n"); - fprintf(stream, "###############\n"); - fprintf(stream, "# User Inputs #\n"); - fprintf(stream, "###############\n"); - fprintf(stream, "\n"); - - fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str()); - fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch); - fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks); - fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false"); - fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx); - fprintf(stream, "dry_allowed_length: %d # default: 2\n", sparams.dry_allowed_length); - fprintf(stream, "dry_base: %.2f # default: 1.75\n", sparams.dry_base); - fprintf(stream, "dry_multiplier: %.1f # default: 0.0\n", sparams.dry_multiplier); - fprintf(stream, "dry_penalty_last_n: %d # default: -1 (0 = disable, -1 = context size)\n", sparams.dry_penalty_last_n); - fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false"); - fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n"); - fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", sparams.penalty_freq); - yaml_dump_string_multiline(stream, "grammar", sparams.grammar.c_str()); - fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n"); - fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false"); - fprintf(stream, "hellaswag_tasks: %zu # default: 400\n", params.hellaswag_tasks); - fprintf(stream, "ignore_eos: %s # default: false\n", sparams.ignore_eos ? "true" : "false"); - - yaml_dump_string_multiline(stream, "in_prefix", params.input_prefix.c_str()); - fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false"); - yaml_dump_string_multiline(stream, "in_suffix", params.input_prefix.c_str()); - fprintf(stream, "interactive: %s # default: false\n", params.interactive ? "true" : "false"); - fprintf(stream, "interactive_first: %s # default: false\n", params.interactive_first ? "true" : "false"); - fprintf(stream, "keep: %d # default: 0\n", params.n_keep); - fprintf(stream, "logdir: %s # default: unset (no logging)\n", params.logdir.c_str()); - - fprintf(stream, "logit_bias:\n"); - for (const auto & logit_bias : sparams.logit_bias) { - fprintf(stream, " %d: %f", logit_bias.token, logit_bias.bias); - } - - fprintf(stream, "lora:\n"); - for (auto & la : params.lora_adapters) { - if (la.scale == 1.0f) { - fprintf(stream, " - %s\n", la.path.c_str()); - } - } - fprintf(stream, "lora_scaled:\n"); - for (auto & la : params.lora_adapters) { - if (la.scale != 1.0f) { - fprintf(stream, " - %s: %f\n", la.path.c_str(), la.scale); - } - } - fprintf(stream, "lora_init_without_apply: %s # default: false\n", params.lora_init_without_apply ? "true" : "false"); - fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu); - fprintf(stream, "min_keep: %d # default: 0 (disabled)\n", sparams.min_keep); - fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat); - fprintf(stream, "mirostat_ent: %f # default: 5.0\n", sparams.mirostat_tau); - fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.mirostat_eta); - fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false"); - fprintf(stream, "model: %s # default: %s\n", params.model.c_str(), DEFAULT_MODEL_PATH); - fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str()); - fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false"); - fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers); - fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict); - fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs); - fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false"); - fprintf(stream, "penalize_nl: %s # default: false\n", sparams.penalize_nl ? "true" : "false"); - fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type); - fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride); - fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present); - yaml_dump_string_multiline(stream, "prompt", params.prompt.c_str()); - fprintf(stream, "prompt_cache: %s\n", params.path_prompt_cache.c_str()); - fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false"); - fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false"); - yaml_dump_vector_int(stream, "prompt_tokens", prompt_tokens); - fprintf(stream, "repeat_penalty: %f # default: 1.1\n", sparams.penalty_repeat); - - fprintf(stream, "reverse_prompt:\n"); - for (std::string ap : params.antiprompt) { - size_t pos = 0; - while ((pos = ap.find('\n', pos)) != std::string::npos) { - ap.replace(pos, 1, "\\n"); - pos += 1; - } - - fprintf(stream, " - %s\n", ap.c_str()); - } - - fprintf(stream, "rope_freq_base: %f # default: 10000.0\n", params.rope_freq_base); - fprintf(stream, "rope_freq_scale: %f # default: 1.0\n", params.rope_freq_scale); - fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false"); - fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false"); - fprintf(stream, "flash_attn: %s # default: false\n", params.flash_attn ? "true" : "false"); - fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp); - - const std::vector tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices()); - yaml_dump_vector_float(stream, "tensor_split", tensor_split_vector); - - fprintf(stream, "threads: %d # default: %u\n", params.cpuparams.n_threads, std::thread::hardware_concurrency()); - fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k); - fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p); - fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p); - fprintf(stream, "xtc_probability: %f # default: 0.0\n", sparams.xtc_probability); - fprintf(stream, "xtc_threshold: %f # default: 0.1\n", sparams.xtc_threshold); - fprintf(stream, "typ_p: %f # default: 1.0\n", sparams.typ_p); - fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false"); - fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false"); -} diff --git a/common/common.h b/common/common.h index f7b22454c..1e5890404 100644 --- a/common/common.h +++ b/common/common.h @@ -39,6 +39,8 @@ struct common_lora_adapter_container : common_lora_adapter_info { struct llama_lora_adapter * adapter; }; +using llama_tokens = std::vector; + // build info extern int LLAMA_BUILD_NUMBER; extern char const * LLAMA_COMMIT; @@ -107,8 +109,8 @@ enum dimre_method { DIMRE_METHOD_MEAN, }; -// sampler parameters -struct common_sampler_params { +// sampling parameters +struct common_params_sampling { uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler int32_t n_prev = 64; // number of previous tokens to remember @@ -137,6 +139,7 @@ struct common_sampler_params { bool penalize_nl = false; // consider newlines as a repeatable token bool ignore_eos = false; bool no_perf = false; // disable performance metrics + bool timing_per_token = false; std::vector dry_sequence_breakers = {"\n", ":", "\"", "*"}; // default sequence breakers for DRY @@ -160,21 +163,30 @@ struct common_sampler_params { std::string print() const; }; +struct common_params_speculative { + std::vector devices; // devices to use for offloading + int32_t n_ctx = 0; // draft context size + int32_t n_max = 16; // maximum number of tokens to draft during speculative decoding + int32_t n_min = 5; // minimum number of draft tokens to use for speculative decoding + int32_t n_gpu_layers = -1; // number of layers to store in VRAM for the draft model (-1 - use default) + float p_split = 0.1f; // speculative decoding split probability + float p_min = 0.9f; // minimum speculative decoding probability (greedy) + + struct cpu_params cpuparams; + struct cpu_params cpuparams_batch; + + std::string model = ""; // draft model for speculative decoding // NOLINT +}; + struct common_params { int32_t n_predict = -1; // new tokens to predict - int32_t n_ctx = 0; // context size + int32_t n_ctx = 4096; // context size int32_t n_batch = 2048; // logical batch size for prompt processing (must be >=32 to use BLAS) int32_t n_ubatch = 512; // physical batch size for prompt processing (must be >=32 to use BLAS) int32_t n_keep = 0; // number of tokens to keep from initial prompt - int32_t n_draft = 5; // number of tokens to draft during speculative decoding int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) int32_t n_parallel = 1; // number of parallel sequences to decode int32_t n_sequences = 1; // number of sequences to decode - float p_split = 0.1f; // speculative decoding split probability - int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) - int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default) - int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors - float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs int32_t grp_attn_n = 1; // group-attention factor int32_t grp_attn_w = 512; // group-attention width int32_t n_print = -1; // print token count every n tokens (-1 = disabled) @@ -185,27 +197,31 @@ struct common_params { float yarn_beta_fast = 32.0f; // YaRN low correction dim float yarn_beta_slow = 1.0f; // YaRN high correction dim int32_t yarn_orig_ctx = 0; // YaRN original context length - float defrag_thold = -1.0f; // KV cache defragmentation threshold + float defrag_thold = 0.1f; // KV cache defragmentation threshold + + // offload params + std::vector devices; // devices to use for offloading + int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) + int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors + float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs + enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs struct cpu_params cpuparams; struct cpu_params cpuparams_batch; - struct cpu_params draft_cpuparams; - struct cpu_params draft_cpuparams_batch; ggml_backend_sched_eval_callback cb_eval = nullptr; void * cb_eval_user_data = nullptr; ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED; - enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs enum llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED; enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings enum llama_attention_type attention_type = LLAMA_ATTENTION_TYPE_UNSPECIFIED; // attention type for embeddings - struct common_sampler_params sparams; + struct common_params_sampling sampling; + struct common_params_speculative speculative; std::string model = ""; // model path // NOLINT - std::string model_draft = ""; // draft model for speculative decoding // NOLINT std::string model_alias = "unknown"; // model alias // NOLINT std::string model_url = ""; // model url to download // NOLINT std::string hf_token = ""; // HF token // NOLINT @@ -216,7 +232,6 @@ struct common_params { std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state // NOLINT std::string input_prefix = ""; // string to prefix user inputs with // NOLINT std::string input_suffix = ""; // string to suffix user inputs with // NOLINT - std::string logdir = ""; // directory in which to save YAML log files // NOLINT std::string lookup_cache_static = ""; // path of static ngram cache file for lookup decoding // NOLINT std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding // NOLINT std::string logits_file = ""; // file for saving *all* logits // NOLINT @@ -462,17 +477,28 @@ struct common_init_result { struct common_init_result common_init_from_params(common_params & params); -struct llama_model_params common_model_params_to_llama (const common_params & params); +struct llama_model_params common_model_params_to_llama ( common_params & params); struct llama_context_params common_context_params_to_llama(const common_params & params); struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params); -struct llama_model * common_load_model_from_url(const char * model_url, const char * path_model, const char * hf_token, const struct llama_model_params & params); -struct llama_model * common_load_model_from_hf(const char * repo, const char * file, const char * path_model, const char * hf_token, const struct llama_model_params & params); +struct llama_model * common_load_model_from_url( + const std::string & model_url, + const std::string & local_path, + const std::string & hf_token, + const struct llama_model_params & params); +struct llama_model * common_load_model_from_hf( + const std::string & repo, + const std::string & remote_path, + const std::string & local_path, + const std::string & hf_token, + const struct llama_model_params & params); // clear LoRA adapters from context, then apply new list of adapters void common_lora_adapters_apply(struct llama_context * ctx, std::vector & lora_adapters); +// // Batch utils +// void common_batch_clear(struct llama_batch & batch); @@ -483,6 +509,16 @@ void common_batch_add( const std::vector & seq_ids, bool logits); +// +// Token utils +// + +// longest common prefix +size_t common_lcp(const llama_tokens & a, const llama_tokens & b); + +// longet common subsequence +size_t common_lcs(const llama_tokens & a, const llama_tokens & b); + // // Vocab utils // @@ -808,15 +844,3 @@ private: static const char * const LLM_KV_SPLIT_NO = "split.no"; static const char * const LLM_KV_SPLIT_COUNT = "split.count"; static const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count"; - -// -// YAML utils -// - -void yaml_dump_vector_float (FILE * stream, const char * prop_name, const std::vector & data); -void yaml_dump_vector_int (FILE * stream, const char * prop_name, const std::vector & data); -void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const char * data); - -void yaml_dump_non_result_info( - FILE * stream, const common_params & params, const llama_context * lctx, - const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc); diff --git a/common/sampling.cpp b/common/sampling.cpp index 161a30387..cd938f0b9 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -99,7 +99,7 @@ struct ring_buffer { }; struct common_sampler { - common_sampler_params params; + common_params_sampling params; struct llama_sampler * grmr; struct llama_sampler * chain; @@ -125,7 +125,7 @@ struct common_sampler { } }; -std::string common_sampler_params::print() const { +std::string common_params_sampling::print() const { char result[1024]; snprintf(result, sizeof(result), @@ -150,7 +150,7 @@ bool common_sampler_trigger_grammar(const struct llama_model * model, common_sam return true; } -struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_sampler_params & params) { +struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params) { llama_sampler_chain_params lparams = llama_sampler_chain_default_params(); lparams.no_perf = params.no_perf; @@ -333,6 +333,45 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co return cur_p.data[cur_p.selected].id; } +std::vector common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector & idxs, const llama_tokens & draft, bool grammar_first) { + GGML_ASSERT(idxs.size() == draft.size() + 1 && "idxs.size() must be draft.size() + 1"); + + std::vector result; + result.reserve(idxs.size()); + + size_t i = 0; + for (; i < draft.size(); i++) { + const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first); + + common_sampler_accept(gsmpl, id, true); + + result.push_back(id); + + if (draft[i] != id) { + break; + } + } + + if (i == draft.size()) { + const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first); + + common_sampler_accept(gsmpl, id, true); + + result.push_back(id); + } + + return result; +} + +std::vector common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first) { + std::vector idxs(draft.size() + 1); + for (size_t i = 0; i < idxs.size(); ++i) { + idxs[i] = i; + } + + return common_sampler_sample_and_accept_n(gsmpl, ctx, idxs, draft, grammar_first); +} + uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl) { return llama_sampler_get_seed(gsmpl->chain); } diff --git a/common/sampling.h b/common/sampling.h index d5cce3420..d3a4c3990 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -36,7 +36,7 @@ struct common_sampler; // llama_sampler API overloads -struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_sampler_params & params); +struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params); void common_sampler_free(struct common_sampler * gsmpl); @@ -60,6 +60,27 @@ void common_perf_print(const struct llama_context * ctx, const struct common_sam // llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first = false); +// generalized version of common_sampler_sample +// +// will cross-reference the sampled tokens with a batch of draft tokens and accept those that match +// if the sampler disagrees at some point, we stop and return the accepted tokens up to now +// +// common_sampler_sample_n(gsmpl, ctx, { idx }, {}); +// +// is equivalent to +// +// common_sampler_sample(gsmpl, ctx, idx); +// common_sampler_accept(gsmpl, token, true); +// +// requires: idxs.size() == draft.size() + 1 +// +// returns at least 1 token, up to idxs.size() +// +std::vector common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector & idxs, const llama_tokens & draft, bool grammar_first = false); + +// assume idxs == [ 0, 1, 2, ..., draft.size() ] +std::vector common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first = false); + uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl); // helpers diff --git a/common/speculative.cpp b/common/speculative.cpp new file mode 100644 index 000000000..e559675c4 --- /dev/null +++ b/common/speculative.cpp @@ -0,0 +1,270 @@ +#include "speculative.h" + +#include "log.h" +#include "common.h" +#include "sampling.h" + +#include + +#define SPEC_VOCAB_MAX_SIZE_DIFFERENCE 128 +#define SPEC_VOCAB_CHECK_START_TOKEN_ID 5 + +struct common_speculative { + struct llama_context * ctx; + struct common_sampler * smpl; + + llama_batch batch; + llama_tokens prompt; +}; + +struct common_speculative * common_speculative_init( + struct llama_context * ctx_dft) { + auto * result = new common_speculative { + /* .ctx = */ ctx_dft, + /* .smpl = */ nullptr, + /* .batch = */ llama_batch_init(llama_n_batch(ctx_dft), 0, 1), + /* .prompt = */ {}, + }; + + // TODO: optimize or pass from outside? +#if 0 + { + common_params_sampling params; + params.no_perf = false; + + params.top_k = 40; + params.top_p = 0.9; + + params.samplers = { + COMMON_SAMPLER_TYPE_TOP_K, + COMMON_SAMPLER_TYPE_TOP_P, + COMMON_SAMPLER_TYPE_INFILL, + }; + + result->smpl = common_sampler_init(llama_get_model(ctx_dft), params); + } +#else + { + common_params_sampling params; + params.no_perf = false; + + params.top_k = 10; + + params.samplers = { + COMMON_SAMPLER_TYPE_TOP_K, + }; + + result->smpl = common_sampler_init(llama_get_model(ctx_dft), params); + } +#endif + + return result; +} + +void common_speculative_free(struct common_speculative * spec) { + common_sampler_free(spec->smpl); + + llama_batch_free(spec->batch); + + delete spec; +} + +bool common_speculative_are_compatible( + const struct llama_context * ctx_tgt, + const struct llama_context * ctx_dft) { + const struct llama_model * model_tgt = llama_get_model(ctx_tgt); + const struct llama_model * model_dft = llama_get_model(ctx_dft); + + const bool vocab_type_tgt = llama_vocab_type(model_tgt); + LOG_DBG("%s: vocab_type tgt: %d\n", __func__, vocab_type_tgt); + + const bool vocab_type_dft = llama_vocab_type(model_dft); + LOG_DBG("%s: vocab_type dft: %d\n", __func__, vocab_type_dft); + + if (vocab_type_tgt != vocab_type_dft) { + LOG_ERR("%s: draft model vocab type must match target model to use speculation but " + "vocab_type_dft = %d while vocab_type_tgt = %d\n", __func__, vocab_type_dft, vocab_type_tgt); + return false; + } + + if (llama_add_bos_token(model_tgt) != llama_add_bos_token(model_dft) || + llama_add_eos_token(model_tgt) != llama_add_eos_token(model_dft) || + llama_token_bos(model_tgt) != llama_token_bos(model_dft) || + llama_token_eos(model_tgt) != llama_token_eos(model_dft)) { + LOG_ERR("%s: draft model special tokens must match target model to use speculation\n", __func__); + LOG_ERR("%s: tgt: bos = %d (%d), eos = %d (%d)\n", __func__, llama_token_bos(model_tgt), llama_add_bos_token(model_tgt), llama_token_eos(model_tgt), llama_add_eos_token(model_tgt)); + LOG_ERR("%s: dft: bos = %d (%d), eos = %d (%d)\n", __func__, llama_token_bos(model_dft), llama_add_bos_token(model_dft), llama_token_eos(model_dft), llama_add_eos_token(model_dft)); + return false; + } + + { + const int n_vocab_tgt = llama_n_vocab(model_tgt); + const int n_vocab_dft = llama_n_vocab(model_dft); + + const int vocab_diff = std::abs(n_vocab_tgt - n_vocab_dft); + + if (vocab_diff > SPEC_VOCAB_MAX_SIZE_DIFFERENCE) { + LOG_ERR("%s: draft model vocab must closely match target model to use speculation but " + "target vocab size %d does not match draft vocab size %d - difference %d, max allowed %d\n", + __func__, n_vocab_tgt, llama_n_vocab(model_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE); + return false; + } + + for (int i = SPEC_VOCAB_CHECK_START_TOKEN_ID; i < std::min(n_vocab_tgt, n_vocab_dft); ++i) { + const char * token_text_tgt = llama_token_get_text(model_tgt, i); + const char * token_text_dft = llama_token_get_text(model_dft, i); + if (std::strcmp(token_text_tgt, token_text_dft) != 0) { + LOG_ERR("%s: draft model vocab must match target model to use speculation but " + "token %d content differs - target '%s', draft '%s'\n", __func__, i, + common_token_to_piece(ctx_tgt, i).c_str(), + common_token_to_piece(ctx_dft, i).c_str()); + return false; + } + } + } + + return true; +} + +llama_tokens common_speculative_gen_draft( + struct common_speculative * spec, + struct common_speculative_params params, + const llama_tokens & prompt_tgt, + llama_token id_last) { + auto & batch = spec->batch; + auto & ctx = spec->ctx; + auto & smpl = spec->smpl; + auto & prompt = spec->prompt; + + int reuse_i = 0; + int reuse_n = 0; + + const int n_ctx = llama_n_ctx(ctx) - params.n_draft; + + const int i_start = std::max(0, (int) prompt_tgt.size() - n_ctx); + + // reuse as much as possible from the old draft context + // ideally, the draft context should be as big as the target context and we will always reuse the entire prompt + for (int i = 0; i < (int) prompt.size(); ++i) { + int cur = 0; + while (i_start + cur < (int) prompt_tgt.size() && + i + cur < (int) prompt.size() && + prompt_tgt[i_start + cur] == prompt[i + cur]) { + cur++; + } + + if ((cur >= params.n_reuse || n_ctx >= (int) prompt_tgt.size()) && cur > reuse_n) { + reuse_i = i; + reuse_n = cur; + } + } + + LOG_DBG("%s: reuse_i = %d, reuse_n = %d, prompt = %d\n", __func__, reuse_i, reuse_n, (int) prompt.size()); + + llama_tokens result; + result.reserve(params.n_draft); + + if (reuse_n == 0) { + llama_kv_cache_clear(ctx); + + prompt.clear(); + } else { + // this happens when a previous draft has been discarded (for example, due to being too small), but the + // target model agreed with it. in this case, we simply pass back the previous results to save compute + if (reuse_i + reuse_n < (int) prompt.size() && prompt[reuse_i + reuse_n] == id_last) { + for (int i = reuse_i + reuse_n + 1; i < (int) prompt.size(); ++i) { + result.push_back(prompt[i]); + + if (params.n_draft <= (int) result.size()) { + break; + } + } + + return result; + } + + if (reuse_i > 0) { + llama_kv_cache_seq_rm (ctx, 0, 0, reuse_i); + llama_kv_cache_seq_add(ctx, 0, reuse_i, -1, -reuse_i); + + prompt.erase(prompt.begin(), prompt.begin() + reuse_i); + } + + if (reuse_n < (int) prompt.size()) { + llama_kv_cache_seq_rm (ctx, 0, reuse_n, -1); + + prompt.erase(prompt.begin() + reuse_n, prompt.end()); + } + } + + // prepare a batch to evaluate any new tokens in the prompt + common_batch_clear(batch); + + for (size_t i = i_start + reuse_n; i < prompt_tgt.size(); ++i) { + //LOG_DBG("i = %d, i_start = %d, reuse_n = %d, i - i_start = %d, id = %6d\n", i, i_start, reuse_n, i - i_start, prompt_tgt[i]); + common_batch_add(batch, prompt_tgt[i], i - i_start, { 0 }, false); + + prompt.push_back(prompt_tgt[i]); + } + + // we should rarely end-up here during normal decoding + if (batch.n_tokens > 0) { + //LOG_DBG("%s: draft prompt batch: %s\n", __func__, string_from(ctx, batch).c_str()); + + llama_decode(ctx, batch); + } + + const llama_pos n_past = prompt.size(); + + LOG_DBG("%s: n_past = %d\n", __func__, n_past); + + common_batch_clear(batch); + common_batch_add (batch, id_last, n_past, { 0 }, true); + + prompt.push_back(id_last); + + //LOG_DBG("%s: draft prompt: %s\n", __func__, string_from(ctx, prompt).c_str()); + + llama_decode(ctx, batch); + + common_sampler_reset(smpl); + + // sample n_draft tokens from the draft model + for (int i = 0; i < params.n_draft; ++i) { + common_batch_clear(batch); + + common_sampler_sample(smpl, ctx, 0, true); + + const auto * cur_p = common_sampler_get_candidates(smpl); + + for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) { + LOG_DBG(" - draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n", + k, i, cur_p->data[k].id, cur_p->data[k].p, common_token_to_piece(ctx, cur_p->data[k].id).c_str()); + } + + // add drafted token for each sequence + const llama_token id = cur_p->data[0].id; + + // only collect very high-confidence draft tokens + if (cur_p->data[0].p < params.p_min) { + break; + } + + common_sampler_accept(smpl, id, true); + + result.push_back(id); + + if (params.n_draft <= (int) result.size()) { + break; + } + + common_batch_add(batch, id, n_past + i + 1, { 0 }, true); + + // evaluate the drafted tokens on the draft model + llama_decode(ctx, batch); + + prompt.push_back(id); + } + + return result; +} diff --git a/common/speculative.h b/common/speculative.h new file mode 100644 index 000000000..50ec03446 --- /dev/null +++ b/common/speculative.h @@ -0,0 +1,28 @@ +#pragma once + +#include "llama.h" +#include "common.h" + +struct common_speculative; + +struct common_speculative_params { + int n_draft = 16; // max drafted tokens + int n_reuse = 256; + + float p_min = 0.9f; // min probabiliy required to accept a token in the draft +}; + +struct common_speculative * common_speculative_init(struct llama_context * ctx_dft); + +void common_speculative_free(struct common_speculative * spec); + +bool common_speculative_are_compatible( + const struct llama_context * ctx_tgt, + const struct llama_context * ctx_dft); + +// sample up to n_draft tokens and add them to the batch using the draft model +llama_tokens common_speculative_gen_draft( + struct common_speculative * spec, + struct common_speculative_params params, + const llama_tokens & prompt, + llama_token id_last); diff --git a/common/train.cpp b/common/train.cpp deleted file mode 100644 index 661ad8382..000000000 --- a/common/train.cpp +++ /dev/null @@ -1,1515 +0,0 @@ -#include "train.h" -#include "common.h" - -#include -#include -#include -#include -#include - -struct random_normal_distribution { - std::mt19937 gen; - std::normal_distribution rd; - float min; - float max; -}; - -struct random_uniform_distribution { - std::mt19937 gen; - std::uniform_real_distribution rd; -}; - -struct train_state * init_train_state() { - struct train_state * state = new struct train_state; - state->train_its = 0; - state->train_samples = 0; - state->train_tokens = 0; - state->train_epochs = 0; - state->shuffle_samples_hash = 0; - state->shuffle_sample_count = 0; - state->shuffle_next_sample = 0; - state->shuffle_rng_state_current = ""; - state->shuffle_rng_state_next = ""; - - state->opt = new struct ggml_opt_context; - state->opt->ctx = NULL; - state->opt->params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM); - state->opt->params.graph_size = LLAMA_TRAIN_MAX_NODES; - state->opt->loss_after = 0.0f; - - return state; -} - -void free_train_state(struct train_state * state) { - delete state->opt; - delete state; -} - -struct random_normal_distribution * init_random_normal_distribution( - int seed, float mean, float std, float min, float max -) { - struct random_normal_distribution * rnd = (struct random_normal_distribution *) malloc(sizeof(struct random_normal_distribution)); - rnd->gen = std::mt19937(seed); - rnd->rd = std::normal_distribution{mean, std}; - rnd->min = min; - rnd->max = max; - return rnd; -} - -struct random_uniform_distribution * init_random_uniform_distribution(int seed, float min, float max) { - struct random_uniform_distribution * rnd = (struct random_uniform_distribution *) malloc(sizeof(struct random_uniform_distribution)); - rnd->gen = std::mt19937(seed); - rnd->rd = std::uniform_real_distribution{min, max}; - return rnd; -} - -void free_random_normal_distribution (struct random_normal_distribution * rnd) { - free(rnd); -} - -void free_random_uniform_distribution(struct random_uniform_distribution * rnd) { - free(rnd); -} - -struct ggml_tensor * randomize_tensor_normal(struct ggml_tensor * tensor, struct random_normal_distribution * rnd) { - float scale = 1.0f; // xavier - switch (ggml_n_dims(tensor)) { - case 1: - scale /= sqrtf((float) tensor->ne[0]); - for (int i0 = 0; i0 < tensor->ne[0]; i0++) { - float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0]); - *dst = scale * frand_normal(rnd); - } - break; - case 2: - scale /= sqrtf((float) tensor->ne[0]+tensor->ne[1]); - for (int i1 = 0; i1 < tensor->ne[1]; i1++) { - for (int i0 = 0; i0 < tensor->ne[0]; i0++) { - float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]); - *dst = scale * frand_normal(rnd); - } - } - break; - case 3: - scale /= sqrtf((float) tensor->ne[0]+tensor->ne[1]); - for (int i2 = 0; i2 < tensor->ne[2]; i2++) { - for (int i1 = 0; i1 < tensor->ne[1]; i1++) { - for (int i0 = 0; i0 < tensor->ne[0]; i0++) { - float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2]); - *dst = scale * frand_normal(rnd); - } - } - } - break; - case 4: - scale /= sqrtf((float) tensor->ne[0]+tensor->ne[1]); - for (int i3 = 0; i3 < tensor->ne[3]; i3++) { - for (int i2 = 0; i2 < tensor->ne[2]; i2++) { - for (int i1 = 0; i1 < tensor->ne[1]; i1++) { - for (int i0 = 0; i0 < tensor->ne[0]; i0++) { - float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]); - *dst = scale * frand_normal(rnd); - } - } - } - } - break; - default: - die("Unsupported tensor->n_dims"); - }; - return tensor; -} - -struct ggml_tensor * randomize_tensor_uniform(struct ggml_tensor * tensor, struct random_uniform_distribution * rnd) { - switch (ggml_n_dims(tensor)) { - case 1: - for (int i0 = 0; i0 < tensor->ne[0]; i0++) { - float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0]); - *dst = frand_uniform(rnd); - } - break; - case 2: - for (int i1 = 0; i1 < tensor->ne[1]; i1++) { - for (int i0 = 0; i0 < tensor->ne[0]; i0++) { - float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1]); - *dst = frand_uniform(rnd); - } - } - break; - case 3: - for (int i2 = 0; i2 < tensor->ne[2]; i2++) { - for (int i1 = 0; i1 < tensor->ne[1]; i1++) { - for (int i0 = 0; i0 < tensor->ne[0]; i0++) { - float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2]); - *dst = frand_uniform(rnd); - } - } - } - break; - case 4: - for (int i3 = 0; i3 < tensor->ne[3]; i3++) { - for (int i2 = 0; i2 < tensor->ne[2]; i2++) { - for (int i1 = 0; i1 < tensor->ne[1]; i1++) { - for (int i0 = 0; i0 < tensor->ne[0]; i0++) { - float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]); - *dst = frand_uniform(rnd); - } - } - } - } - break; - default: - die("Unsupported tensor->n_dims"); - }; - return tensor; -} - -float frand() { - return (float)rand()/((float)(RAND_MAX) + 1.0f); -} - -float frand_normal(struct random_normal_distribution * rnd) { - return fclamp(rnd->rd(rnd->gen), rnd->min, rnd->max); -} - -float frand_uniform(struct random_uniform_distribution * rnd) { - return rnd->rd(rnd->gen); -} - -int clamp(const int v, const int min, const int max) { - return ((v < min) ? (min) : (v > max) ? (max) : v); -} - -float fclamp(const float v, const float min, const float max) { - return ((v < min) ? (min) : (v > max) ? (max) : v); -} - -void assert_shape_1d(struct ggml_tensor * tensor, int64_t ne0) { - GGML_ASSERT(tensor->ne[0] == ne0); - GGML_ASSERT(tensor->ne[1] == 1); - GGML_ASSERT(tensor->ne[2] == 1); - GGML_ASSERT(tensor->ne[3] == 1); -} - -void assert_shape_2d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1) { - GGML_ASSERT(tensor->ne[0] == ne0); - GGML_ASSERT(tensor->ne[1] == ne1); - GGML_ASSERT(tensor->ne[2] == 1); - GGML_ASSERT(tensor->ne[3] == 1); -} - -void assert_shape_3d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2) { - GGML_ASSERT(tensor->ne[0] == ne0); - GGML_ASSERT(tensor->ne[1] == ne1); - GGML_ASSERT(tensor->ne[2] == ne2); - GGML_ASSERT(tensor->ne[3] == 1); -} - -void assert_shape_4d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3) { - GGML_ASSERT(tensor->ne[0] == ne0); - GGML_ASSERT(tensor->ne[1] == ne1); - GGML_ASSERT(tensor->ne[2] == ne2); - GGML_ASSERT(tensor->ne[3] == ne3); -} - -int64_t get_example_targets_batch( - struct llama_context * lctx, - struct ggml_tensor * tokens_input, - struct ggml_tensor * target_probs, - int64_t example_id, - const size_t * samples_offs, - const size_t * samples_begin, - const size_t * samples_size, - size_t samples_count, - const llama_token * train_data, - size_t n_train_data, - bool separate_with_eos, - bool separate_with_bos, - bool fill_with_next_samples, - bool sample_random_offsets -) { - GGML_ASSERT(samples_count > 0); - GGML_ASSERT(ggml_is_matrix(tokens_input)); - GGML_ASSERT(ggml_is_3d(target_probs)); - int64_t n_vocab = target_probs->ne[0]; - int64_t n_tokens = tokens_input->ne[0]; - int64_t n_batch = tokens_input->ne[1]; - GGML_ASSERT(n_vocab == target_probs->ne[0]); - GGML_ASSERT(n_tokens == target_probs->ne[1]); - GGML_ASSERT(n_batch == target_probs->ne[2]); - - int64_t used_samples = 0; - - ggml_set_f32(target_probs, 0.0f); - llama_token bos = llama_token_bos(llama_get_model(lctx)); - llama_token eos = llama_token_eos(llama_get_model(lctx)); - // printf("%s: example_id=%d n_batch=%d n_train_samples=%zu\n", __func__, example_id, n_batch, n_train_samples); - for (int k=0; k= sample_size && fill_with_next_samples) { - if (!sample_separation_eos) { - // insert eos token to separate samples - sample_separation_eos = true; - } else if (!sample_separation_bos) { - // insert bos token to separate samples - sample_separation_bos = true; - token = bos; - } else { - // sample separation is done, continue with next sample - sample_separation_eos = !separate_with_eos; - sample_separation_bos = !separate_with_bos; - sample_offs = 0; - sample_idx = (example_id + used_samples) % samples_count; - sample_begin = samples_begin[sample_idx]; - sample_size = samples_size[sample_idx]; - ++used_samples; - } - } - // note: no else-if here - if (sample_offs < sample_size) { - token = clamp(train_data[sample_begin+sample_offs], 0, (llama_token) (n_vocab - 1)); - ++sample_offs; - } - ggml_set_f32_nd(target_probs, token, (int) i, (int) k, 0, +1.0f); - if (i+1> rng; -} - -std::string mt19937_get_state(const std::mt19937& rng) { - std::stringstream s_rng_state; - s_rng_state.imbue(std::locale::classic()); - s_rng_state << rng; - return s_rng_state.str(); -} - -std::string mt19937_seed_to_state(unsigned seed) { - std::mt19937 rng(seed); - return mt19937_get_state(rng); -} - -std::string shuffle_samples( - const std::string & rng_state, - size_t * shuffled_offs, - size_t * shuffled_begins, - size_t * shuffled_sizes, - const size_t * begins, - const size_t * sizes, - size_t count) { - if (count == 0) return rng_state; - - std::mt19937 rng; - mt19937_set_state(rng, rng_state); - - // sort indices by random value for each index - std::vector idcs; - { - std::vector rnd; - idcs.resize(count); - rnd.resize(count); - for (unsigned i=0; i h_string; - std::hash h_ull; - size_t h = h_string(std::string(fn)); - h = hash_combine(h, h_ull((unsigned long long) sample_count)); - for (size_t i=0; i< sample_count; ++i) { - h = hash_combine(h, h_ull((unsigned long long) samples_begin[i])); - h = hash_combine(h, h_ull((unsigned long long) samples_size[i])); - } - return h; -} - -std::string replace_str(const char * s, const char * needle, const char * replacement) { - std::string str = s; - size_t pos = str.find(needle); - if (pos != std::string::npos) { - str.replace(pos, strlen(needle), replacement); - } - return str; -} - -void print_duration(double fmillis) { - if (fmillis < 1000.0f) { - printf("%.1fms", (float) fmillis); - return; - } - const int64_t one_sec = 1000; - const int64_t one_min = one_sec * 60; - const int64_t one_hour = one_min * 60; - const int64_t one_day = one_hour * 24; - - int64_t millis = (int64_t) fmillis; - int64_t days = millis/one_day; - int64_t hours = (millis - days*one_day)/one_hour; - int64_t minutes = (millis - days*one_day - hours*one_hour)/one_min; - int64_t seconds = (millis - days*one_day - hours*one_hour - minutes*one_min)/one_sec; - - // to print int64_t either cast to (long long int) or use macro PRId64 from - if (days > 0) { - printf("%lldd ", (long long int) days); - } - printf("%02lld:%02lld:%02lld", (long long int) hours, (long long int) minutes, (long long int) seconds); -} - -float cosine_decay(int64_t step, int64_t decay_steps, float minimum) { - if (step > decay_steps) { - step = decay_steps; - } - const float cosine_decay = 0.50f*(1.0f + cosf(3.14159265359f*step/decay_steps)); - const float decay = (1 - minimum)*cosine_decay + minimum; - return decay; -} - -float cosine_decay_restart(int64_t step, int64_t decay_steps, float minimum, float restart_step_mult) { - while (step > decay_steps) { - step -= decay_steps; - decay_steps = (int64_t) (restart_step_mult * decay_steps); - } - return cosine_decay(step, decay_steps, minimum); -} - -float learning_schedule( - int64_t step, - int64_t warmup_steps, - int64_t cos_decay_steps, - float learning_rate, - float overall_minimum, - float cos_decay_minimum, - float cos_decay_restart_step_mult, - bool enable_restart) { - - float result = - (step < warmup_steps) - ? (float) step / (float) warmup_steps - : enable_restart - ? cosine_decay_restart( - step - warmup_steps, - cos_decay_steps, - cos_decay_minimum, - cos_decay_restart_step_mult) - : cosine_decay( - step, - cos_decay_steps, - cos_decay_minimum); - - float min = overall_minimum / learning_rate; - result = min + result * (1.0f - min); - return result; -} - -static bool are_same_layout(struct ggml_tensor * a, struct ggml_tensor * b) { - GGML_ASSERT(a != NULL); - GGML_ASSERT(b != NULL); - GGML_ASSERT(a->type == b->type); - GGML_ASSERT(ggml_are_same_shape(a, b)); - GGML_ASSERT(ggml_is_contiguous(a) && ggml_is_contiguous(b)); - - return true; -} - -void copy_tensor_by_name(struct ggml_tensor * dst, struct ggml_context * ctx, const char * name) { - if (dst == NULL) { - return; - } - struct ggml_tensor * t = ggml_get_tensor(ctx, name); - GGML_ASSERT(are_same_layout(dst, t)); - memcpy(dst->data, t->data, ggml_nbytes(t)); - - if (strlen(ggml_get_name(dst)) == 0) { - ggml_set_name(dst, name); - } -} - -// gguf constants -static const char * LLM_KV_OPTIMIZER_TYPE = "optimizer.type"; -static const char * LLM_KV_OPTIMIZER_TYPE_ADAM = "adam"; -static const char * LLM_KV_OPTIMIZER_TYPE_LBFGS = "lbfgs"; -static const char * LLM_KV_OPTIMIZER_FILE_VERSION = "optimizer.file_version"; -static const char * LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT = "optimizer.convergence_past_count"; -static const char * LLM_KV_OPTIMIZER_PARAMETER_COUNT = "optimizer.parameter_count"; -static const char * LLM_KV_OPTIMIZER_ITERATION_COUNT = "optimizer.iteration_count"; -static const char * LLM_KV_OPTIMIZER_JUST_INITIALIZED = "optimizer.just_initialized"; -static const char * LLM_KV_OPTIMIZER_ADAM_BEST_LOSS = "optimizer.adam.best_loss"; -static const char * LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS = "optimizer.adam.previous_loss"; -static const char * LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT = "optimizer.adam.no_improvement_count"; -static const char * LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT = "optimizer.lbfgs.approx_hessian_count"; -static const char * LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS = "optimizer.lbfgs.best_loss"; -static const char * LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP = "optimizer.lbfgs.line_search_step"; -static const char * LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J = "optimizer.lbfgs.line_search_j"; -static const char * LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K = "optimizer.lbfgs.line_search_k"; -static const char * LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END = "optimizer.lbfgs.line_search_end"; -static const char * LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT = "optimizer.lbfgs.no_improvement_count"; - -static const char * LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS = "optimizer.adam.first_moments"; -static const char * LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS = "optimizer.adam.second_moments"; -static const char * LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES = "optimizer.adam.past_loss_values"; - -static const char * LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS = "optimizer.lbfgs.current_parameters"; -static const char * LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS = "optimizer.lbfgs.previous_parameters"; -static const char * LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS = "optimizer.lbfgs.current_gradients"; -static const char * LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS = "optimizer.lbfgs.previous_gradients"; -static const char * LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION = "optimizer.lbfgs.search_direction"; -static const char * LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES = "optimizer.lbfgs.past_loss_values"; -static const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA = "optimizer.lbfgs.memory_alpha"; -static const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS = "optimizer.lbfgs.memory_ys"; -static const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S = "optimizer.lbfgs.memory_s"; -static const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y = "optimizer.lbfgs.memory_y"; - -static const char * LLM_KV_TRAINING_FILE_VERSION = "training.file_version"; -static const char * LLM_KV_TRAINING_ITERATION_COUNT = "training.iteration_count"; -static const char * LLM_KV_TRAINING_SAMPLE_COUNT = "training.sample_count"; -static const char * LLM_KV_TRAINING_TOKEN_COUNT = "training.token_count"; -static const char * LLM_KV_TRAINING_EPOCH_COUNT = "training.epoch_count"; -static const char * LLM_KV_TRAINING_SHUFFLE_SAMPLES_HASH = "training.shuffle.samples_hash"; -static const char * LLM_KV_TRAINING_SHUFFLE_RNG_STATE = "training.shuffle.rng_state"; -static const char * LLM_KV_TRAINING_SHUFFLE_SAMPLE_COUNT = "training.shuffle.sample_count"; -static const char * LLM_KV_TRAINING_SHUFFLE_NEXT_SAMPLE = "training.shuffle.next_sample"; - -#define GGUF_GET_KEY(ctx, dst, func, type, req, key) \ -{ \ - const std::string skey(key); \ - const int kid = gguf_find_key(ctx, skey.c_str()); \ - if (kid >= 0) { \ - enum gguf_type ktype = gguf_get_kv_type(ctx, kid); \ - if (ktype != (type)) { \ - die_fmt("key %s has wrong type: %s", skey.c_str(), gguf_type_name(ktype)); \ - } \ - (dst) = func(ctx, kid); \ - } else if (req) { \ - die_fmt("key not found in model: %s", skey.c_str()); \ - } \ -} - -void load_opt_context_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct ggml_opt_context * opt) { - // NOTE: gguf_context must be initialized with f_ggml_ctx and no_alloc=false, otherwise tensor data can not be read - - uint32_t file_version; - GGUF_GET_KEY(fctx, file_version, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_FILE_VERSION); - GGML_ASSERT(file_version == 0); - - GGUF_GET_KEY(fctx, opt->params.past, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT); - GGUF_GET_KEY(fctx, opt->iter, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_ITERATION_COUNT); - GGUF_GET_KEY(fctx, opt->just_initialized, gguf_get_val_bool, GGUF_TYPE_BOOL, true, LLM_KV_OPTIMIZER_JUST_INITIALIZED); - - uint64_t nx; - GGUF_GET_KEY(fctx, nx, gguf_get_val_u64, GGUF_TYPE_UINT64, true, LLM_KV_OPTIMIZER_PARAMETER_COUNT); - opt->nx = (size_t) nx; - - // don't call ggml_opt_init until optimizer type and optimizer specific parameters are know - - std::string opt_type; - GGUF_GET_KEY(fctx, opt_type, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_OPTIMIZER_TYPE); - if (opt_type == LLM_KV_OPTIMIZER_TYPE_ADAM) { - opt->params.type = GGML_OPT_TYPE_ADAM; - - GGUF_GET_KEY(fctx, opt->adam.fx_best, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_ADAM_BEST_LOSS); - GGUF_GET_KEY(fctx, opt->adam.fx_prev, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS); - GGUF_GET_KEY(fctx, opt->adam.n_no_improvement, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT); - - ggml_opt_init(opt->ctx, opt, opt->params, opt->nx); - - copy_tensor_by_name(opt->adam.m, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS); - copy_tensor_by_name(opt->adam.v, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS); - copy_tensor_by_name(opt->adam.pf, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES); - } else if (opt_type == LLM_KV_OPTIMIZER_TYPE_LBFGS) { - opt->params.type = GGML_OPT_TYPE_LBFGS; - - GGUF_GET_KEY(fctx, opt->params.lbfgs.m, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT); - GGUF_GET_KEY(fctx, opt->lbfgs.fx_best, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS); - GGUF_GET_KEY(fctx, opt->lbfgs.step, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP); - GGUF_GET_KEY(fctx, opt->lbfgs.j, gguf_get_val_i32, GGUF_TYPE_INT32, true, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J); - GGUF_GET_KEY(fctx, opt->lbfgs.k, gguf_get_val_i32, GGUF_TYPE_INT32, true, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K); - GGUF_GET_KEY(fctx, opt->lbfgs.end, gguf_get_val_i32, GGUF_TYPE_INT32, true, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END); - GGUF_GET_KEY(fctx, opt->lbfgs.n_no_improvement, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT); - - ggml_opt_init(opt->ctx, opt, opt->params, opt->nx); - - copy_tensor_by_name(opt->lbfgs.x, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS); - copy_tensor_by_name(opt->lbfgs.xp, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS); - copy_tensor_by_name(opt->lbfgs.g, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS); - copy_tensor_by_name(opt->lbfgs.gp, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS); - copy_tensor_by_name(opt->lbfgs.d, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION); - copy_tensor_by_name(opt->lbfgs.pf, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES); - copy_tensor_by_name(opt->lbfgs.lmal, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA); - copy_tensor_by_name(opt->lbfgs.lmys, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS); - copy_tensor_by_name(opt->lbfgs.lms, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S); - copy_tensor_by_name(opt->lbfgs.lmy, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y); - } else { - die("unknown optimizer type\n"); - } -} - -void save_opt_context_gguf(struct gguf_context * fctx, struct ggml_opt_context * opt) { - gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_FILE_VERSION, 0); - gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT, opt->params.past); - gguf_set_val_u64(fctx, LLM_KV_OPTIMIZER_PARAMETER_COUNT, (uint64_t) opt->nx); - gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_ITERATION_COUNT, opt->iter); - gguf_set_val_bool(fctx, LLM_KV_OPTIMIZER_JUST_INITIALIZED, opt->just_initialized); - - switch (opt->params.type) { - case GGML_OPT_TYPE_ADAM: - { - gguf_set_val_str(fctx, LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_ADAM); - gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_ADAM_BEST_LOSS, opt->adam.fx_best); - gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS, opt->adam.fx_prev); - gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT, opt->adam.n_no_improvement); - - ggml_set_name(opt->adam.m, LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS); - ggml_set_name(opt->adam.v, LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS); - if (opt->adam.pf) { - ggml_set_name(opt->adam.pf, LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES); - } - - gguf_add_tensor(fctx, opt->adam.m); - gguf_add_tensor(fctx, opt->adam.v); - if (opt->adam.pf) { - gguf_add_tensor(fctx, opt->adam.pf); - } - } break; - case GGML_OPT_TYPE_LBFGS: - { - gguf_set_val_str(fctx, LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_LBFGS); - gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT, opt->params.lbfgs.m); - gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS, opt->lbfgs.fx_best); - gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP, opt->lbfgs.step); - gguf_set_val_i32(fctx, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J, opt->lbfgs.j); - gguf_set_val_i32(fctx, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K, opt->lbfgs.k); - gguf_set_val_i32(fctx, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END, opt->lbfgs.end); - gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT, opt->lbfgs.n_no_improvement); - - ggml_set_name(opt->lbfgs.x, LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS); - ggml_set_name(opt->lbfgs.xp, LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS); - ggml_set_name(opt->lbfgs.g, LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS); - ggml_set_name(opt->lbfgs.gp, LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS); - ggml_set_name(opt->lbfgs.d, LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION); - if (opt->lbfgs.pf) { - ggml_set_name(opt->lbfgs.pf, LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES); - } - ggml_set_name(opt->lbfgs.lmal, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA); - ggml_set_name(opt->lbfgs.lmys, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS); - ggml_set_name(opt->lbfgs.lms, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S); - ggml_set_name(opt->lbfgs.lmy, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y); - - gguf_add_tensor(fctx, opt->lbfgs.x); - gguf_add_tensor(fctx, opt->lbfgs.xp); - gguf_add_tensor(fctx, opt->lbfgs.g); - gguf_add_tensor(fctx, opt->lbfgs.gp); - gguf_add_tensor(fctx, opt->lbfgs.d); - if (opt->lbfgs.pf) { - gguf_add_tensor(fctx, opt->lbfgs.pf); - } - gguf_add_tensor(fctx, opt->lbfgs.lmal); - gguf_add_tensor(fctx, opt->lbfgs.lmys); - gguf_add_tensor(fctx, opt->lbfgs.lms); - gguf_add_tensor(fctx, opt->lbfgs.lmy); - } break; - } -} - -bool load_train_state_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct train_state * train) { - if (gguf_find_key(fctx, LLM_KV_TRAINING_FILE_VERSION) < 0) { - return false; - } - - uint32_t file_version; - GGUF_GET_KEY(fctx, file_version, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_FILE_VERSION); - GGML_ASSERT(file_version <= 1); - - if (file_version == 0) { - - GGUF_GET_KEY(fctx, train->train_its, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_ITERATION_COUNT); - GGUF_GET_KEY(fctx, train->train_samples, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_SAMPLE_COUNT); - GGUF_GET_KEY(fctx, train->train_tokens, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_TOKEN_COUNT); - - } else if (file_version == 1) { - - GGUF_GET_KEY(fctx, train->train_its, gguf_get_val_u64, GGUF_TYPE_UINT64, true, LLM_KV_TRAINING_ITERATION_COUNT); - GGUF_GET_KEY(fctx, train->train_samples, gguf_get_val_u64, GGUF_TYPE_UINT64, true, LLM_KV_TRAINING_SAMPLE_COUNT); - GGUF_GET_KEY(fctx, train->train_tokens, gguf_get_val_u64, GGUF_TYPE_UINT64, true, LLM_KV_TRAINING_TOKEN_COUNT); - GGUF_GET_KEY(fctx, train->train_epochs, gguf_get_val_u64, GGUF_TYPE_UINT64, true, LLM_KV_TRAINING_EPOCH_COUNT); - - GGUF_GET_KEY(fctx, train->shuffle_samples_hash, gguf_get_val_u64, GGUF_TYPE_UINT64, false, LLM_KV_TRAINING_SHUFFLE_SAMPLES_HASH); - GGUF_GET_KEY(fctx, train->shuffle_rng_state_current, gguf_get_val_str, GGUF_TYPE_STRING, false, LLM_KV_TRAINING_SHUFFLE_RNG_STATE); - GGUF_GET_KEY(fctx, train->shuffle_sample_count, gguf_get_val_u64, GGUF_TYPE_UINT64, false, LLM_KV_TRAINING_SHUFFLE_SAMPLE_COUNT); - GGUF_GET_KEY(fctx, train->shuffle_next_sample, gguf_get_val_u64, GGUF_TYPE_UINT64, false, LLM_KV_TRAINING_SHUFFLE_NEXT_SAMPLE); - } - - load_opt_context_gguf(fctx, f_ggml_ctx, train->opt); - return true; -} - -void save_train_state_gguf(struct gguf_context * fctx, struct train_state * train) { - gguf_set_val_u32(fctx, LLM_KV_TRAINING_FILE_VERSION, 1); - gguf_set_val_u64(fctx, LLM_KV_TRAINING_ITERATION_COUNT, train->train_its); - gguf_set_val_u64(fctx, LLM_KV_TRAINING_SAMPLE_COUNT, train->train_samples); - gguf_set_val_u64(fctx, LLM_KV_TRAINING_TOKEN_COUNT, train->train_tokens); - gguf_set_val_u64(fctx, LLM_KV_TRAINING_EPOCH_COUNT, train->train_epochs); - - gguf_set_val_u64(fctx, LLM_KV_TRAINING_SHUFFLE_SAMPLES_HASH, (uint64_t) train->shuffle_samples_hash); - gguf_set_val_str(fctx, LLM_KV_TRAINING_SHUFFLE_RNG_STATE, train->shuffle_rng_state_current.c_str()); - gguf_set_val_u64(fctx, LLM_KV_TRAINING_SHUFFLE_SAMPLE_COUNT, (uint64_t) train->shuffle_sample_count); - gguf_set_val_u64(fctx, LLM_KV_TRAINING_SHUFFLE_NEXT_SAMPLE, (uint64_t) train->shuffle_next_sample); - - save_opt_context_gguf(fctx, train->opt); -} - - -struct llama_file { - // use FILE * so we don't have to re-open the file to mmap - FILE * fp; - size_t size; - - llama_file(const char * fname, const char * mode) { - fp = std::fopen(fname, mode); - if (fp == NULL) { - size = 0; - } else { - seek(0, SEEK_END); - size = tell(); - seek(0, SEEK_SET); - } - } - - size_t tell() const { -#ifdef _WIN32 - __int64 ret = _ftelli64(fp); -#else - long ret = std::ftell(fp); -#endif - GGML_ASSERT(ret != -1); // this really shouldn't fail - return (size_t) ret; - } - - void seek(size_t offset, int whence) { -#ifdef _WIN32 - int ret = _fseeki64(fp, (__int64) offset, whence); -#else - int ret = std::fseek(fp, (long) offset, whence); -#endif - GGML_ASSERT(ret == 0); // same - } - - void read_raw(void * ptr, size_t size) { - if (size == 0) { - return; - } - errno = 0; - std::size_t ret = std::fread(ptr, size, 1, fp); - if (ferror(fp)) { - die_fmt("read error: %s", strerror(errno)); - } - if (ret != 1) { - die("unexpectedly reached end of file"); - } - } - - std::uint32_t read_u32() { - std::uint32_t ret; - read_raw(&ret, sizeof(ret)); - return ret; - } - - std::string read_string(std::uint32_t len) { - std::vector chars(len); - read_raw(chars.data(), len); - return std::string(chars.data(), len); - } - - void write_raw(const void * ptr, size_t size) { - if (size == 0) { - return; - } - errno = 0; - size_t ret = std::fwrite(ptr, size, 1, fp); - if (ret != 1) { - die_fmt("write error: %s", strerror(errno)); - } - } - - void write_u32(std::uint32_t val) { - write_raw(&val, sizeof(val)); - } - - ~llama_file() { - if (fp) { - std::fclose(fp); - } - } -}; - -static size_t utf8_len(char src) { - const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 }; - uint8_t highbits = static_cast(src) >> 4; - return lookup[highbits]; -} - -// mark each byte with its utf8 unit number. -// returns the number of utf8 characters. -// e.g. when bytes == '\x61\xD0\xB0\x62', -// then utf8_units will become [0,0,1,0] -// utf8_nunits will become [1,2,2,1] and 3 is returned. -// bytes where utf8_units is zero, are the begin of an utf8 character. -static size_t mark_utf8_units(const char* bytes, int * utf8_units, int * utf8_nunits, size_t count) { - size_t offs = 0; - size_t count_utf8 = 0; - while(offs < count) { - int len = (int) utf8_len(bytes[offs]); - for (int i=0; i & out_tokens, - std::vector & out_samples_begin, - std::vector & out_samples_size) { - struct llama_file f(filename, "rb"); - - if (f.size == 0) { - out_tokens.clear(); - out_samples_begin.clear(); - out_samples_size.clear(); - printf("%s: warning: empty or not existing training data file '%s'\n", - __func__, filename); - return out_tokens.size(); - } - - // account for possible leading whitespace that will be added by tokenizer - // e.g. '\t' will be tokenized by llama spm tokenizer to [29871, 12] - const int n_max_tokens_overhead = 1; - - std::vector buf; - buf.resize(f.size); - - f.read_raw(buf.data(), f.size); - - std::vector utf8_units; - std::vector utf8_nunits; - utf8_units.resize(buf.size()); - utf8_nunits.resize(buf.size()); - mark_utf8_units(buf.data(), utf8_units.data(), utf8_nunits.data(), buf.size()); - - if (sample_start.size() == 0) { - // tokenize all data at once - out_tokens.resize(buf.size() + n_max_tokens_overhead); - - int n_tokens = llama_tokenize( - llama_get_model(lctx), - buf.data(), - (int) buf.size(), - out_tokens.data(), - (int) out_tokens.size(), - false, false); - if (n_tokens < 0) { - out_tokens.resize(-n_tokens); - n_tokens = llama_tokenize( - llama_get_model(lctx), - buf.data(), - (int) buf.size(), - out_tokens.data(), - (int) out_tokens.size(), - false, false); - } - if (n_tokens >= 0) { - out_tokens.resize(n_tokens); - } - - // generate sample starts at all token positions - out_samples_begin.clear(); - out_samples_begin.push_back(0); - out_samples_size.push_back(std::min((size_t) context_length, out_tokens.size())); - size_t end = (out_tokens.size() >= context_length) ? (out_tokens.size() - context_length) : 0; - for (size_t sample_begin = 1; sample_begin < end; ++sample_begin) { - out_samples_begin.push_back(sample_begin); - out_samples_size.push_back(context_length); - } - } else { - // split data into samples and tokenize each sample - std::string data_str(buf.data(), buf.size()); - out_samples_begin.clear(); - out_samples_size.clear(); - out_tokens.clear(); - - // find all positions of pattern sample_start - size_t sample_begin = data_str.find(sample_start, 0); - while (sample_begin != std::string::npos) { - out_samples_begin.push_back(sample_begin); - const size_t search_start = sample_begin + sample_start.size(); - sample_begin = data_str.find(sample_start, search_start); - } - if (out_samples_begin.size() == 0) { - printf("%s: warning: sample start pattern '%s' not found. inserting single sample at data begin\n", - __func__, sample_start.c_str()); - out_samples_begin.push_back(0); - } - - out_samples_size.resize(out_samples_begin.size(), 0); - - std::vector buf_sample; - std::vector tok_sample; - - const size_t sample_begin_offset = (include_sample_start ? 0 : sample_start.size()); - size_t found_too_big_sample = 0; - size_t found_too_small_sample = 0; - size_t found_empty_sample = 0; - size_t found_min_sample_size = SIZE_MAX; - size_t found_max_sample_size = 0; - - size_t max_token_text_size = 0; - int n_vocab = llama_n_vocab(llama_get_model(lctx)); - for (llama_token token=0; token < n_vocab; ++token) { - max_token_text_size = std::max( - max_token_text_size, - strlen(llama_token_get_text(llama_get_model(lctx), token))); - } - - // upper bound of context byte length. - // strings with this byte length should always tokenize to at least context_length tokens. - size_t context_byte_len = max_token_text_size*context_length; - - for (unsigned i=0; i 0) { - // sample end is in the middle of an utf8 character. - // advance sample_end to the begin of the next utf8 character. - sample_end += utf8_nunits[sample_end] - utf8_units[sample_end]; - } - size_t sample_size = sample_end - sample_begin; - if (sample_size == 0) { - ++found_empty_sample; - } - - if (sample_size > 0) { - // llama_tokenize expects zero terminated string, - // copy sample into buffer and zero terminate it. - buf_sample.resize(sample_size); - memcpy(buf_sample.data(), data_str.data() + sample_begin, sample_size); - - // printf("sample: '%s'\n", buf_sample.data()); - - // tokenize the sample - tok_sample.resize(buf_sample.size() + n_max_tokens_overhead); - int n_tokens = llama_tokenize(llama_get_model(lctx), - buf_sample.data(), - (int) buf_sample.size(), - tok_sample.data(), - (int) tok_sample.size(), - false, false); - if (n_tokens < 0) { - tok_sample.resize(-n_tokens); - n_tokens = llama_tokenize(llama_get_model(lctx), - buf_sample.data(), - (int) buf_sample.size(), - tok_sample.data(), - (int) tok_sample.size(), - false, false); - GGML_ASSERT(n_tokens >= 0); - } - GGML_ASSERT(n_tokens <= (int) tok_sample.size()); - - if ((size_t) n_tokens > context_length) { - ++found_too_big_sample; - } else if ((size_t) n_tokens < context_length) { - ++found_too_small_sample; - } - found_max_sample_size = std::max(found_max_sample_size, (size_t) n_tokens); - found_min_sample_size = std::min(found_min_sample_size, (size_t) n_tokens); - - // write out tokens, start and size of sample - // overwrite the string start position with the token start position - out_samples_begin[i] = out_tokens.size(); - out_samples_size[i] = (size_t) n_tokens; - out_tokens.insert(out_tokens.end(), tok_sample.begin(), tok_sample.begin() + n_tokens); - } else { - out_samples_begin[i] = out_tokens.size(); - out_samples_size[i] = 0; - } - - } - if (found_too_big_sample > 0) { - printf("%s: warning: found %zu samples (max length %zu) that exceed context length of %u. samples will be cut off.\n", - __func__, found_too_big_sample, found_max_sample_size, context_length); - } - - if (found_too_small_sample > 0) { - printf("%s: warning: found %zu samples (min length %zu) that are shorter than context length of %u.\n", - __func__, found_too_small_sample, found_min_sample_size, context_length); - } - - if (found_empty_sample) { - printf("%s: warning: found %zu empty samples.\n", - __func__, found_empty_sample); - } - } - printf("%s: total number of samples: %zu\n", - __func__, out_samples_begin.size()); - - GGML_ASSERT(out_samples_begin.size() == out_samples_size.size()); - - return out_tokens.size(); -} - -std::string get_train_filename(const char * filename, const char * pattern_it, const char * latest, int64_t iteration) { - std::string sit = (iteration >= 0) ? std::to_string(iteration) : std::string(latest); - return replace_str(filename, pattern_it, sit.c_str()); -} - -struct train_params_common get_default_train_params_common() { - struct train_params_common params; - params.fn_train_data = "shakespeare.txt"; - params.fn_checkpoint_in = "checkpoint.gguf"; - params.fn_checkpoint_out = "checkpoint-ITERATION.gguf"; - params.pattern_fn_it = "ITERATION"; - params.fn_latest = "LATEST"; - - params.print_usage = false; - - params.save_every = 10; - - params.seed = -1; - - params.n_ctx = 128; - params.n_threads = 6; - params.n_batch = 8; - params.n_gradient_accumulation = 1; - params.n_epochs = -1; - params.n_gpu_layers = 0; - - params.custom_n_ctx = false; - - params.use_flash = false; - params.use_checkpointing = true; - - params.sample_start = ""; - params.include_sample_start = false; - params.escape = false; - params.overlapping_samples = false; - params.fill_with_next_samples = false; - params.separate_with_eos = false; - params.separate_with_bos = true; - params.sample_random_offsets = false; - params.force_reshuffle = false; - - params.opt_past = 0; - params.opt_delta = 1e-5f; - params.opt_max_no_improvement = 0; - - params.warmup = 100; - params.cos_decay_steps = 1000; - params.cos_decay_restart = 1.1f; - params.cos_decay_min = 0.1f; - params.enable_restart = false; - - params.adam_n_iter = 256; - params.adam_alpha = 1e-3f; - params.adam_min_alpha = 0; - params.adam_decay = 1e-1f; - params.adam_decay_min_ndim = 2; - params.adam_beta1 = 0.9f; - params.adam_beta2 = 0.999f; - params.adam_gclip = 1.0f; - params.adam_eps_f = 0.0f; - - return params; -} - -void print_common_train_usage(int /*argc*/, char ** /*argv*/, const struct train_params_common * params) { - // fprintf(stderr, "usage: %s [options]\n", argv[0]); - // fprintf(stderr, "\n"); - // fprintf(stderr, "options:\n"); - // fprintf(stderr, " -h, --help show this help message and exit\n"); - fprintf(stderr, " --train-data FNAME path from which to load training data (default '%s')\n", params->fn_train_data); - fprintf(stderr, " --checkpoint-in FNAME path from which to load training checkpoint (default '%s')\n", params->fn_checkpoint_in); - fprintf(stderr, " --checkpoint-out FNAME path to save training checkpoint (default '%s')\n", params->fn_checkpoint_out); - fprintf(stderr, " --pattern-fn-it STR pattern in output filenames to be replaced by iteration number (default '%s')\n", params->pattern_fn_it); - fprintf(stderr, " --fn-latest STR string to use instead of iteration number for saving latest output (default '%s')\n", params->fn_latest); - fprintf(stderr, " --save-every N save checkpoint and lora every N iterations. Disabled when N <= 0. (default '%d')\n", params->save_every); - fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1, use random seed for -1)\n"); - fprintf(stderr, " -c N, --ctx N Context size used during training (default %d)\n", params->n_ctx); - fprintf(stderr, " -t N, --threads N Number of threads (default %d)\n", params->n_threads); - fprintf(stderr, " -b N, --batch N Parallel batch size (default %d)\n", params->n_batch); - fprintf(stderr, " --grad-acc N Number of gradient accumulation steps (simulates larger batch size of batch*gradacc) (default %d)\n", params->n_gradient_accumulation); - fprintf(stderr, " --sample-start STR Sets the starting point for samples after the specified pattern. If empty use every token position as sample start. (default '%s')\n", params->sample_start.c_str()); - fprintf(stderr, " --include-sample-start Include the sample start in the samples. (default off)\n"); - fprintf(stderr, " --escape process sample start escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n"); - fprintf(stderr, " --overlapping-samples Samples may overlap, will include sample-start of second and following samples. When off, samples will end at begin of next sample. (default off)\n"); - fprintf(stderr, " --fill-with-next-samples Samples shorter than context length will be followed by the next (shuffled) samples. (default off)\n"); - fprintf(stderr, " --separate-with-eos When fill-with-next-samples, insert end-of-sequence token between samples.%s\n", params->separate_with_eos ? " (default)" : ""); - fprintf(stderr, " --separate-with-bos When fill-with-next-samples, insert begin-of-sequence token between samples.%s\n", params->separate_with_bos ? " (default)" : ""); - fprintf(stderr, " --no-separate-with-eos When fill-with-next-samples, don't insert end-of-sequence token between samples.%s\n", !params->separate_with_eos ? " (default)" : ""); - fprintf(stderr, " --no-separate-with-bos When fill-with-next-samples, don't insert begin-of-sequence token between samples.%s\n", !params->separate_with_bos ? " (default)" : ""); - fprintf(stderr, " --sample-random-offsets Use samples beginning at random offsets. Together with fill-with-next-samples this may help for training endless text generation.%s\n", params->sample_random_offsets ? " (default)" : ""); - fprintf(stderr, " --force-reshuffle Force a reshuffling of data at program start, otherwise the shuffling of loaded checkpoint is resumed.\n"); - fprintf(stderr, " --no-flash Don't use flash attention \n"); - fprintf(stderr, " --use-flash Use flash attention (default)\n"); - fprintf(stderr, " --no-checkpointing Don't use gradient checkpointing\n"); - fprintf(stderr, " --use-checkpointing Use gradient checkpointing (default)\n"); - fprintf(stderr, " --warmup N Only for Adam optimizer. Number of warmup steps (default %d)\n", params->warmup); - fprintf(stderr, " --cos-decay-steps N Only for Adam optimizer. Number of cosine decay steps (default %d)\n", params->cos_decay_steps); - fprintf(stderr, " --cos-decay-restart N Only for Adam optimizer. Increase of cosine decay steps after restart (default %f)\n", params->cos_decay_restart); - fprintf(stderr, " --cos-decay-min N Only for Adam optimizer. Cosine decay minimum (default %f)\n", params->cos_decay_min); - fprintf(stderr, " --enable-restart N Only for Adam optimizer. Enable restarts of cos-decay %s\n", params->enable_restart ? "(default)" : ""); - fprintf(stderr, " --disable-restart N Only for Adam optimizer. Disable restarts of cos-decay %s\n", !params->enable_restart ? "(default)" : ""); - fprintf(stderr, " --opt-past N Number of optimization iterations to track for delta convergence test. Disabled when zero. (default %d)\n", params->opt_past); - fprintf(stderr, " --opt-delta N Maximum delta for delta convergence test. Disabled when <= zero. (default %f)\n", params->opt_delta); - fprintf(stderr, " --opt-max-no-improvement N Maximum number of optimization iterations with no improvement. Disabled when <= zero. (default %d)\n", params->opt_max_no_improvement); - fprintf(stderr, " --epochs N Maximum number epochs to process. (default %d)\n", params->n_epochs); - fprintf(stderr, " --adam-iter N Maximum number of Adam optimization iterations for each batch (default %d)\n", params->adam_n_iter); - fprintf(stderr, " --adam-alpha N Adam learning rate alpha (default %f)\n", params->adam_alpha); - fprintf(stderr, " --adam-min-alpha N Adam minimum learning rate alpha - including warmup phase (default %f)\n", params->adam_min_alpha); - fprintf(stderr, " --adam-decay N AdamW weight decay. Values greater zero enable AdamW instead of regular Adam. (default %f)\n", params->adam_decay); - fprintf(stderr, " --adam-decay-min-ndim N Minimum number of tensor dimensions to apply AdamW weight decay. Weight decay is not applied to tensors with less n_dims. (default %d)\n", params->adam_decay_min_ndim); - fprintf(stderr, " --adam-beta1 N AdamW beta1 in interval [0,1). How much to smooth the first moment of gradients. (default %f)\n", params->adam_beta1); - fprintf(stderr, " --adam-beta2 N AdamW beta2 in interval [0,1). How much to smooth the second moment of gradients. (default %f)\n", params->adam_beta2); - fprintf(stderr, " --adam-gclip N AdamW gradient clipping. Disabled when zero. (default %f)\n", params->adam_gclip); - fprintf(stderr, " --adam-epsf N AdamW epsilon for convergence test. Disabled when <= zero. (default %f)\n", params->adam_eps_f); - fprintf(stderr, " -ngl N, --n-gpu-layers N Number of model layers to offload to GPU (default %d)", params->n_gpu_layers); - fprintf(stderr, "\n"); -} - -bool consume_common_train_arg( - int argc, char ** argv, int * idx, struct train_params_common * params, bool * invalid_param -) { - int& i = *idx; - std::string arg = argv[i]; - const std::string arg_prefix = "--"; - if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { - std::replace(arg.begin(), arg.end(), '_', '-'); - } - if (arg == "--train-data") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->fn_train_data = argv[i]; - } else if (arg == "--checkpoint-in") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->fn_checkpoint_in = argv[i]; - } else if (arg == "--checkpoint-out") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->fn_checkpoint_out = argv[i]; - } else if (arg == "--pattern-fn-it") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->pattern_fn_it = argv[i]; - } else if (arg == "--fn-latest") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->fn_latest = argv[i]; - } else if (arg == "--save-every") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->save_every = std::stoi(argv[i]); - } else if (arg == "-s" || arg == "--seed") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->seed = std::stoi(argv[i]); - } else if (arg == "-c" || arg == "--ctx") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->n_ctx = std::stoi(argv[i]); - params->custom_n_ctx = true; - } else if (arg == "-t" || arg == "--threads") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->n_threads = std::stoi(argv[i]); - } else if (arg == "-b" || arg == "--batch") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->n_batch = std::stoi(argv[i]); - } else if (arg == "--grad-acc") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->n_gradient_accumulation = std::max(1, std::stoi(argv[i])); - } else if (arg == "--sample-start") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->sample_start = std::string(argv[i]); - } else if (arg == "--escape") { - params->escape = true; - } else if (arg == "--include-sample-start") { - params->include_sample_start = true; - } else if (arg == "--overlapping-samples") { - params->overlapping_samples = true; - } else if (arg == "--fill-with-next-samples") { - params->fill_with_next_samples = true; - } else if (arg == "--separate-with-eos") { - params->separate_with_eos = true; - } else if (arg == "--separate-with-bos") { - params->separate_with_bos = true; - } else if (arg == "--no-separate-with-eos") { - params->separate_with_eos = false; - } else if (arg == "--no-separate-with-bos") { - params->separate_with_bos = false; - } else if (arg == "--sample-random-offsets") { - params->sample_random_offsets = true; - } else if (arg == "--force-reshuffle") { - params->force_reshuffle = true; - } else if (arg == "--no-flash") { - params->use_flash = false; - } else if (arg == "--use-flash") { - params->use_flash = true; - } else if (arg == "--no-checkpointing") { - params->use_checkpointing = false; - } else if (arg == "--use-checkpointing") { - params->use_checkpointing = true; - } else if (arg == "--warmup") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->warmup = std::stoi(argv[i]); - } else if (arg == "--cos-decay-steps") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->cos_decay_steps = std::stoi(argv[i]); - } else if (arg == "--cos-decay-restart") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->cos_decay_restart = std::stof(argv[i]); - } else if (arg == "--cos-decay-min") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->cos_decay_min = std::stof(argv[i]); - } else if (arg == "--enable-restart") { - params->enable_restart = true; - } else if (arg == "--disable-restart") { - params->enable_restart = false; - } else if (arg == "--opt-past") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->opt_past = std::stoi(argv[i]); - } else if (arg == "--opt-delta") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->opt_delta = std::stof(argv[i]); - } else if (arg == "--opt-max-no-improvement") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->opt_max_no_improvement = std::stoi(argv[i]); - } else if (arg == "--adam-epsf") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->adam_eps_f = std::stof(argv[i]); - } else if (arg == "--epochs") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->n_epochs = std::stoi(argv[i]); - } else if (arg == "--adam-iter") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->adam_n_iter = std::stoi(argv[i]); - } else if (arg == "--adam-alpha") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->adam_alpha = std::stof(argv[i]); - } else if (arg == "--adam-min-alpha") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->adam_min_alpha = std::stof(argv[i]); - } else if (arg == "--adam-decay") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->adam_decay = std::stof(argv[i]); - } else if (arg == "--adam-decay-min-ndim") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->adam_decay_min_ndim = std::stoi(argv[i]); - } else if (arg == "--adam-beta1") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->adam_beta1 = std::stof(argv[i]); - } else if (arg == "--adam-beta2") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->adam_beta2 = std::stof(argv[i]); - } else if (arg == "--adam-gclip") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - params->adam_gclip = std::stof(argv[i]); - } else if (arg == "-ngl" || arg == "--n-gpu-layers") { - if (++i >= argc) { - *invalid_param = true; - return true; - } - if (llama_supports_gpu_offload()) { - params->n_gpu_layers = std::stoi(argv[i]); - } else { - fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n"); - fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); - } - } else if (arg == "-h" || arg == "--help") { - params->print_usage = true; - return true; - } else { - return false; - } - return true; -} - -void finish_processing_train_args(struct train_params_common * params) { - if (params->escape) { - string_process_escapes(params->sample_start); - } -} - -void train_opt_callback(void * vdata, int accum_step, float * sched, bool * cancel) { - struct train_opt_callback_data * data = (struct train_opt_callback_data *) vdata; - struct train_params_common * params = data->params; - struct train_state * train = data->train; - struct ggml_opt_context * opt = train->opt; - int n_batch = params->n_batch; - int n_ctx = params->n_ctx; - - if (accum_step == 0) { - // time measurement - int64_t now = ggml_time_ms(); - if (now > data->last_time && opt->iter > data->first_iter) { - double dt = (double) (now - data->last_time); - if (data->millis_per_iter == 0.0) { - data->millis_per_iter = dt; - } else { - const double gain = 0.7; - data->millis_per_iter = data->millis_per_iter*(1.0-gain) + dt*gain; - } - } - - double remaining_millis = 0.0; - if (data->millis_per_iter > 0.0) { - const int n_iter = params->adam_n_iter; - const int done_iter = opt->iter - data->first_iter; - const int remaining_iter = n_iter - done_iter; - remaining_millis = remaining_iter * data->millis_per_iter; - } - - // file saving - const bool save_now = (params->save_every > 0) && (opt->iter - data->last_save_iter >= params->save_every); - if (save_now) { - int new_iters = opt->iter - data->last_save_iter; - train->train_its += new_iters; - train->train_tokens += new_iters * opt->params.n_gradient_accumulation * n_batch * n_ctx; - - if (data->save_cb) { - data->save_cb(data->save_data, train); - } - - data->last_save_iter = opt->iter; - } - - // exclude file saving from time measurement, by measuring last_time after saving - data->last_time = ggml_time_ms(); - - *sched = learning_schedule( - opt->iter, - params->warmup, - params->cos_decay_steps, - params->adam_alpha, - params->adam_min_alpha, - params->cos_decay_min, - params->cos_decay_restart, - params->enable_restart); - - int impr_plot = -(int)(1 + (opt->loss_before - opt->loss_after) * 10.0f + 0.5f); - if (impr_plot > 0) impr_plot = 0; - if (std::isnan(opt->loss_before) || std::isnan(opt->loss_after)) impr_plot = 0; - printf("%s: iter=%6d sample=%zu/%zu sched=%f loss=%f", - __func__, opt->iter, std::min(1+train->shuffle_next_sample, train->shuffle_sample_count), train->shuffle_sample_count, - *sched, opt->loss_after); - - - if (data->millis_per_iter > 0) { - printf(" dt="); - print_duration(data->millis_per_iter); - printf(" eta="); - print_duration(remaining_millis); - } - - float improvement = opt->loss_before - opt->loss_after; - const float plot_scale = 10.0f; - int bar_len = (int)(1 + improvement*plot_scale + 0.5); - printf(" |"); - for (int i=0; i"); - printf("\n"); - } - - int64_t used_samples = get_example_targets_batch( - data->lctx, - data->tokens_input, - data->target_probs, - train->shuffle_next_sample, - data->shuffled_samples_offs, - data->shuffled_samples_begin, - data->shuffled_samples_size, - data->samples_count, - data->tokens_data, - data->tokens_size, - params->separate_with_eos, - params->separate_with_bos, - params->fill_with_next_samples, - params->sample_random_offsets); - - train->train_samples += used_samples; - train->shuffle_next_sample += used_samples; - - if (train->shuffle_next_sample >= train->shuffle_sample_count) { - ++train->train_epochs; - printf("%s: reshuffle samples. completed epochs: %llu\n", __func__, (long long unsigned) train->train_epochs); - // note: we may have used some samples from the current shuffling more than once - train->shuffle_rng_state_current = train->shuffle_rng_state_next; - train->shuffle_rng_state_next = shuffle_samples( - train->shuffle_rng_state_current, - data->shuffled_samples_offs, - data->shuffled_samples_begin, - data->shuffled_samples_size, - data->samples_begin, - data->samples_size, - data->samples_count); - train->shuffle_next_sample = 0; - } - - const bool last_epoch_reached = (params->n_epochs > 0 && (int64_t) train->train_epochs - data->first_epoch >= params->n_epochs); - if (last_epoch_reached) { - // allow optimization iteration at last epoch to be completed before canceling - if (data->iter_at_last_epoch < 0) { - data->iter_at_last_epoch = opt->iter; - } else if (opt->iter > data->iter_at_last_epoch) { - *cancel = true; - } - } -} diff --git a/common/train.h b/common/train.h deleted file mode 100644 index 263d940c0..000000000 --- a/common/train.h +++ /dev/null @@ -1,233 +0,0 @@ -// Various helper functions and utilities for training - -#pragma once - -#include -#include -#include - -#include "ggml.h" -#include "llama.h" - -#define LLAMA_TRAIN_MAX_NODES 16384 - -typedef std::string mt19937_state; - -struct train_state { - struct ggml_opt_context * opt; - - uint64_t train_its; - uint64_t train_samples; - uint64_t train_tokens; - uint64_t train_epochs; - - size_t shuffle_samples_hash; // fn, sample_count, *zip(sample_begins, sample_sizes) - mt19937_state shuffle_rng_state_current; - mt19937_state shuffle_rng_state_next; - size_t shuffle_sample_count; - size_t shuffle_next_sample; -}; - -struct train_params_common { - const char * fn_train_data; - const char * fn_checkpoint_in; - const char * fn_checkpoint_out; - const char * pattern_fn_it; - const char * fn_latest; - - bool print_usage; - - int save_every; - - uint32_t seed; - - int n_ctx; - int n_threads; - int n_batch; - int n_gradient_accumulation; - int n_epochs; - int n_gpu_layers; - - bool custom_n_ctx; - - bool use_flash; - bool use_checkpointing; - - std::string sample_start; - bool include_sample_start; - bool escape; - bool overlapping_samples; - bool fill_with_next_samples; - bool separate_with_eos; - bool separate_with_bos; - bool sample_random_offsets; - - bool force_reshuffle; - - int warmup; - int cos_decay_steps; - float cos_decay_restart; - float cos_decay_min; - bool enable_restart; - - int opt_past; - float opt_delta; - int opt_max_no_improvement; - - int adam_n_iter; - float adam_alpha; - float adam_min_alpha; - float adam_decay; - int adam_decay_min_ndim; - float adam_beta1; - float adam_beta2; - float adam_gclip; - float adam_eps_f; -}; - -typedef void (*save_train_files_callback)(void * data, struct train_state * train); - -struct train_opt_callback_data { - struct train_params_common * params; - struct train_state * train; - save_train_files_callback save_cb; - void * save_data; - struct llama_context * lctx; - int last_save_iter; - llama_token * tokens_data; - size_t tokens_size; - size_t * samples_begin; - size_t * samples_size; - size_t * shuffled_samples_offs; - size_t * shuffled_samples_begin; - size_t * shuffled_samples_size; - size_t samples_count; - struct ggml_tensor * tokens_input; - struct ggml_tensor * target_probs; - int first_iter; - int first_epoch; - int iter_at_last_epoch; - int64_t last_time; - double millis_per_iter; -}; - -struct train_state * init_train_state(); -void free_train_state(struct train_state * state); - -struct train_params_common get_default_train_params_common(); -void print_common_train_usage(int /*argc*/, char ** argv, const struct train_params_common * params); - -bool consume_common_train_arg(int argc, char ** argv, int * idx, struct train_params_common * params, bool * invalid_param); -void finish_processing_train_args(struct train_params_common * params); - -struct random_normal_distribution; -struct random_uniform_distribution; - -struct random_normal_distribution * init_random_normal_distribution (int seed, float mean, float std, float min, float max); -struct random_uniform_distribution * init_random_uniform_distribution(int seed, float min, float max); - -void free_random_normal_distribution (struct random_normal_distribution * rnd); -void free_random_uniform_distribution(struct random_uniform_distribution * rnd); - -struct ggml_tensor * randomize_tensor_normal (struct ggml_tensor * tensor, struct random_normal_distribution * rnd); -struct ggml_tensor * randomize_tensor_uniform(struct ggml_tensor * tensor, struct random_uniform_distribution * rnd); - -// generate random float in interval [0,1) -float frand(); -float frand_normal (struct random_normal_distribution * rnd); -float frand_uniform(struct random_uniform_distribution * rnd); - -int clamp (const int v, const int min, const int max); -float fclamp(const float v, const float min, const float max); - -void assert_shape_1d(struct ggml_tensor * tensor, int64_t ne0); -void assert_shape_2d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1); -void assert_shape_3d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2); -void assert_shape_4d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3); - -size_t tokenize_file( - struct llama_context * lctx, - const char * filename, - const std::string & sample_start, - bool include_sample_start, - bool overlapping_samples, - unsigned context_length, - std::vector & out_tokens, - std::vector & out_samples_begin, - std::vector & out_samples_size); - -int64_t get_example_targets_batch( - struct llama_context * lctx, - struct ggml_tensor * tokens_input, - struct ggml_tensor * target_probs, - int64_t example_id, - const size_t * samples_offs, - const size_t * samples_begin, - const size_t * samples_size, - size_t samples_count, - const llama_token * train_data, - size_t n_train_data, - bool separate_with_eos, - bool separate_with_bos, - bool fill_with_next_samples, - bool sample_random_offsets); - - -void mt19937_set_state(std::mt19937& rng, const mt19937_state& rng_state); -mt19937_state mt19937_get_state(const std::mt19937& rng); -mt19937_state mt19937_seed_to_state(unsigned seed); - -mt19937_state shuffle_samples( - const mt19937_state & rng_state, - size_t * shuffled_offs, - size_t * shuffled_begins, - size_t * shuffled_sizes, - const size_t * begins, - const size_t * sizes, - size_t count); - -size_t hash_combine(size_t h1, size_t h2); - -size_t compute_samples_hash( - const char* fn, - const size_t* samples_begin, - const size_t* samples_size, - size_t sample_count); - - -std::string replace_str(const char * s, const char * needle, const char * replacement); - -void print_duration(double milliseconds); - -float cosine_decay( - int64_t step, - int64_t decay_steps, - float minimum); - -float cosine_decay_restart( - int64_t step, - int64_t decay_steps, - float minimum, - float restart_step_mult); - -float learning_schedule( - int64_t step, - int64_t warmup_steps, - int64_t decay_steps, - float learning_rate, - float overall_minimum, - float cos_decay_minimum, - float cos_decay_restart_step_mult, - bool enable_restart); - -void copy_tensor_by_name(struct ggml_tensor * dst, struct ggml_context * ctx, const char * name); - -void load_opt_context_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct ggml_opt_context * opt); -void save_opt_context_gguf(struct gguf_context * fctx, struct ggml_opt_context * opt); - -bool load_train_state_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct train_state * train); -void save_train_state_gguf(struct gguf_context * fctx, struct train_state * train); - -std::string get_train_filename(const char * filename, const char * pattern_it, const char * latest, int64_t iteration); - -void train_opt_callback(void * vdata, int accum_step, float * sched, bool * cancel); diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index a34dabe23..9f1419e29 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -72,7 +72,8 @@ class Model: def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool = False, use_temp_file: bool = False, eager: bool = False, metadata_override: Path | None = None, model_name: str | None = None, - split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, small_first_shard: bool = False): + split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, + small_first_shard: bool = False, hparams: dict[str, Any] | None = None): if type(self) is Model: raise TypeError(f"{type(self).__name__!r} should not be directly instantiated") @@ -87,7 +88,7 @@ class Model: self.is_safetensors = len(self.part_names) > 0 if not self.is_safetensors: self.part_names = Model.get_model_part_names(self.dir_model, "pytorch_model", ".bin") - self.hparams = Model.load_hparams(self.dir_model) + self.hparams = Model.load_hparams(self.dir_model) if hparams is None else hparams self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer", "num_layers"]) self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count) self.tensor_names = None @@ -657,6 +658,9 @@ class Model: if chkhsh == "60824e3c0d9401f89943cbb2fff727f0e2d4c545ba4df2d6e4f09a6db0f5b450": # ref: https://huggingface.co/facebook/chameleon-7b res = "chameleon" + if chkhsh == "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35": + # ref: https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0 + res = "minerva-7b" if res is None: logger.warning("\n") @@ -1541,6 +1545,17 @@ class LlamaModel(Model): special_vocab._set_special_token("eot", 32010) special_vocab.add_to_gguf(self.gguf_writer) + tokenizer_config_file = self.dir_model / 'tokenizer_config.json' + if tokenizer_config_file.is_file(): + with open(tokenizer_config_file, "r", encoding="utf-8") as f: + tokenizer_config_json = json.load(f) + if "add_prefix_space" in tokenizer_config_json: + self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"]) + + # Apply to granite small models only + if self.hparams.get("vocab_size", 32000) == 49152: + self.gguf_writer.add_add_bos_token(False) + def set_gguf_parameters(self): super().set_gguf_parameters() hparams = self.hparams @@ -1557,17 +1572,6 @@ class LlamaModel(Model): self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) - tokenizer_config_file = self.dir_model / 'tokenizer_config.json' - if tokenizer_config_file.is_file(): - with open(tokenizer_config_file, "r", encoding="utf-8") as f: - tokenizer_config_json = json.load(f) - if "add_prefix_space" in tokenizer_config_json: - self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"]) - - # Apply to granite small models only - if self.hparams.get("vocab_size", 32000) == 49152: - self.gguf_writer.add_add_bos_token(False) - @staticmethod def permute(weights: Tensor, n_head: int, n_head_kv: int | None): if n_head_kv is not None and n_head != n_head_kv: @@ -1830,29 +1834,40 @@ class MiniCPMModel(Model): model_arch = gguf.MODEL_ARCH.MINICPM def set_gguf_parameters(self): - block_count = self.hparams["num_hidden_layers"] - self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) - self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) - self.gguf_writer.add_block_count(block_count) - self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) - self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) - self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) - self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"]) - self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) - self.gguf_writer.add_file_type(self.ftype) + super().set_gguf_parameters() + embedding_scale = float(self.hparams["scale_emb"]) + self.gguf_writer.add_embedding_scale(embedding_scale) + logger.info(f"gguf: (minicpm) embedding_scale = {embedding_scale}") + residual_scale = self.hparams["scale_depth"] / self.hparams["num_hidden_layers"] ** 0.5 + self.gguf_writer.add_residual_scale(residual_scale) + logger.info(f"gguf: (minicpm) residual_scale = {residual_scale}") + logit_scale = self.hparams["hidden_size"] / self.hparams["dim_model_base"] + self.gguf_writer.add_logit_scale(logit_scale) + logger.info(f"gguf: (minicpm) logit_scale = {logit_scale}") + if self.hparams.get("rope_scaling") is not None: + if self.hparams["rope_scaling"].get("type") == "longrope": + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LONGROPE) + logger.info(f"gguf: (minicpm) rope_scaling_type = {gguf.RopeScalingType.LONGROPE}") + + def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: + rope_dims = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] + + rope_scaling = self.find_hparam(['rope_scaling'], True) + if rope_scaling is not None: + long_factors = rope_scaling.get('long_factor', None) + short_factors = rope_scaling.get('short_factor', None) + + if long_factors is None or short_factors is None: + raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor') + + if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2: + raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}') + + yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_LONG), torch.tensor(long_factors, dtype=torch.float32)) + yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32)) def set_vocab(self): - self._set_vocab_llama_hf() - - def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor: - if n_kv_head is not None and n_head != n_kv_head: - n_head //= n_kv_head - - return ( - weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) - .swapaxes(1, 2) - .reshape(weights.shape) - ) + self._set_vocab_sentencepiece() def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: del bid # unused @@ -1862,9 +1877,9 @@ class MiniCPMModel(Model): # HF models permute some of the tensors, so we need to undo that if name.endswith(("q_proj.weight")): - data_torch = self._reverse_hf_permute(data_torch, n_head, n_head) + data_torch = LlamaModel.permute(data_torch, n_head, n_head) if name.endswith(("k_proj.weight")): - data_torch = self._reverse_hf_permute(data_torch, n_head, n_kv_head) + data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) return [(self.map_tensor_name(name), data_torch)] @@ -2706,7 +2721,7 @@ class XLMRobertaModel(BertModel): self.gguf_writer.add_token_scores(scores) self.gguf_writer.add_token_types(toktypes) self.gguf_writer.add_add_space_prefix(add_prefix) - self.gguf_writer.add_token_type_count(1) + self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1)) self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces) if precompiled_charsmap: self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap) @@ -3039,6 +3054,11 @@ class OlmoModel(Model): return [(self.map_tensor_name(name), data_torch)] +@Model.register("Olmo2ForCausalLM") +class Olmo2Model(Model): + model_arch = gguf.MODEL_ARCH.OLMO2 + + @Model.register("OlmoeForCausalLM") class OlmoeModel(Model): model_arch = gguf.MODEL_ARCH.OLMOE @@ -3747,10 +3767,7 @@ class JaisModel(Model): # Embeddings scale self.embeddings_scale = 1.0 - # note: For some JAIS flavors, output is tied to (same as) wte in original model - self.output_is_wte = False if 'mup_embeddings_scale' in self.hparams: - self.output_is_wte = True # Hack (?) self.embeddings_scale = self.hparams['mup_embeddings_scale'] elif 'embeddings_scale' in self.hparams: self.embeddings_scale = self.hparams['embeddings_scale'] @@ -3807,10 +3824,7 @@ class JaisModel(Model): if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD): tensors.append((new_name, data_torch * self.embeddings_scale)) - if self.output_is_wte: - tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch * self.width_scale)) elif new_name == self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT): - assert not self.output_is_wte tensors.append((new_name, data_torch * self.width_scale)) else: tensors.append((new_name, data_torch)) diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py index 28cd02e5a..ce3c571df 100755 --- a/convert_hf_to_gguf_update.py +++ b/convert_hf_to_gguf_update.py @@ -17,7 +17,7 @@ # # python3 convert_hf_to_gguf_update.py # -# - Copy-paste the generated get_vocab_base_pre() function into convert_hf_to_gguf.py +# - The convert_hf_to_gguf.py script will have had its get_vocab_base_pre() function updated # - Update llama.cpp with the new pre-tokenizer if necessary # # TODO: generate tokenizer tests for llama.cpp @@ -102,6 +102,7 @@ models = [ {"name": "exaone", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct", }, {"name": "phi-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/microsoft/phi-2", }, {"name": "chameleon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/facebook/chameleon-7b", }, + {"name": "minerva-7b", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", }, ] diff --git a/convert_lora_to_gguf.py b/convert_lora_to_gguf.py index 915e21836..ed1014cae 100755 --- a/convert_lora_to_gguf.py +++ b/convert_lora_to_gguf.py @@ -12,6 +12,7 @@ import json from math import prod from pathlib import Path from typing import TYPE_CHECKING, Any, Callable, Iterable, Iterator, Sequence, SupportsIndex, cast +from transformers import AutoConfig import torch @@ -256,8 +257,8 @@ def parse_args() -> argparse.Namespace: help="only print out what will be done, without writing any new files", ) parser.add_argument( - "--base", type=Path, required=True, - help="directory containing Hugging Face model config files (config.json, tokenizer.json) for the base model that the adapter is based on - only config is needed, actual model weights are not required", + "--base", type=Path, + help="directory containing Hugging Face model config files (config.json, tokenizer.json) for the base model that the adapter is based on - only config is needed, actual model weights are not required. If base model is unspecified, it will be loaded from Hugging Face hub based on the adapter config", ) parser.add_argument( "lora_path", type=Path, @@ -267,6 +268,12 @@ def parse_args() -> argparse.Namespace: return parser.parse_args() +def load_hparams_from_hf(hf_model_id: str) -> dict[str, Any]: + # normally, adapter does not come with base model config, we need to load it from AutoConfig + config = AutoConfig.from_pretrained(hf_model_id) + return config.to_dict() + + if __name__ == '__main__': args = parse_args() logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) @@ -281,7 +288,7 @@ if __name__ == '__main__': ftype = ftype_map[args.outtype] - dir_base_model: Path = args.base + dir_base_model: Path | None = args.base dir_lora: Path = args.lora_path lora_config = dir_lora / "adapter_config.json" input_model = dir_lora / "adapter_model.safetensors" @@ -301,9 +308,29 @@ if __name__ == '__main__': input_model = os.path.join(dir_lora, "adapter_model.bin") lora_model = torch.load(input_model, map_location="cpu", weights_only=True) + # load LoRA config + with open(lora_config, "r") as f: + lparams: dict[str, Any] = json.load(f) + # load base model - logger.info(f"Loading base model: {dir_base_model.name}") - hparams = Model.load_hparams(dir_base_model) + if dir_base_model is None: + if "base_model_name_or_path" in lparams: + model_id = lparams["base_model_name_or_path"] + logger.info(f"Loading base model from Hugging Face: {model_id}") + try: + hparams = load_hparams_from_hf(model_id) + except OSError as e: + logger.error(f"Failed to load base model config: {e}") + logger.error("Please try downloading the base model and add its path to --base") + sys.exit(1) + else: + logger.error("'base_model_name_or_path' is not found in adapter_config.json") + logger.error("Base model config is required. Please download the base model and add its path to --base") + sys.exit(1) + else: + logger.info(f"Loading base model: {dir_base_model.name}") + hparams = Model.load_hparams(dir_base_model) + with torch.inference_mode(): try: model_class = Model.from_model_architecture(hparams["architectures"][0]) @@ -323,13 +350,15 @@ if __name__ == '__main__': self.dir_model_card = dir_lora_model self.lora_alpha = float(lora_alpha) + def set_vocab(self): + pass + def set_type(self): self.gguf_writer.add_type(gguf.GGUFType.ADAPTER) self.gguf_writer.add_string(gguf.Keys.Adapter.TYPE, "lora") def set_gguf_parameters(self): self.gguf_writer.add_float32(gguf.Keys.Adapter.LORA_ALPHA, self.lora_alpha) - super().set_gguf_parameters() def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: # Never add extra tensors (e.g. rope_freqs) for LoRA adapters @@ -350,7 +379,7 @@ if __name__ == '__main__': logger.error(f"Unexpected name '{name}': Not a lora_A or lora_B tensor") if ".embed_tokens.weight" in name or ".lm_head.weight" in name: logger.error("Embeddings is present in the adapter. This can be due to new tokens added during fine tuning") - logger.error("Hint: if you are using TRL, make sure not to call setup_chat_format()") + logger.error("Please refer to https://github.com/ggerganov/llama.cpp/pull/9948") sys.exit(1) if base_name in tensor_map: @@ -384,9 +413,6 @@ if __name__ == '__main__': yield (dest_name + ".lora_a", lora_a) yield (dest_name + ".lora_b", lora_b) - with open(lora_config, "r") as f: - lparams: dict[str, Any] = json.load(f) - alpha: float = lparams["lora_alpha"] model_instance = LoraModel( @@ -399,6 +425,7 @@ if __name__ == '__main__': dry_run=args.dry_run, dir_lora_model=dir_lora, lora_alpha=alpha, + hparams=hparams, ) logger.info("Exporting model...") diff --git a/docs/android.md b/docs/android.md index 320b62240..47530c6c1 100644 --- a/docs/android.md +++ b/docs/android.md @@ -23,10 +23,10 @@ $ curl -L {model-url} -o ~/{model}.gguf Then, if you are not already in the repo directory, `cd` into `llama.cpp` and: ``` -$ ./build/bin/llama-simple -m ~/{model}.gguf -c {context-size} -p "{your-prompt}" +$ ./build/bin/llama-cli -m ~/{model}.gguf -c {context-size} -p "{your-prompt}" ``` -Here, we show `llama-simple`, but any of the executables under `examples` should work, in theory. Be sure to set `context-size` to a reasonable number (say, 4096) to start with; otherwise, memory could spike and kill your terminal. +Here, we show `llama-cli`, but any of the executables under `examples` should work, in theory. Be sure to set `context-size` to a reasonable number (say, 4096) to start with; otherwise, memory could spike and kill your terminal. To see what it might look like visually, here's an old demo of an interactive session running on a Pixel 5 phone: diff --git a/docs/backend/BLIS.md b/docs/backend/BLIS.md index 35d06bd0f..904548577 100644 --- a/docs/backend/BLIS.md +++ b/docs/backend/BLIS.md @@ -27,13 +27,6 @@ We recommend using openmp since it's easier to modify the cores being used. ### llama.cpp compilation -Makefile: - -```bash -make GGML_BLIS=1 -j -# make GGML_BLIS=1 llama-benchmark-matmult -``` - CMake: ```bash diff --git a/docs/backend/CANN.md b/docs/backend/CANN.md index 6bdd9d2da..23f10175a 100644 --- a/docs/backend/CANN.md +++ b/docs/backend/CANN.md @@ -23,6 +23,8 @@ The llama.cpp CANN backend is designed to support Ascend NPU. It utilize the abi ## News +- 2024.11 + - Support F16 and F32 data type model for Ascend 310P NPU. - 2024.8 - Support `Q4_0` and `Q8_0` data type for Ascend NPU. - 2024.7 @@ -40,9 +42,11 @@ The llama.cpp CANN backend is designed to support Ascend NPU. It utilize the abi ### Ascend NPU **Verified devices** + | Ascend NPU | Status | |:-----------------------------:|:-------:| | Atlas 300T A2 | Support | +| Atlas 300I Duo | Support | *Notes:* diff --git a/docs/backend/SYCL.md b/docs/backend/SYCL.md index ea34182e4..8d8312e91 100644 --- a/docs/backend/SYCL.md +++ b/docs/backend/SYCL.md @@ -34,13 +34,16 @@ The SYCL backend would be broken by some PRs due to no online CI. The following release is verified with good quality: -|Commit ID|Tag|Release|Verified Platform| -|-|-|-|-| -|fb76ec31a9914b7761c1727303ab30380fd4f05c|b3038 |[llama-b3038-bin-win-sycl-x64.zip](https://github.com/ggerganov/llama.cpp/releases/download/b3038/llama-b3038-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1
MTL Arc GPU/Windows 11/oneAPI 2024.1| +|Commit ID|Tag|Release|Verified Platform| Update date| +|-|-|-|-|-| +|3bcd40b3c593d14261fb2abfabad3c0fb5b9e318|b4040 |[llama-b4040-bin-win-sycl-x64.zip](https://github.com/ggerganov/llama.cpp/releases/download/b4040/llama-b4040-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1
MTL Arc GPU/Windows 11/oneAPI 2024.1| 2024-11-19| +|fb76ec31a9914b7761c1727303ab30380fd4f05c|b3038 |[llama-b3038-bin-win-sycl-x64.zip](https://github.com/ggerganov/llama.cpp/releases/download/b3038/llama-b3038-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1
MTL Arc GPU/Windows 11/oneAPI 2024.1|| ## News +- 2024.11 + - Use syclcompat to improve the performance on some platforms. This requires to use oneAPI 2025.0 or newer. - 2024.8 - Use oneDNN as the default GEMM library, improve the compatibility for new Intel GPUs. @@ -310,12 +313,14 @@ export CPLUS_INCLUDE_DIR=/path/to/oneMKL/buildWithCublas/include:$CPLUS_INCLUDE_ export CPLUS_INCLUDE_DIR=/path/to/oneMKL/include:$CPLUS_INCLUDE_DIR # Build LLAMA with Nvidia BLAS acceleration through SYCL +# Setting GGML_SYCL_DEVICE_ARCH is optional but can improve performance +GGML_SYCL_DEVICE_ARCH=sm_80 # Example architecture # Option 1: Use FP32 (recommended for better performance in most cases) -cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx +cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx # Option 2: Use FP16 -cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON +cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON # build all binary cmake --build build --config Release -j -v @@ -333,8 +338,9 @@ export CPLUS_INCLUDE_DIR=/path/to/oneMKL/buildWithrocBLAS/include:$CPLUS_INCLUDE ## AMD # Use FP32, FP16 is not supported -# Find your GGML_SYCL_HIP_TARGET with rocminfo, under the key 'Name:' -cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=AMD -DGGML_SYCL_HIP_TARGET=${GGML_SYCL_HIP_TARGET} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx +# Find your GGML_SYCL_DEVICE_ARCH with rocminfo, under the key 'Name:' +GGML_SYCL_DEVICE_ARCH=gfx90a # Example architecture +cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=AMD -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx # build all binary cmake --build build --config Release -j -v @@ -377,7 +383,7 @@ found 2 SYCL devices: |Chosen Device ID|Setting| |-|-| -|0|`export ONEAPI_DEVICE_SELECTOR="level_zero:1"` or no action| +|0|`export ONEAPI_DEVICE_SELECTOR="level_zero:0"` or no action| |1|`export ONEAPI_DEVICE_SELECTOR="level_zero:1"`| |0 & 1|`export ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"`| @@ -644,6 +650,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512 |--------------------|---------------------------------------|---------------------------------------------| | GGML_SYCL | ON (mandatory) | Enable build with SYCL code path.
FP32 path - recommended for better perforemance than FP16 on quantized model| | GGML_SYCL_TARGET | INTEL *(default)* \| NVIDIA \| AMD | Set the SYCL target device type. | +| GGML_SYCL_DEVICE_ARCH | Optional (except for AMD) | Set the SYCL device architecture, optional except for AMD. Setting the device architecture can improve the performance. See the table [--offload-arch](https://github.com/intel/llvm/blob/sycl/sycl/doc/design/OffloadDesign.md#--offload-arch) for a list of valid architectures. | | GGML_SYCL_F16 | OFF *(default)* \|ON *(optional)* | Enable FP16 build with SYCL code path. | | CMAKE_C_COMPILER | `icx` *(Linux)*, `icx/cl` *(Windows)* | Set `icx` compiler for SYCL code path. | | CMAKE_CXX_COMPILER | `icpx` *(Linux)*, `icx` *(Windows)* | Set `icpx/icx` compiler for SYCL code path. | diff --git a/docs/build.md b/docs/build.md index 4e362ebc7..a4964cbd1 100644 --- a/docs/build.md +++ b/docs/build.md @@ -7,124 +7,68 @@ git clone https://github.com/ggerganov/llama.cpp cd llama.cpp ``` -In order to build llama.cpp you have four different options. +The following sections describe how to build with different backends and options. -- Using `make`: - - On Linux or MacOS: +## CPU Build - ```bash - make - ``` +Build llama.cpp using `CMake`: - - On Windows (x86/x64 only, arm64 requires cmake): +```bash +cmake -B build +cmake --build build --config Release +``` - 1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases). - 2. Extract `w64devkit` on your pc. - 3. Run `w64devkit.exe`. - 4. Use the `cd` command to reach the `llama.cpp` folder. - 5. From here you can run: - ```bash - make - ``` +**Notes**: - - Notes: - - For `Q4_0_4_4` quantization type build, add the `GGML_NO_LLAMAFILE=1` flag. For example, use `make GGML_NO_LLAMAFILE=1`. - - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `make -j 8` will run 8 jobs in parallel. - - For faster repeated compilation, install [ccache](https://ccache.dev/). - - For debug builds, run `make LLAMA_DEBUG=1` +- For faster compilation, add the `-j` argument to run multiple jobs in parallel, or use a generator that does this automatically such as Ninja. For example, `cmake --build build --config Release -j 8` will run 8 jobs in parallel. +- For faster repeated compilation, install [ccache](https://ccache.dev/) +- For debug builds, there are two cases: -- Using `CMake`: + 1. Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag): - ```bash - cmake -B build + ```bash + cmake -B build -DCMAKE_BUILD_TYPE=Debug + cmake --build build + ``` + + 2. Multi-config generators (`-G` param set to Visual Studio, XCode...): + + ```bash + cmake -B build -G "Xcode" + cmake --build build --config Debug + ``` + + For more details and a list of supported generators, see the [CMake documentation](https://cmake.org/cmake/help/latest/manual/cmake-generators.7.html). +- For static builds, add `-DBUILD_SHARED_LIBS=OFF`: + ``` + cmake -B build -DBUILD_SHARED_LIBS=OFF cmake --build build --config Release ``` - **Notes**: - - - For `Q4_0_4_4` quantization type build, add the `-DGGML_LLAMAFILE=OFF` cmake option. For example, use `cmake -B build -DGGML_LLAMAFILE=OFF`. - - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `cmake --build build --config Release -j 8` will run 8 jobs in parallel. - - For faster repeated compilation, install [ccache](https://ccache.dev/). - - For debug builds, there are two cases: - - 1. Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag): - - ```bash - cmake -B build -DCMAKE_BUILD_TYPE=Debug - cmake --build build - ``` - - 2. Multi-config generators (`-G` param set to Visual Studio, XCode...): - - ```bash - cmake -B build -G "Xcode" - cmake --build build --config Debug - ``` - - Building for Windows (x86, x64 and arm64) with MSVC or clang as compilers: - - Install Visual Studio 2022, e.g. via the [Community Edition](https://visualstudio.microsoft.com/de/vs/community/). In the installer, select at least the following options (this also automatically installs the required additional tools like CMake,...): - - Tab Workload: Desktop-development with C++ - - Tab Components (select quickly via search): C++-_CMake_ Tools for Windows, _Git_ for Windows, C++-_Clang_ Compiler for Windows, MS-Build Support for LLVM-Toolset (clang) - - Please remember to always use a Developer Command Prompt / PowerShell for VS2022 for git, build, test - - For Windows on ARM (arm64, WoA) build with: - ```bash - cmake --preset arm64-windows-llvm-release -D GGML_OPENMP=OFF - cmake --build build-arm64-windows-llvm-release - ``` - Note: Building for arm64 could also be done just with MSVC (with the build-arm64-windows-MSVC preset, or the standard CMake build instructions). But MSVC does not support inline ARM assembly-code, used e.g. for the accelerated Q4_0_4_8 CPU kernels. - -- Using `gmake` (FreeBSD): - - 1. Install and activate [DRM in FreeBSD](https://wiki.freebsd.org/Graphics) - 2. Add your user to **video** group - 3. Install compilation dependencies. - - ```bash - sudo pkg install gmake automake autoconf pkgconf llvm15 openblas - - gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j4 - ``` - -## Metal Build - -On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU. -To disable the Metal build at compile time use the `GGML_NO_METAL=1` flag or the `GGML_METAL=OFF` cmake option. - -When built with Metal support, you can explicitly disable GPU inference with the `--n-gpu-layers|-ngl 0` command-line -argument. +- Building for Windows (x86, x64 and arm64) with MSVC or clang as compilers: + - Install Visual Studio 2022, e.g. via the [Community Edition](https://visualstudio.microsoft.com/de/vs/community/). In the installer, select at least the following options (this also automatically installs the required additional tools like CMake,...): + - Tab Workload: Desktop-development with C++ + - Tab Components (select quickly via search): C++-_CMake_ Tools for Windows, _Git_ for Windows, C++-_Clang_ Compiler for Windows, MS-Build Support for LLVM-Toolset (clang) + - Please remember to always use a Developer Command Prompt / PowerShell for VS2022 for git, build, test + - For Windows on ARM (arm64, WoA) build with: + ```bash + cmake --preset arm64-windows-llvm-release -D GGML_OPENMP=OFF + cmake --build build-arm64-windows-llvm-release + ``` + Building for arm64 can also be done with the MSVC compiler with the build-arm64-windows-MSVC preset, or the standard CMake build instructions. However, note that the MSVC compiler does not support inline ARM assembly code, used e.g. for the accelerated Q4_0_4_8 CPU kernels. ## BLAS Build -Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS. There are currently several different BLAS implementations available for build and use: +Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Using BLAS doesn't affect the generation performance. There are currently several different BLAS implementations available for build and use: -### Accelerate Framework: +### Accelerate Framework This is only available on Mac PCs and it's enabled by default. You can just build using the normal instructions. -### OpenBLAS: +### OpenBLAS This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS installed on your machine. -- Using `make`: - - On Linux: - ```bash - make GGML_OPENBLAS=1 - ``` - - - On Windows: - - 1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases). - 2. Download the latest version of [OpenBLAS for Windows](https://github.com/xianyi/OpenBLAS/releases). - 3. Extract `w64devkit` on your pc. - 4. From the OpenBLAS zip that you just downloaded copy `libopenblas.a`, located inside the `lib` folder, inside `w64devkit\x86_64-w64-mingw32\lib`. - 5. From the same OpenBLAS zip copy the content of the `include` folder inside `w64devkit\x86_64-w64-mingw32\include`. - 6. Run `w64devkit.exe`. - 7. Use the `cd` command to reach the `llama.cpp` folder. - 8. From here you can run: - - ```bash - make GGML_OPENBLAS=1 - ``` - - Using `CMake` on Linux: ```bash @@ -136,14 +80,6 @@ This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS i Check [BLIS.md](./backend/BLIS.md) for more information. -### SYCL - -SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators. - -llama.cpp based on SYCL is used to **support Intel GPU** (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU). - -For detailed info, please refer to [llama.cpp for SYCL](./backend/SYCL.md). - ### Intel oneMKL Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. Please note that this build config **does not support Intel GPU**. For Intel GPU support, please refer to [llama.cpp for SYCL](./backend/SYCL.md). @@ -161,16 +97,29 @@ Building through oneAPI compilers will make avx_vnni instruction set available f Check [Optimizing and Running LLaMA2 on Intel® CPU](https://www.intel.com/content/www/us/en/content-details/791610/optimizing-and-running-llama2-on-intel-cpu.html) for more information. -### CUDA +### Other BLAS libraries -This provides GPU acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads). +Any other BLAS library can be used by setting the `GGML_BLAS_VENDOR` option. See the [CMake documentation](https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors) for a list of supported vendors. -For Jetson user, if you have Jetson Orin, you can try this: [Offical Support](https://www.jetson-ai-lab.com/tutorial_text-generation.html). If you are using an old model(nano/TX2), need some additional operations before compiling. +## Metal Build + +On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU. +To disable the Metal build at compile time use the `-DGGML_METAL=OFF` cmake option. + +When built with Metal support, you can explicitly disable GPU inference with the `--n-gpu-layers 0` command-line argument. + +## SYCL + +SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators. + +llama.cpp based on SYCL is used to **support Intel GPU** (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU). + +For detailed info, please refer to [llama.cpp for SYCL](./backend/SYCL.md). + +## CUDA + +This provides GPU acceleration using an NVIDIA GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from the [NVIDIA developer site](https://developer.nvidia.com/cuda-downloads). -- Using `make`: - ```bash - make GGML_CUDA=1 - ``` - Using `CMake`: ```bash @@ -186,24 +135,16 @@ The following compilation options are also available to tweak performance: | Option | Legal values | Default | Description | |-------------------------------|------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| GGML_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. | -| GGML_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | -| GGML_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. | | GGML_CUDA_FORCE_MMQ | Boolean | false | Force the use of custom matrix multiplication kernels for quantized models instead of FP16 cuBLAS even if there is no int8 tensor core implementation available (affects V100, RDNA3). MMQ kernels are enabled by default on GPUs with int8 tensor core support. With MMQ force enabled, speed for large batch sizes will be worse but VRAM consumption will be lower. | | GGML_CUDA_FORCE_CUBLAS | Boolean | false | Force the use of FP16 cuBLAS instead of custom matrix multiplication kernels for quantized models | | GGML_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. | -| GGML_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | | GGML_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. | | GGML_CUDA_FA_ALL_QUANTS | Boolean | false | Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer. | -### MUSA +## MUSA This provides GPU acceleration using the MUSA cores of your Moore Threads MTT GPU. Make sure to have the MUSA SDK installed. You can download it from here: [MUSA SDK](https://developer.mthreads.com/sdk/download/musa). -- Using `make`: - ```bash - make GGML_MUSA=1 - ``` - Using `CMake`: ```bash @@ -217,20 +158,16 @@ The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enab Most of the compilation options available for CUDA should also be available for MUSA, though they haven't been thoroughly tested yet. -### hipBLAS +## HIP -This provides BLAS acceleration on HIP-supported AMD GPUs. +This provides GPU acceleration on HIP-supported AMD GPUs. Make sure to have ROCm installed. You can download it from your Linux distro's package manager or from here: [ROCm Quick Start (Linux)](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html#rocm-install-quick). -- Using `make`: - ```bash - make GGML_HIPBLAS=1 - ``` - Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU): ```bash HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \ - cmake -S . -B build -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ + cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ && cmake --build build --config Release -- -j 16 ``` On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DGGML_HIP_UMA=ON`. @@ -247,19 +184,14 @@ You can download it from your Linux distro's package manager or from here: [ROCm ```bash HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -p)" \ HIP_DEVICE_LIB_PATH= \ - cmake -S . -B build -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ + cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ && cmake --build build -- -j 16 ``` -- Using `make` (example for target gfx1030, build with 16 CPU threads): - ```bash - make -j16 GGML_HIPBLAS=1 GGML_HIP_UMA=1 AMDGPU_TARGETS=gfx1030 - ``` - - Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU): ```bash set PATH=%HIP_PATH%\bin;%PATH% - cmake -S . -B build -G Ninja -DAMDGPU_TARGETS=gfx1100 -DGGML_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release + cmake -S . -B build -G Ninja -DAMDGPU_TARGETS=gfx1100 -DGGML_HIP=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release cmake --build build ``` Make sure that `AMDGPU_TARGETS` is set to the GPU arch you want to compile for. The above example uses `gfx1100` that corresponds to Radeon RX 7900XTX/XT/GRE. You can find a list of targets [here](https://llvm.org/docs/AMDGPUUsage.html#processors) @@ -268,23 +200,16 @@ You can download it from your Linux distro's package manager or from here: [ROCm The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used. If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3. -The following compilation options are also available to tweak performance (yes, they refer to CUDA, not HIP, because it uses the same code as the cuBLAS version above): -| Option | Legal values | Default | Description | -|------------------------|------------------------|---------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| GGML_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the HIP dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | -| GGML_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. | -| GGML_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | - -### Vulkan +## Vulkan **Windows** -#### w64devkit +### w64devkit -Download and extract [w64devkit](https://github.com/skeeto/w64devkit/releases). +Download and extract [`w64devkit`](https://github.com/skeeto/w64devkit/releases). -Download and install the [Vulkan SDK](https://vulkan.lunarg.com/sdk/home#windows). When selecting components, only the Vulkan SDK Core is required. +Download and install the [`Vulkan SDK`](https://vulkan.lunarg.com/sdk/home#windows) with the default settings. Launch `w64devkit.exe` and run the following commands to copy Vulkan dependencies: ```sh @@ -300,18 +225,47 @@ Libs: -lvulkan-1 EOF ``` -Switch into the `llama.cpp` directory and run `make GGML_VULKAN=1`. -#### MSYS2 +Switch into the `llama.cpp` directory and build using CMake. +```sh +cmake -B build -DGGML_VULKAN=ON +cmake --build build --config Release +``` + +### Git Bash MINGW64 + +Download and install [`Git-SCM`](https://git-scm.com/downloads/win) with the default settings + +Download and install [`Visual Studio Community Edition`](https://visualstudio.microsoft.com/) and make sure you select `C++` + +Download and install [`CMake`](https://cmake.org/download/) with the default settings + +Download and install the [`Vulkan SDK`](https://vulkan.lunarg.com/sdk/home#windows) with the default settings. + +Go into your `llama.cpp` directory and right click, select `Open Git Bash Here` and then run the following commands + +``` +cmake -B build -DGGML_VULKAN=ON +cmake --build build --config Release +``` + +Now you can load the model in conversation mode using `Vulkan` + +```sh +build/bin/Release/llama-cli -m "[PATH TO MODEL]" -ngl 100 -c 16384 -t 10 -n -2 -cnv +``` + +### MSYS2 Install [MSYS2](https://www.msys2.org/) and then run the following commands in a UCRT terminal to install dependencies. - ```sh - pacman -S git \ - mingw-w64-ucrt-x86_64-gcc \ - mingw-w64-ucrt-x86_64-cmake \ - mingw-w64-ucrt-x86_64-vulkan-devel \ - mingw-w64-ucrt-x86_64-shaderc - ``` -Switch into `llama.cpp` directory and build using CMake. +```sh +pacman -S git \ + mingw-w64-ucrt-x86_64-gcc \ + mingw-w64-ucrt-x86_64-cmake \ + mingw-w64-ucrt-x86_64-vulkan-devel \ + mingw-w64-ucrt-x86_64-shaderc +``` + +Switch into the `llama.cpp` directory and build using CMake. ```sh cmake -B build -DGGML_VULKAN=ON cmake --build build --config Release @@ -360,7 +314,7 @@ cmake --build build --config Release # ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32 ``` -### CANN +## CANN This provides NPU acceleration using the AI cores of your Ascend NPU. And [CANN](https://www.hiascend.com/en/software/cann) is a hierarchical APIs to help you to quickly build AI applications and service based on Ascend NPU. For more information about Ascend NPU in [Ascend Community](https://www.hiascend.com/en/). @@ -375,22 +329,26 @@ cmake --build build --config release You can test with: -`./build/llama-cli -m PATH_TO_MODEL -p "Building a website can be done in 10 steps:" -ngl 32` - -If the fllowing info is output on screen, you are using `llama.cpp by CANN backend`: ```bash -llm_load_tensors: CANN buffer size = 13313.00 MiB +./build/bin/llama-cli -m PATH_TO_MODEL -p "Building a website can be done in 10 steps:" -ngl 32 +``` + +If the following info is output on screen, you are using `llama.cpp` with the CANN backend: +```bash +llm_load_tensors: CANN model buffer size = 13313.00 MiB llama_new_context_with_model: CANN compute buffer size = 1260.81 MiB ``` For detailed info, such as model/device supports, CANN install, please refer to [llama.cpp for CANN](./backend/CANN.md). -### Android +## Android To read documentation for how to build on Android, [click here](./android.md) -### Arm CPU optimized mulmat kernels +## Notes about GPU-accelerated backends -Llama.cpp includes a set of optimized mulmat kernels for the Arm architecture, leveraging Arm® Neon™, int8mm and SVE instructions. These kernels are enabled at build time through the appropriate compiler cpu-type flags, such as `-DCMAKE_C_FLAGS=-march=armv8.2a+i8mm+sve`. Note that these optimized kernels require the model to be quantized into one of the formats: `Q4_0_4_4` (Arm Neon), `Q4_0_4_8` (int8mm) or `Q4_0_8_8` (SVE). The SVE mulmat kernel specifically requires a vector width of 256 bits. When running on devices with a different vector width, it is recommended to use the `Q4_0_4_8` (int8mm) or `Q4_0_4_4` (Arm Neon) formats for better performance. Refer to [examples/quantize/README.md](../examples/quantize/README.md) for more information on the quantization formats. +The GPU may still be used to accelerate some parts of the computation even when using the `-ngl 0` option. You can fully disable GPU acceleration by using `--device none`. -To support `Q4_0_4_4`, you must build with `GGML_NO_LLAMAFILE=1` (`make`) or `-DGGML_LLAMAFILE=OFF` (`cmake`). +In most cases, it is possible to build and use multiple backends at the same time. For example, you can build llama.cpp with both CUDA and Vulkan support by using the `-DGGML_CUDA=ON -DGGML_VULKAN=ON` options with CMake. At runtime, you can specify which backend devices to use with the `--device` option. To see a list of available devices, use the `--list-devices` option. + +Backends can be built as dynamic libraries that can be loaded dynamically at runtime. This allows you to use the same llama.cpp binary on different machines with different GPUs. To enable this feature, use the `GGML_BACKEND_DL` option when building. diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index ead630661..9210e9fea 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -6,20 +6,20 @@ find_package(Threads REQUIRED) # ... +# flags + +llama_add_compile_flags() + # examples include_directories(${CMAKE_CURRENT_SOURCE_DIR}) if (EMSCRIPTEN) else() - add_subdirectory(cvector-generator) - add_subdirectory(baby-llama) add_subdirectory(batched-bench) add_subdirectory(batched) - add_subdirectory(convert-llama2c-to-ggml) add_subdirectory(embedding) add_subdirectory(eval-callback) - add_subdirectory(export-lora) add_subdirectory(gbnf-validator) add_subdirectory(gguf-hash) add_subdirectory(gguf-split) @@ -28,27 +28,36 @@ else() add_subdirectory(imatrix) add_subdirectory(infill) add_subdirectory(llama-bench) - add_subdirectory(llava) add_subdirectory(lookahead) add_subdirectory(lookup) add_subdirectory(main) add_subdirectory(parallel) add_subdirectory(passkey) add_subdirectory(perplexity) - add_subdirectory(quantize-stats) add_subdirectory(quantize) add_subdirectory(retrieval) - if (GGML_RPC) - add_subdirectory(rpc) - endif() if (LLAMA_BUILD_SERVER) - add_subdirectory(server) - endif() - if (GGML_SYCL) - add_subdirectory(sycl) + add_subdirectory(server) endif() add_subdirectory(save-load-state) + add_subdirectory(run) add_subdirectory(simple) + add_subdirectory(simple-chat) add_subdirectory(speculative) + add_subdirectory(speculative-simple) add_subdirectory(tokenize) + if (NOT GGML_BACKEND_DL) + # these examples use the backends directly and cannot be built with dynamic loading + add_subdirectory(convert-llama2c-to-ggml) + add_subdirectory(cvector-generator) + add_subdirectory(export-lora) + add_subdirectory(quantize-stats) + add_subdirectory(llava) + if (GGML_RPC) + add_subdirectory(rpc) + endif() + if (GGML_SYCL) + add_subdirectory(sycl) + endif() + endif() endif() diff --git a/examples/baby-llama/CMakeLists.txt b/examples/baby-llama/CMakeLists.txt deleted file mode 100644 index 71b82105c..000000000 --- a/examples/baby-llama/CMakeLists.txt +++ /dev/null @@ -1,5 +0,0 @@ -set(TARGET llama-baby-llama) -add_executable(${TARGET} baby-llama.cpp) -install(TARGETS ${TARGET} RUNTIME) -target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/baby-llama/baby-llama.cpp b/examples/baby-llama/baby-llama.cpp deleted file mode 100644 index 3ce91070b..000000000 --- a/examples/baby-llama/baby-llama.cpp +++ /dev/null @@ -1,1639 +0,0 @@ -#include "ggml.h" -#include "train.h" - -#include -#include -#include -#include -#include - -#if defined(_MSC_VER) -#pragma warning(disable: 4244 4267) // possible loss of data -#endif - -#ifdef LLAMA_DEFAULT_RMS_EPS -constexpr float rms_norm_eps = LLAMA_DEFAULT_RMS_EPS; -#else -constexpr float rms_norm_eps = 5e-6f; -#endif - -static void ggml_graph_compute_helper(std::vector & buf, ggml_cgraph * graph, int n_threads) { - struct ggml_cplan plan = ggml_graph_plan(graph, n_threads, nullptr); - - if (plan.work_size > 0) { - buf.resize(plan.work_size); - plan.work_data = buf.data(); - } - - ggml_graph_compute(graph, &plan); -} - -static struct ggml_tensor * randomize_tensor( - struct ggml_tensor * tensor, int ndims, const int64_t ne[], float fmin, float fmax -) { - switch (ndims) { - case 1: - for (int i0 = 0; i0 < ne[0]; i0++) { - ((float *)tensor->data)[i0] = frand()*(fmax - fmin) + fmin; - } - break; - case 2: - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((float *)tensor->data)[i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin; - } - } - break; - case 3: - for (int i2 = 0; i2 < ne[2]; i2++) { - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((float *)tensor->data)[i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin; - } - } - } - break; - case 4: - for (int i3 = 0; i3 < ne[3]; i3++) { - for (int i2 = 0; i2 < ne[2]; i2++) { - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((float *)tensor->data)[i3*ne[2]*ne[1]*ne[0] + i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin; - } - } - } - } - break; - default: - assert(false); - } - - return tensor; -} - -struct llama_hparams { - uint32_t n_vocab = 32000; - uint32_t n_ctx = 512; // this is provided as user input? - uint32_t n_embd = 4096; - uint32_t n_mult = 4; - uint32_t n_head = 32; - uint32_t n_layer = 32; - uint32_t n_rot = 64; - - bool operator!=(const llama_hparams & other) const { - return memcmp(this, &other, sizeof(llama_hparams)); - } -}; - -static uint32_t get_n_ff(const struct llama_hparams* hparams) { - const uint32_t n_ff = ((2*(4*hparams->n_embd)/3 + hparams->n_mult - 1)/hparams->n_mult)*hparams->n_mult; - return n_ff; -} - -struct llama_hparams_lora { - uint32_t n_vocab = 32000; - uint32_t n_ctx = 512; // this is provided as user input? - uint32_t n_embd = 4096; - uint32_t n_mult = 4; - uint32_t n_head = 32; - uint32_t n_layer = 32; - uint32_t n_rot = 64; - uint32_t n_lora = 64; - - bool operator!=(const llama_hparams_lora & other) const { - return memcmp(this, &other, sizeof(llama_hparams_lora)) != 0; - } -}; - -struct llama_layer { - // normalization - struct ggml_tensor * attention_norm; - - // attention - struct ggml_tensor * wq; - struct ggml_tensor * wk; - struct ggml_tensor * wv; - struct ggml_tensor * wo; - - // normalization - struct ggml_tensor * ffn_norm; - - // ff - struct ggml_tensor * w1; - struct ggml_tensor * w2; - struct ggml_tensor * w3; -}; - -struct llama_layer_lora { - // normalization - struct ggml_tensor * attention_norm; - - // attention - struct ggml_tensor * wqa; - struct ggml_tensor * wqb; - struct ggml_tensor * wka; - struct ggml_tensor * wkb; - struct ggml_tensor * wva; - struct ggml_tensor * wvb; - struct ggml_tensor * woa; - struct ggml_tensor * wob; - - // normalization - struct ggml_tensor * ffn_norm; - - // ff - struct ggml_tensor * w1; - struct ggml_tensor * w2; - struct ggml_tensor * w3; -}; - - -struct llama_kv_cache { - struct ggml_context * ctx = NULL; - - struct ggml_tensor * k; - struct ggml_tensor * v; - - // llama_ctx_buffer buf; - - int n; // number of tokens currently in the cache -}; - -struct llama_model { - struct ggml_context * ctx = NULL; - - llama_hparams hparams; - - struct ggml_tensor * tok_embeddings; - - struct ggml_tensor * norm; - struct ggml_tensor * output; - - std::vector layers; -}; - -struct llama_model_lora { - struct ggml_context * ctx = NULL; - - llama_hparams_lora hparams; - - struct ggml_tensor * tok_embeddings; - - struct ggml_tensor * norm; - struct ggml_tensor * outputa; - struct ggml_tensor * outputb; - - std::vector layers; -}; - -static void init_model(struct llama_model * model) { - const auto & hparams = model->hparams; - - const uint32_t n_embd = hparams.n_embd; - const uint32_t n_layer = hparams.n_layer; - const uint32_t n_vocab = hparams.n_vocab; - - const uint32_t n_ff = get_n_ff(&hparams); - - struct ggml_context * ctx = model->ctx; - - model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); // ("tok_embeddings.weight", {n_embd, n_vocab}); - model->norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // ("norm.weight", {n_embd}); - model->output = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); // ("output.weight", {n_embd, n_vocab}); - - model->layers.resize(n_layer); - for (uint32_t i = 0; i < n_layer; ++i) { - auto & layer = model->layers[i]; - - // std::string layers_i = "layers." + std::to_string(i); - - layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // (layers_i + ".attention_norm.weight", {n_embd}); - - layer.wq = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); // (layers_i + ".attention.wq.weight", {n_embd, n_embd}); - layer.wk = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); // (layers_i + ".attention.wk.weight", {n_embd, n_embd}); - layer.wv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); // (layers_i + ".attention.wv.weight", {n_embd, n_embd}); - layer.wo = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); // (layers_i + ".attention.wo.weight", {n_embd, n_embd}); - - layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // (layers_i + ".ffn_norm.weight", {n_embd}); - - layer.w1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); // (layers_i + ".feed_forward.w1.weight", {n_embd, n_ff}); - layer.w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd); // (layers_i + ".feed_forward.w2.weight", { n_ff, n_embd}); - layer.w3 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); // (layers_i + ".feed_forward.w3.weight", {n_embd, n_ff}); - } -} - - -static void init_model_lora(struct llama_model_lora * model) { - const auto & hparams = model->hparams; - - const uint32_t n_embd = hparams.n_embd; - const uint32_t n_mult = hparams.n_mult; - const uint32_t n_layer = hparams.n_layer; - const uint32_t n_vocab = hparams.n_vocab; - const uint32_t n_lora = hparams.n_lora; - - const uint32_t n_ff = ((2*(4*n_embd)/3 + n_mult - 1)/n_mult)*n_mult; - - struct ggml_context * ctx = model->ctx; - - model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); // ("tok_embeddings.weight", {n_embd, n_vocab}); - model->norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // ("norm.weight", {n_embd}); - model->outputa = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_vocab); // ("output.weight", {n_embd, n_vocab}); - model->outputb = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // ("output.weight", {n_embd, n_vocab}); - - model->layers.resize(n_layer); - for (uint32_t i = 0; i < n_layer; ++i) { - auto & layer = model->layers[i]; - - // std::string layers_i = "layers." + std::to_string(i); - - layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // (layers_i + ".attention_norm.weight", {n_embd}); - - layer.wqa = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_embd); // (layers_i + ".attention.wq.weight", {n_embd, n_embd}); - layer.wqb = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // (layers_i + ".attention.wq.weight", {n_embd, n_embd}); - layer.wka = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_embd); // (layers_i + ".attention.wk.weight", {n_embd, n_embd}); - layer.wkb = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // (layers_i + ".attention.wk.weight", {n_embd, n_embd}); - layer.wva = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_embd); // (layers_i + ".attention.wv.weight", {n_embd, n_embd}); - layer.wvb = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // (layers_i + ".attention.wv.weight", {n_embd, n_embd}); - layer.woa = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_embd); // (layers_i + ".attention.wo.weight", {n_embd, n_embd}); - layer.wob = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // (layers_i + ".attention.wo.weight", {n_embd, n_embd}); - - layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // (layers_i + ".ffn_norm.weight", {n_embd}); - - layer.w1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); // (layers_i + ".feed_forward.w1.weight", {n_embd, n_ff}); - layer.w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd); // (layers_i + ".feed_forward.w2.weight", { n_ff, n_embd}); - layer.w3 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); // (layers_i + ".feed_forward.w3.weight", {n_embd, n_ff}); - } -} - -static void set_param_model(struct llama_model * model) { - const auto& hparams = model->hparams; - - const uint32_t n_layer = hparams.n_layer; - - struct ggml_context* ctx = model->ctx; - - ggml_set_param(ctx, model->tok_embeddings); - ggml_set_param(ctx, model->norm); - ggml_set_param(ctx, model->output); - - for (uint32_t i = 0; i < n_layer; ++i) { - auto & layer = model->layers[i]; - - ggml_set_param(ctx, layer.attention_norm); - ggml_set_param(ctx, layer.wq); - ggml_set_param(ctx, layer.wk); - ggml_set_param(ctx, layer.wv); - ggml_set_param(ctx, layer.wo); - ggml_set_param(ctx, layer.ffn_norm); - ggml_set_param(ctx, layer.w1); - ggml_set_param(ctx, layer.w2); - ggml_set_param(ctx, layer.w3); - } -} - -static void set_param_model_lora(struct llama_model_lora * model) { - const auto& hparams = model->hparams; - - const uint32_t n_layer = hparams.n_layer; - - struct ggml_context* ctx = model->ctx; - - ggml_set_param(ctx, model->tok_embeddings); - ggml_set_param(ctx, model->norm); - ggml_set_param(ctx, model->outputa); - ggml_set_param(ctx, model->outputb); - - for (uint32_t i = 0; i < n_layer; ++i) { - auto & layer = model->layers[i]; - - ggml_set_param(ctx, layer.attention_norm); - ggml_set_param(ctx, layer.wqa); - ggml_set_param(ctx, layer.wqb); - ggml_set_param(ctx, layer.wka); - ggml_set_param(ctx, layer.wkb); - ggml_set_param(ctx, layer.wva); - ggml_set_param(ctx, layer.wvb); - ggml_set_param(ctx, layer.woa); - ggml_set_param(ctx, layer.wob); - ggml_set_param(ctx, layer.ffn_norm); - ggml_set_param(ctx, layer.w1); - ggml_set_param(ctx, layer.w2); - ggml_set_param(ctx, layer.w3); - } -} - -static void randomize_model(struct llama_model * model, int seed, float mean, float std, float min, float max) { - const auto & hparams = model->hparams; - - const uint32_t n_layer = hparams.n_layer; - - struct random_normal_distribution * rnd = init_random_normal_distribution(seed, mean, std, min, max); - - randomize_tensor_normal(model->tok_embeddings , rnd); - randomize_tensor_normal(model->norm , rnd); - randomize_tensor_normal(model->output , rnd); - - for (uint32_t i = 0; i < n_layer; ++i) { - auto & layer = model->layers[i]; - randomize_tensor_normal(layer.attention_norm, rnd); - - randomize_tensor_normal(layer.wq, rnd); - randomize_tensor_normal(layer.wk, rnd); - randomize_tensor_normal(layer.wv, rnd); - randomize_tensor_normal(layer.wo, rnd); - - randomize_tensor_normal(layer.ffn_norm, rnd); - - randomize_tensor_normal(layer.w1, rnd); - randomize_tensor_normal(layer.w2, rnd); - randomize_tensor_normal(layer.w3, rnd); - } - - free_random_normal_distribution(rnd); -} - - -static void randomize_model_lora( - struct llama_model_lora * model, int seed, float mean, float std, float min, float max -) { - const auto & hparams = model->hparams; - - const uint32_t n_layer = hparams.n_layer; - - struct random_normal_distribution * rnd = init_random_normal_distribution(seed, mean, std, min, max); - - randomize_tensor_normal(model->tok_embeddings, rnd); - randomize_tensor_normal(model->norm , rnd); - randomize_tensor_normal(model->outputa , rnd); - randomize_tensor_normal(model->outputb , rnd); - - for (uint32_t i = 0; i < n_layer; ++i) { - auto & layer = model->layers[i]; - randomize_tensor_normal(layer.attention_norm, rnd); - - randomize_tensor_normal(layer.wqa, rnd); - randomize_tensor_normal(layer.wqb, rnd); - randomize_tensor_normal(layer.wka, rnd); - randomize_tensor_normal(layer.wkb, rnd); - randomize_tensor_normal(layer.wva, rnd); - randomize_tensor_normal(layer.wvb, rnd); - randomize_tensor_normal(layer.woa, rnd); - randomize_tensor_normal(layer.wob, rnd); - - randomize_tensor_normal(layer.ffn_norm, rnd); - - randomize_tensor_normal(layer.w1, rnd); - randomize_tensor_normal(layer.w2, rnd); - randomize_tensor_normal(layer.w3, rnd); - } - - free_random_normal_distribution(rnd); -} - -static void init_kv_cache(struct llama_kv_cache* cache, struct llama_model * model, int n_batch) { - const auto & hparams = model->hparams; - - const uint32_t n_ctx = hparams.n_ctx; - const uint32_t n_embd = hparams.n_embd; - const uint32_t n_layer = hparams.n_layer; - - const int64_t n_mem = n_layer*n_ctx*n_batch; - const int64_t n_elements = n_embd*n_mem; - - // cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2u*MB); - - // struct ggml_init_params params; - // params.mem_size = cache.buf.size; - // params.mem_buffer = cache.buf.addr; - // params.no_alloc = false; - if (!cache->ctx) { - struct ggml_init_params params; - params.mem_size = 2u*n_elements*ggml_type_size(GGML_TYPE_F32) + 2u*1024*1024; - params.mem_buffer = NULL; - params.no_alloc = false; - - cache->ctx = ggml_init(params); - - if (!cache->ctx) { - fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__); - exit(1); - } - } - - cache->k = ggml_new_tensor_1d(cache->ctx, GGML_TYPE_F32, n_elements); - cache->v = ggml_new_tensor_1d(cache->ctx, GGML_TYPE_F32, n_elements); -} - -static bool init_kv_cache_lora(struct llama_kv_cache* cache, struct llama_model_lora * model, int n_batch) { - const auto & hparams = model->hparams; - - const uint32_t n_ctx = hparams.n_ctx; - const uint32_t n_embd = hparams.n_embd; - const uint32_t n_layer = hparams.n_layer; - - const int64_t n_mem = n_layer*n_ctx*n_batch; - const int64_t n_elements = n_embd*n_mem; - - // cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2u*MB); - - // struct ggml_init_params params; - // params.mem_size = cache.buf.size; - // params.mem_buffer = cache.buf.addr; - // params.no_alloc = false; - if (!cache->ctx) { - struct ggml_init_params params; - params.mem_size = 2u*n_elements*ggml_type_size(GGML_TYPE_F32) + 2u*1024*1024; - params.mem_buffer = NULL; - params.no_alloc = false; - - cache->ctx = ggml_init(params); - - if (!cache->ctx) { - fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__); - return false; - } - } - - cache->k = ggml_new_tensor_1d(cache->ctx, GGML_TYPE_F32, n_elements); - cache->v = ggml_new_tensor_1d(cache->ctx, GGML_TYPE_F32, n_elements); - - return true; -} - -static struct ggml_tensor * forward( - struct llama_model * model, - struct llama_kv_cache * cache, - struct ggml_context * ctx0, - struct ggml_cgraph * gf, - struct ggml_tensor * tokens_input, - const int n_tokens, - const int n_past -) { - const int N = n_tokens; - - struct llama_kv_cache& kv_self = *cache; - const auto & hparams = model->hparams; - const int n_ctx = hparams.n_ctx; - const int n_embd = hparams.n_embd; - const int n_layer = hparams.n_layer; - const int n_head = hparams.n_head; - const int n_rot = hparams.n_rot; - - struct ggml_tensor * tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); - memcpy(tokens->data, tokens_input->data, N*ggml_element_size(tokens)); - - struct ggml_tensor * kc = kv_self.k; - struct ggml_tensor * vc = kv_self.v; - - struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); - { - int * data = (int *) KQ_pos->data; - for (int i = 0; i < N; ++i) { - data[i] = n_past + i; - } - } - - // inpL shape [n_embd,N,1,1] - struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens); - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * inpSA = inpL; - - struct ggml_tensor * cur; - - // lctx.use_buf(ctx0, 0); - - // norm - { - // cur shape [n_embd,N,1,1] - cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps); - - // cur = attention_norm*cur - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model->layers[il].attention_norm, cur), - cur); - } - - // self-attention - { - // compute Q and K and RoPE them - // wq shape [n_embd, n_embd, 1, 1] - // wk shape [n_embd, n_embd, 1, 1] - // Qcur shape [n_embd/n_head, n_head, N, 1] - // Kcur shape [n_embd/n_head, n_head, N, 1] - struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0); - struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0); - - // store key and value to memory - { - // compute the transposed [N, n_embd] V matrix - // wv shape [n_embd, n_embd, 1, 1] - // Vcur shape [n_embd, N, 1, 1] - struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wv, cur), n_embd, N))); - - // kv_self.k shape [n_embd * n_ctx * n_layer, 1] - // kv_self.v shape [n_embd * n_ctx * n_layer, 1] - // k shape [n_embd * N, 1] == kv_self.k[:,n_past:n_past+N,il,0] - // v shape [N, n_embd, 1, 1] == kv_self.v[:,n_past:n_past+N,il,0] - - /* { - struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past)); - struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd, - ( n_ctx)*ggml_element_size(kv_self.v), - (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v)); - - // important: storing RoPE-ed version of K in the KV cache! - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k)); - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v)); - } //*/ - - kc = ggml_set_1d(ctx0, kc, ggml_reshape_1d(ctx0, Kcur, n_embd*N), (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past)); - vc = ggml_set_2d(ctx0, vc, Vcur, ( n_ctx)*ggml_element_size(kv_self.v), - (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v)); - } - - // Qcur shape [n_embd/n_head, n_head, N, 1] - // Q shape [n_embd/n_head, N, n_head, 1] - struct ggml_tensor * Q = - ggml_permute(ctx0, - Qcur, - 0, 2, 1, 3); - - // kv_self.k shape [n_embd * n_ctx * n_layer, 1] - // K shape [n_embd/n_head, n_past + N, n_head, 1] - struct ggml_tensor * K = - ggml_permute(ctx0, - ggml_reshape_3d(ctx0, - ggml_view_1d(ctx0, kc, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kc)*n_embd), - n_embd/n_head, n_head, n_past + N), - 0, 2, 1, 3); - - // K * Q - // KQ shape [n_past + N, N, n_head, 1] - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - - // KQ_scaled = KQ / sqrt(n_embd/n_head) - // KQ_scaled shape [n_past + N, N, n_head, 1] - struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, 1.0f/sqrtf(float(n_embd)/n_head)); - - // KQ_masked = mask_past(KQ_scaled) - // KQ_masked shape [n_past + N, N, n_head, 1] - struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past); - - // KQ = soft_max(KQ_masked) - // KQ_soft_max shape [n_past + N, N, n_head, 1] - struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked); - - // split cached V into n_head heads - //// V shape [n_past + N, n_embd/n_head, n_head, 1] - // V shape [n_past + N, n_embd/n_head, n_head, 1] == kv_self.v[:,:(n_past+N),il,1] - struct ggml_tensor * V = - ggml_view_3d(ctx0, vc, - n_past + N, n_embd/n_head, n_head, - n_ctx*ggml_element_size(vc), - n_ctx*ggml_element_size(vc)*n_embd/n_head, - il*n_ctx*ggml_element_size(vc)*n_embd); - - // KQV shape [n_embd/n_head, N, n_head, 1] - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max); - - // KQV_merged = KQV.permute(0, 2, 1, 3) - // KQV_merged shape [n_embd/n_head, n_head, N, 1] - struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - // KQV_merged shape - - // cur = KQV_merged.contiguous().view(n_embd, N) - // cur shape [n_embd,N,1,1] - cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV_merged), n_embd, N); - // cur = ggml_cpy(ctx0, - // KQV_merged, - // ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N)); - - // projection (no bias) - // cur shape [n_embd,N,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].wo, - cur); - } - - // lctx.use_buf(ctx0, 1); - - // inpFF shape [n_embd,N,1,1] - struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA); - - // feed-forward network - { - // norm - { - // cur shape [n_embd,N,1,1] - cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps); - - // cur = ffn_norm*cur - // cur shape [n_embd,N,1,1] - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model->layers[il].ffn_norm, cur), - cur); - } - - // tmp shape [n_ff,N,1,1] - struct ggml_tensor * tmp = ggml_mul_mat(ctx0, - model->layers[il].w3, - cur); - - // cur shape [n_ff,N,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].w1, - cur); - - // SILU activation - // cur shape [n_ff,N,1,1] - cur = ggml_silu(ctx0, cur); - - // cur shape [n_ff,N,1,1] - cur = ggml_mul(ctx0, cur, tmp); - - // cur shape [n_embd,N,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].w2, - cur); - } - - // cur shape [n_embd,N,1,1] - cur = ggml_add(ctx0, cur, inpFF); - - // input for next layer - // inpL shape [n_embd,N,1,1] - inpL = cur; - } - - // norm - { - - // inpL shape [n_embd,N,1,1] - inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps); - - // inpL = norm*inpL - // inpL shape [n_embd,N,1,1] - inpL = ggml_mul(ctx0, - ggml_repeat(ctx0, model->norm, inpL), - inpL); - - //embeddings = inpL; - } - - // lm_head - // inpL shape [n_vocab,N,1,1] - inpL = ggml_mul_mat(ctx0, model->output, inpL); - - // run the computation - ggml_build_forward_expand(gf, inpL); - - return inpL; -} - -static struct ggml_tensor * forward_batch( - struct llama_model * model, - struct llama_kv_cache * cache, - struct ggml_context * ctx0, - struct ggml_cgraph * gf, - struct ggml_tensor * tokens_input, - const int n_tokens, - const int n_past, - const int n_batch -) { - const int N = n_tokens; - - struct llama_kv_cache& kv_self = *cache; - const auto & hparams = model->hparams; - const int n_ctx = hparams.n_ctx; - const int n_vocab = hparams.n_vocab; - const int n_embd = hparams.n_embd; - const int n_layer = hparams.n_layer; - const int n_head = hparams.n_head; - const int n_rot = hparams.n_rot; - const int n_ff = get_n_ff(&hparams); - - struct ggml_tensor * tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N*n_batch); - memcpy(tokens->data, tokens_input->data, ggml_element_size(tokens)*N*n_batch); - - struct ggml_tensor * kc = kv_self.k; - struct ggml_tensor * vc = kv_self.v; - - struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); - { - int * data = (int *) KQ_pos->data; - for (int i = 0; i < N; ++i) { - data[i] = n_past + i; - } - } - - // inpL shape [n_embd,N*n_batch,1] - struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens); - assert_shape_2d(inpL, n_embd, N*n_batch); - - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * inpSA = inpL; - - struct ggml_tensor * cur; - - // lctx.use_buf(ctx0, 0); - - // norm - { - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps); - assert_shape_2d(cur, n_embd, N*n_batch); - - // cur = attention_norm*cur - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model->layers[il].attention_norm, cur), - cur); - assert_shape_2d(cur, n_embd, N*n_batch); - } - - // self-attention - { - // compute Q and K and RoPE them - // wq shape [n_embd, n_embd, 1, 1] - // wk shape [n_embd, n_embd, 1, 1] - // Qcur shape [n_embd/n_head, n_head, N, n_batch] - // Kcur shape [n_embd/n_head, n_head, N, n_batch] - struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), KQ_pos, n_rot, 0); - struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), KQ_pos, n_rot, 0); - assert_shape_4d(Qcur, n_embd/n_head, n_head, N, n_batch); - assert_shape_4d(Kcur, n_embd/n_head, n_head, N, n_batch); - - // store key and value to memory - { - // compute the transposed [N, n_embd] V matrix - // wv shape [n_embd, n_embd, 1, 1] - // Vcur shape [N, n_embd, n_batch, 1] - struct ggml_tensor * Vcur = ggml_cont(ctx0, - ggml_permute(ctx0, - ggml_reshape_3d(ctx0, - ggml_mul_mat(ctx0, - model->layers[il].wv, - cur), - n_embd, N, n_batch), - 1, 0, 2, 3)); - - assert_shape_3d(Vcur, N, n_embd, n_batch); - - // kv_self.k shape [n_embd * n_ctx * n_batch * n_layer] - // kv_self.v shape [n_ctx * n_embd * n_batch * n_layer] - // k shape [n_embd * N, n_batch] == kv_self.k[:,n_past:n_past+N,:,il] - // v shape [N, n_embd, n_batch, 1] == kv_self.v[:,n_past:n_past+N,:,il] - - /* { - struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past)); - struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd, - ( n_ctx)*ggml_element_size(kv_self.v), - (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v)); - - // important: storing RoPE-ed version of K in the KV cache! - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k)); - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v)); - } //*/ - - kc = ggml_set_2d(ctx0, kc, - ggml_reshape_2d(ctx0, Kcur, n_embd*N, n_batch), - ggml_element_size(kc)*n_embd*n_ctx, - (ggml_element_size(kc)*n_embd)*(il*n_batch*n_ctx + n_past)); - vc = ggml_set_2d(ctx0, vc, - ggml_reshape_2d(ctx0, Vcur, N*n_embd, n_batch), - ggml_element_size(vc)*n_ctx*n_embd, - ggml_element_size(vc)*(n_past + il*n_embd*n_batch*n_ctx)); - - assert_shape_1d(kc, n_embd * n_ctx * n_batch * n_layer); - assert_shape_1d(vc, n_embd * n_ctx * n_batch * n_layer); - } - - // Qcur shape [n_embd/n_head, n_head, N, n_batch] - // Q shape [n_embd/n_head, N, n_head, n_batch] - struct ggml_tensor * Q = - ggml_permute(ctx0, - Qcur, - 0, 2, 1, 3); - assert_shape_4d(Q, n_embd/n_head, N, n_head, n_batch); - - // kv_self.k shape [n_embd * n_ctx * n_batch * n_layer] - // K shape [n_embd/n_head, n_past + N, n_head, n_batch] - struct ggml_tensor * K = - ggml_permute(ctx0, - ggml_reshape_4d(ctx0, - ggml_view_3d(ctx0, - kc, - n_embd, - (n_past + N), - n_batch, - n_embd*ggml_element_size(kc), - n_ctx*n_embd*ggml_element_size(kc), - il*n_batch*n_ctx*n_embd*ggml_element_size(kc)), - n_embd/n_head, n_head, n_past + N, n_batch), - 0, 2, 1, 3); - assert_shape_4d(K, n_embd/n_head, n_past + N, n_head, n_batch); - - // K * Q - // KQ shape [n_past + N, N, n_head, n_batch] - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - assert_shape_4d(KQ, n_past + N, N, n_head, n_batch); - - // KQ_scaled = KQ / sqrt(n_embd/n_head) - // KQ_scaled shape [n_past + N, N, n_head, n_batch] - struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, 1.0f/sqrtf(float(n_embd)/n_head)); - assert_shape_4d(KQ_scaled, n_past + N, N, n_head, n_batch); - - // KQ_masked = mask_past(KQ_scaled) - // KQ_masked shape [n_past + N, N, n_head, n_batch] - struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past); - assert_shape_4d(KQ_masked, n_past + N, N, n_head, n_batch); - - // KQ = soft_max(KQ_masked) - // KQ_soft_max shape [n_past + N, N, n_head, n_batch] - struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked); - assert_shape_4d(KQ_soft_max, n_past + N, N, n_head, n_batch); - - // split cached V into n_head heads - // kv_self.v shape [n_ctx * n_embd * n_batch * n_layer] - // V shape [n_past + N, n_embd/n_head, n_head, n_batch] == kv_self.v[:(n_past+N),:,:,il] - struct ggml_tensor * V = - ggml_view_4d(ctx0, vc, - n_past + N, n_embd/n_head, n_head, n_batch, - ggml_element_size(vc)*n_ctx, - ggml_element_size(vc)*n_ctx*n_embd/n_head, - ggml_element_size(vc)*n_ctx*n_embd, - il*n_batch*n_ctx*n_embd*ggml_element_size(vc)); - assert_shape_4d(V, n_past + N, n_embd/n_head, n_head, n_batch); - - // KQV shape [n_embd/n_head, N, n_head, n_batch] - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max); - assert_shape_4d(KQV, n_embd/n_head, N, n_head, n_batch); - - // KQV_merged = KQV.permute(0, 2, 1, 3) - // KQV_merged shape [n_embd/n_head, n_head, N, n_batch] - struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - assert_shape_4d(KQV_merged, n_embd/n_head, n_head, N, n_batch); - // KQV_merged shape - - // cur = KQV_merged.contiguous().view(n_embd, N) - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV_merged), n_embd, N*n_batch); - assert_shape_2d(cur, n_embd, N*n_batch); - // cur = ggml_cpy(ctx0, - // KQV_merged, - // ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N)); - - // projection (no bias) - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].wo, - cur); - assert_shape_2d(cur, n_embd, N*n_batch); - } - - // lctx.use_buf(ctx0, 1); - - // inpFF shape [n_embd,N*n_batch,1,1] - struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA); - assert_shape_2d(inpFF, n_embd, N*n_batch); - - // feed-forward network - { - // norm - { - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps); - assert_shape_2d(cur, n_embd, N*n_batch); - - // cur = ffn_norm*cur - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model->layers[il].ffn_norm, cur), - cur); - assert_shape_2d(cur, n_embd, N*n_batch); - } - - // tmp shape [n_ff,N*n_batch,1,1] - struct ggml_tensor * tmp = ggml_mul_mat(ctx0, - model->layers[il].w3, - cur); - assert_shape_2d(tmp, n_ff, N*n_batch); - - // cur shape [n_ff,N*n_batch,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].w1, - cur); - assert_shape_2d(cur, n_ff, N*n_batch); - - // SILU activation - // cur shape [n_ff,N*n_batch,1,1] - cur = ggml_silu(ctx0, cur); - assert_shape_2d(cur, n_ff, N*n_batch); - - // cur shape [n_ff,N*n_batch,1,1] - cur = ggml_mul(ctx0, cur, tmp); - assert_shape_2d(cur, n_ff, N*n_batch); - - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].w2, - cur); - assert_shape_2d(cur, n_embd, N*n_batch); - } - - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_add(ctx0, cur, inpFF); - assert_shape_2d(cur, n_embd, N*n_batch); - - // input for next layer - // inpL shape [n_embd,N*n_batch,1,1] - inpL = cur; - assert_shape_2d(inpL, n_embd, N*n_batch); - } - - // norm - { - - // inpL shape [n_embd,N*n_batch,1,1] - inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps); - assert_shape_2d(inpL, n_embd, N*n_batch); - - // inpL = norm*inpL - // inpL shape [n_embd,N*n_batch,1,1] - inpL = ggml_mul(ctx0, - ggml_repeat(ctx0, model->norm, inpL), - inpL); - - assert_shape_2d(inpL, n_embd, N*n_batch); - - //embeddings = inpL; - } - - // lm_head - // inpL shape [n_vocab,N*n_batch,1,1] - inpL = ggml_mul_mat(ctx0, model->output, inpL); - assert_shape_2d(inpL, n_vocab, N*n_batch); - - { - // inpL shape [n_vocab,N,n_batch,1] - inpL = ggml_reshape_3d(ctx0, - inpL, - n_vocab, N, n_batch); - assert_shape_3d(inpL, n_vocab, N, n_batch); - } - - // run the computation - ggml_build_forward_expand(gf, inpL); - - return inpL; -} - -static struct ggml_tensor * forward_lora( - struct llama_model_lora * model, - struct llama_kv_cache * cache, - struct ggml_context * ctx0, - struct ggml_cgraph * gf, - struct ggml_tensor * tokens_input, - const int n_tokens, - const int n_past -) { - const int N = n_tokens; - - struct llama_kv_cache& kv_self = *cache; - const auto & hparams = model->hparams; - - const int n_ctx = hparams.n_ctx; - const int n_embd = hparams.n_embd; - const int n_layer = hparams.n_layer; - const int n_head = hparams.n_head; - const int n_rot = hparams.n_rot; - - struct ggml_tensor * tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); - memcpy(tokens->data, tokens_input->data, N*ggml_element_size(tokens)); - - struct ggml_tensor * kc = kv_self.k; - struct ggml_tensor * vc = kv_self.v; - - struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); - { - int * data = (int *) KQ_pos->data; - for (int i = 0; i < N; ++i) { - data[i] = n_past + i; - } - } - - // inpL shape [n_embd,N,1,1] - struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens); - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * inpSA = inpL; - - struct ggml_tensor * cur; - - // norm - { - // cur shape [n_embd,N,1,1] - cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps); - - // cur = attention_norm*cur - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model->layers[il].attention_norm, cur), - cur); - } - - // self-attention - { - // compute Q and K and RoPE them - // wq shape [n_embd, n_embd, 1, 1] - // wk shape [n_embd, n_embd, 1, 1] - // Qcur shape [n_embd/n_head, n_head, N, 1] - // Kcur shape [n_embd/n_head, n_head, N, 1] - struct ggml_tensor * Qcur = ggml_rope(ctx0, - ggml_reshape_3d(ctx0, - ggml_mul_mat(ctx0, - model->layers[il].wqa, - ggml_mul_mat(ctx0, - model->layers[il].wqb, - cur)), - n_embd/n_head, n_head, N), - KQ_pos, n_rot, 0); - struct ggml_tensor * Kcur = ggml_rope(ctx0, - ggml_reshape_3d(ctx0, - ggml_mul_mat(ctx0, - model->layers[il].wka, - ggml_mul_mat(ctx0, - model->layers[il].wkb, - cur)), - n_embd/n_head, n_head, N), - KQ_pos, n_rot, 0); - - // store key and value to memory - { - // compute the transposed [N, n_embd] V matrix - // wv shape [n_embd, n_embd, 1, 1] - // Vcur shape [n_embd, N, 1, 1] - struct ggml_tensor * Vcur = ggml_cont(ctx0, - ggml_transpose(ctx0, - ggml_reshape_2d(ctx0, - ggml_mul_mat(ctx0, - model->layers[il].wva, - ggml_mul_mat(ctx0, - model->layers[il].wvb, - cur)), - n_embd, N))); - - // kv_self.k shape [n_embd * n_ctx * n_layer, 1] - // kv_self.v shape [n_embd * n_ctx * n_layer, 1] - // k shape [n_embd * N, 1] == kv_self.k[:,n_past:n_past+N,il,0] - // v shape [N, n_embd, 1, 1] == kv_self.v[:,n_past:n_past+N,il,0] - - /* { - struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past)); - struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd, - ( n_ctx)*ggml_element_size(kv_self.v), - (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v)); - - // important: storing RoPE-ed version of K in the KV cache! - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k)); - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v)); - } //*/ - - kc = ggml_set_1d(ctx0, kc, ggml_reshape_1d(ctx0, Kcur, n_embd*N), (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past)); - vc = ggml_set_2d(ctx0, vc, Vcur, ( n_ctx)*ggml_element_size(kv_self.v), - (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v)); - } - - // Qcur shape [n_embd/n_head, n_head, N, 1] - // Q shape [n_embd/n_head, N, n_head, 1] - struct ggml_tensor * Q = - ggml_permute(ctx0, - Qcur, - 0, 2, 1, 3); - - // kv_self.k shape [n_embd * n_ctx * n_layer, 1] - // K shape [n_embd/n_head, n_past + N, n_head, 1] - struct ggml_tensor * K = - ggml_permute(ctx0, - ggml_reshape_3d(ctx0, - ggml_view_1d(ctx0, kc, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kc)*n_embd), - n_embd/n_head, n_head, n_past + N), - 0, 2, 1, 3); - - // K * Q - // KQ shape [n_past + N, N, n_head, 1] - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - - // KQ_scaled = KQ / sqrt(n_embd/n_head) - // KQ_scaled shape [n_past + N, N, n_head, 1] - struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, 1.0f/sqrtf(float(n_embd)/n_head)); - - // KQ_masked = mask_past(KQ_scaled) - // KQ_masked shape [n_past + N, N, n_head, 1] - struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past); - - // KQ = soft_max(KQ_masked) - // KQ_soft_max shape [n_past + N, N, n_head, 1] - struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked); - - // split cached V into n_head heads - //// V shape [n_past + N, n_embd/n_head, n_head, 1] - // V shape [n_past + N, n_embd/n_head, n_head, 1] == kv_self.v[:,:(n_past+N),il,1] - struct ggml_tensor * V = - ggml_view_3d(ctx0, vc, - n_past + N, n_embd/n_head, n_head, - n_ctx*ggml_element_size(vc), - n_ctx*ggml_element_size(vc)*n_embd/n_head, - il*n_ctx*ggml_element_size(vc)*n_embd); - - // KQV shape [n_embd/n_head, N, n_head, 1] - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max); - - // KQV_merged = KQV.permute(0, 2, 1, 3) - // KQV_merged shape [n_embd/n_head, n_head, N, 1] - struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - // KQV_merged shape - - // cur = KQV_merged.contiguous().view(n_embd, N) - // cur shape [n_embd,N,1,1] - cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV_merged), n_embd, N); - // cur = ggml_cpy(ctx0, - // KQV_merged, - // ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N)); - - // projection (no bias) - // cur shape [n_embd,N,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].woa, - ggml_mul_mat(ctx0, - model->layers[il].wob, - cur)); - } - - // inpFF shape [n_embd,N,1,1] - struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA); - - // feed-forward network - { - // norm - { - // cur shape [n_embd,N,1,1] - cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps); - - // cur = ffn_norm*cur - // cur shape [n_embd,N,1,1] - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model->layers[il].ffn_norm, cur), - cur); - } - - // tmp shape [n_ff,N,1,1] - struct ggml_tensor * tmp = ggml_mul_mat(ctx0, - model->layers[il].w3, - cur); - - // cur shape [n_ff,N,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].w1, - cur); - - // SILU activation - // cur shape [n_ff,N,1,1] - cur = ggml_silu(ctx0, cur); - - // cur shape [n_ff,N,1,1] - cur = ggml_mul(ctx0, cur, tmp); - - // cur shape [n_embd,N,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].w2, - cur); - } - - // cur shape [n_embd,N,1,1] - cur = ggml_add(ctx0, cur, inpFF); - - // input for next layer - // inpL shape [n_embd,N,1,1] - inpL = cur; - } - - // norm - { - - // inpL shape [n_embd,N,1,1] - inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps); - - // inpL = norm*inpL - // inpL shape [n_embd,N,1,1] - inpL = ggml_mul(ctx0, - ggml_repeat(ctx0, model->norm, inpL), - inpL); - - //embeddings = inpL; - } - - - // lm_head - // inpL shape [n_vocab,N,1,1] - inpL = ggml_mul_mat(ctx0, - model->outputa, - ggml_mul_mat(ctx0, - model->outputb, - inpL)); - - // ggml_set_scratch(ctx0, { 0, 0, nullptr, }); - // run the computation - ggml_build_forward_expand(gf, inpL); - - return inpL; -} - -static void sample_softmax(struct ggml_tensor * logits, struct ggml_tensor * probs, struct ggml_tensor * best_samples) { - assert(ggml_is_matrix(logits)); - assert(ggml_is_matrix(probs)); - assert(ggml_is_vector(best_samples)); - assert(logits->ne[1] == best_samples->ne[0]); - assert(logits->ne[0] == probs->ne[0]); - assert(logits->ne[1] == probs->ne[1]); - for (int i = 0; i < logits->ne[1]; ++i) { - float max_logit = ggml_get_f32_1d(logits, i * logits->ne[0]); - ggml_set_i32_1d(best_samples, i, 0); - for (int k = 0; k < logits->ne[0]; ++k) { - float logit = ggml_get_f32_1d(logits, i * logits->ne[0] + k); - if (logit > max_logit) { - max_logit = logit; - ggml_set_i32_1d(best_samples, i, k); - } - } - float psum = 0; - for (int k = 0; k < logits->ne[0]; ++k) { - float logit = ggml_get_f32_1d(logits, i * logits->ne[0] + k); - float p = (logit == -INFINITY) ? 0 : expf(logit - max_logit); - psum += p; - ggml_set_f32_1d(probs, i * probs->ne[0] + k, p); - } - for (int k = 0; k < logits->ne[0]; ++k) { - float p = ggml_get_f32_1d(probs, i*probs->ne[0] + k); - ggml_set_f32_1d(probs, i * probs->ne[0] + k, p / psum); - } - } -} - -static void sample_softmax_batch( - struct ggml_context * ctx, struct ggml_tensor * logits, struct ggml_tensor * probs, - struct ggml_tensor * best_samples -) { - GGML_ASSERT(ggml_is_matrix(best_samples)); - GGML_ASSERT(ggml_is_3d(logits)); - GGML_ASSERT(ggml_is_3d(probs)); - int n_tokens = best_samples->ne[0]; - int n_batch = best_samples->ne[1]; - int n_vocab = logits->ne[0]; - GGML_ASSERT(n_tokens == logits->ne[1]); - GGML_ASSERT(n_batch == logits->ne[2]); - GGML_ASSERT(n_vocab == probs->ne[0]); - GGML_ASSERT(n_tokens == probs->ne[1]); - GGML_ASSERT(n_batch == probs->ne[2]); - - for (int k = 0; k < n_batch; ++k) { - struct ggml_tensor * best_samples_k = ggml_view_1d(ctx, - best_samples, - best_samples->ne[0], - k*best_samples->nb[1]); - struct ggml_tensor * logits_k = ggml_view_2d(ctx, - logits, - logits->ne[0], - logits->ne[1], - logits->nb[1], - k*logits->nb[2]); - struct ggml_tensor * probs_k = ggml_view_2d(ctx, - probs, - probs->ne[0], - probs->ne[1], - probs->nb[1], - k*probs->nb[2]); - sample_softmax(logits_k, probs_k, best_samples_k); - } -} - -static void print_row(struct ggml_tensor * probs, int i) { - for (int k = 0; k < probs->ne[0]; ++k) { - float p = ggml_get_f32_1d(probs, i*probs->ne[0] + k); - printf(" %.2f", p); - } - printf("\n"); -} - -static void print_matrix(struct ggml_tensor * probs) { - assert(ggml_is_matrix(probs)); - for (int i = 0; i < probs->ne[1]; ++i) { - for (int k = 0; k < probs->ne[0]; ++k) { - float p = ggml_get_f32_1d(probs, i*probs->ne[0] + k); - printf(" %.2f", p); - } - printf("\n"); - } -} - -static void print_token(int token, int n_vocab) { - for (int k = 0; k < token; ++k) { - printf(" "); - } - printf("X"); - for (int k = token+1; k < n_vocab; ++k) { - printf(" "); - } - printf("\n"); -} - -static void print_tokens(struct ggml_tensor * tokens, int n_vocab) { - for (int i=0; ine[0]; ++i) { - int token = ggml_get_i32_1d(tokens, i); - print_token(token, n_vocab); - } -} - -static void get_example_targets(int example_id, struct ggml_tensor * tokens_input, struct ggml_tensor * targets) { - int n_tokens = tokens_input->ne[0]; - int n_vocab = targets->ne[0]; - float randomness = 0.0f; - // ggml_set_zero(targets); - ggml_set_f32(targets, -1.0f); - ggml_set_i32_1d(tokens_input, 0, 0); - for (int i=1; i 1.0f) ? 1.0f : z; // clamp to [0..1] - int token = std::max(1,std::min(1+(int)(z*(float)(n_vocab-1)), n_vocab-1)); - ggml_set_f32_1d(targets, (i-1)*n_vocab + token, +1.0f); - if (ine[0]; - int n_batch = tokens_input->ne[1]; - GGML_ASSERT(n_tokens == targets->ne[1]); - GGML_ASSERT(n_batch == targets->ne[2]); - - for (int k=0; kne[0], - k*tokens_input->nb[1]); - struct ggml_tensor * targets_k = ggml_view_2d(ctx, - targets, - targets->ne[0], - targets->ne[1], - targets->nb[1], - k*targets->nb[2]); - get_example_targets(example_id*n_batch + k, tokens_input_k, targets_k); - } -} - -static void lshift_examples(struct ggml_tensor * tokens_input, struct ggml_tensor * targets, int n_shift) { - int n_tokens = tokens_input->ne[0]; - int n_vocab = targets->ne[0]; - for (int i=0; i work_buffer; - - for (int ex=0; ex "" [extra-main-args] -# - -if [ $# -lt 2 ]; then - echo "Usage: ./base-translate.sh \"\" [extra-main-args]" - exit 1 -fi - -eargs="" -if [ $# -gt 2 ]; then - eargs="${@:3}" -fi - -ftmp="__llama.cpp_example_tmp__.txt" -trap "rm -f $ftmp" EXIT - -echo "Translate from English to French: - -=== - -sea otter, peppermint, plush girafe: - -sea otter => loutre de mer -peppermint => menthe poivrée -plush girafe => girafe peluche - -=== - -violin - -violin => violon - -=== - -phone, computer, mouse, keyboard: - -phone => téléphone -computer => ordinateur -mouse => souris -keyboard => clavier - -=== -" > $ftmp - -echo "$2 -" >> $ftmp - -model=$1 - -# generate the most likely continuation until the string "===" is found -./llama-cli -m $model -f $ftmp -n 64 --temp 0 --repeat-penalty 1.0 --no-penalize-nl -r "===" $eargs diff --git a/examples/batched-bench/CMakeLists.txt b/examples/batched-bench/CMakeLists.txt index 959acaeee..68ad707f3 100644 --- a/examples/batched-bench/CMakeLists.txt +++ b/examples/batched-bench/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-batched-bench) add_executable(${TARGET} batched-bench.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/batched/CMakeLists.txt b/examples/batched/CMakeLists.txt index 77e33343b..0d439f498 100644 --- a/examples/batched/CMakeLists.txt +++ b/examples/batched/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-batched) add_executable(${TARGET} batched.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp index 3b554033e..ba219cd4b 100644 --- a/examples/batched/batched.cpp +++ b/examples/batched/batched.cpp @@ -68,10 +68,10 @@ int main(int argc, char ** argv) { llama_sampler * smpl = llama_sampler_chain_init(sparams); - llama_sampler_chain_add(smpl, llama_sampler_init_top_k(params.sparams.top_k)); - llama_sampler_chain_add(smpl, llama_sampler_init_top_p(params.sparams.top_p, params.sparams.min_keep)); - llama_sampler_chain_add(smpl, llama_sampler_init_temp (params.sparams.temp)); - llama_sampler_chain_add(smpl, llama_sampler_init_dist (params.sparams.seed)); + llama_sampler_chain_add(smpl, llama_sampler_init_top_k(params.sampling.top_k)); + llama_sampler_chain_add(smpl, llama_sampler_init_top_p(params.sampling.top_p, params.sampling.min_keep)); + llama_sampler_chain_add(smpl, llama_sampler_init_temp (params.sampling.temp)); + llama_sampler_chain_add(smpl, llama_sampler_init_dist (params.sampling.seed)); if (ctx == NULL) { LOG_ERR("%s: error: failed to create the llama_context\n" , __func__); diff --git a/examples/chat-persistent.sh b/examples/chat-persistent.sh index d9cab9836..9d761ebb8 100755 --- a/examples/chat-persistent.sh +++ b/examples/chat-persistent.sh @@ -23,8 +23,9 @@ CUR_PROMPT_CACHE="${CHAT_SAVE_DIR}/current-cache.bin" NEXT_PROMPT_FILE="${CHAT_SAVE_DIR}/next-prompt.txt" NEXT_PROMPT_CACHE="${CHAT_SAVE_DIR}/next-cache.bin" -SESSION_SIZE_MSG_PATTERN='main: session file matches [[:digit:]]+ / [[:digit:]]+' -SAMPLE_TIME_MSG_PATTERN='sample time =[[:space:]]+[[:digit:]]+.[[:digit:]]+ ms /[[:space:]]+[[:digit:]]+' +SESSION_AND_SAMPLE_PATTERN='main: session file matches [[:digit:]]+ / [[:digit:]]+'\ +'|'\ +'sampling time =[[:space:]]+[[:digit:]]+.[[:digit:]]+ ms /[[:space:]]+[[:digit:]]+' SED_DELETE_MESSAGES="/^(${USER_NAME}:|${AI_NAME}:|\\.\\.\\.)/,\$d" CTX_SIZE=2048 @@ -129,15 +130,12 @@ while read -e line; do printf ' ' - # HACK get num tokens from debug message - # TODO get both messages in one go - if ! session_size_msg="$(tail -n30 "$LOG" | grep -oE "$SESSION_SIZE_MSG_PATTERN")" || - ! sample_time_msg="$(tail -n10 "$LOG" | grep -oE "$SAMPLE_TIME_MSG_PATTERN")"; then + if ! session_and_sample_msg=$(tail -n30 "$LOG" | grep -oE "$SESSION_AND_SAMPLE_PATTERN"); then echo >&2 "Couldn't get number of tokens from ./llama-cli output!" exit 1 fi - n_tokens=$(($(cut -d/ -f2 <<<"$session_size_msg") + $(cut -d/ -f2 <<<"$sample_time_msg"))) + n_tokens=$(awk '{sum+=$1} END {print sum}' <<< "$(cut -d/ -f2 <<< "$session_and_sample_msg")") if ((n_tokens > CTX_ROTATE_POINT)); then tail -c+$((n_prompt_len_pre + 1)) "$CUR_PROMPT_FILE" >>"$NEXT_PROMPT_FILE" diff --git a/examples/convert-llama2c-to-ggml/CMakeLists.txt b/examples/convert-llama2c-to-ggml/CMakeLists.txt index a6790e617..44e5f722a 100644 --- a/examples/convert-llama2c-to-ggml/CMakeLists.txt +++ b/examples/convert-llama2c-to-ggml/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-convert-llama2c-to-ggml) add_executable(${TARGET} convert-llama2c-to-ggml.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/convert-llama2c-to-ggml/README.md b/examples/convert-llama2c-to-ggml/README.md index 5774ac83c..46a42da69 100644 --- a/examples/convert-llama2c-to-ggml/README.md +++ b/examples/convert-llama2c-to-ggml/README.md @@ -2,11 +2,8 @@ This example reads weights from project [llama2.c](https://github.com/karpathy/llama2.c) and saves them in ggml compatible format. The vocab that is available in `models/ggml-vocab.bin` is used by default. -To convert the model first download the models from the [llama2.c](https://github.com/karpathy/llama2.c) repository: +To convert the model first download the models from the [llama2.c](https://github.com/karpathy/llama2.c) repository. -`$ make -j` - -After successful compilation, following usage options are available: ``` usage: ./llama-convert-llama2c-to-ggml [options] diff --git a/examples/convert_legacy_llama.py b/examples/convert_legacy_llama.py index 9ab9ab06e..c4ec5c524 100755 --- a/examples/convert_legacy_llama.py +++ b/examples/convert_legacy_llama.py @@ -840,6 +840,8 @@ class OutputFile: self.gguf.add_base_model_version(key, base_model_entry["version"]) if "organization" in base_model_entry: self.gguf.add_base_model_organization(key, base_model_entry["organization"]) + if "description" in base_model_entry: + self.gguf.add_base_model_description(key, base_model_entry["description"]) if "url" in base_model_entry: self.gguf.add_base_model_url(key, base_model_entry["url"]) if "doi" in base_model_entry: @@ -849,12 +851,32 @@ class OutputFile: if "repo_url" in base_model_entry: self.gguf.add_base_model_repo_url(key, base_model_entry["repo_url"]) + if metadata.datasets is not None: + self.gguf.add_dataset_count(len(metadata.datasets)) + for key, dataset_entry in enumerate(metadata.datasets): + if "name" in dataset_entry: + self.gguf.add_dataset_name(key, dataset_entry["name"]) + if "author" in dataset_entry: + self.gguf.add_dataset_author(key, dataset_entry["author"]) + if "version" in dataset_entry: + self.gguf.add_dataset_version(key, dataset_entry["version"]) + if "organization" in dataset_entry: + self.gguf.add_dataset_organization(key, dataset_entry["organization"]) + if "description" in dataset_entry: + self.gguf.add_dataset_description(key, dataset_entry["description"]) + if "url" in dataset_entry: + self.gguf.add_dataset_url(key, dataset_entry["url"]) + if "doi" in dataset_entry: + self.gguf.add_dataset_doi(key, dataset_entry["doi"]) + if "uuid" in dataset_entry: + self.gguf.add_dataset_uuid(key, dataset_entry["uuid"]) + if "repo_url" in dataset_entry: + self.gguf.add_dataset_repo_url(key, dataset_entry["repo_url"]) + if metadata.tags is not None: self.gguf.add_tags(metadata.tags) if metadata.languages is not None: self.gguf.add_languages(metadata.languages) - if metadata.datasets is not None: - self.gguf.add_datasets(metadata.datasets) def add_meta_arch(self, params: Params) -> None: # Metadata About The Neural Architecture Itself diff --git a/examples/cvector-generator/CMakeLists.txt b/examples/cvector-generator/CMakeLists.txt index 0a559d60c..49ad9561c 100644 --- a/examples/cvector-generator/CMakeLists.txt +++ b/examples/cvector-generator/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-cvector-generator) add_executable(${TARGET} cvector-generator.cpp pca.hpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/deprecation-warning/deprecation-warning.cpp b/examples/deprecation-warning/deprecation-warning.cpp index 11b35d2c2..c2958ea12 100644 --- a/examples/deprecation-warning/deprecation-warning.cpp +++ b/examples/deprecation-warning/deprecation-warning.cpp @@ -12,7 +12,7 @@ int main(int argc, char** argv) { } // Get only the program name from the full path - auto pos = filename.find_last_of('/'); + auto pos = filename.find_last_of("/\\"); if (pos != std::string::npos) { filename = filename.substr(pos+1); } diff --git a/examples/embedding/CMakeLists.txt b/examples/embedding/CMakeLists.txt index 8256e789a..809040307 100644 --- a/examples/embedding/CMakeLists.txt +++ b/examples/embedding/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-embedding) add_executable(${TARGET} embedding.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/eval-callback/CMakeLists.txt b/examples/eval-callback/CMakeLists.txt index a48753d38..95915ed91 100644 --- a/examples/eval-callback/CMakeLists.txt +++ b/examples/eval-callback/CMakeLists.txt @@ -2,8 +2,9 @@ set(TARGET llama-eval-callback) add_executable(${TARGET} eval-callback.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) set(TEST_TARGET test-eval-callback) -add_test(NAME ${TEST_TARGET} COMMAND llama-eval-callback --hf-repo ggml-org/models --hf-file tinyllamas/stories260K.gguf --model stories260K.gguf --prompt hello --seed 42 -ngl 0) +add_test(NAME ${TEST_TARGET} + COMMAND llama-eval-callback --hf-repo ggml-org/models --hf-file tinyllamas/stories260K.gguf --model stories260K.gguf --prompt hello --seed 42 -ngl 0) set_property(TEST ${TEST_TARGET} PROPERTY LABELS eval-callback curl) diff --git a/examples/export-lora/CMakeLists.txt b/examples/export-lora/CMakeLists.txt index 1cef6e716..310455787 100644 --- a/examples/export-lora/CMakeLists.txt +++ b/examples/export-lora/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-export-lora) add_executable(${TARGET} export-lora.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/gbnf-validator/CMakeLists.txt b/examples/gbnf-validator/CMakeLists.txt index 4edd6ec73..d2cb524c0 100644 --- a/examples/gbnf-validator/CMakeLists.txt +++ b/examples/gbnf-validator/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-gbnf-validator) add_executable(${TARGET} gbnf-validator.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/gen-docs/CMakeLists.txt b/examples/gen-docs/CMakeLists.txt index c94cda776..25de0af35 100644 --- a/examples/gen-docs/CMakeLists.txt +++ b/examples/gen-docs/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-gen-docs) add_executable(${TARGET} gen-docs.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/gguf-hash/CMakeLists.txt b/examples/gguf-hash/CMakeLists.txt index 633f45535..15c5c68c6 100644 --- a/examples/gguf-hash/CMakeLists.txt +++ b/examples/gguf-hash/CMakeLists.txt @@ -4,12 +4,19 @@ install(TARGETS ${TARGET} RUNTIME) # clibs dependencies include_directories(deps/) + add_library(xxhash OBJECT deps/xxhash/xxhash.c deps/xxhash/xxhash.h) target_link_libraries(${TARGET} PRIVATE xxhash) + add_library(sha1 OBJECT deps/sha1/sha1.c deps/sha1/sha1.h) target_link_libraries(${TARGET} PRIVATE sha1) +if (NOT MSVC) + # disable warnings in 3rd party code + target_compile_options(sha1 PRIVATE -w) +endif() + add_library(sha256 OBJECT deps/sha256/sha256.c deps/sha256/sha256.h) target_link_libraries(${TARGET} PRIVATE sha256) target_link_libraries(${TARGET} PRIVATE ggml ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/gguf-split/CMakeLists.txt b/examples/gguf-split/CMakeLists.txt index f63887da7..c407e2f0a 100644 --- a/examples/gguf-split/CMakeLists.txt +++ b/examples/gguf-split/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-gguf-split) add_executable(${TARGET} gguf-split.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/gguf/CMakeLists.txt b/examples/gguf/CMakeLists.txt index a9569b411..fb04eb83f 100644 --- a/examples/gguf/CMakeLists.txt +++ b/examples/gguf/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-gguf) add_executable(${TARGET} gguf.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE ggml ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/gritlm/CMakeLists.txt b/examples/gritlm/CMakeLists.txt index 86dfddca3..fa1b4dc70 100644 --- a/examples/gritlm/CMakeLists.txt +++ b/examples/gritlm/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-gritlm) add_executable(${TARGET} gritlm.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/imatrix/CMakeLists.txt b/examples/imatrix/CMakeLists.txt index d4c8265bd..412696c47 100644 --- a/examples/imatrix/CMakeLists.txt +++ b/examples/imatrix/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-imatrix) add_executable(${TARGET} imatrix.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/imatrix/README.md b/examples/imatrix/README.md index bb5faec94..9c056986b 100644 --- a/examples/imatrix/README.md +++ b/examples/imatrix/README.md @@ -25,8 +25,6 @@ For faster computation, make sure to use GPU offloading via the `-ngl` argument ## Example ```bash -GGML_CUDA=1 make -j - # generate importance matrix (imatrix.dat) ./llama-imatrix -m ggml-model-f16.gguf -f train-data.txt -ngl 99 diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index 70ff47768..45206f4a7 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -637,10 +637,19 @@ int main(int argc, char ** argv) { LOG_INF("%s\n", common_params_get_system_info(params).c_str()); } - if (!compute_imatrix(ctx, params)) { - return 1; + if (params.prompt.empty()) { + if (params.in_files.empty()) { + LOG_ERR("Error: No prompt provided and no precomputed matrices (--in-file) to combine.\n"); + return 1; + } + LOG_INF("No prompt provided; combining precomputed matrices only.\n"); + } else { + if (!compute_imatrix(ctx, params)) { + return 1; + } } + g_collector.save_imatrix(); LOG("\n"); diff --git a/examples/infill/CMakeLists.txt b/examples/infill/CMakeLists.txt index 9b1aa3b63..fb26628d8 100644 --- a/examples/infill/CMakeLists.txt +++ b/examples/infill/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-infill) add_executable(${TARGET} infill.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/infill/README.md b/examples/infill/README.md index 810a0c5e7..df4d976f2 100644 --- a/examples/infill/README.md +++ b/examples/infill/README.md @@ -14,7 +14,7 @@ In this section, we cover the most commonly used options for running the `infill - `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`). - `-i, --interactive`: Run the program in interactive mode, allowing you to provide input directly and receive real-time responses. - `-n N, --n-predict N`: Set the number of tokens to predict when generating text. Adjusting this value can influence the length of the generated text. -- `-c N, --ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. +- `-c N, --ctx-size N`: Set the size of the prompt context. The default is 4096, but if a LLaMA model was built with a longer context, increasing this value will provide better results for longer input/inference. - `--spm-infill`: Use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. ## Input Prompts diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index f18362c91..ef7008957 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -43,50 +43,6 @@ static std::vector * g_output_tokens; static bool is_interacting = false; -static void write_logfile( - const llama_context * ctx, const common_params & params, const llama_model * model, - const std::vector & input_tokens, const std::string & output, - const std::vector & output_tokens -) { - if (params.logdir.empty()) { - return; - } - - const std::string timestamp = string_get_sortable_timestamp(); - - const bool success = fs_create_directory_with_parents(params.logdir); - if (!success) { - LOG_ERR("%s: warning: failed to create logdir %s, cannot write logfile\n", - __func__, params.logdir.c_str()); - return; - } - - const std::string logfile_path = params.logdir + timestamp + ".yml"; - FILE * logfile = fopen(logfile_path.c_str(), "w"); - - if (logfile == NULL) { - LOG_ERR("%s: failed to open logfile %s\n", __func__, logfile_path.c_str()); - return; - } - - fprintf(logfile, "binary: infill\n"); - char model_desc[128]; - llama_model_desc(model, model_desc, sizeof(model_desc)); - yaml_dump_non_result_info(logfile, params, ctx, timestamp, input_tokens, model_desc); - - fprintf(logfile, "\n"); - fprintf(logfile, "######################\n"); - fprintf(logfile, "# Generation Results #\n"); - fprintf(logfile, "######################\n"); - fprintf(logfile, "\n"); - - yaml_dump_string_multiline(logfile, "output", output.c_str()); - yaml_dump_vector_int(logfile, "output_tokens", output_tokens); - - llama_perf_dump_yaml(logfile, ctx); - fclose(logfile); -} - #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32) static void sigint_handler(int signo) { if (signo == SIGINT) { @@ -96,7 +52,6 @@ static void sigint_handler(int signo) { console::cleanup(); LOG("\n"); common_perf_print(*g_ctx, *g_smpl); - write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens); // make sure all logs are flushed LOG("Interrupted by user\n"); @@ -118,7 +73,7 @@ int main(int argc, char ** argv) { common_init(); - auto & sparams = params.sparams; + auto & sparams = params.sampling; console::init(params.simple_io, params.use_color); atexit([]() { console::cleanup(); }); @@ -625,7 +580,6 @@ int main(int argc, char ** argv) { LOG("\n"); common_perf_print(ctx, smpl); - write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens); llama_free(ctx); llama_free_model(model); diff --git a/examples/llama-bench/CMakeLists.txt b/examples/llama-bench/CMakeLists.txt index 5bdbea4e2..17e3b9b87 100644 --- a/examples/llama-bench/CMakeLists.txt +++ b/examples/llama-bench/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-bench) add_executable(${TARGET} llama-bench.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index e7873a143..bac606f47 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -6,28 +6,28 @@ #include #include #include +#include #include #include -#include #include #include #include #include #include #include -#include #include +#include +#include "common.h" #include "ggml.h" #include "llama.h" -#include "common.h" #ifdef _WIN32 -#define WIN32_LEAN_AND_MEAN -#ifndef NOMINMAX -# define NOMINMAX -#endif -#include +# define WIN32_LEAN_AND_MEAN +# ifndef NOMINMAX +# define NOMINMAX +# endif +# include #endif // utils @@ -36,8 +36,7 @@ static uint64_t get_time_ns() { return std::chrono::nanoseconds(clock::now().time_since_epoch()).count(); } -template -static std::string join(const std::vector & values, const std::string & delim) { +template static std::string join(const std::vector & values, const std::string & delim) { std::ostringstream str; for (size_t i = 0; i < values.size(); i++) { str << values[i]; @@ -48,38 +47,35 @@ static std::string join(const std::vector & values, const std::string & delim return str.str(); } -template -static std::vector transform_to_str(const std::vector & values, F f) { +template static std::vector transform_to_str(const std::vector & values, F f) { std::vector str_values; std::transform(values.begin(), values.end(), std::back_inserter(str_values), f); return str_values; } -template -static T avg(const std::vector & v) { +template static T avg(const std::vector & v) { if (v.empty()) { return 0; } T sum = std::accumulate(v.begin(), v.end(), T(0)); - return sum / (T)v.size(); + return sum / (T) v.size(); } -template -static T stdev(const std::vector & v) { +template static T stdev(const std::vector & v) { if (v.size() <= 1) { return 0; } - T mean = avg(v); + T mean = avg(v); T sq_sum = std::inner_product(v.begin(), v.end(), v.begin(), T(0)); - T stdev = std::sqrt(sq_sum / (T)(v.size() - 1) - mean * mean * (T)v.size() / (T)(v.size() - 1)); + T stdev = std::sqrt(sq_sum / (T) (v.size() - 1) - mean * mean * (T) v.size() / (T) (v.size() - 1)); return stdev; } static std::string get_cpu_info() { std::vector cpu_list; for (size_t i = 0; i < ggml_backend_dev_count(); i++) { - auto * dev = ggml_backend_dev_get(i); - auto dev_type = ggml_backend_dev_type(dev); + auto * dev = ggml_backend_dev_get(i); + auto dev_type = ggml_backend_dev_type(dev); if (dev_type == GGML_BACKEND_DEVICE_TYPE_CPU || dev_type == GGML_BACKEND_DEVICE_TYPE_ACCEL) { cpu_list.push_back(ggml_backend_dev_description(dev)); } @@ -90,8 +86,8 @@ static std::string get_cpu_info() { static std::string get_gpu_info() { std::vector gpu_list; for (size_t i = 0; i < ggml_backend_dev_count(); i++) { - auto * dev = ggml_backend_dev_get(i); - auto dev_type = ggml_backend_dev_type(dev); + auto * dev = ggml_backend_dev_get(i); + auto dev_type = ggml_backend_dev_type(dev); if (dev_type == GGML_BACKEND_DEVICE_TYPE_GPU) { gpu_list.push_back(ggml_backend_dev_description(dev)); } @@ -100,17 +96,24 @@ static std::string get_gpu_info() { } // command line params -enum output_formats {NONE, CSV, JSON, JSONL, MARKDOWN, SQL}; +enum output_formats { NONE, CSV, JSON, JSONL, MARKDOWN, SQL }; static const char * output_format_str(output_formats format) { switch (format) { - case NONE: return "none"; - case CSV: return "csv"; - case JSON: return "json"; - case JSONL: return "jsonl"; - case MARKDOWN: return "md"; - case SQL: return "sql"; - default: GGML_ABORT("invalid output format"); + case NONE: + return "none"; + case CSV: + return "csv"; + case JSON: + return "json"; + case JSONL: + return "jsonl"; + case MARKDOWN: + return "md"; + case SQL: + return "sql"; + default: + GGML_ABORT("invalid output format"); } } @@ -135,10 +138,14 @@ static bool output_format_from_str(const std::string & s, output_formats & forma static const char * split_mode_str(llama_split_mode mode) { switch (mode) { - case LLAMA_SPLIT_MODE_NONE: return "none"; - case LLAMA_SPLIT_MODE_LAYER: return "layer"; - case LLAMA_SPLIT_MODE_ROW: return "row"; - default: GGML_ABORT("invalid split mode"); + case LLAMA_SPLIT_MODE_NONE: + return "none"; + case LLAMA_SPLIT_MODE_LAYER: + return "layer"; + case LLAMA_SPLIT_MODE_ROW: + return "row"; + default: + GGML_ABORT("invalid split mode"); } } @@ -149,59 +156,59 @@ static std::string pair_str(const std::pair & p) { } struct cmd_params { - std::vector model; - std::vector n_prompt; - std::vector n_gen; + std::vector model; + std::vector n_prompt; + std::vector n_gen; std::vector> n_pg; - std::vector n_batch; - std::vector n_ubatch; - std::vector type_k; - std::vector type_v; - std::vector n_threads; - std::vector cpu_mask; - std::vector cpu_strict; - std::vector poll; - std::vector n_gpu_layers; - std::vector rpc_servers; - std::vector split_mode; - std::vector main_gpu; - std::vector no_kv_offload; - std::vector flash_attn; - std::vector> tensor_split; - std::vector use_mmap; - std::vector embeddings; - ggml_numa_strategy numa; - int reps; - ggml_sched_priority prio; - int delay; - bool verbose; - bool progress; - output_formats output_format; - output_formats output_format_stderr; + std::vector n_batch; + std::vector n_ubatch; + std::vector type_k; + std::vector type_v; + std::vector n_threads; + std::vector cpu_mask; + std::vector cpu_strict; + std::vector poll; + std::vector n_gpu_layers; + std::vector rpc_servers; + std::vector split_mode; + std::vector main_gpu; + std::vector no_kv_offload; + std::vector flash_attn; + std::vector> tensor_split; + std::vector use_mmap; + std::vector embeddings; + ggml_numa_strategy numa; + int reps; + ggml_sched_priority prio; + int delay; + bool verbose; + bool progress; + output_formats output_format; + output_formats output_format_stderr; }; static const cmd_params cmd_params_defaults = { - /* model */ {"models/7B/ggml-model-q4_0.gguf"}, - /* n_prompt */ {512}, - /* n_gen */ {128}, + /* model */ { "models/7B/ggml-model-q4_0.gguf" }, + /* n_prompt */ { 512 }, + /* n_gen */ { 128 }, /* n_pg */ {}, - /* n_batch */ {2048}, - /* n_ubatch */ {512}, - /* type_k */ {GGML_TYPE_F16}, - /* type_v */ {GGML_TYPE_F16}, - /* n_threads */ {cpu_get_num_math()}, - /* cpu_mask */ {"0x0"}, - /* cpu_strict */ {false}, - /* poll */ {50}, - /* n_gpu_layers */ {99}, - /* rpc_servers */ {""}, - /* split_mode */ {LLAMA_SPLIT_MODE_LAYER}, - /* main_gpu */ {0}, - /* no_kv_offload */ {false}, - /* flash_attn */ {false}, - /* tensor_split */ {std::vector(llama_max_devices(), 0.0f)}, - /* use_mmap */ {true}, - /* embeddings */ {false}, + /* n_batch */ { 2048 }, + /* n_ubatch */ { 512 }, + /* type_k */ { GGML_TYPE_F16 }, + /* type_v */ { GGML_TYPE_F16 }, + /* n_threads */ { cpu_get_num_math() }, + /* cpu_mask */ { "0x0" }, + /* cpu_strict */ { false }, + /* poll */ { 50 }, + /* n_gpu_layers */ { 99 }, + /* rpc_servers */ { "" }, + /* split_mode */ { LLAMA_SPLIT_MODE_LAYER }, + /* main_gpu */ { 0 }, + /* no_kv_offload */ { false }, + /* flash_attn */ { false }, + /* tensor_split */ { std::vector(llama_max_devices(), 0.0f) }, + /* use_mmap */ { true }, + /* embeddings */ { false }, /* numa */ GGML_NUMA_STRATEGY_DISABLED, /* reps */ 5, /* prio */ GGML_SCHED_PRIO_NORMAL, @@ -218,44 +225,68 @@ static void print_usage(int /* argc */, char ** argv) { printf("options:\n"); printf(" -h, --help\n"); printf(" -m, --model (default: %s)\n", join(cmd_params_defaults.model, ",").c_str()); - printf(" -p, --n-prompt (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str()); + printf(" -p, --n-prompt (default: %s)\n", + join(cmd_params_defaults.n_prompt, ",").c_str()); printf(" -n, --n-gen (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str()); - printf(" -pg (default: %s)\n", join(transform_to_str(cmd_params_defaults.n_pg, pair_str), ",").c_str()); - printf(" -b, --batch-size (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str()); - printf(" -ub, --ubatch-size (default: %s)\n", join(cmd_params_defaults.n_ubatch, ",").c_str()); - printf(" -ctk, --cache-type-k (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str()); - printf(" -ctv, --cache-type-v (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str()); - printf(" -t, --threads (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str()); - printf(" -C, --cpu-mask (default: %s)\n", join(cmd_params_defaults.cpu_mask, ",").c_str()); - printf(" --cpu-strict <0|1> (default: %s)\n", join(cmd_params_defaults.cpu_strict, ",").c_str()); + printf(" -pg (default: %s)\n", + join(transform_to_str(cmd_params_defaults.n_pg, pair_str), ",").c_str()); + printf(" -b, --batch-size (default: %s)\n", + join(cmd_params_defaults.n_batch, ",").c_str()); + printf(" -ub, --ubatch-size (default: %s)\n", + join(cmd_params_defaults.n_ubatch, ",").c_str()); + printf(" -ctk, --cache-type-k (default: %s)\n", + join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str()); + printf(" -ctv, --cache-type-v (default: %s)\n", + join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str()); + printf(" -t, --threads (default: %s)\n", + join(cmd_params_defaults.n_threads, ",").c_str()); + printf(" -C, --cpu-mask (default: %s)\n", + join(cmd_params_defaults.cpu_mask, ",").c_str()); + printf(" --cpu-strict <0|1> (default: %s)\n", + join(cmd_params_defaults.cpu_strict, ",").c_str()); printf(" --poll <0...100> (default: %s)\n", join(cmd_params_defaults.poll, ",").c_str()); - printf(" -ngl, --n-gpu-layers (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str()); + printf(" -ngl, --n-gpu-layers (default: %s)\n", + join(cmd_params_defaults.n_gpu_layers, ",").c_str()); if (llama_supports_rpc()) { - printf(" -rpc, --rpc (default: %s)\n", join(cmd_params_defaults.rpc_servers, ",").c_str()); + printf(" -rpc, --rpc (default: %s)\n", + join(cmd_params_defaults.rpc_servers, ",").c_str()); } - printf(" -sm, --split-mode (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str()); - printf(" -mg, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); - printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); - printf(" -fa, --flash-attn <0|1> (default: %s)\n", join(cmd_params_defaults.flash_attn, ",").c_str()); - printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str()); + printf(" -sm, --split-mode (default: %s)\n", + join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str()); + printf(" -mg, --main-gpu (default: %s)\n", + join(cmd_params_defaults.main_gpu, ",").c_str()); + printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", + join(cmd_params_defaults.no_kv_offload, ",").c_str()); + printf(" -fa, --flash-attn <0|1> (default: %s)\n", + join(cmd_params_defaults.flash_attn, ",").c_str()); + printf(" -mmp, --mmap <0|1> (default: %s)\n", + join(cmd_params_defaults.use_mmap, ",").c_str()); printf(" --numa (default: disabled)\n"); - printf(" -embd, --embeddings <0|1> (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str()); + printf(" -embd, --embeddings <0|1> (default: %s)\n", + join(cmd_params_defaults.embeddings, ",").c_str()); printf(" -ts, --tensor-split (default: 0)\n"); printf(" -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); printf(" --prio <0|1|2|3> (default: %d)\n", cmd_params_defaults.prio); printf(" --delay <0...N> (seconds) (default: %d)\n", cmd_params_defaults.delay); - printf(" -o, --output (default: %s)\n", output_format_str(cmd_params_defaults.output_format)); - printf(" -oe, --output-err (default: %s)\n", output_format_str(cmd_params_defaults.output_format_stderr)); + printf(" -o, --output (default: %s)\n", + output_format_str(cmd_params_defaults.output_format)); + printf(" -oe, --output-err (default: %s)\n", + output_format_str(cmd_params_defaults.output_format_stderr)); printf(" -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0"); printf(" --progress (default: %s)\n", cmd_params_defaults.progress ? "1" : "0"); printf("\n"); - printf("Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\n"); + printf( + "Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter " + "multiple times.\n"); } static ggml_type ggml_type_from_name(const std::string & s) { if (s == "f16") { return GGML_TYPE_F16; } + if (s == "bf16") { + return GGML_TYPE_BF16; + } if (s == "q8_0") { return GGML_TYPE_Q8_0; } @@ -278,22 +309,21 @@ static ggml_type ggml_type_from_name(const std::string & s) { return GGML_TYPE_COUNT; } - static cmd_params parse_cmd_params(int argc, char ** argv) { - cmd_params params; - std::string arg; - bool invalid_param = false; - const std::string arg_prefix = "--"; - const char split_delim = ','; + cmd_params params; + std::string arg; + bool invalid_param = false; + const std::string arg_prefix = "--"; + const char split_delim = ','; - params.verbose = cmd_params_defaults.verbose; - params.output_format = cmd_params_defaults.output_format; + params.verbose = cmd_params_defaults.verbose; + params.output_format = cmd_params_defaults.output_format; params.output_format_stderr = cmd_params_defaults.output_format_stderr; - params.reps = cmd_params_defaults.reps; - params.numa = cmd_params_defaults.numa; - params.prio = cmd_params_defaults.prio; - params.delay = cmd_params_defaults.delay; - params.progress = cmd_params_defaults.progress; + params.reps = cmd_params_defaults.reps; + params.numa = cmd_params_defaults.numa; + params.prio = cmd_params_defaults.prio; + params.delay = cmd_params_defaults.delay; + params.progress = cmd_params_defaults.progress; for (int i = 1; i < argc; i++) { arg = argv[i]; @@ -335,7 +365,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } - params.n_pg.push_back({std::stoi(p[0]), std::stoi(p[1])}); + params.n_pg.push_back({ std::stoi(p[0]), std::stoi(p[1]) }); } else if (arg == "-b" || arg == "--batch-size") { if (++i >= argc) { invalid_param = true; @@ -355,7 +385,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } - auto p = string_split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); std::vector types; for (const auto & t : p) { ggml_type gt = ggml_type_from_name(t); @@ -374,7 +404,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } - auto p = string_split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); std::vector types; for (const auto & t : p) { ggml_type gt = ggml_type_from_name(t); @@ -434,7 +464,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } - auto p = string_split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); std::vector modes; for (const auto & m : p) { llama_split_mode mode; @@ -473,10 +503,16 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { break; } else { std::string value(argv[i]); - /**/ if (value == "distribute" || value == "" ) { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; } - else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; } - else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; } - else { invalid_param = true; break; } + /**/ if (value == "distribute" || value == "") { + params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; + } else if (value == "isolate") { + params.numa = GGML_NUMA_STRATEGY_ISOLATE; + } else if (value == "numactl") { + params.numa = GGML_NUMA_STRATEGY_NUMACTL; + } else { + invalid_param = true; + break; + } } } else if (arg == "-fa" || arg == "--flash-attn") { if (++i >= argc) { @@ -506,9 +542,9 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } for (auto ts : string_split(argv[i], split_delim)) { // split string by ; and / - const std::regex regex{R"([;/]+)"}; - std::sregex_token_iterator it{ts.begin(), ts.end(), regex, -1}; - std::vector split_arg{it, {}}; + const std::regex regex{ R"([;/]+)" }; + std::sregex_token_iterator it{ ts.begin(), ts.end(), regex, -1 }; + std::vector split_arg{ it, {} }; GGML_ASSERT(split_arg.size() <= llama_max_devices()); std::vector tensor_split(llama_max_devices()); @@ -567,52 +603,94 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } // set defaults - if (params.model.empty()) { params.model = cmd_params_defaults.model; } - if (params.n_prompt.empty()) { params.n_prompt = cmd_params_defaults.n_prompt; } - if (params.n_gen.empty()) { params.n_gen = cmd_params_defaults.n_gen; } - if (params.n_pg.empty()) { params.n_pg = cmd_params_defaults.n_pg; } - if (params.n_batch.empty()) { params.n_batch = cmd_params_defaults.n_batch; } - if (params.n_ubatch.empty()) { params.n_ubatch = cmd_params_defaults.n_ubatch; } - if (params.type_k.empty()) { params.type_k = cmd_params_defaults.type_k; } - if (params.type_v.empty()) { params.type_v = cmd_params_defaults.type_v; } - if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; } - if (params.rpc_servers.empty()) { params.rpc_servers = cmd_params_defaults.rpc_servers; } - if (params.split_mode.empty()) { params.split_mode = cmd_params_defaults.split_mode; } - if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; } - if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; } - if (params.flash_attn.empty()) { params.flash_attn = cmd_params_defaults.flash_attn; } - if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; } - if (params.use_mmap.empty()) { params.use_mmap = cmd_params_defaults.use_mmap; } - if (params.embeddings.empty()) { params.embeddings = cmd_params_defaults.embeddings; } - if (params.n_threads.empty()) { params.n_threads = cmd_params_defaults.n_threads; } - if (params.cpu_mask.empty()) { params.cpu_mask = cmd_params_defaults.cpu_mask; } - if (params.cpu_strict.empty()) { params.cpu_strict = cmd_params_defaults.cpu_strict; } - if (params.poll.empty()) { params.poll = cmd_params_defaults.poll; } + if (params.model.empty()) { + params.model = cmd_params_defaults.model; + } + if (params.n_prompt.empty()) { + params.n_prompt = cmd_params_defaults.n_prompt; + } + if (params.n_gen.empty()) { + params.n_gen = cmd_params_defaults.n_gen; + } + if (params.n_pg.empty()) { + params.n_pg = cmd_params_defaults.n_pg; + } + if (params.n_batch.empty()) { + params.n_batch = cmd_params_defaults.n_batch; + } + if (params.n_ubatch.empty()) { + params.n_ubatch = cmd_params_defaults.n_ubatch; + } + if (params.type_k.empty()) { + params.type_k = cmd_params_defaults.type_k; + } + if (params.type_v.empty()) { + params.type_v = cmd_params_defaults.type_v; + } + if (params.n_gpu_layers.empty()) { + params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; + } + if (params.rpc_servers.empty()) { + params.rpc_servers = cmd_params_defaults.rpc_servers; + } + if (params.split_mode.empty()) { + params.split_mode = cmd_params_defaults.split_mode; + } + if (params.main_gpu.empty()) { + params.main_gpu = cmd_params_defaults.main_gpu; + } + if (params.no_kv_offload.empty()) { + params.no_kv_offload = cmd_params_defaults.no_kv_offload; + } + if (params.flash_attn.empty()) { + params.flash_attn = cmd_params_defaults.flash_attn; + } + if (params.tensor_split.empty()) { + params.tensor_split = cmd_params_defaults.tensor_split; + } + if (params.use_mmap.empty()) { + params.use_mmap = cmd_params_defaults.use_mmap; + } + if (params.embeddings.empty()) { + params.embeddings = cmd_params_defaults.embeddings; + } + if (params.n_threads.empty()) { + params.n_threads = cmd_params_defaults.n_threads; + } + if (params.cpu_mask.empty()) { + params.cpu_mask = cmd_params_defaults.cpu_mask; + } + if (params.cpu_strict.empty()) { + params.cpu_strict = cmd_params_defaults.cpu_strict; + } + if (params.poll.empty()) { + params.poll = cmd_params_defaults.poll; + } return params; } struct cmd_params_instance { - std::string model; - int n_prompt; - int n_gen; - int n_batch; - int n_ubatch; - ggml_type type_k; - ggml_type type_v; - int n_threads; - std::string cpu_mask; - bool cpu_strict; - int poll; - int n_gpu_layers; - std::string rpc_servers; - llama_split_mode split_mode; - int main_gpu; - bool no_kv_offload; - bool flash_attn; + std::string model; + int n_prompt; + int n_gen; + int n_batch; + int n_ubatch; + ggml_type type_k; + ggml_type type_v; + int n_threads; + std::string cpu_mask; + bool cpu_strict; + int poll; + int n_gpu_layers; + std::string rpc_servers; + llama_split_mode split_mode; + int main_gpu; + bool no_kv_offload; + bool flash_attn; std::vector tensor_split; - bool use_mmap; - bool embeddings; + bool use_mmap; + bool embeddings; llama_model_params to_llama_mparams() const { llama_model_params mparams = llama_model_default_params(); @@ -621,35 +699,31 @@ struct cmd_params_instance { if (!rpc_servers.empty()) { mparams.rpc_servers = rpc_servers.c_str(); } - mparams.split_mode = split_mode; - mparams.main_gpu = main_gpu; + mparams.split_mode = split_mode; + mparams.main_gpu = main_gpu; mparams.tensor_split = tensor_split.data(); - mparams.use_mmap = use_mmap; + mparams.use_mmap = use_mmap; return mparams; } bool equal_mparams(const cmd_params_instance & other) const { - return model == other.model && - n_gpu_layers == other.n_gpu_layers && - rpc_servers == other.rpc_servers && - split_mode == other.split_mode && - main_gpu == other.main_gpu && - use_mmap == other.use_mmap && + return model == other.model && n_gpu_layers == other.n_gpu_layers && rpc_servers == other.rpc_servers && + split_mode == other.split_mode && main_gpu == other.main_gpu && use_mmap == other.use_mmap && tensor_split == other.tensor_split; } llama_context_params to_llama_cparams() const { llama_context_params cparams = llama_context_default_params(); - cparams.n_ctx = n_prompt + n_gen; - cparams.n_batch = n_batch; - cparams.n_ubatch = n_ubatch; - cparams.type_k = type_k; - cparams.type_v = type_v; + cparams.n_ctx = n_prompt + n_gen; + cparams.n_batch = n_batch; + cparams.n_ubatch = n_ubatch; + cparams.type_k = type_k; + cparams.type_v = type_v; cparams.offload_kqv = !no_kv_offload; - cparams.flash_attn = flash_attn; - cparams.embeddings = embeddings; + cparams.flash_attn = flash_attn; + cparams.embeddings = embeddings; return cparams; } @@ -659,6 +733,7 @@ static std::vector get_cmd_params_instances(const cmd_param std::vector instances; // this ordering minimizes the number of times that each model needs to be reloaded + // clang-format off for (const auto & m : params.model) for (const auto & nl : params.n_gpu_layers) for (const auto & rpc : params.rpc_servers) @@ -764,109 +839,94 @@ static std::vector get_cmd_params_instances(const cmd_param instances.push_back(instance); } } + // clang-format on return instances; } struct test { static const std::string build_commit; - static const int build_number; - static const bool cuda; - static const bool vulkan; - static const bool kompute; - static const bool metal; - static const bool sycl; - static const bool gpu_blas; - static const bool blas; + static const int build_number; static const std::string cpu_info; static const std::string gpu_info; - std::string model_filename; - std::string model_type; - uint64_t model_size; - uint64_t model_n_params; - int n_batch; - int n_ubatch; - int n_threads; - std::string cpu_mask; - bool cpu_strict; - int poll; - bool has_rpc; - ggml_type type_k; - ggml_type type_v; - int n_gpu_layers; - llama_split_mode split_mode; - int main_gpu; - bool no_kv_offload; - bool flash_attn; - std::vector tensor_split; - bool use_mmap; - bool embeddings; - int n_prompt; - int n_gen; - std::string test_time; - std::vector samples_ns; + std::string model_filename; + std::string model_type; + uint64_t model_size; + uint64_t model_n_params; + int n_batch; + int n_ubatch; + int n_threads; + std::string cpu_mask; + bool cpu_strict; + int poll; + ggml_type type_k; + ggml_type type_v; + int n_gpu_layers; + llama_split_mode split_mode; + int main_gpu; + bool no_kv_offload; + bool flash_attn; + std::vector tensor_split; + bool use_mmap; + bool embeddings; + int n_prompt; + int n_gen; + std::string test_time; + std::vector samples_ns; test(const cmd_params_instance & inst, const llama_model * lmodel, const llama_context * ctx) { model_filename = inst.model; char buf[128]; llama_model_desc(lmodel, buf, sizeof(buf)); - model_type = buf; - model_size = llama_model_size(lmodel); + model_type = buf; + model_size = llama_model_size(lmodel); model_n_params = llama_model_n_params(lmodel); - n_batch = inst.n_batch; - n_ubatch = inst.n_ubatch; - n_threads = inst.n_threads; - cpu_mask = inst.cpu_mask; - cpu_strict = inst.cpu_strict; - poll = inst.poll; - has_rpc = !inst.rpc_servers.empty(); - type_k = inst.type_k; - type_v = inst.type_v; - n_gpu_layers = inst.n_gpu_layers; - split_mode = inst.split_mode; - main_gpu = inst.main_gpu; - no_kv_offload = inst.no_kv_offload; - flash_attn = inst.flash_attn; - tensor_split = inst.tensor_split; - use_mmap = inst.use_mmap; - embeddings = inst.embeddings; - n_prompt = inst.n_prompt; - n_gen = inst.n_gen; + n_batch = inst.n_batch; + n_ubatch = inst.n_ubatch; + n_threads = inst.n_threads; + cpu_mask = inst.cpu_mask; + cpu_strict = inst.cpu_strict; + poll = inst.poll; + type_k = inst.type_k; + type_v = inst.type_v; + n_gpu_layers = inst.n_gpu_layers; + split_mode = inst.split_mode; + main_gpu = inst.main_gpu; + no_kv_offload = inst.no_kv_offload; + flash_attn = inst.flash_attn; + tensor_split = inst.tensor_split; + use_mmap = inst.use_mmap; + embeddings = inst.embeddings; + n_prompt = inst.n_prompt; + n_gen = inst.n_gen; // RFC 3339 date-time format - time_t t = time(NULL); + time_t t = time(NULL); std::strftime(buf, sizeof(buf), "%FT%TZ", gmtime(&t)); test_time = buf; (void) ctx; } - uint64_t avg_ns() const { - return ::avg(samples_ns); - } + uint64_t avg_ns() const { return ::avg(samples_ns); } - uint64_t stdev_ns() const { - return ::stdev(samples_ns); - } + uint64_t stdev_ns() const { return ::stdev(samples_ns); } std::vector get_ts() const { - int n_tokens = n_prompt + n_gen; + int n_tokens = n_prompt + n_gen; std::vector ts; - std::transform(samples_ns.begin(), samples_ns.end(), std::back_inserter(ts), [n_tokens](uint64_t t) { return 1e9 * n_tokens / t; }); + std::transform(samples_ns.begin(), samples_ns.end(), std::back_inserter(ts), + [n_tokens](uint64_t t) { return 1e9 * n_tokens / t; }); return ts; } - double avg_ts() const { - return ::avg(get_ts()); - } + double avg_ts() const { return ::avg(get_ts()); } - double stdev_ts() const { - return ::stdev(get_ts()); - } + double stdev_ts() const { return ::stdev(get_ts()); } static std::string get_backend() { std::vector backends; for (size_t i = 0; i < ggml_backend_reg_count(); i++) { - auto * reg = ggml_backend_reg_get(i); + auto * reg = ggml_backend_reg_get(i); std::string name = ggml_backend_reg_name(reg); if (name != "CPU") { backends.push_back(ggml_backend_reg_name(reg)); @@ -877,38 +937,27 @@ struct test { static const std::vector & get_fields() { static const std::vector fields = { - "build_commit", "build_number", - "cuda", "vulkan", "kompute", "metal", "sycl", "rpc", "gpu_blas", "blas", - "cpu_info", "gpu_info", - "model_filename", "model_type", "model_size", "model_n_params", - "n_batch", "n_ubatch", - "n_threads", "cpu_mask", "cpu_strict", "poll", - "type_k", "type_v", - "n_gpu_layers", "split_mode", - "main_gpu", "no_kv_offload", "flash_attn", - "tensor_split", "use_mmap", "embeddings", - "n_prompt", "n_gen", "test_time", - "avg_ns", "stddev_ns", - "avg_ts", "stddev_ts", + "build_commit", "build_number", "cpu_info", "gpu_info", "backends", "model_filename", + "model_type", "model_size", "model_n_params", "n_batch", "n_ubatch", "n_threads", + "cpu_mask", "cpu_strict", "poll", "type_k", "type_v", "n_gpu_layers", + "split_mode", "main_gpu", "no_kv_offload", "flash_attn", "tensor_split", "use_mmap", + "embeddings", "n_prompt", "n_gen", "test_time", "avg_ns", "stddev_ns", + "avg_ts", "stddev_ts", }; return fields; } - enum field_type {STRING, BOOL, INT, FLOAT}; + enum field_type { STRING, BOOL, INT, FLOAT }; static field_type get_field_type(const std::string & field) { - if (field == "build_number" || field == "n_batch" || field == "n_ubatch" || - field == "n_threads" || field == "poll" || - field == "model_size" || field == "model_n_params" || - field == "n_gpu_layers" || field == "main_gpu" || - field == "n_prompt" || field == "n_gen" || - field == "avg_ns" || field == "stddev_ns") { + if (field == "build_number" || field == "n_batch" || field == "n_ubatch" || field == "n_threads" || + field == "poll" || field == "model_size" || field == "model_n_params" || field == "n_gpu_layers" || + field == "main_gpu" || field == "n_prompt" || field == "n_gen" || field == "avg_ns" || + field == "stddev_ns") { return INT; } - if (field == "cuda" || field == "vulkan" || field == "kompute" || field == "metal" || - field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" || - field == "cpu_strict" || - field == "flash_attn" || field == "use_mmap" || field == "embeddings") { + if (field == "f16_kv" || field == "no_kv_offload" || field == "cpu_strict" || field == "flash_attn" || + field == "use_mmap" || field == "embeddings") { return BOOL; } if (field == "avg_ts" || field == "stddev_ts") { @@ -919,7 +968,7 @@ struct test { std::vector get_values() const { std::string tensor_split_str; - int max_nonzero = 0; + int max_nonzero = 0; for (size_t i = 0; i < llama_max_devices(); i++) { if (tensor_split[i] > 0) { max_nonzero = i; @@ -933,44 +982,53 @@ struct test { tensor_split_str += "/"; } } - std::vector values = { - build_commit, std::to_string(build_number), - std::to_string(cuda), std::to_string(vulkan), std::to_string(vulkan), - std::to_string(metal), std::to_string(sycl), std::to_string(has_rpc), std::to_string(gpu_blas), std::to_string(blas), - cpu_info, gpu_info, - model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params), - std::to_string(n_batch), std::to_string(n_ubatch), - std::to_string(n_threads), cpu_mask, std::to_string(cpu_strict), std::to_string(poll), - ggml_type_name(type_k), ggml_type_name(type_v), - std::to_string(n_gpu_layers), split_mode_str(split_mode), - std::to_string(main_gpu), std::to_string(no_kv_offload), std::to_string(flash_attn), - tensor_split_str, std::to_string(use_mmap), std::to_string(embeddings), - std::to_string(n_prompt), std::to_string(n_gen), test_time, - std::to_string(avg_ns()), std::to_string(stdev_ns()), - std::to_string(avg_ts()), std::to_string(stdev_ts()) - }; + std::vector values = { build_commit, + std::to_string(build_number), + cpu_info, + gpu_info, + get_backend(), + model_filename, + model_type, + std::to_string(model_size), + std::to_string(model_n_params), + std::to_string(n_batch), + std::to_string(n_ubatch), + std::to_string(n_threads), + cpu_mask, + std::to_string(cpu_strict), + std::to_string(poll), + ggml_type_name(type_k), + ggml_type_name(type_v), + std::to_string(n_gpu_layers), + split_mode_str(split_mode), + std::to_string(main_gpu), + std::to_string(no_kv_offload), + std::to_string(flash_attn), + tensor_split_str, + std::to_string(use_mmap), + std::to_string(embeddings), + std::to_string(n_prompt), + std::to_string(n_gen), + test_time, + std::to_string(avg_ns()), + std::to_string(stdev_ns()), + std::to_string(avg_ts()), + std::to_string(stdev_ts()) }; return values; } std::map get_map() const { std::map map; - auto fields = get_fields(); - auto values = get_values(); - std::transform(fields.begin(), fields.end(), values.begin(), - std::inserter(map, map.end()), std::make_pair); + auto fields = get_fields(); + auto values = get_values(); + std::transform(fields.begin(), fields.end(), values.begin(), std::inserter(map, map.end()), + std::make_pair); return map; } }; const std::string test::build_commit = LLAMA_COMMIT; const int test::build_number = LLAMA_BUILD_NUMBER; -const bool test::cuda = !!ggml_cpu_has_cuda(); -const bool test::vulkan = !!ggml_cpu_has_vulkan(); -const bool test::kompute = !!ggml_cpu_has_kompute(); -const bool test::metal = !!ggml_cpu_has_metal(); -const bool test::gpu_blas = !!ggml_cpu_has_gpublas(); -const bool test::blas = !!ggml_cpu_has_blas(); -const bool test::sycl = !!ggml_cpu_has_sycl(); const std::string test::cpu_info = get_cpu_info(); const std::string test::gpu_info = get_gpu_info(); @@ -978,9 +1036,12 @@ struct printer { virtual ~printer() {} FILE * fout; + virtual void print_header(const cmd_params & params) { (void) params; } + virtual void print_test(const test & t) = 0; - virtual void print_footer() { } + + virtual void print_footer() {} }; struct csv_printer : public printer { @@ -996,7 +1057,7 @@ struct csv_printer : public printer { return escaped; } - void print_header(const cmd_params & params) override { + void print_header(const cmd_params & params) override { std::vector fields = test::get_fields(); fprintf(fout, "%s\n", join(fields, ",").c_str()); (void) params; @@ -1009,7 +1070,6 @@ struct csv_printer : public printer { } }; - static std::string escape_json(const std::string & value) { std::string escaped; for (auto c : value) { @@ -1017,7 +1077,7 @@ static std::string escape_json(const std::string & value) { escaped += "\\\""; } else if (c == '\\') { escaped += "\\\\"; - } else if (c <= 0x1f) { + } else if (c <= 0x1f) { char buf[8]; snprintf(buf, sizeof(buf), "\\u%04x", c); escaped += buf; @@ -1050,7 +1110,8 @@ struct json_printer : public printer { void print_fields(const std::vector & fields, const std::vector & values) { assert(fields.size() == values.size()); for (size_t i = 0; i < fields.size(); i++) { - fprintf(fout, " \"%s\": %s,\n", fields.at(i).c_str(), format_json_value(fields.at(i), values.at(i)).c_str()); + fprintf(fout, " \"%s\": %s,\n", fields.at(i).c_str(), + format_json_value(fields.at(i), values.at(i)).c_str()); } } @@ -1068,12 +1129,9 @@ struct json_printer : public printer { fflush(fout); } - void print_footer() override { - fprintf(fout, "\n]\n"); - } + void print_footer() override { fprintf(fout, "\n]\n"); } }; - struct jsonl_printer : public printer { void print_fields(const std::vector & fields, const std::vector & values) { assert(fields.size() == values.size()); @@ -1133,7 +1191,7 @@ struct markdown_printer : public printer { return 13; } - int width = std::max((int)field.length(), 10); + int width = std::max((int) field.length(), 10); if (test::get_field_type(field) == test::STRING) { return -width; @@ -1175,7 +1233,8 @@ struct markdown_printer : public printer { fields.emplace_back("size"); fields.emplace_back("params"); fields.emplace_back("backend"); - bool is_cpu_backend = test::get_backend() == "CPU" || test::get_backend() == "BLAS"; + bool is_cpu_backend = test::get_backend().find("CPU") != std::string::npos || + test::get_backend().find("BLAS") != std::string::npos; if (!is_cpu_backend) { fields.emplace_back("n_gpu_layers"); } @@ -1246,18 +1305,18 @@ struct markdown_printer : public printer { fprintf(fout, "|"); for (const auto & field : fields) { std::string value; - char buf[128]; + char buf[128]; if (field == "model") { value = t.model_type; } else if (field == "size") { - if (t.model_size < 1024*1024*1024) { + if (t.model_size < 1024 * 1024 * 1024) { snprintf(buf, sizeof(buf), "%.2f MiB", t.model_size / 1024.0 / 1024.0); } else { snprintf(buf, sizeof(buf), "%.2f GiB", t.model_size / 1024.0 / 1024.0 / 1024.0); } value = buf; } else if (field == "params") { - if (t.model_n_params < 1000*1000*1000) { + if (t.model_n_params < 1000 * 1000 * 1000) { snprintf(buf, sizeof(buf), "%.2f M", t.model_n_params / 1e6); } else { snprintf(buf, sizeof(buf), "%.2f B", t.model_n_params / 1e9); @@ -1265,9 +1324,6 @@ struct markdown_printer : public printer { value = buf; } else if (field == "backend") { value = test::get_backend(); - if (t.has_rpc) { - value += "+RPC"; - } } else if (field == "test") { if (t.n_prompt > 0 && t.n_gen == 0) { snprintf(buf, sizeof(buf), "pp%d", t.n_prompt); @@ -1322,7 +1378,8 @@ struct sql_printer : public printer { std::vector fields = test::get_fields(); fprintf(fout, "CREATE TABLE IF NOT EXISTS test (\n"); for (size_t i = 0; i < fields.size(); i++) { - fprintf(fout, " %s %s%s\n", fields.at(i).c_str(), get_sql_field_type(fields.at(i)).c_str(), i < fields.size() - 1 ? "," : ""); + fprintf(fout, " %s %s%s\n", fields.at(i).c_str(), get_sql_field_type(fields.at(i)).c_str(), + i < fields.size() - 1 ? "," : ""); } fprintf(fout, ");\n"); fprintf(fout, "\n"); @@ -1343,8 +1400,8 @@ struct sql_printer : public printer { static void test_prompt(llama_context * ctx, int n_prompt, int n_batch, int n_threads) { llama_set_n_threads(ctx, n_threads, n_threads); - const llama_model * model = llama_get_model(ctx); - const int32_t n_vocab = llama_n_vocab(model); + const llama_model * model = llama_get_model(ctx); + const int32_t n_vocab = llama_n_vocab(model); std::vector tokens(n_batch); @@ -1352,7 +1409,7 @@ static void test_prompt(llama_context * ctx, int n_prompt, int n_batch, int n_th while (n_processed < n_prompt) { int n_tokens = std::min(n_prompt - n_processed, n_batch); - tokens[0] = n_processed == 0 && llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab; + tokens[0] = n_processed == 0 && llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab; for (int i = 1; i < n_tokens; i++) { tokens[i] = std::rand() % n_vocab; } @@ -1366,8 +1423,8 @@ static void test_prompt(llama_context * ctx, int n_prompt, int n_batch, int n_th static void test_gen(llama_context * ctx, int n_gen, int n_threads) { llama_set_n_threads(ctx, n_threads, n_threads); - const llama_model * model = llama_get_model(ctx); - const int32_t n_vocab = llama_n_vocab(model); + const llama_model * model = llama_get_model(ctx); + const int32_t n_vocab = llama_n_vocab(model); llama_token token = llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab; @@ -1420,6 +1477,17 @@ int main(int argc, char ** argv) { cmd_params params = parse_cmd_params(argc, argv); + // initialize backends + ggml_backend_load_all(); + auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU); + if (!cpu_dev) { + fprintf(stderr, "%s: error: CPU backend is not loaded\n", __func__); + return 1; + } + auto * cpu_reg = ggml_backend_dev_backend_reg(cpu_dev); + auto * ggml_threadpool_new_fn = (decltype(ggml_threadpool_new) *) ggml_backend_reg_get_proc_address(cpu_reg, "ggml_threadpool_new"); + auto * ggml_threadpool_free_fn = (decltype(ggml_threadpool_free) *) ggml_backend_reg_get_proc_address(cpu_reg, "ggml_threadpool_free"); + // initialize llama.cpp if (!params.verbose) { llama_log_set(llama_null_log_callback, NULL); @@ -1430,7 +1498,7 @@ int main(int argc, char ** argv) { set_process_priority(params.prio); // initialize printer - std::unique_ptr p = create_printer(params.output_format); + std::unique_ptr p = create_printer(params.output_format); std::unique_ptr p_err = create_printer(params.output_format_stderr); if (p) { @@ -1445,13 +1513,13 @@ int main(int argc, char ** argv) { std::vector params_instances = get_cmd_params_instances(params); - llama_model * lmodel = nullptr; + llama_model * lmodel = nullptr; const cmd_params_instance * prev_inst = nullptr; - int params_idx = 0; + int params_idx = 0; auto params_count = params_instances.size(); for (const auto & inst : params_instances) { - params_idx ++; + params_idx++; if (params.progress) { fprintf(stderr, "llama-bench: benchmark %d/%ld: starting\n", params_idx, params_count); } @@ -1494,7 +1562,7 @@ int main(int argc, char ** argv) { tpp.poll = t.poll; tpp.prio = params.prio; - struct ggml_threadpool* threadpool = ggml_threadpool_new(&tpp); + struct ggml_threadpool * threadpool = ggml_threadpool_new_fn(&tpp); if (!threadpool) { fprintf(stderr, "%s: threadpool create failed : n_threads %d\n", __func__, tpp.n_threads); exit(1); @@ -1524,13 +1592,15 @@ int main(int argc, char ** argv) { if (t.n_prompt > 0) { if (params.progress) { - fprintf(stderr, "llama-bench: benchmark %d/%ld: prompt run %d/%d\n", params_idx, params_count, i + 1, params.reps); + fprintf(stderr, "llama-bench: benchmark %d/%ld: prompt run %d/%d\n", params_idx, params_count, + i + 1, params.reps); } test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads); } if (t.n_gen > 0) { if (params.progress) { - fprintf(stderr, "llama-bench: benchmark %d/%ld: generation run %d/%d\n", params_idx, params_count, i + 1, params.reps); + fprintf(stderr, "llama-bench: benchmark %d/%ld: generation run %d/%d\n", params_idx, params_count, + i + 1, params.reps); } test_gen(ctx, t.n_gen, t.n_threads); } @@ -1553,7 +1623,7 @@ int main(int argc, char ** argv) { llama_free(ctx); - ggml_threadpool_free(threadpool); + ggml_threadpool_free_fn(threadpool); } llama_free_model(lmodel); diff --git a/examples/llava/CMakeLists.txt b/examples/llava/CMakeLists.txt index bbf5fec58..5d32f377f 100644 --- a/examples/llava/CMakeLists.txt +++ b/examples/llava/CMakeLists.txt @@ -11,7 +11,7 @@ target_include_directories(llava PUBLIC .) target_include_directories(llava PUBLIC ../..) target_include_directories(llava PUBLIC ../../common) -target_compile_features(llava PRIVATE cxx_std_11) +target_compile_features(llava PRIVATE cxx_std_17) add_library(llava_static STATIC $) if (BUILD_SHARED_LIBS) @@ -35,11 +35,11 @@ add_executable(${TARGET} llava-cli.cpp) set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-llava-cli) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) set(TARGET llama-minicpmv-cli) add_executable(${TARGET} minicpmv-cli.cpp) set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-minicpmv-cli) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 14e02c8dd..d7c94352b 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -4,6 +4,7 @@ // Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch #include "clip.h" #include "ggml.h" +#include "ggml-cpu.h" #include "ggml-alloc.h" #include "ggml-backend.h" @@ -11,6 +12,10 @@ #include "ggml-cuda.h" #endif +#ifdef GGML_USE_SYCL +#include "ggml-sycl.h" +#endif + #ifdef GGML_USE_METAL #include "ggml-metal.h" #endif @@ -39,10 +44,17 @@ #include #include -#define LOG_INF(...) do { fprintf(stdout, __VA_ARGS__); } while (0) -#define LOG_WRN(...) do { fprintf(stderr, __VA_ARGS__); } while (0) -#define LOG_ERR(...) do { fprintf(stderr, __VA_ARGS__); } while (0) -#define LOG_DBG(...) do { fprintf(stderr, __VA_ARGS__); } while (0) +#if defined(LLAVA_LOG_OFF) +# define LOG_INF(...) +# define LOG_WRN(...) +# define LOG_ERR(...) +# define LOG_DBG(...) +#else // defined(LLAVA_LOG_OFF) +# define LOG_INF(...) do { fprintf(stdout, __VA_ARGS__); } while (0) +# define LOG_WRN(...) do { fprintf(stderr, __VA_ARGS__); } while (0) +# define LOG_ERR(...) do { fprintf(stderr, __VA_ARGS__); } while (0) +# define LOG_DBG(...) do { fprintf(stdout, __VA_ARGS__); } while (0) +#endif // defined(LLAVA_LOG_OFF) //#define CLIP_DEBUG_FUNCTIONS @@ -1161,6 +1173,11 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { LOG_INF("%s: CLIP using Vulkan backend\n", __func__); #endif +#ifdef GGML_USE_SYCL + new_clip->backend = ggml_backend_sycl_init(0); + LOG_INF("%s: CLIP using SYCL backend\n", __func__); +#endif + if (!new_clip->backend) { new_clip->backend = ggml_backend_cpu_init(); LOG_INF("%s: CLIP using CPU backend\n", __func__); diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index 161098585..2691c6e6b 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -191,7 +191,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_ LOG("\n"); - struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sparams); + struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sampling); if (!smpl) { LOG_ERR("%s: failed to initialize sampling subsystem\n", __func__); exit(1); diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index be6988540..4ca53a0b8 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -11,13 +11,17 @@ #include #include -#define die(msg) do { fputs("error: " msg "\n", stderr); exit(1); } while (0) -#define die_fmt(fmt, ...) do { fprintf(stderr, "error: " fmt "\n", __VA_ARGS__); exit(1); } while (0) - -#define LOG_INF(...) do { fprintf(stdout, __VA_ARGS__); } while (0) -#define LOG_WRN(...) do { fprintf(stderr, __VA_ARGS__); } while (0) -#define LOG_ERR(...) do { fprintf(stderr, __VA_ARGS__); } while (0) -#define LOG_DBG(...) do { fprintf(stdout, __VA_ARGS__); } while (0) +#if defined(LLAVA_LOG_OFF) +# define LOG_INF(...) +# define LOG_WRN(...) +# define LOG_ERR(...) +# define LOG_DBG(...) +#else // defined(LLAVA_LOG_OFF) +# define LOG_INF(...) do { fprintf(stdout, __VA_ARGS__); } while (0) +# define LOG_WRN(...) do { fprintf(stderr, __VA_ARGS__); } while (0) +# define LOG_ERR(...) do { fprintf(stderr, __VA_ARGS__); } while (0) +# define LOG_DBG(...) do { fprintf(stdout, __VA_ARGS__); } while (0) +#endif // defined(LLAVA_LOG_OFF) // RGB uint8 image struct clip_image_u8 { @@ -498,10 +502,16 @@ static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long errno = 0; size_t ret = fread(buffer, 1, fileSize, file); // Read the file into the buffer if (ferror(file)) { - die_fmt("read error: %s", strerror(errno)); + LOG_ERR("read error: %s", strerror(errno)); + free(buffer); + fclose(file); + return false; } if (ret != (size_t) fileSize) { - die("unexpectedly reached end of file"); + LOG_ERR("unexpectedly reached end of file"); + free(buffer); + fclose(file); + return false; } fclose(file); // Close the file diff --git a/examples/llava/minicpmv-cli.cpp b/examples/llava/minicpmv-cli.cpp index cbecec343..e9cbb51ed 100644 --- a/examples/llava/minicpmv-cli.cpp +++ b/examples/llava/minicpmv-cli.cpp @@ -237,7 +237,7 @@ static struct common_sampler * llama_init(struct llava_context * ctx_llava, comm LOG_INF("\n"); - struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sparams); + struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sampling); return smpl; } diff --git a/examples/lookahead/CMakeLists.txt b/examples/lookahead/CMakeLists.txt index f0ae5cd89..346861314 100644 --- a/examples/lookahead/CMakeLists.txt +++ b/examples/lookahead/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-lookahead) add_executable(${TARGET} lookahead.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/lookahead/lookahead.cpp b/examples/lookahead/lookahead.cpp index 3c0ccfea2..8d0ef8b3d 100644 --- a/examples/lookahead/lookahead.cpp +++ b/examples/lookahead/lookahead.cpp @@ -115,7 +115,7 @@ int main(int argc, char ** argv) { llama_batch batch = llama_batch_init(params.n_ctx, 0, W + G + 1); // target model sampling context - struct common_sampler * smpl = common_sampler_init(model, params.sparams); + struct common_sampler * smpl = common_sampler_init(model, params.sampling); // verification n-grams std::vector ngrams_cur(G); diff --git a/examples/lookup/CMakeLists.txt b/examples/lookup/CMakeLists.txt index ef19fe25e..fba78ceda 100644 --- a/examples/lookup/CMakeLists.txt +++ b/examples/lookup/CMakeLists.txt @@ -2,22 +2,22 @@ set(TARGET llama-lookup) add_executable(${TARGET} lookup.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) set(TARGET llama-lookup-create) add_executable(${TARGET} lookup-create.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) set(TARGET llama-lookup-merge) add_executable(${TARGET} lookup-merge.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) set(TARGET llama-lookup-stats) add_executable(${TARGET} lookup-stats.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/lookup/lookup-stats.cpp b/examples/lookup/lookup-stats.cpp index 7faebe7ba..dff07c075 100644 --- a/examples/lookup/lookup-stats.cpp +++ b/examples/lookup/lookup-stats.cpp @@ -21,7 +21,7 @@ int main(int argc, char ** argv){ common_init(); - const int n_draft = params.n_draft; + const int n_draft = params.speculative.n_max; // init llama.cpp llama_backend_init(); @@ -40,6 +40,7 @@ int main(int argc, char ** argv){ common_ngram_cache ngram_cache_context; common_ngram_cache ngram_cache_dynamic; common_ngram_cache ngram_cache_static; + int64_t t_draft_flat_us = 0; int64_t t_draft_us = 0; diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp index a04728b18..4d92bb238 100644 --- a/examples/lookup/lookup.cpp +++ b/examples/lookup/lookup.cpp @@ -22,7 +22,7 @@ int main(int argc, char ** argv){ common_init(); // max. number of additional tokens to draft if match is found - const int n_draft = params.n_draft; + const int n_draft = params.speculative.n_max; const bool dump_kv_cache = params.dump_kv_cache; @@ -102,7 +102,7 @@ int main(int argc, char ** argv){ bool has_eos = false; - struct common_sampler * smpl = common_sampler_init(model, params.sparams); + struct common_sampler * smpl = common_sampler_init(model, params.sampling); std::vector draft; diff --git a/examples/main-cmake-pkg/CMakeLists.txt b/examples/main-cmake-pkg/CMakeLists.txt index 3b38db292..5563f4de0 100644 --- a/examples/main-cmake-pkg/CMakeLists.txt +++ b/examples/main-cmake-pkg/CMakeLists.txt @@ -29,4 +29,4 @@ add_executable(${TARGET} ${CMAKE_CURRENT_LIST_DIR}/../main/main.cpp) target_include_directories(${TARGET} PRIVATE ${_common_path}) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/main/CMakeLists.txt b/examples/main/CMakeLists.txt index 5f6efaa9a..af3d9150f 100644 --- a/examples/main/CMakeLists.txt +++ b/examples/main/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-cli) add_executable(${TARGET} main.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/main/README.md b/examples/main/README.md index 145216938..7787f7b11 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -66,7 +66,7 @@ In this section, we cover the most commonly used options for running the `llama- - `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (e.g [https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/resolve/main/gemma-1.1-7b-it.Q4_K_M.gguf?download=true](https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/resolve/main/gemma-1.1-7b-it.Q4_K_M.gguf?download=true)). - `-i, --interactive`: Run the program in interactive mode, allowing you to provide input directly and receive real-time responses. - `-n N, --n-predict N`: Set the number of tokens to predict when generating text. Adjusting this value can influence the length of the generated text. -- `-c N, --ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. +- `-c N, --ctx-size N`: Set the size of the prompt context. The default is 4096, but if a LLaMA model was built with a longer context, increasing this value will provide better results for longer input/inference. - `-mli, --multiline-input`: Allows you to write or paste multiple lines without ending each in '\' - `-t N, --threads N`: Set the number of threads to use during generation. For optimal performance, it is recommended to set this value to the number of physical CPU cores your system has. - `-ngl N, --n-gpu-layers N`: When compiled with GPU support, this option allows offloading some layers to the GPU for computation. Generally results in increased performance. @@ -131,7 +131,7 @@ During text generation, LLaMA models have a limited context size, which means th ### Context Size -- `-c N, --ctx-size N`: Set the size of the prompt context (default: 0, 0 = loaded from model). The LLaMA models were built with a context of 2048-8192, which will yield the best results on longer input/inference. +- `-c N, --ctx-size N`: Set the size of the prompt context (default: 4096, 0 = loaded from model). If a LLaMA model was built with a longer context, increasing this value will yield the best results on longer input/inference. ### Extended Context Size @@ -348,6 +348,7 @@ These options provide extra functionality and customization when running the LLa - `-h, --help`: Display a help message showing all available options and their default values. This is particularly useful for checking the latest options and default values, as they can change frequently, and the information in this document may become outdated. - `--verbose-prompt`: Print the prompt before generating text. +- `--no-display-prompt`: Don't print prompt at generation. - `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. - `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. - `-hfr URL --hf-repo URL`: The url to the Hugging Face model repository. Used in conjunction with `--hf-file` or `-hff`. The model is downloaded and stored in the file provided by `-m` or `--model`. If `-m` is not provided, the model is auto-stored in the path specified by the `LLAMA_CACHE` environment variable or in an OS-specific local cache. diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 2c6ab8e20..a9aa9cab7 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -62,49 +62,6 @@ static bool file_is_empty(const std::string & path) { return f.tellg() == 0; } -static void write_logfile( - const llama_context * ctx, const common_params & params, const llama_model * model, - const std::vector & input_tokens, const std::string & output, - const std::vector & output_tokens -) { - if (params.logdir.empty()) { - return; - } - - const std::string timestamp = string_get_sortable_timestamp(); - - const bool success = fs_create_directory_with_parents(params.logdir); - if (!success) { - LOG_ERR("%s: failed to create logdir %s, cannot write logfile\n", __func__, params.logdir.c_str()); - return; - } - - const std::string logfile_path = params.logdir + timestamp + ".yml"; - FILE * logfile = fopen(logfile_path.c_str(), "w"); - - if (logfile == NULL) { - LOG_ERR("%s: failed to open logfile %s\n", __func__, logfile_path.c_str()); - return; - } - - fprintf(logfile, "binary: main\n"); - char model_desc[128]; - llama_model_desc(model, model_desc, sizeof(model_desc)); - yaml_dump_non_result_info(logfile, params, ctx, timestamp, input_tokens, model_desc); - - fprintf(logfile, "\n"); - fprintf(logfile, "######################\n"); - fprintf(logfile, "# Generation Results #\n"); - fprintf(logfile, "######################\n"); - fprintf(logfile, "\n"); - - yaml_dump_string_multiline(logfile, "output", output.c_str()); - yaml_dump_vector_int(logfile, "output_tokens", output_tokens); - - llama_perf_dump_yaml(logfile, ctx); - fclose(logfile); -} - #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32) static void sigint_handler(int signo) { if (signo == SIGINT) { @@ -115,7 +72,6 @@ static void sigint_handler(int signo) { console::cleanup(); LOG("\n"); common_perf_print(*g_ctx, *g_smpl); - write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, *g_output_s, *g_output_tokens); // make sure all logs are flushed LOG("Interrupted by user\n"); @@ -144,7 +100,7 @@ int main(int argc, char ** argv) { common_init(); - auto & sparams = params.sparams; + auto & sparams = params.sampling; // save choice to use color for later // (note for later: this is a slightly awkward choice) @@ -209,6 +165,10 @@ int main(int argc, char ** argv) { LOG_INF("%s: llama threadpool init, n_threads = %d\n", __func__, (int) params.cpuparams.n_threads); + auto * reg = ggml_backend_dev_backend_reg(ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU)); + auto * ggml_threadpool_new_fn = (decltype(ggml_threadpool_new) *) ggml_backend_reg_get_proc_address(reg, "ggml_threadpool_new"); + auto * ggml_threadpool_free_fn = (decltype(ggml_threadpool_free) *) ggml_backend_reg_get_proc_address(reg, "ggml_threadpool_free"); + struct ggml_threadpool_params tpp_batch = ggml_threadpool_params_from_cpu_params(params.cpuparams_batch); struct ggml_threadpool_params tpp = @@ -218,7 +178,7 @@ int main(int argc, char ** argv) { struct ggml_threadpool * threadpool_batch = NULL; if (!ggml_threadpool_params_match(&tpp, &tpp_batch)) { - threadpool_batch = ggml_threadpool_new(&tpp_batch); + threadpool_batch = ggml_threadpool_new_fn(&tpp_batch); if (!threadpool_batch) { LOG_ERR("%s: batch threadpool create failed : n_threads %d\n", __func__, tpp_batch.n_threads); return 1; @@ -228,7 +188,7 @@ int main(int argc, char ** argv) { tpp.paused = true; } - struct ggml_threadpool * threadpool = ggml_threadpool_new(&tpp); + struct ggml_threadpool * threadpool = ggml_threadpool_new_fn(&tpp); if (!threadpool) { LOG_ERR("%s: threadpool create failed : n_threads %d\n", __func__, tpp.n_threads); return 1; @@ -912,7 +872,6 @@ int main(int argc, char ** argv) { LOG("\n\n"); common_perf_print(ctx, smpl); - write_logfile(ctx, params, model, input_tokens, output_s, output_tokens); common_sampler_free(smpl); @@ -921,8 +880,8 @@ int main(int argc, char ** argv) { llama_backend_free(); - ggml_threadpool_free(threadpool); - ggml_threadpool_free(threadpool_batch); + ggml_threadpool_free_fn(threadpool); + ggml_threadpool_free_fn(threadpool_batch); return 0; } diff --git a/examples/parallel/CMakeLists.txt b/examples/parallel/CMakeLists.txt index c13557bac..847e916de 100644 --- a/examples/parallel/CMakeLists.txt +++ b/examples/parallel/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-parallel) add_executable(${TARGET} parallel.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp index 43c8f3ed5..fd2b1c011 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -160,7 +160,7 @@ int main(int argc, char ** argv) { for (size_t i = 0; i < clients.size(); ++i) { auto & client = clients[i]; client.id = i; - client.smpl = common_sampler_init(model, params.sparams); + client.smpl = common_sampler_init(model, params.sampling); } std::vector tokens_system; diff --git a/examples/passkey/CMakeLists.txt b/examples/passkey/CMakeLists.txt index dc467a5d3..9bc5110c2 100644 --- a/examples/passkey/CMakeLists.txt +++ b/examples/passkey/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-passkey) add_executable(${TARGET} passkey.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/perplexity/CMakeLists.txt b/examples/perplexity/CMakeLists.txt index be0f2fd02..3e6864093 100644 --- a/examples/perplexity/CMakeLists.txt +++ b/examples/perplexity/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-perplexity) add_executable(${TARGET} perplexity.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index e803ff143..64a84607c 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -34,55 +34,6 @@ struct results_log_softmax { float prob; }; -static void write_logfile( - const llama_context * ctx, const common_params & params, const llama_model * model, - const struct results_perplexity & results -) { - if (params.logdir.empty()) { - return; - } - - if (params.hellaswag) { - LOG_WRN("%s: logging results is not implemented for HellaSwag. No files will be written.\n", __func__); - return; - } - - const std::string timestamp = string_get_sortable_timestamp(); - - const bool success = fs_create_directory_with_parents(params.logdir); - if (!success) { - LOG_WRN("%s: failed to create logdir %s, cannot write logfile\n", - __func__, params.logdir.c_str()); - return; - } - - const std::string logfile_path = params.logdir + timestamp + ".yml"; - FILE * logfile = fopen(logfile_path.c_str(), "w"); - - if (logfile == NULL) { - LOG_ERR("%s: failed to open logfile %s\n", __func__, logfile_path.c_str()); - return; - } - - fprintf(logfile, "binary: main\n"); - char model_desc[128]; - llama_model_desc(model, model_desc, sizeof(model_desc)); - yaml_dump_non_result_info(logfile, params, ctx, timestamp, results.tokens, model_desc); - - fprintf(logfile, "\n"); - fprintf(logfile, "######################\n"); - fprintf(logfile, "# Perplexity Results #\n"); - fprintf(logfile, "######################\n"); - fprintf(logfile, "\n"); - - yaml_dump_vector_float(logfile, "logits", results.logits); - fprintf(logfile, "ppl_value: %f\n", results.ppl_value); - yaml_dump_vector_float(logfile, "probs", results.probs); - - llama_perf_dump_yaml(logfile, ctx); - fclose(logfile); -} - static std::vector softmax(const std::vector& logits) { std::vector probs(logits.size()); float max_logit = logits[0]; @@ -2072,8 +2023,6 @@ int main(int argc, char ** argv) { LOG("\n"); llama_perf_context_print(ctx); - write_logfile(ctx, params, model, results); - llama_free(ctx); llama_free_model(model); diff --git a/examples/quantize-stats/CMakeLists.txt b/examples/quantize-stats/CMakeLists.txt index bb986a716..9a3a0d3cd 100644 --- a/examples/quantize-stats/CMakeLists.txt +++ b/examples/quantize-stats/CMakeLists.txt @@ -3,4 +3,4 @@ add_executable(${TARGET} quantize-stats.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT}) target_include_directories(${TARGET} PRIVATE ../../common) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp index e372856c6..912caf346 100644 --- a/examples/quantize-stats/quantize-stats.cpp +++ b/examples/quantize-stats/quantize-stats.cpp @@ -142,7 +142,7 @@ static bool tensor_is_contiguous(const struct ggml_tensor * tensor) { } static void test_roundtrip_on_chunk( - const ggml_tensor * layer, int64_t offset, int64_t chunk_size, const ggml_type_traits & qfns, bool use_reference, + const ggml_tensor * layer, int64_t offset, int64_t chunk_size, const ggml_type_traits & qfns, const ggml_type_traits_cpu & qfns_cpu, bool use_reference, float * input_scratch, char * quantized_scratch, float * output_scratch, error_stats & stats ) { if (layer->type == GGML_TYPE_F16) { @@ -156,7 +156,7 @@ static void test_roundtrip_on_chunk( if (use_reference) { qfns.from_float_ref(input_scratch, quantized_scratch, chunk_size); } else { - qfns.from_float(input_scratch, quantized_scratch, chunk_size); + qfns_cpu.from_float(input_scratch, quantized_scratch, chunk_size); } qfns.to_float(quantized_scratch, output_scratch, chunk_size); @@ -166,7 +166,7 @@ static void test_roundtrip_on_chunk( // Run quantization function for a single layer and update error stats static void test_roundtrip_on_layer( - std::string & name, bool print_layer_stats, const ggml_type_traits & qfns, bool use_reference, + std::string & name, bool print_layer_stats, const ggml_type_traits & qfns, const ggml_type_traits_cpu & qfns_cpu, bool use_reference, const ggml_tensor * layer, std::vector & input_scratch, std::vector & quantized_scratch, std::vector & output_scratch, error_stats & total_error, int max_thread = 0 ) { @@ -187,13 +187,13 @@ static void test_roundtrip_on_layer( int num_chunks = (nelements + chunk_size - 1)/chunk_size; if (num_chunks < 2 || max_thread < 2) { - test_roundtrip_on_chunk(layer, 0, nelements, qfns, use_reference, input_scratch_ptr, quantized_scratch.data(), + test_roundtrip_on_chunk(layer, 0, nelements, qfns, qfns_cpu, use_reference, input_scratch_ptr, quantized_scratch.data(), output_scratch.data(), print_layer_stats ? layer_error : total_error); } else { auto & stats = print_layer_stats ? layer_error : total_error; std::mutex mutex; uint64_t counter = 0; - auto compute = [&mutex, &counter, &stats, &qfns, nelements, layer, use_reference, input_scratch_ptr, + auto compute = [&mutex, &counter, &stats, &qfns, &qfns_cpu, nelements, layer, use_reference, input_scratch_ptr, &quantized_scratch, &output_scratch, chunk_size] () { error_stats local_stats {}; while (true) { @@ -205,7 +205,7 @@ static void test_roundtrip_on_layer( } lock.unlock(); uint64_t chunk = offset + chunk_size < nelements ? chunk_size : nelements - offset; - test_roundtrip_on_chunk(layer, offset, chunk, qfns, use_reference, input_scratch_ptr + offset, + test_roundtrip_on_chunk(layer, offset, chunk, qfns, qfns_cpu, use_reference, input_scratch_ptr + offset, quantized_scratch.data() + 4*offset, output_scratch.data() + offset, local_stats); } }; @@ -371,8 +371,9 @@ int main(int argc, char ** argv) { if (!params.include_types.empty() && std::find(params.include_types.begin(), params.include_types.end(), i) == params.include_types.end()) { continue; } - const auto * qfns = ggml_get_type_traits(type); - if (qfns->from_float && qfns->to_float) { + const auto * qfns = ggml_get_type_traits(type); + const auto * qfns_cpu = ggml_get_type_traits_cpu(type); + if (qfns_cpu->from_float && qfns->to_float) { if (params.verbose) { printf("testing %s ...\n", ggml_type_name(type)); } @@ -393,7 +394,7 @@ int main(int argc, char ** argv) { test_roundtrip_on_layer( layer_name, params.per_layer_stats, - *qfns, + *qfns, *qfns_cpu, params.reference, kv_tensor.second, input_scratch, diff --git a/examples/quantize/CMakeLists.txt b/examples/quantize/CMakeLists.txt index 62680cda4..47e5cbe30 100644 --- a/examples/quantize/CMakeLists.txt +++ b/examples/quantize/CMakeLists.txt @@ -3,4 +3,4 @@ add_executable(${TARGET} quantize.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_include_directories(${TARGET} PRIVATE ../../common) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/retrieval/CMakeLists.txt b/examples/retrieval/CMakeLists.txt index 66610f311..512a602ec 100644 --- a/examples/retrieval/CMakeLists.txt +++ b/examples/retrieval/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-retrieval) add_executable(${TARGET} retrieval.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/retrieval/retrieval.cpp b/examples/retrieval/retrieval.cpp index 1768aae51..e78a8596d 100644 --- a/examples/retrieval/retrieval.cpp +++ b/examples/retrieval/retrieval.cpp @@ -282,8 +282,8 @@ int main(int argc, char ** argv) { return a.second > b.second; }); - LOG("Top %d similar chunks:\n", params.sparams.top_k); - for (int i = 0; i < std::min(params.sparams.top_k, (int) chunks.size()); i++) { + LOG("Top %d similar chunks:\n", params.sampling.top_k); + for (int i = 0; i < std::min(params.sampling.top_k, (int) chunks.size()); i++) { LOG("filename: %s\n", chunks[similarities[i].first].filename.c_str()); LOG("filepos: %lld\n", (long long int) chunks[similarities[i].first].filepos); LOG("similarity: %f\n", similarities[i].second); diff --git a/examples/rpc/rpc-server.cpp b/examples/rpc/rpc-server.cpp index 8354e37e5..5fe70dac7 100644 --- a/examples/rpc/rpc-server.cpp +++ b/examples/rpc/rpc-server.cpp @@ -1,3 +1,5 @@ +#include "ggml-cpu.h" + #ifdef GGML_USE_CUDA #include "ggml-cuda.h" #endif diff --git a/examples/run/CMakeLists.txt b/examples/run/CMakeLists.txt new file mode 100644 index 000000000..52add51ef --- /dev/null +++ b/examples/run/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET llama-run) +add_executable(${TARGET} run.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/run/README.md b/examples/run/README.md new file mode 100644 index 000000000..6e926811f --- /dev/null +++ b/examples/run/README.md @@ -0,0 +1,7 @@ +# llama.cpp/example/run + +The purpose of this example is to demonstrate a minimal usage of llama.cpp for running models. + +```bash +./llama-run Meta-Llama-3.1-8B-Instruct.gguf +... diff --git a/examples/run/run.cpp b/examples/run/run.cpp new file mode 100644 index 000000000..cac2faefc --- /dev/null +++ b/examples/run/run.cpp @@ -0,0 +1,409 @@ +#if defined(_WIN32) +#include +#else +#include +#endif + +#include +#include +#include +#include +#include +#include +#include +#include + +#include "llama-cpp.h" + +typedef std::unique_ptr char_array_ptr; + +struct Argument { + std::string flag; + std::string help_text; +}; + +struct Options { + std::string model_path, prompt_non_interactive; + int ngl = 99; + int n_ctx = 2048; +}; + +class ArgumentParser { + public: + ArgumentParser(const char * program_name) : program_name(program_name) {} + + void add_argument(const std::string & flag, std::string & var, const std::string & help_text = "") { + string_args[flag] = &var; + arguments.push_back({flag, help_text}); + } + + void add_argument(const std::string & flag, int & var, const std::string & help_text = "") { + int_args[flag] = &var; + arguments.push_back({flag, help_text}); + } + + int parse(int argc, const char ** argv) { + for (int i = 1; i < argc; ++i) { + std::string arg = argv[i]; + if (string_args.count(arg)) { + if (i + 1 < argc) { + *string_args[arg] = argv[++i]; + } else { + fprintf(stderr, "error: missing value for %s\n", arg.c_str()); + print_usage(); + return 1; + } + } else if (int_args.count(arg)) { + if (i + 1 < argc) { + if (parse_int_arg(argv[++i], *int_args[arg]) != 0) { + fprintf(stderr, "error: invalid value for %s: %s\n", arg.c_str(), argv[i]); + print_usage(); + return 1; + } + } else { + fprintf(stderr, "error: missing value for %s\n", arg.c_str()); + print_usage(); + return 1; + } + } else { + fprintf(stderr, "error: unrecognized argument %s\n", arg.c_str()); + print_usage(); + return 1; + } + } + + if (string_args["-m"]->empty()) { + fprintf(stderr, "error: -m is required\n"); + print_usage(); + return 1; + } + + return 0; + } + + private: + const char * program_name; + std::unordered_map string_args; + std::unordered_map int_args; + std::vector arguments; + + int parse_int_arg(const char * arg, int & value) { + char * end; + const long val = std::strtol(arg, &end, 10); + if (*end == '\0' && val >= INT_MIN && val <= INT_MAX) { + value = static_cast(val); + return 0; + } + return 1; + } + + void print_usage() const { + printf("\nUsage:\n"); + printf(" %s [OPTIONS]\n\n", program_name); + printf("Options:\n"); + for (const auto & arg : arguments) { + printf(" %-10s %s\n", arg.flag.c_str(), arg.help_text.c_str()); + } + + printf("\n"); + } +}; + +class LlamaData { + public: + llama_model_ptr model; + llama_sampler_ptr sampler; + llama_context_ptr context; + std::vector messages; + + int init(const Options & opt) { + model = initialize_model(opt.model_path, opt.ngl); + if (!model) { + return 1; + } + + context = initialize_context(model, opt.n_ctx); + if (!context) { + return 1; + } + + sampler = initialize_sampler(); + return 0; + } + + private: + // Initializes the model and returns a unique pointer to it + llama_model_ptr initialize_model(const std::string & model_path, const int ngl) { + llama_model_params model_params = llama_model_default_params(); + model_params.n_gpu_layers = ngl; + + llama_model_ptr model(llama_load_model_from_file(model_path.c_str(), model_params)); + if (!model) { + fprintf(stderr, "%s: error: unable to load model\n", __func__); + } + + return model; + } + + // Initializes the context with the specified parameters + llama_context_ptr initialize_context(const llama_model_ptr & model, const int n_ctx) { + llama_context_params ctx_params = llama_context_default_params(); + ctx_params.n_ctx = n_ctx; + ctx_params.n_batch = n_ctx; + + llama_context_ptr context(llama_new_context_with_model(model.get(), ctx_params)); + if (!context) { + fprintf(stderr, "%s: error: failed to create the llama_context\n", __func__); + } + + return context; + } + + // Initializes and configures the sampler + llama_sampler_ptr initialize_sampler() { + llama_sampler_ptr sampler(llama_sampler_chain_init(llama_sampler_chain_default_params())); + llama_sampler_chain_add(sampler.get(), llama_sampler_init_min_p(0.05f, 1)); + llama_sampler_chain_add(sampler.get(), llama_sampler_init_temp(0.8f)); + llama_sampler_chain_add(sampler.get(), llama_sampler_init_dist(LLAMA_DEFAULT_SEED)); + + return sampler; + } +}; + +// Add a message to `messages` and store its content in `owned_content` +static void add_message(const char * role, const std::string & text, LlamaData & llama_data, + std::vector & owned_content) { + char_array_ptr content(new char[text.size() + 1]); + std::strcpy(content.get(), text.c_str()); + llama_data.messages.push_back({role, content.get()}); + owned_content.push_back(std::move(content)); +} + +// Function to apply the chat template and resize `formatted` if needed +static int apply_chat_template(const LlamaData & llama_data, std::vector & formatted, const bool append) { + int result = llama_chat_apply_template(llama_data.model.get(), nullptr, llama_data.messages.data(), + llama_data.messages.size(), append, formatted.data(), formatted.size()); + if (result > static_cast(formatted.size())) { + formatted.resize(result); + result = llama_chat_apply_template(llama_data.model.get(), nullptr, llama_data.messages.data(), + llama_data.messages.size(), append, formatted.data(), formatted.size()); + } + + return result; +} + +// Function to tokenize the prompt +static int tokenize_prompt(const llama_model_ptr & model, const std::string & prompt, + std::vector & prompt_tokens) { + const int n_prompt_tokens = -llama_tokenize(model.get(), prompt.c_str(), prompt.size(), NULL, 0, true, true); + prompt_tokens.resize(n_prompt_tokens); + if (llama_tokenize(model.get(), prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), true, + true) < 0) { + GGML_ABORT("failed to tokenize the prompt\n"); + } + + return n_prompt_tokens; +} + +// Check if we have enough space in the context to evaluate this batch +static int check_context_size(const llama_context_ptr & ctx, const llama_batch & batch) { + const int n_ctx = llama_n_ctx(ctx.get()); + const int n_ctx_used = llama_get_kv_cache_used_cells(ctx.get()); + if (n_ctx_used + batch.n_tokens > n_ctx) { + printf("\033[0m\n"); + fprintf(stderr, "context size exceeded\n"); + return 1; + } + + return 0; +} + +// convert the token to a string +static int convert_token_to_string(const llama_model_ptr & model, const llama_token token_id, std::string & piece) { + char buf[256]; + int n = llama_token_to_piece(model.get(), token_id, buf, sizeof(buf), 0, true); + if (n < 0) { + GGML_ABORT("failed to convert token to piece\n"); + } + + piece = std::string(buf, n); + return 0; +} + +static void print_word_and_concatenate_to_response(const std::string & piece, std::string & response) { + printf("%s", piece.c_str()); + fflush(stdout); + response += piece; +} + +// helper function to evaluate a prompt and generate a response +static int generate(LlamaData & llama_data, const std::string & prompt, std::string & response) { + std::vector prompt_tokens; + const int n_prompt_tokens = tokenize_prompt(llama_data.model, prompt, prompt_tokens); + if (n_prompt_tokens < 0) { + return 1; + } + + // prepare a batch for the prompt + llama_batch batch = llama_batch_get_one(prompt_tokens.data(), prompt_tokens.size()); + llama_token new_token_id; + while (true) { + check_context_size(llama_data.context, batch); + if (llama_decode(llama_data.context.get(), batch)) { + GGML_ABORT("failed to decode\n"); + } + + // sample the next token, check is it an end of generation? + new_token_id = llama_sampler_sample(llama_data.sampler.get(), llama_data.context.get(), -1); + if (llama_token_is_eog(llama_data.model.get(), new_token_id)) { + break; + } + + std::string piece; + if (convert_token_to_string(llama_data.model, new_token_id, piece)) { + return 1; + } + + print_word_and_concatenate_to_response(piece, response); + + // prepare the next batch with the sampled token + batch = llama_batch_get_one(&new_token_id, 1); + } + + return 0; +} + +static int parse_arguments(const int argc, const char ** argv, Options & opt) { + ArgumentParser parser(argv[0]); + parser.add_argument("-m", opt.model_path, "model"); + parser.add_argument("-p", opt.prompt_non_interactive, "prompt"); + parser.add_argument("-c", opt.n_ctx, "context_size"); + parser.add_argument("-ngl", opt.ngl, "n_gpu_layers"); + if (parser.parse(argc, argv)) { + return 1; + } + + return 0; +} + +static int read_user_input(std::string & user) { + std::getline(std::cin, user); + return user.empty(); // Indicate an error or empty input +} + +// Function to generate a response based on the prompt +static int generate_response(LlamaData & llama_data, const std::string & prompt, std::string & response) { + // Set response color + printf("\033[33m"); + if (generate(llama_data, prompt, response)) { + fprintf(stderr, "failed to generate response\n"); + return 1; + } + + // End response with color reset and newline + printf("\n\033[0m"); + return 0; +} + +// Helper function to apply the chat template and handle errors +static int apply_chat_template_with_error_handling(const LlamaData & llama_data, std::vector & formatted, + const bool is_user_input, int & output_length) { + const int new_len = apply_chat_template(llama_data, formatted, is_user_input); + if (new_len < 0) { + fprintf(stderr, "failed to apply the chat template\n"); + return -1; + } + + output_length = new_len; + return 0; +} + +// Helper function to handle user input +static bool handle_user_input(std::string & user_input, const std::string & prompt_non_interactive) { + if (!prompt_non_interactive.empty()) { + user_input = prompt_non_interactive; + return true; // No need for interactive input + } + + printf("\033[32m> \033[0m"); + return !read_user_input(user_input); // Returns false if input ends the loop +} + +// Function to tokenize the prompt +static int chat_loop(LlamaData & llama_data, std::string & prompt_non_interactive) { + std::vector owned_content; + std::vector fmtted(llama_n_ctx(llama_data.context.get())); + int prev_len = 0; + + while (true) { + // Get user input + std::string user_input; + if (!handle_user_input(user_input, prompt_non_interactive)) { + break; + } + + add_message("user", prompt_non_interactive.empty() ? user_input : prompt_non_interactive, llama_data, + owned_content); + + int new_len; + if (apply_chat_template_with_error_handling(llama_data, fmtted, true, new_len) < 0) { + return 1; + } + + std::string prompt(fmtted.begin() + prev_len, fmtted.begin() + new_len); + std::string response; + if (generate_response(llama_data, prompt, response)) { + return 1; + } + } + return 0; +} + +static void log_callback(const enum ggml_log_level level, const char * text, void *) { + if (level == GGML_LOG_LEVEL_ERROR) { + fprintf(stderr, "%s", text); + } +} + +static bool is_stdin_a_terminal() { +#if defined(_WIN32) + HANDLE hStdin = GetStdHandle(STD_INPUT_HANDLE); + DWORD mode; + return GetConsoleMode(hStdin, &mode); +#else + return isatty(STDIN_FILENO); +#endif +} + +static std::string read_pipe_data() { + std::ostringstream result; + result << std::cin.rdbuf(); // Read all data from std::cin + return result.str(); +} + +int main(int argc, const char ** argv) { + Options opt; + if (parse_arguments(argc, argv, opt)) { + return 1; + } + + if (!is_stdin_a_terminal()) { + if (!opt.prompt_non_interactive.empty()) { + opt.prompt_non_interactive += "\n\n"; + } + + opt.prompt_non_interactive += read_pipe_data(); + } + + llama_log_set(log_callback, nullptr); + LlamaData llama_data; + if (llama_data.init(opt)) { + return 1; + } + + if (chat_loop(llama_data, opt.prompt_non_interactive)) { + return 1; + } + + return 0; +} diff --git a/examples/save-load-state/CMakeLists.txt b/examples/save-load-state/CMakeLists.txt index 0fb5e359b..0f50e50de 100644 --- a/examples/save-load-state/CMakeLists.txt +++ b/examples/save-load-state/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-save-load-state) add_executable(${TARGET} save-load-state.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp index 8c49a52a6..2f0cf9baa 100644 --- a/examples/save-load-state/save-load-state.cpp +++ b/examples/save-load-state/save-load-state.cpp @@ -9,7 +9,7 @@ int main(int argc, char ** argv) { common_params params; params.prompt = "The quick brown fox"; - params.sparams.seed = 1234; + params.sampling.seed = 1234; if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) { return 1; @@ -42,7 +42,7 @@ int main(int argc, char ** argv) { llama_sampler * smpl = llama_sampler_chain_init(sparams); - llama_sampler_chain_add(smpl, llama_sampler_init_dist(params.sparams.seed)); + llama_sampler_chain_add(smpl, llama_sampler_init_dist(params.sampling.seed)); // tokenize prompt auto tokens = common_tokenize(ctx, params.prompt, true); @@ -106,7 +106,7 @@ int main(int argc, char ** argv) { llama_sampler * smpl2 = llama_sampler_chain_init(sparams); - llama_sampler_chain_add(smpl2, llama_sampler_init_dist(params.sparams.seed)); + llama_sampler_chain_add(smpl2, llama_sampler_init_dist(params.sampling.seed)); printf("\nsecond run: %s", params.prompt.c_str()); @@ -169,7 +169,7 @@ int main(int argc, char ** argv) { llama_sampler * smpl3 = llama_sampler_chain_init(sparams); - llama_sampler_chain_add(smpl3, llama_sampler_init_dist(params.sparams.seed)); + llama_sampler_chain_add(smpl3, llama_sampler_init_dist(params.sampling.seed)); printf("\nsingle seq run: %s", params.prompt.c_str()); diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index 3e717e882..0035859a6 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -15,21 +15,7 @@ set(TARGET_SRCS httplib.h ) set(PUBLIC_ASSETS - colorthemes.css - style.css - theme-beeninorder.css - theme-ketivah.css - theme-mangotango.css - theme-playground.css - theme-polarnight.css - theme-snowstorm.css index.html - index-new.html - index.js - completion.js - system-prompts.js - prompt-formats.js - json-schema-to-grammar.mjs loading.html ) @@ -42,11 +28,20 @@ foreach(asset ${PUBLIC_ASSETS}) OUTPUT "${output}" COMMAND "${CMAKE_COMMAND}" "-DINPUT=${input}" "-DOUTPUT=${output}" -P "${PROJECT_SOURCE_DIR}/scripts/xxd.cmake" ) + set_source_files_properties(${output} PROPERTIES GENERATED TRUE) endforeach() add_executable(${TARGET} ${TARGET_SRCS}) install(TARGETS ${TARGET} RUNTIME) +# clean up generated files in pre-build step +foreach(asset ${PUBLIC_ASSETS}) + set(output "${CMAKE_CURRENT_BINARY_DIR}/${asset}.hpp") + add_custom_command(TARGET ${TARGET} PRE_BUILD + COMMAND "${CMAKE_COMMAND}" -E remove -f "${output}" + ) +endforeach() + target_link_libraries(${TARGET} PRIVATE common ${CMAKE_THREAD_LIBS_INIT}) if (LLAMA_SERVER_SSL) @@ -59,4 +54,4 @@ if (WIN32) TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32) endif() -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/server/README.md b/examples/server/README.md index ba8fd14ac..4d1da8da0 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -39,7 +39,7 @@ The project is under active development, and we are [looking for feedback and co | `--cpu-strict-batch <0\|1>` | use strict CPU placement (default: same as --cpu-strict) | | `--prio-batch N` | set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0)
| | `--poll-batch <0\|1>` | use polling to wait for work (default: same as --poll) | -| `-c, --ctx-size N` | size of the prompt context (default: 0, 0 = loaded from model)
(env: LLAMA_ARG_CTX_SIZE) | +| `-c, --ctx-size N` | size of the prompt context (default: 4096, 0 = loaded from model)
(env: LLAMA_ARG_CTX_SIZE) | | `-n, --predict, --n-predict N` | number of tokens to predict (default: -1, -1 = infinity, -2 = until context filled)
(env: LLAMA_ARG_N_PREDICT) | | `-b, --batch-size N` | logical maximum batch size (default: 2048)
(env: LLAMA_ARG_BATCH) | | `-ub, --ubatch-size N` | physical maximum batch size (default: 512)
(env: LLAMA_ARG_UBATCH) | @@ -64,11 +64,13 @@ The project is under active development, and we are [looking for feedback and co | `-nkvo, --no-kv-offload` | disable KV offload
(env: LLAMA_ARG_NO_KV_OFFLOAD) | | `-ctk, --cache-type-k TYPE` | KV cache data type for K (default: f16)
(env: LLAMA_ARG_CACHE_TYPE_K) | | `-ctv, --cache-type-v TYPE` | KV cache data type for V (default: f16)
(env: LLAMA_ARG_CACHE_TYPE_V) | -| `-dt, --defrag-thold N` | KV cache defragmentation threshold (default: -1.0, < 0 - disabled)
(env: LLAMA_ARG_DEFRAG_THOLD) | +| `-dt, --defrag-thold N` | KV cache defragmentation threshold (default: 0.1, < 0 - disabled)
(env: LLAMA_ARG_DEFRAG_THOLD) | | `-np, --parallel N` | number of parallel sequences to decode (default: 1)
(env: LLAMA_ARG_N_PARALLEL) | | `--mlock` | force system to keep model in RAM rather than swapping or compressing
(env: LLAMA_ARG_MLOCK) | | `--no-mmap` | do not memory-map model (slower load but may reduce pageouts if not using mlock)
(env: LLAMA_ARG_NO_MMAP) | | `--numa TYPE` | attempt optimizations that help on some NUMA systems
- distribute: spread execution evenly over all nodes
- isolate: only spawn threads on CPUs on the node that execution started on
- numactl: use the CPU map provided by numactl
if run without this previously, it is recommended to drop the system page cache before using this
see https://github.com/ggerganov/llama.cpp/issues/1437
(env: LLAMA_ARG_NUMA) | +| `-dev, --device ` | comma-separated list of devices to use for offloading (none = don't offload)
use --list-devices to see a list of available devices
(env: LLAMA_ARG_DEVICE) | +| `--list-devices` | print list of available devices and exit | | `-ngl, --gpu-layers, --n-gpu-layers N` | number of layers to store in VRAM
(env: LLAMA_ARG_N_GPU_LAYERS) | | `-sm, --split-mode {none,layer,row}` | how to split the model across multiple GPUs, one of:
- none: use one GPU only
- layer (default): split layers and KV across GPUs
- row: split rows across GPUs
(env: LLAMA_ARG_SPLIT_MODE) | | `-ts, --tensor-split N0,N1,N2,...` | fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1
(env: LLAMA_ARG_TENSOR_SPLIT) | @@ -85,7 +87,6 @@ The project is under active development, and we are [looking for feedback and co | `-hfr, --hf-repo REPO` | Hugging Face model repository (default: unused)
(env: LLAMA_ARG_HF_REPO) | | `-hff, --hf-file FILE` | Hugging Face model file (default: unused)
(env: LLAMA_ARG_HF_FILE) | | `-hft, --hf-token TOKEN` | Hugging Face access token (default: value from HF_TOKEN environment variable)
(env: HF_TOKEN) | -| `-ld, --logdir LOGDIR` | path under which to save YAML logs (no logging if unset) | | `--log-disable` | Log disable | | `--log-file FNAME` | Log to file | | `--log-colors` | Enable colored logging
(env: LLAMA_LOG_COLORS) | @@ -99,25 +100,27 @@ The project is under active development, and we are [looking for feedback and co | Argument | Explanation | | -------- | ----------- | -| `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'
(default: top_k;typ_p;top_p;min_p;temperature) | +| `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'
(default: dry;top_k;typ_p;top_p;min_p;xtc;temperature) | | `-s, --seed SEED` | RNG seed (default: -1, use random seed for -1) | -| `--sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: kfypmt) | +| `--sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: dkypmxt) | | `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) | | `--penalize-nl` | penalize newline tokens (default: false) | | `--temp N` | temperature (default: 0.8) | | `--top-k N` | top-k sampling (default: 40, 0 = disabled) | | `--top-p N` | top-p sampling (default: 0.9, 1.0 = disabled) | | `--min-p N` | min-p sampling (default: 0.1, 0.0 = disabled) | +| `--xtc-probability N` | xtc probability (default: 0.0, 0.0 = disabled) | +| `--xtc-threshold N` | xtc threshold (default: 0.1, 1.0 = disabled) | | `--typical N` | locally typical sampling, parameter p (default: 1.0, 1.0 = disabled) | | `--repeat-last-n N` | last n tokens to consider for penalize (default: 64, 0 = disabled, -1 = ctx_size) | | `--repeat-penalty N` | penalize repeat sequence of tokens (default: 1.0, 1.0 = disabled) | | `--presence-penalty N` | repeat alpha presence penalty (default: 0.0, 0.0 = disabled) | | `--frequency-penalty N` | repeat alpha frequency penalty (default: 0.0, 0.0 = disabled) | -| `--dry-multiplier N` | DRY sampling multiplier (default: 0.0, 0.0 = disabled) | -| `--dry-base N` | DRY sampling base value (default: 1.75) | -| `--dry-allowed-length N` | allowed length for DRY sampling (default: 2) | -| `--dry-penalty-last-n N` | DRY penalty for the last n tokens (default: -1, 0 = disable, -1 = context size) | -| `--dry-sequence-breaker STRING` | add sequence breaker for DRY sampling, clearing out default breakers (`['\n', ':', '"', '*']`) in the process; use `"none"` to not use any sequence breakers +| `--dry-multiplier N` | set DRY sampling multiplier (default: 0.0, 0.0 = disabled) | +| `--dry-base N` | set DRY sampling base value (default: 1.75) | +| `--dry-allowed-length N` | set allowed length for DRY sampling (default: 2) | +| `--dry-penalty-last-n N` | set DRY penalty for the last n tokens (default: -1, 0 = disable, -1 = context size) | +| `--dry-sequence-breaker STRING` | add sequence breaker for DRY sampling, clearing out default breakers ('\n', ':', '"', '*') in the process; use "none" to not use any sequence breakers
| | `--dynatemp-range N` | dynamic temperature range (default: 0.0, 0.0 = disabled) | | `--dynatemp-exp N` | dynamic temperature exponent (default: 1.0) | | `--mirostat N` | use Mirostat sampling.
Top K, Nucleus and Locally Typical samplers are ignored if used.
(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) | @@ -157,9 +160,16 @@ The project is under active development, and we are [looking for feedback and co | `--props` | enable changing global properties via POST /props (default: disabled)
(env: LLAMA_ARG_ENDPOINT_PROPS) | | `--no-slots` | disables slots monitoring endpoint
(env: LLAMA_ARG_NO_ENDPOINT_SLOTS) | | `--slot-save-path PATH` | path to save slot kv cache (default: disabled) | -| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted:
https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
(env: LLAMA_ARG_CHAT_TEMPLATE) | +| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
list of built-in templates:
chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, exaone3, gemma, granite, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, monarch, openchat, orion, phi3, rwkv-world, vicuna, vicuna-orca, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE) | | `-sps, --slot-prompt-similarity SIMILARITY` | how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.50, 0.0 = disabled)
| | `--lora-init-without-apply` | load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: disabled) | +| `--draft-max, --draft, --draft-n N` | number of tokens to draft for speculative decoding (default: 16) | +| `--draft-min, --draft-n-min N` | minimum number of draft tokens to use for speculative decoding (default: 5) | +| `--draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.9) | +| `-cd, --ctx-size-draft N` | size of the prompt context for the draft model (default: 0, 0 = loaded from model) | +| `-devd, --device-draft ` | comma-separated list of devices to use for offloading the draft model (none = don't offload)
use --list-devices to see a list of available devices | +| `-ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | number of layers to store in VRAM for the draft model | +| `-md, --model-draft FNAME` | draft model for speculative decoding (default: unused) | Note: If both command line argument and environment variable are both set for the same param, the argument will take precedence over env var. @@ -187,12 +197,6 @@ services: `llama-server` is built alongside everything else from the root of the project -- Using `make`: - - ```bash - make llama-server - ``` - - Using `CMake`: ```bash @@ -206,15 +210,6 @@ services: `llama-server` can also be built with SSL support using OpenSSL 3 -- Using `make`: - - ```bash - # NOTE: For non-system openssl, use the following: - # CXXFLAGS="-I /path/to/openssl/include" - # LDFLAGS="-L /path/to/openssl/lib" - make LLAMA_SERVER_SSL=true llama-server - ``` - - Using `CMake`: ```bash @@ -222,6 +217,37 @@ services: cmake --build build --config Release -t llama-server ``` +## Web UI + +The project includes a web-based user interface that enables interaction with the model through the `/chat/completions` endpoint. + +The web UI is developed using: +- `vue` framework for frontend development +- `tailwindcss` and `daisyui` for styling +- `vite` for build tooling + +A pre-built version is available as a single HTML file under `/public` directory. + +To build or to run the dev server (with hot reload): + +```sh +# make sure you have nodejs installed +cd examples/server/webui +npm i + +# to run the dev server +npm run dev + +# to build the public/index.html +npm run build +``` + +NOTE: if you are using the vite dev server, you can change the API base URL to llama.cpp. To do that, run this code snippet in browser's console: + +```js +localStorage.setItem('base', 'http://localhost:8080') +``` + ## Quick Start To get started right away, run the following command, making sure to use the correct path for the model you have: @@ -316,100 +342,106 @@ node index.js ### POST `/completion`: Given a `prompt`, it returns the predicted completion. - *Options:* +*Options:* - `prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, if `cache_prompt` is `true`, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. A `BOS` token is inserted at the start, if all of the following conditions are true: +`prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, if `cache_prompt` is `true`, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. A `BOS` token is inserted at the start, if all of the following conditions are true: - - The prompt is a string or an array with the first element given as a string - - The model's `tokenizer.ggml.add_bos_token` metadata is `true` + - The prompt is a string or an array with the first element given as a string + - The model's `tokenizer.ggml.add_bos_token` metadata is `true` - These input shapes and data type are allowed for `prompt`: +These input shapes and data type are allowed for `prompt`: - - Single string: `"string"` - - Single sequence of tokens: `[12, 34, 56]` - - Mixed tokens and strings: `[12, 34, "string", 56, 78]` + - Single string: `"string"` + - Single sequence of tokens: `[12, 34, 56]` + - Mixed tokens and strings: `[12, 34, "string", 56, 78]` - Multiple prompts are also supported. In this case, the completion result will be an array. +Multiple prompts are also supported. In this case, the completion result will be an array. - - Only strings: `["string1", "string2"]` - - Strings and sequences of tokens: `["string1", [12, 34, 56]]` - - Mixed types: `[[12, 34, "string", 56, 78], [12, 34, 56], "string"]` + - Only strings: `["string1", "string2"]` + - Strings and sequences of tokens: `["string1", [12, 34, 56]]` + - Mixed types: `[[12, 34, "string", 56, 78], [12, 34, 56], "string"]` - `temperature`: Adjust the randomness of the generated text. Default: `0.8` +`temperature`: Adjust the randomness of the generated text. Default: `0.8` - `dynatemp_range`: Dynamic temperature range. The final temperature will be in the range of `[temperature - dynatemp_range; temperature + dynatemp_range]` Default: `0.0`, which is disabled. +`dynatemp_range`: Dynamic temperature range. The final temperature will be in the range of `[temperature - dynatemp_range; temperature + dynatemp_range]` Default: `0.0`, which is disabled. - `dynatemp_exponent`: Dynamic temperature exponent. Default: `1.0` +`dynatemp_exponent`: Dynamic temperature exponent. Default: `1.0` - `top_k`: Limit the next token selection to the K most probable tokens. Default: `40` +`top_k`: Limit the next token selection to the K most probable tokens. Default: `40` - `top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P. Default: `0.95` +`top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P. Default: `0.95` - `min_p`: The minimum probability for a token to be considered, relative to the probability of the most likely token. Default: `0.05` +`min_p`: The minimum probability for a token to be considered, relative to the probability of the most likely token. Default: `0.05` - `n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. Default: `-1`, where `-1` is infinity. +`n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. Default: `-1`, where `-1` is infinity. - `n_indent`: Specify the minimum line indentation for the generated text in number of whitespace characters. Useful for code completion tasks. Default: `0` +`n_indent`: Specify the minimum line indentation for the generated text in number of whitespace characters. Useful for code completion tasks. Default: `0` - `n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded. The number excludes the BOS token. - By default, this value is set to `0`, meaning no tokens are kept. Use `-1` to retain all tokens from the prompt. +`n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded. The number excludes the BOS token. +By default, this value is set to `0`, meaning no tokens are kept. Use `-1` to retain all tokens from the prompt. - `stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`. +`stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`. - `stop`: Specify a JSON array of stopping strings. - These words will not be included in the completion, so make sure to add them to the prompt for the next iteration. Default: `[]` +`stop`: Specify a JSON array of stopping strings. +These words will not be included in the completion, so make sure to add them to the prompt for the next iteration. Default: `[]` - `typical_p`: Enable locally typical sampling with parameter p. Default: `1.0`, which is disabled. +`typical_p`: Enable locally typical sampling with parameter p. Default: `1.0`, which is disabled. - `repeat_penalty`: Control the repetition of token sequences in the generated text. Default: `1.1` +`repeat_penalty`: Control the repetition of token sequences in the generated text. Default: `1.1` - `repeat_last_n`: Last n tokens to consider for penalizing repetition. Default: `64`, where `0` is disabled and `-1` is ctx-size. +`repeat_last_n`: Last n tokens to consider for penalizing repetition. Default: `64`, where `0` is disabled and `-1` is ctx-size. - `penalize_nl`: Penalize newline tokens when applying the repeat penalty. Default: `true` +`penalize_nl`: Penalize newline tokens when applying the repeat penalty. Default: `true` - `presence_penalty`: Repeat alpha presence penalty. Default: `0.0`, which is disabled. +`presence_penalty`: Repeat alpha presence penalty. Default: `0.0`, which is disabled. - `frequency_penalty`: Repeat alpha frequency penalty. Default: `0.0`, which is disabled. +`frequency_penalty`: Repeat alpha frequency penalty. Default: `0.0`, which is disabled. - `dry_multiplier`: Set the DRY (Don't Repeat Yourself) repetition penalty multiplier. Default: `0.0`, which is disabled. +`dry_multiplier`: Set the DRY (Don't Repeat Yourself) repetition penalty multiplier. Default: `0.0`, which is disabled. - `dry_base`: Set the DRY repetition penalty base value. Default: `1.75` +`dry_base`: Set the DRY repetition penalty base value. Default: `1.75` - `dry_allowed_length`: Tokens that extend repetition beyond this receive exponentially increasing penalty: multiplier * base ^ (length of repeating sequence before token - allowed length). Default: `2` +`dry_allowed_length`: Tokens that extend repetition beyond this receive exponentially increasing penalty: multiplier * base ^ (length of repeating sequence before token - allowed length). Default: `2` - `dry_penalty_last_n`: How many tokens to scan for repetitions. Default: `-1`, where `0` is disabled and `-1` is context size. +`dry_penalty_last_n`: How many tokens to scan for repetitions. Default: `-1`, where `0` is disabled and `-1` is context size. - `dry_sequence_breakers`: Specify an array of sequence breakers for DRY sampling. Only a JSON array of strings is accepted. Default: `['\n', ':', '"', '*']` +`dry_sequence_breakers`: Specify an array of sequence breakers for DRY sampling. Only a JSON array of strings is accepted. Default: `['\n', ':', '"', '*']` - `mirostat`: Enable Mirostat sampling, controlling perplexity during text generation. Default: `0`, where `0` is disabled, `1` is Mirostat, and `2` is Mirostat 2.0. +`xtc_probability`: Set the chance for token removal via XTC sampler. Default: `0.0`, which is disabled. - `mirostat_tau`: Set the Mirostat target entropy, parameter tau. Default: `5.0` +`xtc_threshold`: Set a minimum probability threshold for tokens to be removed via XTC sampler. Default: `0.1` (> `0.5` disables XTC) - `mirostat_eta`: Set the Mirostat learning rate, parameter eta. Default: `0.1` +`mirostat`: Enable Mirostat sampling, controlling perplexity during text generation. Default: `0`, where `0` is disabled, `1` is Mirostat, and `2` is Mirostat 2.0. - `grammar`: Set grammar for grammar-based sampling. Default: no grammar +`mirostat_tau`: Set the Mirostat target entropy, parameter tau. Default: `5.0` - `json_schema`: Set a JSON schema for grammar-based sampling (e.g. `{"items": {"type": "string"}, "minItems": 10, "maxItems": 100}` of a list of strings, or `{}` for any JSON). See [tests](../../tests/test-json-schema-to-grammar.cpp) for supported features. Default: no JSON schema. +`mirostat_eta`: Set the Mirostat learning rate, parameter eta. Default: `0.1` - `seed`: Set the random number generator (RNG) seed. Default: `-1`, which is a random seed. +`grammar`: Set grammar for grammar-based sampling. Default: no grammar - `ignore_eos`: Ignore end of stream token and continue generating. Default: `false` +`json_schema`: Set a JSON schema for grammar-based sampling (e.g. `{"items": {"type": "string"}, "minItems": 10, "maxItems": 100}` of a list of strings, or `{}` for any JSON). See [tests](../../tests/test-json-schema-to-grammar.cpp) for supported features. Default: no JSON schema. - `logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced. The tokens can also be represented as strings, e.g. `[["Hello, World!",-0.5]]` will reduce the likelihood of all the individual tokens that represent the string `Hello, World!`, just like the `presence_penalty` does. Default: `[]` +`seed`: Set the random number generator (RNG) seed. Default: `-1`, which is a random seed. - `n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token given the sampling settings. Note that for temperature < 0 the tokens are sampled greedily but token probabilities are still being calculated via a simple softmax of the logits without considering any other sampler settings. Default: `0` +`ignore_eos`: Ignore end of stream token and continue generating. Default: `false` - `min_keep`: If greater than 0, force samplers to return N possible tokens at minimum. Default: `0` +`logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced. The tokens can also be represented as strings, e.g. `[["Hello, World!",-0.5]]` will reduce the likelihood of all the individual tokens that represent the string `Hello, World!`, just like the `presence_penalty` does. Default: `[]` - `t_max_predict_ms`: Set a time limit in milliseconds for the prediction (a.k.a. text-generation) phase. The timeout will trigger if the generation takes more than the specified time (measured since the first token was generated) and if a new-line character has already been generated. Useful for FIM applications. Default: `0`, which is disabled. +`n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token given the sampling settings. Note that for temperature < 0 the tokens are sampled greedily but token probabilities are still being calculated via a simple softmax of the logits without considering any other sampler settings. Default: `0` - `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:`. In this case, `[img-12]` will be replaced by the embeddings of the image with id `12` in the following `image_data` array: `{..., "image_data": [{"data": "", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA. +`min_keep`: If greater than 0, force samplers to return N possible tokens at minimum. Default: `0` - `id_slot`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot. Default: `-1` +`t_max_predict_ms`: Set a time limit in milliseconds for the prediction (a.k.a. text-generation) phase. The timeout will trigger if the generation takes more than the specified time (measured since the first token was generated) and if a new-line character has already been generated. Useful for FIM applications. Default: `0`, which is disabled. - `cache_prompt`: Re-use KV cache from a previous request if possible. This way the common prefix does not have to be re-processed, only the suffix that differs between the requests. Because (depending on the backend) the logits are **not** guaranteed to be bit-for-bit identical for different batch sizes (prompt processing vs. token generation) enabling this option can cause nondeterministic results. Default: `false` +`image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:`. In this case, `[img-12]` will be replaced by the embeddings of the image with id `12` in the following `image_data` array: `{..., "image_data": [{"data": "", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA. - `samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["top_k", "typical_p", "top_p", "min_p", "temperature"]` - these are all the available values. +`id_slot`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot. Default: `-1` + +`cache_prompt`: Re-use KV cache from a previous request if possible. This way the common prefix does not have to be re-processed, only the suffix that differs between the requests. Because (depending on the backend) the logits are **not** guaranteed to be bit-for-bit identical for different batch sizes (prompt processing vs. token generation) enabling this option can cause nondeterministic results. Default: `true` + +`samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["dry", "top_k", "typ_p", "top_p", "min_p", "xtc", "temperature"]` - these are all the available values. + + `timings_per_token`: Include prompt processing and text generation speed information in each response. Default: `false` **Response format** @@ -452,13 +484,13 @@ Notice that each `probs` is an array of length `n_probs`. ### POST `/tokenize`: Tokenize a given text - *Options:* +*Options:* - `content`: (Required) The text to tokenize. +`content`: (Required) The text to tokenize. - `add_special`: (Optional) Boolean indicating if special tokens, i.e. `BOS`, should be inserted. Default: `false` +`add_special`: (Optional) Boolean indicating if special tokens, i.e. `BOS`, should be inserted. Default: `false` - `with_pieces`: (Optional) Boolean indicating whether to return token pieces along with IDs. Default: `false` +`with_pieces`: (Optional) Boolean indicating whether to return token pieces along with IDs. Default: `false` **Response:** @@ -495,52 +527,52 @@ With input 'á' (utf8 hex: C3 A1) on tinyllama/stories260k ### POST `/detokenize`: Convert tokens to text - *Options:* +*Options:* - `tokens`: Set the tokens to detokenize. +`tokens`: Set the tokens to detokenize. ### POST `/embedding`: Generate embedding of a given text The same as [the embedding example](../embedding) does. - *Options:* +*Options:* - `content`: Set the text to process. +`content`: Set the text to process. - `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `content`. You can determine the place of the image in the content as in the following: `Image: [img-21].\nCaption: This is a picture of a house`. In this case, `[img-21]` will be replaced by the embeddings of the image with id `21` in the following `image_data` array: `{..., "image_data": [{"data": "", "id": 21}]}`. Use `image_data` only with multimodal models, e.g., LLaVA. +`image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `content`. You can determine the place of the image in the content as in the following: `Image: [img-21].\nCaption: This is a picture of a house`. In this case, `[img-21]` will be replaced by the embeddings of the image with id `21` in the following `image_data` array: `{..., "image_data": [{"data": "", "id": 21}]}`. Use `image_data` only with multimodal models, e.g., LLaVA. ### POST `/reranking`: Rerank documents according to a given query Similar to https://jina.ai/reranker/ but might change in the future. Requires a reranker model (such as [bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3)) and the `--embedding --pooling rank` options. - *Options:* +*Options:* - `query`: The query against which the documents will be ranked. +`query`: The query against which the documents will be ranked. - `documents`: An array strings representing the documents to be ranked. +`documents`: An array strings representing the documents to be ranked. - *Aliases:* - - `/rerank` - - `/v1/rerank` - - `/v1/reranking` +*Aliases:* + - `/rerank` + - `/v1/rerank` + - `/v1/reranking` - *Examples:* +*Examples:* - ```shell - curl http://127.0.0.1:8012/v1/rerank \ - -H "Content-Type: application/json" \ - -d '{ - "model": "some-model", - "query": "What is panda?", - "top_n": 3, - "documents": [ - "hi", - "it is a bear", - "The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." - ] - }' | jq - ``` +```shell +curl http://127.0.0.1:8012/v1/rerank \ + -H "Content-Type: application/json" \ + -d '{ + "model": "some-model", + "query": "What is panda?", + "top_n": 3, + "documents": [ + "hi", + "it is a bear", + "The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." + ] + }' | jq +``` ### POST `/infill`: For code infilling. @@ -606,57 +638,57 @@ To use this endpoint with POST method, you need to start server with `--props` Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only models with a [supported chat template](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) can be used optimally with this endpoint. By default, the ChatML template will be used. - *Options:* +*Options:* - See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such as `mirostat` are supported. +See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such as `mirostat` are supported. - The `response_format` parameter supports both plain JSON output (e.g. `{"type": "json_object"}`) and schema-constrained JSON (e.g. `{"type": "json_object", "schema": {"type": "string", "minLength": 10, "maxLength": 100}}` or `{"type": "json_schema", "schema": {"properties": { "name": { "title": "Name", "type": "string" }, "date": { "title": "Date", "type": "string" }, "participants": { "items": {"type: "string" }, "title": "Participants", "type": "string" } } } }`), similar to other OpenAI-inspired API providers. +The `response_format` parameter supports both plain JSON output (e.g. `{"type": "json_object"}`) and schema-constrained JSON (e.g. `{"type": "json_object", "schema": {"type": "string", "minLength": 10, "maxLength": 100}}` or `{"type": "json_schema", "schema": {"properties": { "name": { "title": "Name", "type": "string" }, "date": { "title": "Date", "type": "string" }, "participants": { "items": {"type: "string" }, "title": "Participants", "type": "string" } } } }`), similar to other OpenAI-inspired API providers. - The `tools` / `tool_choice` parameters are only supported if the server is started with `--jinja`. The template included in the GGUF may not support tools, in that case you may want to override it w/ `--chat-template-file ...`. +The `tools` / `tool_choice` parameters are only supported if the server is started with `--jinja`. The template included in the GGUF may not support tools, in that case you may want to override it w/ `--chat-template-file ...`. - *Examples:* +*Examples:* - You can use either Python `openai` library with appropriate checkpoints: +You can use either Python `openai` library with appropriate checkpoints: - ```python - import openai +```python +import openai - client = openai.OpenAI( - base_url="http://localhost:8080/v1", # "http://:port" - api_key = "sk-no-key-required" - ) +client = openai.OpenAI( + base_url="http://localhost:8080/v1", # "http://:port" + api_key = "sk-no-key-required" +) - completion = client.chat.completions.create( - model="gpt-3.5-turbo", - messages=[ - {"role": "system", "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."}, - {"role": "user", "content": "Write a limerick about python exceptions"} - ] - ) +completion = client.chat.completions.create( +model="gpt-3.5-turbo", +messages=[ + {"role": "system", "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."}, + {"role": "user", "content": "Write a limerick about python exceptions"} +] +) - print(completion.choices[0].message) - ``` +print(completion.choices[0].message) +``` - ... or raw HTTP requests: +... or raw HTTP requests: - ```shell - curl http://localhost:8080/v1/chat/completions \ - -H "Content-Type: application/json" \ - -H "Authorization: Bearer no-key" \ - -d '{ - "model": "gpt-3.5-turbo", - "messages": [ - { - "role": "system", - "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests." - }, - { - "role": "user", - "content": "Write a limerick about python exceptions" - } - ] - }' - ``` +```shell +curl http://localhost:8080/v1/chat/completions \ +-H "Content-Type: application/json" \ +-H "Authorization: Bearer no-key" \ +-d '{ +"model": "gpt-3.5-turbo", +"messages": [ +{ + "role": "system", + "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests." +}, +{ + "role": "user", + "content": "Write a limerick about python exceptions" +} +] +}' +``` ... and even tool usage (needs `--jinja` flag): @@ -737,41 +769,44 @@ Given a ChatML-formatted json description in `messages`, it returns the predicte ### POST `/v1/embeddings`: OpenAI-compatible embeddings API - *Options:* +*Options:* - See [OpenAI Embeddings API documentation](https://platform.openai.com/docs/api-reference/embeddings). +See [OpenAI Embeddings API documentation](https://platform.openai.com/docs/api-reference/embeddings). - *Examples:* +*Examples:* - - input as string +- input as string - ```shell - curl http://localhost:8080/v1/embeddings \ - -H "Content-Type: application/json" \ - -H "Authorization: Bearer no-key" \ - -d '{ - "input": "hello", - "model":"GPT-4", - "encoding_format": "float" - }' - ``` + ```shell + curl http://localhost:8080/v1/embeddings \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer no-key" \ + -d '{ + "input": "hello", + "model":"GPT-4", + "encoding_format": "float" + }' + ``` - - `input` as string array +- `input` as string array - ```shell - curl http://localhost:8080/v1/embeddings \ - -H "Content-Type: application/json" \ - -H "Authorization: Bearer no-key" \ - -d '{ - "input": ["hello", "world"], - "model":"GPT-4", - "encoding_format": "float" - }' - ``` + ```shell + curl http://localhost:8080/v1/embeddings \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer no-key" \ + -d '{ + "input": ["hello", "world"], + "model":"GPT-4", + "encoding_format": "float" + }' + ``` ### GET `/slots`: Returns the current slots processing state -This endpoint can be disabled with `--no-slots` +> [!WARNING] +> This endpoint is intended for debugging and may be modified in future versions. For security reasons, we strongly advise against enabling it in production environments. + +This endpoint is disabled by default and can be enabled with `--slots` If query param `?fail_on_no_slot=1` is set, this endpoint will respond with status code 503 if there is no available slots. @@ -788,6 +823,7 @@ Example: "grammar": "", "id": 0, "ignore_eos": false, + "is_processing": false, "logit_bias": [], "min_p": 0.05000000074505806, "mirostat": 0, @@ -820,7 +856,6 @@ Example: "temperature" ], "seed": 42, - "state": 1, "stop": [ "\n" ], @@ -834,10 +869,6 @@ Example: ] ``` -Possible values for `slot[i].state` are: -- `0`: SLOT_STATE_IDLE -- `1`: SLOT_STATE_PROCESSING - ### GET `/metrics`: Prometheus compatible metrics exporter This endpoint is only accessible if `--metrics` is set. @@ -854,9 +885,9 @@ Available metrics: ### POST `/slots/{id_slot}?action=save`: Save the prompt cache of the specified slot to a file. - *Options:* +*Options:* - `filename`: Name of the file to save the slot's prompt cache. The file will be saved in the directory specified by the `--slot-save-path` server parameter. +`filename`: Name of the file to save the slot's prompt cache. The file will be saved in the directory specified by the `--slot-save-path` server parameter. **Response format** @@ -874,9 +905,9 @@ Available metrics: ### POST `/slots/{id_slot}?action=restore`: Restore the prompt cache of the specified slot from a file. - *Options:* +*Options:* - `filename`: Name of the file to restore the slot's prompt cache from. The file should be located in the directory specified by the `--slot-save-path` server parameter. +`filename`: Name of the file to restore the slot's prompt cache from. The file should be located in the directory specified by the `--slot-save-path` server parameter. **Response format** @@ -1008,6 +1039,16 @@ Apart from error types supported by OAI, we also have custom types that are spec } ``` +### Legacy completion web UI + +A new chat-based UI has replaced the old completion-based since [this PR](https://github.com/ggerganov/llama.cpp/pull/10175). If you want to use the old completion, start the server with `--path ./examples/server/public_legacy` + +For example: + +```sh +./llama-server -m my_model.gguf -c 8192 --path ./examples/server/public_legacy +``` + ### Extending or building alternative Web Front End You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method. diff --git a/examples/server/chat.mjs b/examples/server/chat.mjs index a79c8a3cd..4fef5655a 100644 --- a/examples/server/chat.mjs +++ b/examples/server/chat.mjs @@ -1,7 +1,7 @@ import * as readline from 'node:readline' import { stdin, stdout } from 'node:process' import { readFileSync } from 'node:fs' -import { SchemaConverter } from './public/json-schema-to-grammar.mjs' +import { SchemaConverter } from './public_legacy/json-schema-to-grammar.mjs' const args = process.argv.slice(2); const grammarJsonSchemaFile = args.find( diff --git a/examples/server/deps.sh b/examples/server/deps.sh deleted file mode 100755 index d28378901..000000000 --- a/examples/server/deps.sh +++ /dev/null @@ -1,10 +0,0 @@ -#!/bin/bash -# Download and update deps for binary - -# get the directory of this script file -DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" -PUBLIC=$DIR/public - -echo "download js bundle files" -curl https://npm.reversehttp.com/@preact/signals-core,@preact/signals,htm/preact,preact,preact/hooks > $PUBLIC/index.js -echo >> $PUBLIC/index.js # add newline diff --git a/examples/server/public/index.html b/examples/server/public/index.html index a95f5c6df..250729a44 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -1,1303 +1,351 @@ + + - llama.cpp - chat - - - - + ${c} + `},window.copyStr=Bf;const n=lo(()=>t.value.render(e.source));return()=>ao("div",{innerHTML:n.value})},{props:["source"]}),G3=Kn({template:document.getElementById("settings-modal-short-input").innerHTML,props:{label:{type:String,required:!1},configKey:String,configDefault:Object,configInfo:Object,modelValue:[Object,String,Number]}}),he={getAllConversations(){const e=[];for(const t in localStorage)t.startsWith("conv-")&&e.push(JSON.parse(localStorage.getItem(t)));return e.sort((t,u)=>u.lastModified-t.lastModified),e},getOneConversation(e){return JSON.parse(localStorage.getItem(e)||"null")},appendMsg(e,t){if(t.content===null)return;const u=he.getOneConversation(e)||{id:e,lastModified:Date.now(),messages:[]};u.messages.push(t),u.lastModified=Date.now(),localStorage.setItem(e,JSON.stringify(u))},getNewConvId(){return`conv-${Date.now()}`},remove(e){localStorage.removeItem(e)},filterAndKeepMsgs(e,t){const 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ResizeObserver(()=>{this.isGenerating&&fn(!0)}).observe(e)},methods:{hideSidebar(){document.getElementById("toggle-drawer").checked=!1},setSelectedTheme(e){this.selectedTheme=e,he.setTheme(e)},newConversation(){this.isGenerating||(this.viewingConvId=he.getNewConvId(),this.editingMsg=null,this.fetchMessages(),fn(),this.hideSidebar())},setViewingConv(e){this.isGenerating||(this.viewingConvId=e,this.editingMsg=null,this.fetchMessages(),fn(),this.hideSidebar())},deleteConv(e){this.isGenerating||window.confirm("Are you sure to delete this conversation?")&&(he.remove(e),this.viewingConvId===e&&(this.viewingConvId=he.getNewConvId(),this.editingMsg=null),this.fetchConversation(),this.fetchMessages())},downloadConv(e){const t=he.getOneConversation(e);if(!t){alert("Conversation not found.");return}const u=JSON.stringify(t,null,2),n=new Blob([u],{type:"application/json"}),r=URL.createObjectURL(n),i=document.createElement("a");i.href=r,i.download=`conversation_${e}.json`,document.body.appendChild(i),i.click(),document.body.removeChild(i),URL.revokeObjectURL(r)},async sendMessage(){if(!this.inputMsg)return;const e=this.viewingConvId;he.appendMsg(e,{id:Date.now(),role:"user",content:this.inputMsg}),this.fetchConversation(),this.fetchMessages(),this.inputMsg="",this.editingMsg=null,this.generateMessage(e),fn()},async generateMessage(e){if(!this.isGenerating){this.pendingMsg={id:Date.now()+1,role:"assistant",content:null},this.isGenerating=!0,this.editingMsg=null;try{const t=new AbortController;this.stopGeneration=()=>t.abort();const u={messages:[{role:"system",content:this.config.systemMessage},...this.messages],stream:!0,cache_prompt:!0,samplers:this.config.samplers,temperature:this.config.temperature,dynatemp_range:this.config.dynatemp_range,dynatemp_exponent:this.config.dynatemp_exponent,top_k:this.config.top_k,top_p:this.config.top_p,min_p:this.config.min_p,typical_p:this.config.typical_p,xtc_probability:this.config.xtc_probability,xtc_threshold:this.config.xtc_threshold,repeat_last_n:this.config.repeat_last_n,repeat_penalty:this.config.repeat_penalty,presence_penalty:this.config.presence_penalty,frequency_penalty:this.config.frequency_penalty,dry_multiplier:this.config.dry_multiplier,dry_base:this.config.dry_base,dry_allowed_length:this.config.dry_allowed_length,dry_penalty_last_n:this.config.dry_penalty_last_n,max_tokens:this.config.max_tokens,...this.config.custom.length?JSON.parse(this.config.custom):{},...this.config.apiKey?{api_key:this.config.apiKey}:{}},n={controller:t,api_url:q3,endpoint:"/chat/completions"};for await(const r of eb(prompt,u,n)){const i=r.data.stop,s=r.data.choices[0].delta.content,o=this.pendingMsg.content||"";s&&(this.pendingMsg={id:this.pendingMsg.id,role:"assistant",content:o+s})}he.appendMsg(e,this.pendingMsg),this.fetchConversation(),this.fetchMessages(),setTimeout(()=>document.getElementById("msg-input").focus(),1)}catch(t){if(t.name==="AbortError")he.appendMsg(e,this.pendingMsg),this.fetchConversation(),this.fetchMessages();else{console.error(t),alert(t);const u=he.popMsg(e);this.inputMsg=u?u.content:""}}this.pendingMsg=null,this.isGenerating=!1,this.stopGeneration=()=>{},this.fetchMessages(),fn()}},regenerateMsg(e){if(this.isGenerating)return;const t=this.viewingConvId;he.filterAndKeepMsgs(t,u=>u.idn.id + Failed to start app. Please try clearing localStorage and try again.
+
+ + `} + -
- +
+
+ + + +
+ +
+
+

Conversations

+ + + +
+ + +
+ + New conversation +
+
+ {{ conv.messages[0].content }} +
+
+ Conversations are saved to browser's localStorage +
+
+
+ + +
+ +
+ + + +
llama.cpp
+ + +
+
+ + + + +
+ + + +
+
+ + + +
+
    +
  • + +
    • +
  • + +
    • +
+
+
+
+ + +
+
+ + {{ messages.length === 0 ? 'Send a message to start' : '' }} +
+
+
+
+ + + + +
+
+ + +
+ + + + + +
+
+ + +
+
+ + +
+
+
+ + +
+ + + +
+
+ +
+ + + + +
+

Settings

+
+

Settings below are saved in browser's localStorage

+ + + + + +
+ Other sampler settings +
+ + + + +
+
+ +
+ Penalties settings +
+ +
+
+ +
+ Advanced config +
+ +
+
+
+ + +
+ + + +
+
+ +
-
+ + + + diff --git a/examples/server/public/colorthemes.css b/examples/server/public_legacy/colorthemes.css similarity index 100% rename from examples/server/public/colorthemes.css rename to examples/server/public_legacy/colorthemes.css diff --git a/examples/server/public/completion.js b/examples/server/public_legacy/completion.js similarity index 96% rename from examples/server/public/completion.js rename to examples/server/public_legacy/completion.js index 36818f764..30df7c2fa 100644 --- a/examples/server/public/completion.js +++ b/examples/server/public_legacy/completion.js @@ -29,7 +29,7 @@ export async function* llama(prompt, params = {}, config = {}) { const completionParams = { ...paramDefaults, ...params, prompt }; - const response = await fetch(`${api_url}/completion`, { + const response = await fetch(`${api_url}${config.endpoint || '/completion'}`, { method: 'POST', body: JSON.stringify(completionParams), headers: { @@ -78,7 +78,12 @@ export async function* llama(prompt, params = {}, config = {}) { for (const line of lines) { const match = regex.exec(line); if (match) { - result[match[1]] = match[2] + result[match[1]] = match[2]; + if (result.data === '[DONE]') { + cont = false; + break; + } + // since we know this is llama.cpp, let's just decode the json in data if (result.data) { result.data = JSON.parse(result.data); diff --git a/examples/server/public/favicon.ico b/examples/server/public_legacy/favicon.ico similarity index 100% rename from examples/server/public/favicon.ico rename to examples/server/public_legacy/favicon.ico diff --git a/examples/server/public/index-new.html b/examples/server/public_legacy/index-new.html similarity index 100% rename from examples/server/public/index-new.html rename to examples/server/public_legacy/index-new.html diff --git a/examples/server/public_legacy/index.html b/examples/server/public_legacy/index.html new file mode 100644 index 000000000..a95f5c6df --- /dev/null +++ b/examples/server/public_legacy/index.html @@ -0,0 +1,1303 @@ + + + + + + llama.cpp - chat + + + + + + + +
+ +
+
+ + + diff --git a/examples/server/public/index.js b/examples/server/public_legacy/index.js similarity index 100% rename from examples/server/public/index.js rename to examples/server/public_legacy/index.js diff --git a/examples/server/public/json-schema-to-grammar.mjs b/examples/server/public_legacy/json-schema-to-grammar.mjs similarity index 100% rename from examples/server/public/json-schema-to-grammar.mjs rename to examples/server/public_legacy/json-schema-to-grammar.mjs diff --git a/examples/server/public_legacy/loading.html b/examples/server/public_legacy/loading.html new file mode 100644 index 000000000..c3fd19a0f --- /dev/null +++ b/examples/server/public_legacy/loading.html @@ -0,0 +1,12 @@ + + + + + + +
+ The model is loading. Please wait.
+ The user interface will appear soon. +
+ + diff --git a/examples/server/public/prompt-formats.js b/examples/server/public_legacy/prompt-formats.js similarity index 100% rename from examples/server/public/prompt-formats.js rename to examples/server/public_legacy/prompt-formats.js diff --git a/examples/server/public/style.css b/examples/server/public_legacy/style.css similarity index 100% rename from examples/server/public/style.css rename to examples/server/public_legacy/style.css diff --git a/examples/server/public/system-prompts.js b/examples/server/public_legacy/system-prompts.js similarity index 100% rename from examples/server/public/system-prompts.js rename to examples/server/public_legacy/system-prompts.js diff --git a/examples/server/public/theme-beeninorder.css b/examples/server/public_legacy/theme-beeninorder.css similarity index 100% rename from examples/server/public/theme-beeninorder.css rename to examples/server/public_legacy/theme-beeninorder.css diff --git a/examples/server/public/theme-ketivah.css b/examples/server/public_legacy/theme-ketivah.css similarity index 100% rename from examples/server/public/theme-ketivah.css rename to examples/server/public_legacy/theme-ketivah.css diff --git a/examples/server/public/theme-mangotango.css b/examples/server/public_legacy/theme-mangotango.css similarity index 100% rename from examples/server/public/theme-mangotango.css rename to examples/server/public_legacy/theme-mangotango.css diff --git a/examples/server/public/theme-playground.css b/examples/server/public_legacy/theme-playground.css similarity index 100% rename from examples/server/public/theme-playground.css rename to examples/server/public_legacy/theme-playground.css diff --git a/examples/server/public/theme-polarnight.css b/examples/server/public_legacy/theme-polarnight.css similarity index 100% rename from examples/server/public/theme-polarnight.css rename to examples/server/public_legacy/theme-polarnight.css diff --git a/examples/server/public/theme-snowstorm.css b/examples/server/public_legacy/theme-snowstorm.css similarity index 100% rename from examples/server/public/theme-snowstorm.css rename to examples/server/public_legacy/theme-snowstorm.css diff --git a/examples/server/public_simplechat/simplechat.js b/examples/server/public_simplechat/simplechat.js index 8e0df3b61..2fcd24a86 100644 --- a/examples/server/public_simplechat/simplechat.js +++ b/examples/server/public_simplechat/simplechat.js @@ -407,6 +407,9 @@ class SimpleChat { if (curLine.startsWith("data:")) { curLine = curLine.substring(5); } + if (curLine.trim() === "[DONE]") { + break; + } let curJson = JSON.parse(curLine); console.debug("DBUG:SC:PART:Json:", curJson); this.append_response(this.response_extract_stream(curJson, apiEP)); diff --git a/examples/server/server.cpp b/examples/server/server.cpp index dfd53b1e3..007c6ec82 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2,10 +2,11 @@ #include "arg.h" #include "common.h" -#include "log.h" -#include "sampling.h" #include "json-schema-to-grammar.h" #include "llama.h" +#include "log.h" +#include "sampling.h" +#include "speculative.h" // Change JSON_ASSERT from assert() to GGML_ASSERT: #define JSON_ASSERT GGML_ASSERT @@ -14,21 +15,7 @@ #define MIMETYPE_JSON "application/json; charset=utf-8" // auto generated files (update with ./deps.sh) -#include "colorthemes.css.hpp" -#include "style.css.hpp" -#include "theme-beeninorder.css.hpp" -#include "theme-ketivah.css.hpp" -#include "theme-mangotango.css.hpp" -#include "theme-playground.css.hpp" -#include "theme-polarnight.css.hpp" -#include "theme-snowstorm.css.hpp" #include "index.html.hpp" -#include "index-new.html.hpp" -#include "index.js.hpp" -#include "completion.js.hpp" -#include "system-prompts.js.hpp" -#include "prompt-formats.js.hpp" -#include "json-schema-to-grammar.mjs.hpp" #include "loading.html.hpp" #include @@ -113,7 +100,7 @@ struct server_task_result { struct slot_params { bool stream = true; - bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt + bool cache_prompt = true; // remember the prompt to avoid reprocessing all prompt int32_t n_keep = 0; // number of tokens to keep from initial prompt int32_t n_discard = 0; // number of tokens after n_keep that may be discarded when shifting context, 0 defaults to half @@ -126,12 +113,21 @@ struct slot_params { json input_prefix; json input_suffix; std::vector antiprompt; + + struct common_params_sampling sampling; + struct common_params_speculative speculative; }; struct server_slot { int id; int id_task = -1; + llama_batch batch_spec; + + llama_context * ctx_dft = nullptr; + + common_speculative * spec = nullptr; + // the index relative to completion multi-task request size_t index = 0; @@ -172,6 +168,8 @@ struct server_slot { bool stopped_word = false; bool stopped_limit = false; + bool timings_per_token = false; + bool oaicompat = false; std::string oaicompat_model; @@ -182,7 +180,6 @@ struct server_slot { // sampling json json_schema; - struct common_sampler_params sparams; struct common_sampler * smpl = nullptr; llama_token sampled; @@ -219,7 +216,7 @@ struct server_slot { generated_token_probs.clear(); } - bool has_budget(common_params &global_params) { + bool has_budget(const common_params & global_params) { if (params.n_predict == -1 && global_params.n_predict == -1) { return true; // limitless } @@ -239,6 +236,10 @@ struct server_slot { return state != SLOT_STATE_IDLE; } + bool can_speculate() const { + return ctx_dft && params.speculative.n_max > 0 && params.cache_prompt; + } + void add_token(const completion_token_output & token) { if (!is_processing()) { SLT_WRN(*this, "%s", "slot is not processing\n"); @@ -251,6 +252,7 @@ struct server_slot { if (is_processing()) { SLT_INF(*this, "stop processing: n_past = %d, truncated = %d\n", n_past, truncated); + t_last_used = ggml_time_us(); t_token_generation = (ggml_time_us() - t_start_generation) / 1e3; state = SLOT_STATE_IDLE; callback_on_release(id); @@ -353,8 +355,8 @@ struct server_queue { std::condition_variable condition_tasks; // callback functions - std::function callback_new_task; - std::function callback_update_slots; + std::function callback_new_task; + std::function callback_update_slots; // Add a new task to the end of the queue int post(server_task task, bool front = false) { @@ -406,7 +408,7 @@ struct server_queue { } // Register function to process a new task - void on_new_task(std::function callback) { + void on_new_task(std::function callback) { callback_new_task = std::move(callback); } @@ -456,7 +458,7 @@ struct server_queue { lock.unlock(); QUE_DBG("processing task, id = %d\n", task.id); - callback_new_task(task); + callback_new_task(std::move(task)); } // all tasks in the current loop is processed, slots data is now ready @@ -569,11 +571,14 @@ struct server_response { }; struct server_context { + common_params params_base; + llama_model * model = nullptr; llama_context * ctx = nullptr; std::vector loras; - common_params params; + llama_model * model_dft = nullptr; + llama_context_params cparams_dft; llama_batch batch = {}; @@ -606,32 +611,41 @@ struct server_context { model = nullptr; } + if (model_dft) { + llama_free_model(model_dft); + model_dft = nullptr; + } + // Clear any sampling context for (server_slot & slot : slots) { - if (slot.smpl != nullptr) { - common_sampler_free(slot.smpl); - } + common_sampler_free(slot.smpl); + slot.smpl = nullptr; + + llama_free(slot.ctx_dft); + slot.ctx_dft = nullptr; + + common_speculative_free(slot.spec); + slot.spec = nullptr; + + llama_batch_free(slot.batch_spec); } llama_batch_free(batch); } - bool load_model(const common_params & params_) { - params = params_; + bool load_model(const common_params & params) { + SRV_INF("loading model '%s'\n", params.model.c_str()); - // reserve one extra sequence (seq_id == 0) for extra features - params.n_parallel += 1; + params_base = params; - common_init_result llama_init = common_init_from_params(params); + common_init_result llama_init = common_init_from_params(params_base); model = llama_init.model; ctx = llama_init.context; loras = llama_init.lora_adapters; - params.n_parallel -= 1; // but be sneaky about it - if (model == nullptr) { - SRV_ERR("failed to load model, '%s'\n", params.model.c_str()); + SRV_ERR("failed to load model, '%s'\n", params_base.model.c_str()); return false; } @@ -640,6 +654,48 @@ struct server_context { add_bos_token = llama_add_bos_token(model); has_eos_token = !llama_add_eos_token(model); + if (!params_base.speculative.model.empty()) { + SRV_INF("loading draft model '%s'\n", params_base.speculative.model.c_str()); + + auto params_dft = params_base; + + params_dft.devices = params_base.speculative.devices; + params_dft.model = params_base.speculative.model; + params_dft.n_ctx = params_base.speculative.n_ctx == 0 ? params_base.n_ctx / params_base.n_parallel : params_base.speculative.n_ctx; + params_dft.n_gpu_layers = params_base.speculative.n_gpu_layers; + params_dft.n_parallel = 1; + + common_init_result llama_init_dft = common_init_from_params(params_dft); + + model_dft = llama_init_dft.model; + + if (model_dft == nullptr) { + SRV_ERR("failed to load draft model, '%s'\n", params_base.speculative.model.c_str()); + return false; + } + + if (!common_speculative_are_compatible(ctx, llama_init_dft.context)) { + SRV_ERR("the draft model '%s' is not compatible with the target model '%s'\n", params_base.speculative.model.c_str(), params_base.model.c_str()); + + llama_free (llama_init_dft.context); + llama_free_model(llama_init_dft.model); + + return false; + } + + const int n_ctx_dft = llama_n_ctx(llama_init_dft.context); + + cparams_dft = common_context_params_to_llama(params_dft); + cparams_dft.n_batch = n_ctx_dft; + + // force F16 KV cache for the draft model for extra performance + cparams_dft.type_k = GGML_TYPE_F16; + cparams_dft.type_v = GGML_TYPE_F16; + + // the context is not needed - we will create one for each slot + llama_free(llama_init_dft.context); + } + return true; } @@ -656,30 +712,51 @@ struct server_context { return true; } catch (const std::exception & e) { SRV_ERR("failed to apply template: %s\n", e.what()); - return false; } } else { - const int res = llama_chat_apply_template(model, nullptr, chat, 1, true, nullptr, 0); - - return res > 0; + std::vector model_template(2048, 0); // longest known template is about 1200 bytes + std::string template_key = "tokenizer.chat_template"; + int32_t res = llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size()); + if (res >= 0) { + std::string tmpl = std::string(model_template.data(), model_template.size()); + int32_t chat_res = llama_chat_apply_template(model, tmpl.c_str(), chat, 1, true, nullptr, 0); + return chat_res > 0; + } } + return false; } void init() { - const int32_t n_ctx_slot = n_ctx / params.n_parallel; + const int32_t n_ctx_slot = n_ctx / params_base.n_parallel; - SRV_INF("initializing slots, n_slots = %d\n", params.n_parallel); + SRV_INF("initializing slots, n_slots = %d\n", params_base.n_parallel); - for (int i = 0; i < params.n_parallel; i++) { + for (int i = 0; i < params_base.n_parallel; i++) { server_slot slot; slot.id = i; slot.n_ctx = n_ctx_slot; - slot.n_predict = params.n_predict; + slot.n_predict = params_base.n_predict; + + if (model_dft) { + slot.batch_spec = llama_batch_init(params_base.speculative.n_max + 1, 0, 1); + + slot.ctx_dft = llama_new_context_with_model(model_dft, cparams_dft); + if (slot.ctx_dft == nullptr) { + SRV_ERR("%s", "failed to create draft context\n"); + return; + } + + slot.spec = common_speculative_init(slot.ctx_dft); + if (slot.spec == nullptr) { + SRV_ERR("%s", "failed to create speculator\n"); + return; + } + } SLT_INF(slot, "new slot n_ctx_slot = %d\n", slot.n_ctx); - slot.sparams = params.sparams; + slot.params.sampling = params_base.sampling; slot.callback_on_release = [this](int) { queue_tasks.pop_deferred_task(); @@ -699,7 +776,7 @@ struct server_context { const int32_t n_batch = llama_n_batch(ctx); // only a single seq_id per token is needed - batch = llama_batch_init(std::max(n_batch, params.n_parallel), 0, 1); + batch = llama_batch_init(std::max(n_batch, params_base.n_parallel), 0, 1); } metrics.init(); @@ -715,12 +792,12 @@ struct server_context { return nullptr; } - server_slot * get_available_slot(const std::string & prompt) { + server_slot * get_available_slot(const server_task & task) { server_slot * ret = nullptr; // find the slot that has at least n% prompt similarity - if (ret == nullptr && slot_prompt_similarity != 0.0f && !prompt.empty()) { - int max_lcp_len = 0; + if (ret == nullptr && slot_prompt_similarity != 0.0f) { + int lcs_len = 0; float similarity = 0; for (server_slot & slot : slots) { @@ -730,25 +807,26 @@ struct server_context { } // skip the slot if it does not contains cached tokens - if (slot.prompt_tokens.empty()) { + if (slot.cache_tokens.empty()) { continue; } - // length of the Longest Common Prefix between the current slot's prompt and the input prompt - int lcp_len = longest_common_prefix(slot.cache_tokens, slot.prompt_tokens); + // length of the Longest Common Subsequence between the current slot's prompt and the input prompt + int cur_lcs_len = common_lcs(slot.cache_tokens, task.prompt_tokens); - // fraction of the common substring length compared to the current slot's prompt length - similarity = static_cast(lcp_len) / static_cast(slot.prompt_tokens.size()); + // fraction of the common subsequence length compared to the current slot's prompt length + float cur_similarity = static_cast(cur_lcs_len) / static_cast(slot.cache_tokens.size()); // select the current slot if the criteria match - if (lcp_len > max_lcp_len && similarity > slot_prompt_similarity) { - max_lcp_len = lcp_len; + if (cur_lcs_len > lcs_len && cur_similarity > slot_prompt_similarity) { + lcs_len = cur_lcs_len; + similarity = cur_similarity; ret = &slot; } } if (ret != nullptr) { - SLT_DBG(*ret, "selected slot by lcp similarity, max_lcp_len = %d, similarity = %f\n", max_lcp_len, similarity); + SLT_DBG(*ret, "selected slot by lcs similarity, lcs_len = %d, similarity = %f\n", lcs_len, similarity); } } @@ -777,9 +855,11 @@ struct server_context { } bool launch_slot_with_task(server_slot & slot, const server_task & task) { - slot_params default_params; // Sampling parameter defaults are loaded from the global server context (but individual requests can still override them) - auto default_sparams = params.sparams; + slot_params defaults; + defaults.sampling = params_base.sampling; + defaults.speculative = params_base.speculative; + const auto & data = task.data; if (data.count("__oaicompat") != 0) { @@ -790,42 +870,52 @@ struct server_context { slot.oaicompat_model = ""; } - slot.params.stream = json_value(data, "stream", false); - slot.params.cache_prompt = json_value(data, "cache_prompt", false); - slot.params.n_predict = json_value(data, "n_predict", json_value(data, "max_tokens", default_params.n_predict)); - slot.params.n_indent = json_value(data, "n_indent", default_params.n_indent); - slot.sparams.top_k = json_value(data, "top_k", default_sparams.top_k); - slot.sparams.top_p = json_value(data, "top_p", default_sparams.top_p); - slot.sparams.min_p = json_value(data, "min_p", default_sparams.min_p); - slot.sparams.xtc_probability = json_value(data, "xtc_probability", default_sparams.xtc_probability); - slot.sparams.xtc_threshold = json_value(data, "xtc_threshold", default_sparams.xtc_threshold); - slot.sparams.typ_p = json_value(data, "typical_p", default_sparams.typ_p); - slot.sparams.temp = json_value(data, "temperature", default_sparams.temp); - slot.sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range); - slot.sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent); - slot.sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n); - slot.sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat); - slot.sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq); - slot.sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present); - slot.sparams.dry_multiplier = json_value(data, "dry_multiplier", default_sparams.dry_multiplier); - slot.sparams.dry_base = json_value(data, "dry_base", default_sparams.dry_base); - slot.sparams.dry_allowed_length = json_value(data, "dry_allowed_length", default_sparams.dry_allowed_length); - slot.sparams.dry_penalty_last_n = json_value(data, "dry_penalty_last_n", default_sparams.dry_penalty_last_n); - slot.sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat); - slot.sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau); - slot.sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta); - slot.sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl); - slot.params.n_keep = json_value(data, "n_keep", default_params.n_keep); - slot.params.n_discard = json_value(data, "n_discard", default_params.n_discard); - slot.sparams.seed = json_value(data, "seed", default_sparams.seed); - slot.sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs); - slot.sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep); - //slot.params.t_max_prompt_ms = json_value(data, "t_max_prompt_ms", default_params.t_max_prompt_ms); // TODO: implement - slot.params.t_max_predict_ms = json_value(data, "t_max_predict_ms", default_params.t_max_predict_ms); + slot.timings_per_token = json_value(data, "timings_per_token", false); - if (slot.sparams.dry_base < 1.0f) - { - slot.sparams.dry_base = default_sparams.dry_base; + slot.params.stream = json_value(data, "stream", false); + slot.params.cache_prompt = json_value(data, "cache_prompt", true); + slot.params.n_predict = json_value(data, "n_predict", json_value(data, "max_tokens", defaults.n_predict)); + slot.params.n_indent = json_value(data, "n_indent", defaults.n_indent); + slot.params.n_keep = json_value(data, "n_keep", defaults.n_keep); + slot.params.n_discard = json_value(data, "n_discard", defaults.n_discard); + //slot.params.t_max_prompt_ms = json_value(data, "t_max_prompt_ms", defaults.t_max_prompt_ms); // TODO: implement + slot.params.t_max_predict_ms = json_value(data, "t_max_predict_ms", defaults.t_max_predict_ms); + + slot.params.sampling.top_k = json_value(data, "top_k", defaults.sampling.top_k); + slot.params.sampling.top_p = json_value(data, "top_p", defaults.sampling.top_p); + slot.params.sampling.min_p = json_value(data, "min_p", defaults.sampling.min_p); + slot.params.sampling.xtc_probability = json_value(data, "xtc_probability", defaults.sampling.xtc_probability); + slot.params.sampling.xtc_threshold = json_value(data, "xtc_threshold", defaults.sampling.xtc_threshold); + slot.params.sampling.typ_p = json_value(data, "typical_p", defaults.sampling.typ_p); + slot.params.sampling.temp = json_value(data, "temperature", defaults.sampling.temp); + slot.params.sampling.dynatemp_range = json_value(data, "dynatemp_range", defaults.sampling.dynatemp_range); + slot.params.sampling.dynatemp_exponent = json_value(data, "dynatemp_exponent", defaults.sampling.dynatemp_exponent); + slot.params.sampling.penalty_last_n = json_value(data, "repeat_last_n", defaults.sampling.penalty_last_n); + slot.params.sampling.penalty_repeat = json_value(data, "repeat_penalty", defaults.sampling.penalty_repeat); + slot.params.sampling.penalty_freq = json_value(data, "frequency_penalty", defaults.sampling.penalty_freq); + slot.params.sampling.penalty_present = json_value(data, "presence_penalty", defaults.sampling.penalty_present); + slot.params.sampling.dry_multiplier = json_value(data, "dry_multiplier", defaults.sampling.dry_multiplier); + slot.params.sampling.dry_base = json_value(data, "dry_base", defaults.sampling.dry_base); + slot.params.sampling.dry_allowed_length = json_value(data, "dry_allowed_length", defaults.sampling.dry_allowed_length); + slot.params.sampling.dry_penalty_last_n = json_value(data, "dry_penalty_last_n", defaults.sampling.dry_penalty_last_n); + slot.params.sampling.mirostat = json_value(data, "mirostat", defaults.sampling.mirostat); + slot.params.sampling.mirostat_tau = json_value(data, "mirostat_tau", defaults.sampling.mirostat_tau); + slot.params.sampling.mirostat_eta = json_value(data, "mirostat_eta", defaults.sampling.mirostat_eta); + slot.params.sampling.penalize_nl = json_value(data, "penalize_nl", defaults.sampling.penalize_nl); + slot.params.sampling.seed = json_value(data, "seed", defaults.sampling.seed); + slot.params.sampling.n_probs = json_value(data, "n_probs", defaults.sampling.n_probs); + slot.params.sampling.min_keep = json_value(data, "min_keep", defaults.sampling.min_keep); + + slot.params.speculative.n_min = json_value(data, "speculative.n_min", defaults.speculative.n_min); + slot.params.speculative.n_max = json_value(data, "speculative.n_max", defaults.speculative.n_max); + slot.params.speculative.p_min = json_value(data, "speculative.p_min", defaults.speculative.p_min); + + slot.params.speculative.n_min = std::min(slot.params.speculative.n_max, slot.params.speculative.n_min); + slot.params.speculative.n_min = std::max(slot.params.speculative.n_min, 2); + slot.params.speculative.n_max = std::max(slot.params.speculative.n_max, 0); + + if (slot.params.sampling.dry_base < 1.0f) { + slot.params.sampling.dry_base = defaults.sampling.dry_base; } // sequence breakers for DRY @@ -834,8 +924,8 @@ struct server_context { // Ref: https://github.com/oobabooga/text-generation-webui/blob/d1af7a41ade7bd3c3a463bfa640725edb818ebaf/extensions/openai/typing.py#L39 if (data.contains("dry_sequence_breakers")) { - slot.sparams.dry_sequence_breakers = json_value(data, "dry_sequence_breakers", std::vector()); - if (slot.sparams.dry_sequence_breakers.empty()) { + slot.params.sampling.dry_sequence_breakers = json_value(data, "dry_sequence_breakers", std::vector()); + if (slot.params.sampling.dry_sequence_breakers.empty()) { send_error(task, "Error: dry_sequence_breakers must be a non-empty array of strings", ERROR_TYPE_INVALID_REQUEST); return false; } @@ -849,14 +939,14 @@ struct server_context { } if (data.contains("json_schema") && !data.contains("grammar")) { try { - auto schema = json_value(data, "json_schema", json::object()); - slot.sparams.grammar = json_schema_to_grammar(schema); + auto schema = json_value(data, "json_schema", json::object()); + slot.params.sampling.grammar = json_schema_to_grammar(schema); } catch (const std::exception & e) { send_error(task, std::string("\"json_schema\": ") + e.what(), ERROR_TYPE_INVALID_REQUEST); return false; } } else { - slot.sparams.grammar = json_value(data, "grammar", default_sparams.grammar); + slot.params.sampling.grammar = json_value(data, "grammar", defaults.sampling.grammar); } if (slot.n_predict > 0 && slot.params.n_predict > slot.n_predict) { @@ -866,10 +956,10 @@ struct server_context { } { - slot.sparams.logit_bias.clear(); + slot.params.sampling.logit_bias.clear(); if (json_value(data, "ignore_eos", false) && has_eos_token) { - slot.sparams.logit_bias.push_back({llama_token_eos(model), -INFINITY}); + slot.params.sampling.logit_bias.push_back({llama_token_eos(model), -INFINITY}); } const auto & logit_bias = data.find("logit_bias"); @@ -890,12 +980,12 @@ struct server_context { if (el[0].is_number_integer()) { llama_token tok = el[0].get(); if (tok >= 0 && tok < n_vocab) { - slot.sparams.logit_bias.push_back({tok, bias}); + slot.params.sampling.logit_bias.push_back({tok, bias}); } } else if (el[0].is_string()) { auto toks = common_tokenize(model, el[0].get(), false); for (auto tok : toks) { - slot.sparams.logit_bias.push_back({tok, bias}); + slot.params.sampling.logit_bias.push_back({tok, bias}); } } } @@ -905,7 +995,7 @@ struct server_context { { slot.antiprompts.clear(); - slot.sparams.grammar_trigger_words.clear(); + slot.params.sampling.grammar_trigger_words.clear(); auto copy_string_array = [&](const json & data, const std::string & key, std::vector & vec) { const auto & arr = data.find(key); @@ -921,24 +1011,32 @@ struct server_context { std::vector stop_words; copy_string_array(data, "stop", stop_words); - copy_string_array(data, "grammar_trigger_words", slot.sparams.grammar_trigger_words); + copy_string_array(data, "grammar_trigger_words", slot.params.sampling.grammar_trigger_words); - slot.antiprompts.build(ctx, stop_words, slot.sparams.grammar_trigger_words); + slot.antiprompts.build(ctx, stop_words, slot.params.sampling.grammar_trigger_words); } { const auto & samplers = data.find("samplers"); - if (samplers != data.end() && samplers->is_array()) { - std::vector sampler_names; - for (const auto & name : *samplers) { - if (name.is_string()) { - sampler_names.emplace_back(name); + if (samplers != data.end()) { + if (samplers->is_array()) { + std::vector sampler_names; + for (const auto & name : *samplers) { + if (name.is_string()) { + sampler_names.emplace_back(name); + } } + slot.params.sampling.samplers = common_sampler_types_from_names(sampler_names, false); + } else if (samplers->is_string()){ + std::string sampler_string; + for (const auto & name : *samplers) { + sampler_string += name; + } + slot.params.sampling.samplers = common_sampler_types_from_chars(sampler_string); } - slot.sparams.samplers = common_sampler_types_from_names(sampler_names, false); } else { - slot.sparams.samplers = default_sparams.samplers; + slot.params.sampling.samplers = defaults.sampling.samplers; } } @@ -947,7 +1045,7 @@ struct server_context { common_sampler_free(slot.smpl); } - slot.smpl = common_sampler_init(model, slot.sparams); + slot.smpl = common_sampler_init(model, slot.params.sampling); if (slot.smpl == nullptr) { // for now, the only error that may happen here is invalid grammar send_error(task, "Failed to parse grammar", ERROR_TYPE_INVALID_REQUEST); @@ -955,6 +1053,12 @@ struct server_context { } } + if (slot.ctx_dft) { + llama_batch_free(slot.batch_spec); + + slot.batch_spec = llama_batch_init(slot.params.speculative.n_max + 1, 0, 1); + } + slot.state = SLOT_STATE_STARTED; SLT_INF(slot, "%s", "processing task\n"); @@ -974,12 +1078,12 @@ struct server_context { auto match = slot.antiprompts.findSingleTokenMatch(result.tok); // remember which tokens were sampled - used for repetition penalties during sampling - const std::string token_str = common_token_to_piece(ctx, result.tok, params.special || (match.pos != std::string::npos && match.is_grammar_trigger)); + const std::string token_str = common_token_to_piece(ctx, result.tok, params_base.special || (match.pos != std::string::npos && match.is_grammar_trigger)); slot.sampled = result.tok; if (match.pos != std::string::npos && !match.is_partial) { if (match.is_grammar_trigger) { - common_sampler_trigger_grammar(model, slot.smpl, common_token_to_piece(ctx, result.tok, params.special)); + common_sampler_trigger_grammar(model, slot.smpl, common_token_to_piece(ctx, result.tok, params_base.special)); } else { slot.stopped_word = true; slot.stopping_word = match.pattern; @@ -1061,7 +1165,7 @@ struct server_context { } // check the limits - if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params)) { + if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params_base)) { slot.stopped_limit = true; slot.has_next_token = false; @@ -1154,51 +1258,56 @@ struct server_context { json get_formated_generation(const server_slot & slot) const { std::vector samplers; - samplers.reserve(slot.sparams.samplers.size()); - for (const auto & sampler : slot.sparams.samplers) { + samplers.reserve(slot.params.sampling.samplers.size()); + for (const auto & sampler : slot.params.sampling.samplers) { samplers.emplace_back(common_sampler_type_to_str(sampler)); } return json { {"n_ctx", slot.n_ctx}, {"n_predict", slot.n_predict}, // Server configured n_predict - {"model", params.model_alias}, - {"seed", slot.sparams.seed}, + {"model", params_base.model_alias}, + {"seed", slot.params.sampling.seed}, {"seed_cur", slot.smpl ? common_sampler_get_seed(slot.smpl) : 0}, - {"temperature", slot.sparams.temp}, - {"dynatemp_range", slot.sparams.dynatemp_range}, - {"dynatemp_exponent", slot.sparams.dynatemp_exponent}, - {"top_k", slot.sparams.top_k}, - {"top_p", slot.sparams.top_p}, - {"min_p", slot.sparams.min_p}, - {"xtc_probability", slot.sparams.xtc_probability}, - {"xtc_threshold", slot.sparams.xtc_threshold}, - {"typical_p", slot.sparams.typ_p}, - {"repeat_last_n", slot.sparams.penalty_last_n}, - {"repeat_penalty", slot.sparams.penalty_repeat}, - {"presence_penalty", slot.sparams.penalty_present}, - {"frequency_penalty", slot.sparams.penalty_freq}, - {"dry_multiplier", slot.sparams.dry_multiplier}, - {"dry_base", slot.sparams.dry_base}, - {"dry_allowed_length", slot.sparams.dry_allowed_length}, - {"dry_penalty_last_n", slot.sparams.dry_penalty_last_n}, - {"dry_sequence_breakers", slot.sparams.dry_sequence_breakers}, - {"mirostat", slot.sparams.mirostat}, - {"mirostat_tau", slot.sparams.mirostat_tau}, - {"mirostat_eta", slot.sparams.mirostat_eta}, - {"penalize_nl", slot.sparams.penalize_nl}, - {"stop", slot.antiprompts.stop_words}, + {"temperature", slot.params.sampling.temp}, + {"dynatemp_range", slot.params.sampling.dynatemp_range}, + {"dynatemp_exponent", slot.params.sampling.dynatemp_exponent}, + {"top_k", slot.params.sampling.top_k}, + {"top_p", slot.params.sampling.top_p}, + {"min_p", slot.params.sampling.min_p}, + {"xtc_probability", slot.params.sampling.xtc_probability}, + {"xtc_threshold", slot.params.sampling.xtc_threshold}, + {"typical_p", slot.params.sampling.typ_p}, + {"repeat_last_n", slot.params.sampling.penalty_last_n}, + {"repeat_penalty", slot.params.sampling.penalty_repeat}, + {"presence_penalty", slot.params.sampling.penalty_present}, + {"frequency_penalty", slot.params.sampling.penalty_freq}, + {"dry_multiplier", slot.params.sampling.dry_multiplier}, + {"dry_base", slot.params.sampling.dry_base}, + {"dry_allowed_length", slot.params.sampling.dry_allowed_length}, + {"dry_penalty_last_n", slot.params.sampling.dry_penalty_last_n}, + {"dry_sequence_breakers", slot.params.sampling.dry_sequence_breakers}, + {"mirostat", slot.params.sampling.mirostat}, + {"mirostat_tau", slot.params.sampling.mirostat_tau}, + {"mirostat_eta", slot.params.sampling.mirostat_eta}, + {"penalize_nl", slot.params.sampling.penalize_nl}, + {"stop", slot.params.antiprompt}, {"grammar_trigger", slot.antiprompts.grammar_trigger_words}, {"max_tokens", slot.params.n_predict}, // User configured n_predict {"n_keep", slot.params.n_keep}, {"n_discard", slot.params.n_discard}, - {"ignore_eos", slot.sparams.ignore_eos}, + {"ignore_eos", slot.params.sampling.ignore_eos}, {"stream", slot.params.stream}, - //{"logit_bias", slot.sparams.logit_bias}, - {"n_probs", slot.sparams.n_probs}, - {"min_keep", slot.sparams.min_keep}, - {"grammar", slot.sparams.grammar}, + //{"logit_bias", slot.params.sampling.logit_bias}, + {"n_probs", slot.params.sampling.n_probs}, + {"min_keep", slot.params.sampling.min_keep}, + {"grammar", slot.params.sampling.grammar}, {"samplers", samplers}, + {"speculative", slot.can_speculate()}, + {"speculative.n_max", slot.params.speculative.n_max}, + {"speculative.n_min", slot.params.speculative.n_min}, + {"speculative.p_min", slot.params.speculative.p_min}, + {"timings_per_token", slot.timings_per_token}, }; } @@ -1235,7 +1344,7 @@ struct server_context { {"index", slot.index}, }; - if (slot.sparams.n_probs > 0) { + if (slot.params.sampling.n_probs > 0) { const llama_tokens to_send_toks = common_tokenize(ctx, tkn.text_to_send, false); const size_t probs_pos = std::min(slot.n_sent_token_probs, slot.generated_token_probs.size()); const size_t probs_stop_pos = std::min(slot.n_sent_token_probs + to_send_toks.size(), slot.generated_token_probs.size()); @@ -1256,6 +1365,10 @@ struct server_context { res.data["model"] = slot.oaicompat_model; } + if (slot.timings_per_token) { + res.data["timings"] = slot.get_formated_timings(); + } + queue_results.send(res); } @@ -1268,7 +1381,7 @@ struct server_context { {"content", !slot.params.stream ? slot.generated_text : ""}, {"id_slot", slot.id}, {"stop", true}, - {"model", params.model_alias}, + {"model", params_base.model_alias}, {"tokens_predicted", slot.n_decoded}, {"tokens_evaluated", slot.n_prompt_tokens}, {"generation_settings", get_formated_generation(slot)}, @@ -1284,7 +1397,7 @@ struct server_context { {"index", slot.index}, }; - if (slot.sparams.n_probs > 0) { + if (slot.params.sampling.n_probs > 0) { std::vector probs; if (!slot.params.stream && slot.stopped_word) { const llama_tokens stop_word_toks = common_tokenize(ctx, slot.stopping_word, false); @@ -1312,16 +1425,16 @@ struct server_context { void send_embedding(const server_slot & slot, const llama_batch & batch) { server_task_result res; - res.id = slot.id_task; - res.error = false; - res.stop = true; + res.id = slot.id_task; + res.error = false; + res.stop = true; const int n_embd = llama_n_embd(model); std::vector embd_res(n_embd, 0.0f); for (int i = 0; i < batch.n_tokens; ++i) { - if (!batch.logits[i] || batch.seq_id[i][0] != slot.id + 1) { + if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) { continue; } @@ -1356,12 +1469,12 @@ struct server_context { void send_rerank(const server_slot & slot, const llama_batch & batch) { server_task_result res; - res.id = slot.id_task; - res.error = false; - res.stop = true; + res.id = slot.id_task; + res.error = false; + res.stop = true; for (int i = 0; i < batch.n_tokens; ++i) { - if (!batch.logits[i] || batch.seq_id[i][0] != slot.id + 1) { + if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) { continue; } @@ -1441,10 +1554,10 @@ struct server_context { data.at("input_prefix"), data.at("input_suffix"), data.at("input_extra"), - params.n_batch, - params.n_predict, + params_base.n_batch, + params_base.n_predict, slots[0].n_ctx, // TODO: there should be a better way - params.spm_infill, + params_base.spm_infill, tokenized_prompts[i] ); create_task(data, tokens); @@ -1534,24 +1647,13 @@ struct server_context { // Functions to process the task // - void process_single_task(const server_task & task) { + void process_single_task(server_task task) { switch (task.type) { case SERVER_TASK_TYPE_INFERENCE: { const int id_slot = json_value(task.data, "id_slot", -1); - server_slot * slot; - - if (id_slot != -1) { - slot = get_slot_by_id(id_slot); - } else { - std::string prompt; - if (task.data.contains("prompt") && task.data.at("prompt").is_string()) { - prompt = json_value(task.data, "prompt", std::string()); - } - - slot = get_available_slot(prompt); - } + server_slot * slot = id_slot != -1 ? get_slot_by_id(id_slot) : get_available_slot(task); if (slot == nullptr) { // if no slot is available, we defer this task for processing later @@ -1601,11 +1703,11 @@ struct server_context { for (server_slot & slot : slots) { json slot_data = get_formated_generation(slot); - slot_data["id"] = slot.id; - slot_data["id_task"] = slot.id_task; - slot_data["state"] = slot.state; - slot_data["prompt"] = common_detokenize(ctx, slot.prompt_tokens); - slot_data["next_token"] = { + slot_data["id"] = slot.id; + slot_data["id_task"] = slot.id_task; + slot_data["is_processing"] = slot.is_processing(); + slot_data["prompt"] = common_detokenize(ctx, slot.prompt_tokens); + slot_data["next_token"] = { {"has_next_token", slot.has_next_token}, {"has_new_line", slot.has_new_line}, {"n_remain", slot.n_remaining}, @@ -1616,10 +1718,10 @@ struct server_context { {"stopping_word", slot.stopping_word}, }; - if (slot_data["state"] == SLOT_STATE_IDLE) { - n_idle_slots++; - } else { + if (slot.is_processing()) { n_processing_slots++; + } else { + n_idle_slots++; } slots_data.push_back(slot_data); @@ -1681,7 +1783,7 @@ struct server_context { std::string filename = task.data.at("filename"); std::string filepath = task.data.at("filepath"); - const size_t nwrite = llama_state_seq_save_file(ctx, filepath.c_str(), slot->id + 1, slot->cache_tokens.data(), token_count); + const size_t nwrite = llama_state_seq_save_file(ctx, filepath.c_str(), slot->id, slot->cache_tokens.data(), token_count); const int64_t t_end = ggml_time_us(); const double t_save_ms = (t_end - t_start) / 1000.0; @@ -1723,7 +1825,7 @@ struct server_context { slot->cache_tokens.resize(slot->n_ctx); size_t token_count = 0; - size_t nread = llama_state_seq_load_file(ctx, filepath.c_str(), slot->id + 1, slot->cache_tokens.data(), slot->cache_tokens.size(), &token_count); + size_t nread = llama_state_seq_load_file(ctx, filepath.c_str(), slot->id, slot->cache_tokens.data(), slot->cache_tokens.size(), &token_count); if (nread == 0) { slot->cache_tokens.resize(0); send_error(task, "Unable to restore slot, no available space in KV cache or invalid slot save file", ERROR_TYPE_INVALID_REQUEST); @@ -1766,7 +1868,7 @@ struct server_context { // Erase token cache const size_t n_erased = slot->cache_tokens.size(); - llama_kv_cache_seq_rm(ctx, slot->id + 1, -1, -1); + llama_kv_cache_seq_rm(ctx, slot->id, -1, -1); slot->cache_tokens.clear(); server_task_result result; @@ -1828,7 +1930,7 @@ struct server_context { // TODO: simplify and improve for (server_slot & slot : slots) { if (slot.is_processing() && slot.n_past + 1 >= slot.n_ctx) { - if (!params.ctx_shift) { + if (!params_base.ctx_shift) { // this check is redundant (for good) // we should never get here, because generation should already stopped in process_token() slot.release(); @@ -1843,8 +1945,8 @@ struct server_context { SLT_WRN(slot, "slot context shift, n_keep = %d, n_left = %d, n_discard = %d\n", n_keep, n_left, n_discard); - llama_kv_cache_seq_rm (ctx, slot.id + 1, n_keep , n_keep + n_discard); - llama_kv_cache_seq_add(ctx, slot.id + 1, n_keep + n_discard, slot.n_past, -n_discard); + llama_kv_cache_seq_rm (ctx, slot.id, n_keep , n_keep + n_discard); + llama_kv_cache_seq_add(ctx, slot.id, n_keep + n_discard, slot.n_past, -n_discard); if (slot.params.cache_prompt) { for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++) { @@ -1871,7 +1973,7 @@ struct server_context { slot.i_batch = batch.n_tokens; - common_batch_add(batch, slot.sampled, slot.n_past, { slot.id + 1 }, true); + common_batch_add(batch, slot.sampled, slot.n_past, { slot.id }, true); slot.n_past += 1; @@ -1894,7 +1996,7 @@ struct server_context { int32_t batch_type = batch.n_tokens > 0 ? 0 : -1; // next, batch any pending prompts without exceeding n_batch - if (params.cont_batching || batch.n_tokens == 0) { + if (params_base.cont_batching || batch.n_tokens == 0) { for (auto & slot : slots) { // this slot still has a prompt to be processed if (slot.state == SLOT_STATE_PROCESSING_PROMPT || slot.state == SLOT_STATE_STARTED) { @@ -1947,7 +2049,7 @@ struct server_context { continue; } } else { - if (!params.ctx_shift) { + if (!params_base.ctx_shift) { // if context shift is disabled, we make sure prompt size is smaller than KV size // TODO: there should be a separate parameter that control prompt truncation // context shift should be applied only during the generation phase @@ -1990,14 +2092,14 @@ struct server_context { if (slot.params.cache_prompt) { // reuse any previously computed tokens that are common with the new prompt - slot.n_past = longest_common_prefix(slot.cache_tokens, prompt_tokens); + slot.n_past = common_lcp(slot.cache_tokens, prompt_tokens); // reuse chunks from the cached prompt by shifting their KV cache in the new position - if (params.n_cache_reuse > 0) { + if (params_base.n_cache_reuse > 0) { size_t head_c = slot.n_past; // cache size_t head_p = slot.n_past; // current prompt - SLT_DBG(slot, "trying to reuse chunks with size > %d, slot.n_past = %d\n", params.n_cache_reuse, slot.n_past); + SLT_DBG(slot, "trying to reuse chunks with size > %d, slot.n_past = %d\n", params_base.n_cache_reuse, slot.n_past); while (head_c < slot.cache_tokens.size() && head_p < prompt_tokens.size()) { @@ -2010,7 +2112,7 @@ struct server_context { n_match++; } - if (n_match >= (size_t) params.n_cache_reuse) { + if (n_match >= (size_t) params_base.n_cache_reuse) { SLT_INF(slot, "reusing chunk with size %zu, shifting KV cache [%zu, %zu) -> [%zu, %zu)\n", n_match, head_c, head_c + n_match, head_p, head_p + n_match); //for (size_t i = head_p; i < head_p + n_match; i++) { // SLT_DBG(slot, "cache token %3zu: %6d '%s'\n", i, prompt_tokens[i], common_token_to_piece(ctx, prompt_tokens[i]).c_str()); @@ -2018,8 +2120,8 @@ struct server_context { const int64_t kv_shift = (int64_t) head_p - (int64_t) head_c; - llama_kv_cache_seq_rm (ctx, slot.id + 1, head_p, head_c); - llama_kv_cache_seq_add(ctx, slot.id + 1, head_c, -1, kv_shift); + llama_kv_cache_seq_rm (ctx, slot.id, head_p, head_c); + llama_kv_cache_seq_add(ctx, slot.id, head_c, -1, kv_shift); for (size_t i = 0; i < n_match; i++) { slot.cache_tokens[head_p + i] = slot.cache_tokens[head_c + i]; @@ -2068,9 +2170,9 @@ struct server_context { } // keep only the common part - if (!llama_kv_cache_seq_rm(ctx, slot.id + 1, slot.n_past, -1)) { + if (!llama_kv_cache_seq_rm(ctx, slot.id, slot.n_past, -1)) { // could not partially delete (likely using a non-Transformer model) - llama_kv_cache_seq_rm(ctx, slot.id + 1, -1, -1); + llama_kv_cache_seq_rm(ctx, slot.id, -1, -1); // there is no common part left slot.n_past = 0; @@ -2083,7 +2185,7 @@ struct server_context { // add prompt tokens for processing in the current batch while (slot.n_past < slot.n_prompt_tokens && batch.n_tokens < n_batch) { - common_batch_add(batch, prompt_tokens[slot.n_past], slot.n_past, { slot.id + 1 }, false); + common_batch_add(batch, prompt_tokens[slot.n_past], slot.n_past, { slot.id }, false); if (slot.params.cache_prompt) { slot.cache_tokens.push_back(prompt_tokens[slot.n_past]); @@ -2198,26 +2300,33 @@ struct server_context { continue; // continue loop of slots } - completion_token_output result; - const llama_token id = common_sampler_sample(slot.smpl, ctx, slot.i_batch - i); + llama_token id = common_sampler_sample(slot.smpl, ctx, slot.i_batch - i); + + slot.i_batch = -1; common_sampler_accept(slot.smpl, id, true); slot.n_decoded += 1; + + const int64_t t_current = ggml_time_us(); + if (slot.n_decoded == 1) { - slot.t_start_generation = ggml_time_us(); + slot.t_start_generation = t_current; slot.t_prompt_processing = (slot.t_start_generation - slot.t_start_process_prompt) / 1e3; metrics.on_prompt_eval(slot); } + slot.t_token_generation = (t_current - slot.t_start_generation) / 1e3; + + completion_token_output result; result.tok = id; const auto * cur_p = common_sampler_get_candidates(slot.smpl); - for (size_t i = 0; i < (size_t) slot.sparams.n_probs; ++i) { + for (size_t i = 0; i < (size_t) slot.params.sampling.n_probs; ++i) { result.probs.push_back({ cur_p->data[i].id, - i >= cur_p->size ? 0.0f : cur_p->data[i].p, + i >= cur_p->size ? 0.0f : cur_p->data[i].p, }); } @@ -2227,9 +2336,94 @@ struct server_context { slot.print_timings(); send_final_response(slot); metrics.on_prediction(slot); + continue; + } + } + + // do speculative decoding + for (auto & slot : slots) { + if (!slot.is_processing() || !slot.can_speculate()) { + continue; } - slot.i_batch = -1; + if (slot.state != SLOT_STATE_GENERATING) { + continue; + } + + // determine the max draft that fits the current slot state + int n_draft_max = slot.params.speculative.n_max; + + // note: n_past is not yet increased for the `id` token sampled above + // also, need to leave space for 1 extra token to allow context shifts + n_draft_max = std::min(n_draft_max, slot.n_ctx - slot.n_past - 2); + + if (slot.n_remaining > 0) { + n_draft_max = std::min(n_draft_max, slot.n_remaining - 1); + } + + SLT_DBG(slot, "max possible draft: %d\n", n_draft_max); + + if (n_draft_max < slot.params.speculative.n_min) { + SLT_DBG(slot, "the max possible draft is too small: %d < %d - skipping speculative decoding\n", n_draft_max, slot.params.speculative.n_min); + + continue; + } + + llama_token id = slot.sampled; + + struct common_speculative_params params_spec; + params_spec.n_draft = n_draft_max; + params_spec.n_reuse = llama_n_ctx(slot.ctx_dft) - slot.params.speculative.n_max; + params_spec.p_min = slot.params.speculative.p_min; + + llama_tokens draft = common_speculative_gen_draft(slot.spec, params_spec, slot.cache_tokens, id); + + // ignore small drafts + if (slot.params.speculative.n_min > (int) draft.size()) { + SLT_DBG(slot, "ignoring small draft: %d < %d\n", (int) draft.size(), slot.params.speculative.n_min); + + continue; + } + + // construct the speculation batch + common_batch_clear(slot.batch_spec); + common_batch_add (slot.batch_spec, id, slot.n_past, { slot.id }, true); + + for (size_t i = 0; i < draft.size(); ++i) { + common_batch_add(slot.batch_spec, draft[i], slot.n_past + 1 + i, { slot.id }, true); + } + + SLT_DBG(slot, "decoding speculative batch, size = %d\n", slot.batch_spec.n_tokens); + + llama_decode(ctx, slot.batch_spec); + + // the accepted tokens from the speculation + const auto ids = common_sampler_sample_and_accept_n(slot.smpl, ctx, draft); + + slot.n_past += ids.size(); + slot.n_decoded += ids.size(); + + slot.cache_tokens.push_back(id); + slot.cache_tokens.insert(slot.cache_tokens.end(), ids.begin(), ids.end() - 1); + + llama_kv_cache_seq_rm(ctx, slot.id, slot.n_past, -1); + + for (size_t i = 0; i < ids.size(); ++i) { + completion_token_output result; + + result.tok = ids[i]; + + if (!process_token(result, slot)) { + // release slot because of stop condition + slot.release(); + slot.print_timings(); + send_final_response(slot); + metrics.on_prediction(slot); + break; + } + } + + SLT_DBG(slot, "accepted %d/%d draft tokens, new n_past = %d\n", (int) ids.size() - 1, (int) draft.size(), slot.n_past); } } @@ -2325,16 +2519,6 @@ int main(int argc, char ** argv) { std::atomic state{SERVER_STATE_LOADING_MODEL}; svr->set_default_headers({{"Server", "llama.cpp"}}); - - // CORS preflight - svr->Options(R"(.*)", [](const httplib::Request &, httplib::Response & res) { - // Access-Control-Allow-Origin is already set by middleware - res.set_header("Access-Control-Allow-Credentials", "true"); - res.set_header("Access-Control-Allow-Methods", "POST"); - res.set_header("Access-Control-Allow-Headers", "*"); - return res.set_content("", "text/html"); // blank response, no data - }); - svr->set_logger(log_server_request); auto res_error = [](httplib::Response & res, const json & error_data) { @@ -2405,8 +2589,8 @@ int main(int argc, char ** argv) { return true; } - // If path is public, skip validation - if (public_endpoints.find(req.path) != public_endpoints.end()) { + // If path is public or is static file, skip validation + if (public_endpoints.find(req.path) != public_endpoints.end() || req.path == "/") { return true; } @@ -2447,6 +2631,14 @@ int main(int argc, char ** argv) { // register server middlewares svr->set_pre_routing_handler([&middleware_validate_api_key, &middleware_server_state](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + // If this is OPTIONS request, skip validation because browsers don't include Authorization header + if (req.method == "OPTIONS") { + res.set_header("Access-Control-Allow-Credentials", "true"); + res.set_header("Access-Control-Allow-Methods", "GET, POST"); + res.set_header("Access-Control-Allow-Headers", "*"); + res.set_content("", "text/html"); // blank response, no data + return httplib::Server::HandlerResponse::Handled; // skip further processing + } if (!middleware_server_state(req, res)) { return httplib::Server::HandlerResponse::Handled; } @@ -2717,16 +2909,16 @@ int main(int argc, char ** argv) { }; const auto handle_props = [&ctx_server, &res_ok](const httplib::Request &, httplib::Response & res) { - auto chat_template = llama_chat_template_from_model(ctx_server.model, ctx_server.params.chat_template, /* prefer_tool_use= */ false); + auto chat_template = llama_chat_template_from_model(ctx_server.model, ctx_server.params_base.chat_template, /* prefer_tool_use= */ false); json data = { { "default_generation_settings", ctx_server.default_generation_settings_for_props }, - { "total_slots", ctx_server.params.n_parallel }, + { "total_slots", ctx_server.params_base.n_parallel }, { "bos_token", common_token_to_piece(ctx_server.ctx, llama_token_bos(ctx_server.model), true) }, { "eos_token", common_token_to_piece(ctx_server.ctx, llama_token_eos(ctx_server.model), true) }, { "chat_template", chat_template.source()}, }; - if (ctx_server.params.use_jinja) { - auto tool_use_chat_template = llama_chat_template_from_model(ctx_server.model, ctx_server.params.chat_template, /* prefer_tool_use= */ true); + if (ctx_server.params_base.use_jinja) { + auto tool_use_chat_template = llama_chat_template_from_model(ctx_server.model, ctx_server.params_base.chat_template, /* prefer_tool_use= */ true); if (tool_use_chat_template.source() != chat_template.source()) { data["chat_template_tool_use"] = tool_use_chat_template.source(); } @@ -2736,7 +2928,7 @@ int main(int argc, char ** argv) { }; const auto handle_props_change = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) { - if (!ctx_server.params.endpoint_props) { + if (!ctx_server.params_base.endpoint_props) { res_error(res, format_error_response("This server does not support changing global properties. Start it with `--props`", ERROR_TYPE_NOT_SUPPORTED)); return; } @@ -2749,8 +2941,8 @@ int main(int argc, char ** argv) { }; const auto handle_completions_generic = [&ctx_server, &res_error, &res_ok](server_task_inf_type inf_type, json & data, httplib::Response & res) { - if (ctx_server.params.embedding || ctx_server.params.reranking) { - res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings` or `--reranking`", ERROR_TYPE_NOT_SUPPORTED)); + if (ctx_server.params_base.embedding) { + res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED)); return; } @@ -2855,8 +3047,8 @@ int main(int argc, char ** argv) { // TODO: maybe merge this function with "handle_completions_generic" const auto handle_chat_completions = [&ctx_server, ¶ms, &res_error, &res_ok, verbose](const httplib::Request & req, httplib::Response & res) { - if (ctx_server.params.embedding || ctx_server.params.reranking) { - res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings` or `--reranking`", ERROR_TYPE_NOT_SUPPORTED)); + if (ctx_server.params_base.embedding) { + res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED)); return; } @@ -2996,11 +3188,6 @@ int main(int argc, char ** argv) { }; const auto handle_embeddings = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) { - // TODO: somehow clean up this checks in the future - if (!ctx_server.params.embedding || ctx_server.params.reranking) { - res_error(res, format_error_response("This server does not support embeddings. Start it with `--embeddings` and without `--reranking`", ERROR_TYPE_NOT_SUPPORTED)); - return; - } const json body = json::parse(req.body); bool is_openai = false; @@ -3052,10 +3239,11 @@ int main(int argc, char ** argv) { }; const auto handle_rerank = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) { - if (!ctx_server.params.reranking) { - res_error(res, format_error_response("This server does not support reranking. Start it with `--reranking`", ERROR_TYPE_NOT_SUPPORTED)); + if (!ctx_server.params_base.reranking || ctx_server.params_base.embedding) { + res_error(res, format_error_response("This server does not support reranking. Start it with `--reranking` and without `--embedding`", ERROR_TYPE_NOT_SUPPORTED)); return; } + const json body = json::parse(req.body); // TODO: implement @@ -3170,13 +3358,6 @@ int main(int argc, char ** argv) { res.status = 200; // HTTP OK }; - auto handle_static_file = [](unsigned char * content, size_t len, const char * mime_type) { - return [content, len, mime_type](const httplib::Request &, httplib::Response & res) { - res.set_content(reinterpret_cast(content), len, mime_type); - return false; - }; - }; - // // Router // @@ -3184,33 +3365,17 @@ int main(int argc, char ** argv) { // register static assets routes if (!params.public_path.empty()) { // Set the base directory for serving static files - svr->set_base_dir(params.public_path); - } - - if (!params.api_keys.empty()) { - // for now, if API key is set, web UI is unusable - svr->Get("/", [&](const httplib::Request &, httplib::Response & res) { - return res.set_content("Web UI is disabled because API key is set.", "text/html; charset=utf-8"); - }); + bool is_found = svr->set_mount_point("/", params.public_path); + if (!is_found) { + LOG_ERR("%s: static assets path not found: %s\n", __func__, params.public_path.c_str()); + return 1; + } } else { - // using embedded static files - svr->Get("/", handle_static_file(index_html, index_html_len, "text/html; charset=utf-8")); - svr->Get("/index.js", handle_static_file(index_js, index_js_len, "text/javascript; charset=utf-8")); - svr->Get("/completion.js", handle_static_file(completion_js, completion_js_len, "text/javascript; charset=utf-8")); - svr->Get("/json-schema-to-grammar.mjs", handle_static_file(json_schema_to_grammar_mjs, json_schema_to_grammar_mjs_len, "text/javascript; charset=utf-8")); - - // add new-ui files - svr->Get("/colorthemes.css", handle_static_file(colorthemes_css, colorthemes_css_len, "text/css; charset=utf-8")); - svr->Get("/style.css", handle_static_file(style_css, style_css_len, "text/css; charset=utf-8")); - svr->Get("/theme-beeninorder.css", handle_static_file(theme_beeninorder_css, theme_beeninorder_css_len, "text/css; charset=utf-8")); - svr->Get("/theme-ketivah.css", handle_static_file(theme_ketivah_css, theme_ketivah_css_len, "text/css; charset=utf-8")); - svr->Get("/theme-mangotango.css", handle_static_file(theme_mangotango_css, theme_mangotango_css_len, "text/css; charset=utf-8")); - svr->Get("/theme-playground.css", handle_static_file(theme_playground_css, theme_playground_css_len, "text/css; charset=utf-8")); - svr->Get("/theme-polarnight.css", handle_static_file(theme_polarnight_css, theme_polarnight_css_len, "text/css; charset=utf-8")); - svr->Get("/theme-snowstorm.css", handle_static_file(theme_snowstorm_css, theme_snowstorm_css_len, "text/css; charset=utf-8")); - svr->Get("/index-new.html", handle_static_file(index_new_html, index_new_html_len, "text/html; charset=utf-8")); - svr->Get("/system-prompts.js", handle_static_file(system_prompts_js, system_prompts_js_len, "text/javascript; charset=utf-8")); - svr->Get("/prompt-formats.js", handle_static_file(prompt_formats_js, prompt_formats_js_len, "text/javascript; charset=utf-8")); + // using embedded static index.html + svr->Get("/", [](const httplib::Request &, httplib::Response & res) { + res.set_content(reinterpret_cast(index_html), index_html_len, "text/html; charset=utf-8"); + return false; + }); } // register API routes @@ -3258,8 +3423,18 @@ int main(int argc, char ** argv) { llama_backend_free(); }; - // bind HTTP listen port, run the HTTP server in a thread - if (!svr->bind_to_port(params.hostname, params.port)) { + // bind HTTP listen port + bool was_bound = false; + if (params.port == 0) { + int bound_port = svr->bind_to_any_port(params.hostname); + if ((was_bound = (bound_port >= 0))) { + params.port = bound_port; + } + } else { + was_bound = svr->bind_to_port(params.hostname, params.port); + } + + if (!was_bound) { //LOG_ERROR("couldn't bind HTTP server socket", { // {"hostname", params.hostname}, // {"port", params.port}, @@ -3268,6 +3443,8 @@ int main(int argc, char ** argv) { clean_up(); return 1; } + + // run the HTTP server in a thread std::thread t([&]() { svr->listen_after_bind(); }); svr->wait_until_ready(); @@ -3309,7 +3486,7 @@ int main(int argc, char ** argv) { ctx_server.queue_tasks.terminate(); }; - LOG_INF("%s: server is listening on %s:%d - starting the main loop\n", __func__, params.hostname.c_str(), params.port); + LOG_INF("%s: server is listening on http://%s:%d - starting the main loop\n", __func__, params.hostname.c_str(), params.port); ctx_server.queue_tasks.start_loop(); diff --git a/examples/server/tests/.gitignore b/examples/server/tests/.gitignore index 1d17dae13..90ee7fe6d 100644 --- a/examples/server/tests/.gitignore +++ b/examples/server/tests/.gitignore @@ -1 +1,2 @@ .venv +tmp diff --git a/examples/server/tests/README.md b/examples/server/tests/README.md index 26dbf582c..eb7c54d8e 100644 --- a/examples/server/tests/README.md +++ b/examples/server/tests/README.md @@ -1,19 +1,9 @@ # Server tests -Python based server tests scenario using [BDD](https://en.wikipedia.org/wiki/Behavior-driven_development) -and [behave](https://behave.readthedocs.io/en/latest/): - -* [issues.feature](./features/issues.feature) Pending issues scenario -* [parallel.feature](./features/parallel.feature) Scenario involving multi slots and concurrent requests -* [security.feature](./features/security.feature) Security, CORS and API Key -* [server.feature](./features/server.feature) Server base scenario: completion, embedding, tokenization, etc... +Python based server tests scenario using [pytest](https://docs.pytest.org/en/stable/). Tests target GitHub workflows job runners with 4 vCPU. -Requests are -using [aiohttp](https://docs.aiohttp.org/en/stable/client_reference.html), [asyncio](https://docs.python.org/fr/3/library/asyncio.html) -based http client. - Note: If the host architecture inference speed is faster than GitHub runners one, parallel scenario may randomly fail. To mitigate it, you can increase values in `n_predict`, `kv_size`. @@ -39,28 +29,19 @@ It's possible to override some scenario steps values with environment variables: |--------------------------|------------------------------------------------------------------------------------------------| | `PORT` | `context.server_port` to set the listening port of the server during scenario, default: `8080` | | `LLAMA_SERVER_BIN_PATH` | to change the server binary path, default: `../../../build/bin/llama-server` | -| `DEBUG` | "ON" to enable steps and server verbose mode `--verbose` | +| `DEBUG` | to enable steps and server verbose mode `--verbose` | | `N_GPU_LAYERS` | number of model layers to offload to VRAM `-ngl --n-gpu-layers` | -### Run @bug, @wip or @wrong_usage annotated scenario - -Feature or Scenario must be annotated with `@llama.cpp` to be included in the default scope. - -- `@bug` annotation aims to link a scenario with a GitHub issue. -- `@wrong_usage` are meant to show user issue that are actually an expected behavior -- `@wip` to focus on a scenario working in progress -- `@slow` heavy test, disabled by default - -To run a scenario annotated with `@bug`, start: +To run slow tests: ```shell -DEBUG=ON ./tests.sh --no-skipped --tags bug --stop +SLOW_TESTS=1 ./tests.sh ``` -After changing logic in `steps.py`, ensure that `@bug` and `@wrong_usage` scenario are updated. +To run with stdout/stderr display in real time (verbose output, but useful for debugging): ```shell -./tests.sh --no-skipped --tags bug,wrong_usage || echo "should failed but compile" +DEBUG=1 ./tests.sh -s -v -x ``` Some tests (especially `@slow` ones) require model downloads. Since this can time out the tests, you can pre-download them in the cache ahead of time with: @@ -69,3 +50,5 @@ Some tests (especially `@slow` ones) require model downloads. Since this can tim pip install -r examples/server/tests/requirements.txt python scripts/fetch_server_test_models.py ``` + +To see all available arguments, please refer to [pytest documentation](https://docs.pytest.org/en/stable/how-to/usage.html) diff --git a/examples/server/tests/conftest.py b/examples/server/tests/conftest.py new file mode 100644 index 000000000..017d1bb84 --- /dev/null +++ b/examples/server/tests/conftest.py @@ -0,0 +1,15 @@ +import pytest +from utils import * + + +# ref: https://stackoverflow.com/questions/22627659/run-code-before-and-after-each-test-in-py-test +@pytest.fixture(autouse=True) +def stop_server_after_each_test(): + # do nothing before each test + yield + # stop all servers after each test + instances = set( + server_instances + ) # copy the set to prevent 'Set changed size during iteration' + for server in instances: + server.stop() diff --git a/examples/server/tests/features/ctx_shift.feature b/examples/server/tests/features/ctx_shift.feature deleted file mode 100644 index ae6c6b01b..000000000 --- a/examples/server/tests/features/ctx_shift.feature +++ /dev/null @@ -1,66 +0,0 @@ -@llama.cpp -@ctx_shift -Feature: llama.cpp server - - Background: Server startup - Given a server listening on localhost:8080 - And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models - And a model file test-model.gguf - And a model alias tinyllama-2 - And BOS token is 1 - And 42 as server seed - And 256 KV cache size - And 32 as batch size - And 2 slots - - # the prompt is 301 tokens - # the slot context is 256/2 = 128 tokens - # the prompt is truncated to keep the last 109 tokens - # 64 tokens are generated thanks to shifting the context when it gets full - Scenario: Inference with context shift - And 64 server max tokens to predict - Then the server is starting - Then the server is healthy - Given a prompt: - """ - Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. - Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. - Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. - Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. - """ - And a completion request with no api error - Then 64 tokens are predicted matching fun|Annaks|popcorns|pictry|bowl - And the completion is truncated - And 109 prompt tokens are processed - - Scenario Outline: Inference without context shift - And server max tokens to predict - And disable context shifting - Then the server is starting - Then the server is healthy - Given a prompt: - """ - Hi how are you - """ - And a completion request with no api error - Then tokens are predicted matching twind|Anna - And the completion is truncated - And 8 prompt tokens are processed - Examples: - | n_predict | n_token_output | truncated | - | 64 | 64 | not | - | -1 | 120 | | - - Scenario: Inference without context shift (expected error: prompt too long) - And disable context shifting - Then the server is starting - Then the server is healthy - Given a prompt: - """ - Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. - Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. - Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. - Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. - """ - And a completion request with 400 api error - diff --git a/examples/server/tests/features/embeddings.feature b/examples/server/tests/features/embeddings.feature deleted file mode 100644 index f4fe2ee43..000000000 --- a/examples/server/tests/features/embeddings.feature +++ /dev/null @@ -1,113 +0,0 @@ -@llama.cpp -@embeddings -Feature: llama.cpp server - - Background: Server startup - Given a server listening on localhost:8080 - And a model url https://huggingface.co/ggml-org/models/resolve/main/bert-bge-small/ggml-model-f16.gguf - And a model file bert-bge-small.gguf - And a model alias bert-bge-small - And 42 as server seed - And 2 slots - # the bert-bge-small model has context size of 512 - # since the generated prompts are as big as the batch size, we need to set the batch size to <= 512 - # ref: https://huggingface.co/BAAI/bge-small-en-v1.5/blob/5c38ec7c405ec4b44b94cc5a9bb96e735b38267a/config.json#L20 - And 128 as batch size - And 128 as ubatch size - And 512 KV cache size - And enable embeddings endpoint - Then the server is starting - Then the server is healthy - - Scenario: Embedding - When embeddings are computed for: - """ - What is the capital of Bulgaria ? - """ - Then embeddings are generated - - Scenario: Embedding (error: prompt too long) - When embeddings are computed for: - """ - Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. - Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. - Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. - Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. - Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. - Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. - Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. - Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. - """ - And embeddings request with 500 api error - - Scenario: OAI Embeddings compatibility - Given a model bert-bge-small - When an OAI compatible embeddings computation request for: - """ - What is the capital of Spain ? - """ - Then embeddings are generated - - Scenario: OAI Embeddings compatibility with multiple inputs - Given a model bert-bge-small - Given a prompt: - """ - In which country Paris is located ? - """ - And a prompt: - """ - Is Madrid the capital of Spain ? - """ - When an OAI compatible embeddings computation request for multiple inputs - Then embeddings are generated - - Scenario: Multi users embeddings - Given a prompt: - """ - Write a very long story about AI. - """ - And a prompt: - """ - Write another very long music lyrics. - """ - And a prompt: - """ - Write a very long poem. - """ - And a prompt: - """ - Write a very long joke. - """ - Given concurrent embedding requests - Then the server is busy - Then the server is idle - Then all embeddings are generated - - Scenario: Multi users OAI compatibility embeddings - Given a prompt: - """ - In which country Paris is located ? - """ - And a prompt: - """ - Is Madrid the capital of Spain ? - """ - And a prompt: - """ - What is the biggest US city ? - """ - And a prompt: - """ - What is the capital of Bulgaria ? - """ - And a model bert-bge-small - Given concurrent OAI embedding requests - Then the server is busy - Then the server is idle - Then all embeddings are generated - - Scenario: All embeddings should be the same - Given 10 fixed prompts - And a model bert-bge-small - Given concurrent OAI embedding requests - Then all embeddings are the same diff --git a/examples/server/tests/features/environment.py b/examples/server/tests/features/environment.py deleted file mode 100644 index 2ee5564d4..000000000 --- a/examples/server/tests/features/environment.py +++ /dev/null @@ -1,73 +0,0 @@ -import os -import signal -import socket -import sys -import time -import traceback -from contextlib import closing -from subprocess import TimeoutExpired - - -def before_scenario(context, scenario): - context.debug = 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON' - if context.debug: - print("DEBUG=ON") - print(f"\x1b[33;42mStarting new scenario: {scenario.name}!\x1b[0m") - port = 8080 - if 'PORT' in os.environ: - port = int(os.environ['PORT']) - if is_server_listening("localhost", port): - assert False, "Server already started" - - -def after_scenario(context, scenario): - try: - if 'server_process' not in context or context.server_process is None: - return - if scenario.status == "failed": - if 'GITHUB_ACTIONS' in os.environ: - print(f"\x1b[33;101mSCENARIO FAILED: {scenario.name} server logs:\x1b[0m\n") - if os.path.isfile('llama.log'): - with closing(open('llama.log', 'r')) as f: - for line in f: - print(line) - if not is_server_listening(context.server_fqdn, context.server_port): - print("\x1b[33;101mERROR: Server stopped listening\x1b[0m") - if 'slow' in set(str(t) for t in scenario.tags): - print("\x1b[33;101mERROR: Make sure to precache models before running slow scenarios:\n python scripts/fetch_server_test_models.py\x1b[0m") - - if context.server_process.poll() is not None: - assert False, f"Server not running pid={context.server_process.pid} ..." - - server_graceful_shutdown(context) # SIGINT - - try: - context.server_process.wait(0.5) - except TimeoutExpired: - print(f"server still alive after 500ms, force-killing pid={context.server_process.pid} ...") - context.server_process.kill() # SIGKILL - context.server_process.wait() - - while is_server_listening(context.server_fqdn, context.server_port): - time.sleep(0.1) - except Exception: - print("ignoring error in after_scenario:") - traceback.print_exc(file=sys.stdout) - - -def server_graceful_shutdown(context): - print(f"shutting down server pid={context.server_process.pid} ...") - if os.name == 'nt': - interrupt = signal.CTRL_C_EVENT - else: - interrupt = signal.SIGINT - context.server_process.send_signal(interrupt) - - -def is_server_listening(server_fqdn, server_port): - with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock: - result = sock.connect_ex((server_fqdn, server_port)) - _is_server_listening = result == 0 - if _is_server_listening: - print(f"server is listening on {server_fqdn}:{server_port}...") - return _is_server_listening diff --git a/examples/server/tests/features/infill.feature b/examples/server/tests/features/infill.feature deleted file mode 100644 index a0bbfef77..000000000 --- a/examples/server/tests/features/infill.feature +++ /dev/null @@ -1,36 +0,0 @@ -@llama.cpp -@infill -Feature: llama.cpp server - - # The current model is made by adding FIM tokens to the existing stories260K - # We may want to use a better model in the future, maybe something like SmolLM 360M - - Background: Server startup - Given a server listening on localhost:8080 - And a model file tinyllamas/stories260K-infill.gguf from HF repo ggml-org/models - And a model file test-model-infill.gguf - And a model alias tinyllama-infill - And 42 as server seed - And 1024 as batch size - And 1024 as ubatch size - And 2048 KV cache size - And 64 max tokens to predict - And 0.0 temperature - Then the server is starting - Then the server is healthy - - Scenario: Infill without input_extra - Given a prompt "Complete this" - And an infill input extra none none - And an infill input prefix "#include \n#include \"llama.h\"\n\nint main() {\n int n_threads = llama_" - And an infill input suffix "}\n" - And an infill request with no api error - Then 64 tokens are predicted matching One|day|she|saw|big|scary|bird - - Scenario: Infill with input_extra - Given a prompt "Complete this" - And an infill input extra "llama.h" "LLAMA_API int32_t llama_n_threads();\n" - And an infill input prefix "#include \n#include \"llama.h\"\n\nint main() {\n int n_threads = llama_" - And an infill input suffix "}\n" - And an infill request with no api error - Then 64 tokens are predicted matching cuts|Jimmy|mom|came|into|the|room" diff --git a/examples/server/tests/features/issues.feature b/examples/server/tests/features/issues.feature deleted file mode 100644 index 7b13e44ca..000000000 --- a/examples/server/tests/features/issues.feature +++ /dev/null @@ -1,5 +0,0 @@ -# List of ongoing issues -# run with: DEBUG=ON ./tests.sh --no-skipped --tags bug -@bug -Feature: Issues - # No confirmed issue at the moment diff --git a/examples/server/tests/features/lora.feature b/examples/server/tests/features/lora.feature deleted file mode 100644 index 7b85988ac..000000000 --- a/examples/server/tests/features/lora.feature +++ /dev/null @@ -1,36 +0,0 @@ -@llama.cpp -@lora -Feature: llama.cpp server - - Background: Server startup - Given a server listening on localhost:8080 - And a model url https://huggingface.co/ggml-org/stories15M_MOE/resolve/main/stories15M_MOE-F16.gguf - And a model file stories15M_MOE-F16.gguf - And a model alias stories15M_MOE - And a lora adapter file from https://huggingface.co/ggml-org/stories15M_MOE/resolve/main/moe_shakespeare15M.gguf - And 42 as server seed - And 1024 as batch size - And 1024 as ubatch size - And 2048 KV cache size - And 64 max tokens to predict - And 0.0 temperature - Then the server is starting - Then the server is healthy - - Scenario: Completion LoRA disabled - Given switch off lora adapter 0 - Given a prompt: - """ - Look in thy glass - """ - And a completion request with no api error - Then 64 tokens are predicted matching little|girl|three|years|old - - Scenario: Completion LoRA enabled - Given switch on lora adapter 0 - Given a prompt: - """ - Look in thy glass - """ - And a completion request with no api error - Then 64 tokens are predicted matching eye|love|glass|sun diff --git a/examples/server/tests/features/parallel.feature b/examples/server/tests/features/parallel.feature deleted file mode 100644 index 423d0f1d4..000000000 --- a/examples/server/tests/features/parallel.feature +++ /dev/null @@ -1,131 +0,0 @@ -@llama.cpp -@parallel -Feature: Parallel - - Background: Server startup - Given a server listening on localhost:8080 - And a model file tinyllamas/split/stories15M-00001-of-00003.gguf from HF repo ggml-org/models - And a model file test-model-00001-of-00003.gguf - And 42 as server seed - And 128 as batch size - And 256 KV cache size - And 2 slots - And continuous batching - Then the server is starting - Then the server is healthy - - Scenario Outline: Multi users completion - Given a prompt: - """ - Write a very long story about AI. - """ - And a prompt: - """ - Write another very long music lyrics. - """ - And max tokens to predict - Given concurrent completion requests - Then the server is busy - Then the server is idle - And all slots are idle - Then all prompts are predicted with tokens - Examples: - | n_predict | - | 128 | - - Scenario Outline: Multi users OAI completions compatibility - Given a system prompt You are a writer. - And a model tinyllama-2 - Given a prompt: - """ - Write a very long book. - """ - And a prompt: - """ - Write another a poem. - """ - And max tokens to predict - And streaming is - Given concurrent OAI completions requests - Then the server is busy - Then the server is idle - Then all prompts are predicted with tokens - Examples: - | streaming | n_predict | - | disabled | 128 | - | enabled | 64 | - - Scenario Outline: Multi users OAI completions compatibility no v1 - Given a system prompt You are a writer. - And a model tinyllama-2 - Given a prompt: - """ - Write a very long book. - """ - And a prompt: - """ - Write another a poem. - """ - And max tokens to predict - And streaming is - Given concurrent OAI completions requests no v1 - Then the server is busy - Then the server is idle - Then all prompts are predicted with tokens - Examples: - | streaming | n_predict | - | disabled | 128 | - | enabled | 64 | - - Scenario Outline: Multi users with number of prompts exceeding number of slots - Given a system prompt You are a writer. - And a model tinyllama-2 - Given a prompt: - """ - Write a very long book. - """ - And a prompt: - """ - Write another a poem. - """ - And a prompt: - """ - What is LLM? - """ - And a prompt: - """ - The sky is blue and I love it. - """ - And max tokens to predict - And streaming is - Given concurrent OAI completions requests - Then the server is busy - Then the server is idle - Then all prompts are predicted with tokens - Examples: - | streaming | n_predict | - | disabled | 128 | - | enabled | 64 | - - Scenario: Multi users with total number of tokens to predict exceeds the KV Cache size #3969 - Given a prompt: - """ - Write a very long story about AI. - """ - And a prompt: - """ - Write another very long music lyrics. - """ - And a prompt: - """ - Write a very long poem. - """ - And a prompt: - """ - Write a very long joke. - """ - And 128 max tokens to predict - Given concurrent completion requests - Then the server is busy - Then the server is idle - Then all prompts are predicted diff --git a/examples/server/tests/features/passkey.feature b/examples/server/tests/features/passkey.feature deleted file mode 100644 index ff0a82cc4..000000000 --- a/examples/server/tests/features/passkey.feature +++ /dev/null @@ -1,56 +0,0 @@ -# run with: ./tests.sh --no-skipped --tags passkey -@passkey -@slow -Feature: Passkey / Self-extend with context shift - - Background: Server startup - Given a server listening on localhost:8080 - - # Generates a long text of junk and inserts a secret passkey number inside it. - # Then we query the LLM for the secret passkey. - # see #3856 and #4810 - Scenario Outline: Passkey - Given a model file from HF repo - And as batch size - And as number of junk - And server max tokens to predict - And 42 as seed - And 0.0 temperature - And KV cache size - And 1 slots - And group attention factor to extend context size through self-extend - And group attention width to extend context size through self-extend - # Can be override with N_GPU_LAYERS - And GPU offloaded layers - Then the server is starting - # Higher timeout because the model may need to be downloaded from the internet - Then the server is healthy with timeout 120 seconds - Given available models - Then model 0 is trained on tokens context - Given a prefix prompt: - """ - here is an important info hidden inside a lot of irrelevant text. Find it and memorize them. I will quiz you about the important information there. - """ - And a passkey prompt template: - """ - The pass key is Remember it. is the pass key. - """ - And a junk suffix prompt: - """ - The grass is green. The sky is blue. The sun is yellow. Here we go. There and back again. - """ - And a suffix prompt: - """ - What is the pass key? The pass key is - """ - Given a "" passkey challenge prompt with the passkey inserted every junk - And a completion request with no api error - Then tokens are predicted matching - - Examples: - | hf_repo | hf_file | n_ctx_train | ngl | n_ctx | n_batch | n_ga | n_ga_w | n_junk | i_pos | passkey | n_predicted | re_content | - | TheBloke/phi-2-GGUF | phi-2.Q4_K_M.gguf | 2048 | 5 | 8192 | 512 | 4 | 512 | 250 | 50 | 42 | 1 | 42 | - | TheBloke/phi-2-GGUF | phi-2.Q4_K_M.gguf | 2048 | 5 | 8192 | 512 | 2 | 512 | 250 | 50 | 42 | 1 | \b((?!42)\w)+\b | - #| TheBloke/Llama-2-7B-GGUF | llama-2-7b.Q2_K.gguf | 4096 | 3 | 16384 | 512 | 4 | 512 | 500 | 300 | 1234 | 5 | 1234 | - #| TheBloke/Mixtral-8x7B-v0.1-GGUF | mixtral-8x7b-v0.1.Q2_K.gguf | 32768 | 2 | 16384 | 512 | 4 | 512 | 500 | 100 | 0987 | 5 | 0 - # 987 | diff --git a/examples/server/tests/features/rerank.feature b/examples/server/tests/features/rerank.feature deleted file mode 100644 index c36cc8e21..000000000 --- a/examples/server/tests/features/rerank.feature +++ /dev/null @@ -1,42 +0,0 @@ -@llama.cpp -@rerank -Feature: llama.cpp server - - Background: Server startup - Given a server listening on localhost:8080 - And a model url https://huggingface.co/ggml-org/models/resolve/main/jina-reranker-v1-tiny-en/ggml-model-f16.gguf - And a model file jina-reranker-v1-tiny-en.gguf - And a model alias jina-reranker-v1-tiny-en - And 42 as server seed - And 2 slots - And 512 as batch size - And 512 as ubatch size - And 512 KV cache size - And enable reranking endpoint - Then the server is starting - Then the server is healthy - - Scenario: Rerank - Given a rerank query: - """ - Machine learning is - """ - And a rerank document: - """ - A machine is a physical system that uses power to apply forces and control movement to perform an action. The term is commonly applied to artificial devices, such as those employing engines or motors, but also to natural biological macromolecules, such as molecular machines. - """ - And a rerank document: - """ - Learning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. The ability to learn is possessed by humans, non-human animals, and some machines; there is also evidence for some kind of learning in certain plants. - """ - And a rerank document: - """ - Machine learning is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. - """ - And a rerank document: - """ - Paris, capitale de la France, est une grande ville européenne et un centre mondial de l'art, de la mode, de la gastronomie et de la culture. Son paysage urbain du XIXe siècle est traversé par de larges boulevards et la Seine. - """ - When reranking request - Then reranking results are returned - Then reranking highest score is index 2 and lowest score is index 3 diff --git a/examples/server/tests/features/results.feature b/examples/server/tests/features/results.feature deleted file mode 100644 index e8e1b5414..000000000 --- a/examples/server/tests/features/results.feature +++ /dev/null @@ -1,118 +0,0 @@ -@llama.cpp -@results -Feature: Results - - Background: Server startup - Given a server listening on localhost:8080 - And a model file tinyllamas/split/stories15M-00001-of-00003.gguf from HF repo ggml-org/models - And a model file test-model-00001-of-00003.gguf - And 128 as batch size - And 1024 KV cache size - And 128 max tokens to predict - And continuous batching - - Scenario Outline: consistent results with same seed - Given slots - And 1.0 temperature - Then the server is starting - Then the server is healthy - - Given 4 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 42 - - Given concurrent completion requests - Then the server is busy - Then the server is idle - And all slots are idle - Then all predictions are equal - Examples: - | n_slots | - | 1 | - # FIXME: unified KV cache nondeterminism - # | 2 | - - Scenario Outline: different results with different seed - Given slots - And 1.0 temperature - Then the server is starting - Then the server is healthy - - Given 1 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 42 - Given 1 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 43 - Given 1 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 44 - Given 1 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 45 - - Given concurrent completion requests - Then the server is busy - Then the server is idle - And all slots are idle - Then all predictions are different - Examples: - | n_slots | - | 1 | - | 2 | - - Scenario Outline: consistent results with same seed and varying batch size - Given 4 slots - And temperature - # And 0 as draft - Then the server is starting - Then the server is healthy - - Given 1 prompts "Write a very long story about AI." with seed 42 - And concurrent completion requests - # Then the server is busy # Not all slots will be utilized. - Then the server is idle - And all slots are idle - - Given prompts "Write a very long story about AI." with seed 42 - And concurrent completion requests - # Then the server is busy # Not all slots will be utilized. - Then the server is idle - And all slots are idle - - Then all predictions are equal - Examples: - | n_parallel | temp | - | 1 | 0.0 | - | 1 | 1.0 | - # FIXME: unified KV cache nondeterminism - # See https://github.com/ggerganov/whisper.cpp/issues/1941#issuecomment-1986923227 - # and https://github.com/ggerganov/llama.cpp/pull/6122#discussion_r1531405574 - # and https://github.com/ggerganov/llama.cpp/pull/7347 . - # | 2 | 0.0 | - # | 4 | 0.0 | - # | 2 | 1.0 | - # | 4 | 1.0 | - - Scenario Outline: consistent token probs with same seed and prompt - Given slots - And KV cache size - And 1.0 temperature - And max tokens to predict - Then the server is starting - Then the server is healthy - - Given 1 prompts "The meaning of life is" with seed 42 - And concurrent completion requests - # Then the server is busy # Not all slots will be utilized. - Then the server is idle - And all slots are idle - - Given prompts "The meaning of life is" with seed 42 - And concurrent completion requests - # Then the server is busy # Not all slots will be utilized. - Then the server is idle - And all slots are idle - - Then all token probabilities are equal - Examples: - | n_slots | n_kv | n_predict | n_parallel | - | 4 | 1024 | 1 | 1 | - # FIXME: unified KV cache nondeterminism - # See https://github.com/ggerganov/whisper.cpp/issues/1941#issuecomment-1986923227 - # and https://github.com/ggerganov/llama.cpp/pull/6122#discussion_r1531405574 - # and https://github.com/ggerganov/llama.cpp/pull/7347 . - # | 4 | 1024 | 1 | 4 | - # | 4 | 1024 | 100 | 1 | - # This test still fails even the above patches; the first token probabilities are already different. - # | 4 | 1024 | 100 | 4 | diff --git a/examples/server/tests/features/security.feature b/examples/server/tests/features/security.feature deleted file mode 100644 index 0a3c5cc77..000000000 --- a/examples/server/tests/features/security.feature +++ /dev/null @@ -1,68 +0,0 @@ -@llama.cpp -@security -Feature: Security - - Background: Server startup with an api key defined - Given a server listening on localhost:8080 - And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models - And a server api key THIS_IS_THE_KEY - Then the server is starting - Then the server is healthy - - Scenario Outline: Completion with some user api key - Given a prompt test - And a user api key - And 4 max tokens to predict - And a completion request with api error - - Examples: Prompts - | api_key | api_error | - | THIS_IS_THE_KEY | no | - | THIS_IS_THE_KEY | no | - | hackeme | raised | - | | raised | - - Scenario Outline: OAI Compatibility - Given a system prompt test - And a user prompt test - And a model test - And 2 max tokens to predict - And streaming is disabled - And a user api key - Given an OAI compatible chat completions request with api error - - Examples: Prompts - | api_key | api_error | - | THIS_IS_THE_KEY | no | - | THIS_IS_THE_KEY | no | - | hackme | raised | - - Scenario Outline: OAI Compatibility (invalid response formats) - Given a system prompt test - And a user prompt test - And a response format - And a model test - And 2 max tokens to predict - And streaming is disabled - Given an OAI compatible chat completions request with raised api error - - Examples: Prompts - | response_format | - | {"type": "sound"} | - | {"type": "json_object", "schema": 123} | - | {"type": "json_object", "schema": {"type": 123}} | - | {"type": "json_object", "schema": {"type": "hiccup"}} | - - - Scenario Outline: CORS Options - Given a user api key THIS_IS_THE_KEY - When an OPTIONS request is sent from - Then CORS header is set to - - Examples: Headers - | origin | cors_header | cors_header_value | - | localhost | Access-Control-Allow-Origin | localhost | - | web.mydomain.fr | Access-Control-Allow-Origin | web.mydomain.fr | - | origin | Access-Control-Allow-Credentials | true | - | web.mydomain.fr | Access-Control-Allow-Methods | POST | - | web.mydomain.fr | Access-Control-Allow-Headers | * | diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature deleted file mode 100644 index 15e24c624..000000000 --- a/examples/server/tests/features/server.feature +++ /dev/null @@ -1,120 +0,0 @@ -@llama.cpp -@server -Feature: llama.cpp server - - Background: Server startup - Given a server listening on localhost:8080 - And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models - And a model file test-model.gguf - And a model alias tinyllama-2 - And BOS token is 1 - And 42 as server seed - # KV Cache corresponds to the total amount of tokens - # that can be stored across all independent sequences: #4130 - # see --ctx-size and #5568 - And 256 KV cache size - And 32 as batch size - And 2 slots - And 64 server max tokens to predict - And prometheus compatible metrics exposed - Then the server is starting - Then the server is healthy - - Scenario: Health - Then the server is ready - And all slots are idle - - - Scenario Outline: Completion - Given a prompt - And max tokens to predict - And a completion request with no api error - Then tokens are predicted matching - And the completion is truncated - And prompt tokens are processed - And prometheus metrics are exposed - And metric llamacpp:tokens_predicted is - - Examples: Prompts - | prompt | n_predict | re_content | n_prompt | n_predicted | truncated | - | I believe the meaning of life is | 8 | (read\|going)+ | 18 | 8 | not | - | Write a joke about AI from a very long prompt which will not be truncated | 256 | (princesses\|everyone\|kids\|Anna\|forest)+ | 46 | 64 | not | - - Scenario: Completion prompt truncated - Given a prompt: - """ - Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. - Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. - Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. - Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. - """ - And a completion request with no api error - Then 64 tokens are predicted matching fun|Annaks|popcorns|pictry|bowl - And the completion is truncated - And 109 prompt tokens are processed - - - Scenario Outline: OAI Compatibility - Given a model - And a system prompt - And a user prompt - And max tokens to predict - And streaming is - Given an OAI compatible chat completions request with no api error - Then tokens are predicted matching - And prompt tokens are processed - And the completion is truncated - - Examples: Prompts - | model | system_prompt | user_prompt | max_tokens | re_content | n_prompt | n_predicted | enable_streaming | truncated | - | llama-2 | Book | What is the best book | 8 | (Here\|what)+ | 77 | 8 | disabled | not | - | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 128 | (thanks\|happy\|bird\|Annabyear)+ | -1 | 64 | enabled | | - - - Scenario Outline: OAI Compatibility w/ response format - Given a model test - And a system prompt test - And a user prompt test - And a response format - And 10 max tokens to predict - Given an OAI compatible chat completions request with no api error - Then tokens are predicted matching - - Examples: Prompts - | response_format | n_predicted | re_content | - | {"type": "json_object", "schema": {"const": "42"}} | 6 | "42" | - | {"type": "json_object", "schema": {"items": [{"type": "integer"}]}} | 10 | \[ -300 \] | - | {"type": "json_object"} | 10 | \{ " Jacky. | - - - Scenario: Tokenize / Detokenize - When tokenizing: - """ - What is the capital of France ? - """ - Then tokens can be detokenized - And tokens do not begin with BOS - - Scenario: Tokenize w/ BOS - Given adding special tokens - When tokenizing: - """ - What is the capital of Germany? - """ - Then tokens begin with BOS - Given first token is removed - Then tokens can be detokenized - - Scenario: Tokenize with pieces - When tokenizing with pieces: - """ - What is the capital of Germany? - 媽 - """ - Then tokens are given with pieces - - Scenario: Models available - Given available models - Then 1 models are supported - Then model 0 is identified by tinyllama-2 - Then model 0 is trained on 128 tokens context diff --git a/examples/server/tests/features/slotsave.feature b/examples/server/tests/features/slotsave.feature deleted file mode 100644 index 1c281c074..000000000 --- a/examples/server/tests/features/slotsave.feature +++ /dev/null @@ -1,58 +0,0 @@ -@llama.cpp -@slotsave -Feature: llama.cpp server slot management - - Background: Server startup - Given a server listening on localhost:8080 - And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models - And prompt caching is enabled - And 2 slots - And . as slot save path - And 2048 KV cache size - And 42 as server seed - And 24 max tokens to predict - Then the server is starting - Then the server is healthy - - Scenario: Save and Restore Slot - # First prompt in slot 1 should be fully processed - Given a user prompt "What is the capital of France?" - And using slot id 1 - And a completion request with no api error - Then 24 tokens are predicted matching (Lily|cake) - And 22 prompt tokens are processed - When the slot 1 is saved with filename "slot1.bin" - Then the server responds with status code 200 - # Since we have cache, this should only process the last tokens - Given a user prompt "What is the capital of Germany?" - And a completion request with no api error - Then 24 tokens are predicted matching (Thank|special) - And 7 prompt tokens are processed - # Loading the original cache into slot 0, - # we should only be processing 1 prompt token and get the same output - When the slot 0 is restored with filename "slot1.bin" - Then the server responds with status code 200 - Given a user prompt "What is the capital of France?" - And using slot id 0 - And a completion request with no api error - Then 24 tokens are predicted matching (Lily|cake) - And 1 prompt tokens are processed - # For verification that slot 1 was not corrupted during slot 0 load, same thing - Given a user prompt "What is the capital of Germany?" - And using slot id 1 - And a completion request with no api error - Then 24 tokens are predicted matching (Thank|special) - And 1 prompt tokens are processed - - Scenario: Erase Slot - Given a user prompt "What is the capital of France?" - And using slot id 1 - And a completion request with no api error - Then 24 tokens are predicted matching (Lily|cake) - And 22 prompt tokens are processed - When the slot 1 is erased - Then the server responds with status code 200 - Given a user prompt "What is the capital of France?" - And a completion request with no api error - Then 24 tokens are predicted matching (Lily|cake) - And 22 prompt tokens are processed diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py deleted file mode 100644 index a990a07cf..000000000 --- a/examples/server/tests/features/steps/steps.py +++ /dev/null @@ -1,1730 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -import asyncio -import json -import os -import parse -import re -import requests -import socket -import subprocess -import sys -import threading -import time -from collections.abc import Sequence -from contextlib import closing -from re import RegexFlag -from typing import Any, Literal, cast - -import aiohttp -import numpy as np -import openai -from openai.types.chat import ChatCompletionChunk -from behave import register_type, step # pyright: ignore[reportAttributeAccessIssue] -from behave.api.async_step import async_run_until_complete -from prometheus_client import parser - -# pyright: reportRedeclaration=false - -DEFAULT_TIMEOUT_SECONDS = aiohttp.ClientTimeout(total=600) - -@parse.with_pattern(r".*") -def parse_maybe_empty_string(text): - return text.strip() - -register_type(MaybeEmptyString=parse_maybe_empty_string) - - -@step("a server listening on {server_fqdn}:{server_port}") -def step_server_config(context, server_fqdn: str, server_port: str): - context.server_fqdn = server_fqdn - context.server_port = int(server_port) - context.n_threads = None - context.n_gpu_layer = None - if 'PORT' in os.environ: - context.server_port = int(os.environ['PORT']) - print(f"$PORT set, overriding server port with to {context.server_port}") - if 'FQDN' in os.environ: - context.server_fqdn = os.environ['FQDN'] - print(f"$FQDN set, overriding server fqdn with to {context.server_fqdn}") - if 'N_GPU_LAYERS' in os.environ: - context.n_gpu_layer = int(os.environ['N_GPU_LAYERS']) - print(f"$N_GPU_LAYERS set, overriding n_gpu_layer with to {context.n_gpu_layer}") - - context.base_url = f'http://{context.server_fqdn}:{context.server_port}' - - context.model_alias = None - context.model_file = None - context.model_hf_repo = None - context.model_hf_file = None - context.model_url = None - context.n_batch = None - context.n_ubatch = None - context.n_ctx = None - context.n_ga = None - context.n_ga_w = None - context.n_predict = None - context.n_prompts = 0 - context.n_server_predict = None - context.slot_save_path = None - context.id_slot = None - context.cache_prompt = None - context.n_slots = None - context.prompt_prefix = None - context.prompt_suffix = None - context.server_api_key = None - context.server_continuous_batching = False - context.server_embeddings = False - context.server_reranking = False - context.server_metrics = False - context.server_process = None - context.seed = None - context.draft = None - context.server_seed = None - context.user_api_key = None - context.response_format = None - context.tools = None - context.tool_choice = None - context.parallel_tool_calls = None - context.temperature = None - context.lora_file = None - context.disable_ctx_shift = False - context.warmup = True - context.use_jinja = False - context.chat_template_file = None - context.greedy_sampling = False - - # infill - context.infill_input_extra = None - context.infill_input_suffix = '' - context.infill_input_prefix = '' - - context.tasks_result = [] - context.concurrent_tasks = [] - context.prompts = [] - - context.reranking_query = None - context.reranking_documents = [] - context.reranking_results = None - - -@step('a model file {hf_file} from HF repo {hf_repo}') -def step_download_hf_model(context, hf_file: str, hf_repo: str): - context.model_hf_repo = hf_repo - context.model_hf_file = hf_file - -@step('a lora adapter file from {lora_file_url}') -def step_download_lora_file(context, lora_file_url: str): - file_name = lora_file_url.split('/').pop() - context.lora_file = f'../../../{file_name}' - with open(context.lora_file, 'wb') as f: - f.write(requests.get(lora_file_url).content) - -@step('a model file {model_file}') -def step_model_file(context, model_file: str): - context.model_file = model_file - - -@step('a model url {model_url}') -def step_model_url(context, model_url: str): - context.model_url = model_url - - -@step('a model alias {model_alias}') -def step_model_alias(context, model_alias: str): - context.model_alias = model_alias - - -@step('{seed:d} as server seed') -def step_seed(context, seed: int): - context.server_seed = seed - - -@step('{ngl:d} GPU offloaded layers') -def step_n_gpu_layer(context, ngl: int): - if 'N_GPU_LAYERS' in os.environ: - new_ngl = int(os.environ['N_GPU_LAYERS']) - if context.debug: - print(f"-ngl upgraded from {ngl} to {new_ngl}") - ngl = new_ngl - context.n_gpu_layer = ngl - - -@step('{n_threads:d} threads') -def step_n_threads(context, n_threads: int): - context.n_thread = n_threads - - -@step('{draft:d} as draft') -def step_draft(context, draft: int): - context.draft = draft - - -@step('{n_ctx:d} KV cache size') -def step_n_ctx(context, n_ctx: int): - context.n_ctx = n_ctx - - -@step('{n_slots:d} slots') -def step_n_slots(context, n_slots: int): - context.n_slots = n_slots - - -@step('{n_predict:d} server max tokens to predict') -def step_server_n_predict(context, n_predict: int): - context.n_server_predict = n_predict if n_predict > 0 else None - - -@step('{slot_save_path} as slot save path') -def step_slot_save_path(context, slot_save_path: str): - context.slot_save_path = slot_save_path - - -@step('jinja templates are enabled') -def step_use_jinja(context): - context.use_jinja = True - - -@step('no warmup') -def step_no_warmup(context): - context.warmup = False - - -@step('greedy sampling') -def step_greedy_sampling(context): - context.greedy_sampling = True - - -@step('a chat template file {file}') -def step_chat_template_file(context, file): - context.chat_template_file = file - - -@step('a test chat template file named {name:MaybeEmptyString}') -def step_test_chat_template_file_named(context, name): - name = name.strip() - if name: - context.chat_template_file = f'../../../tests/chat/templates/{name}.jinja' - - -@step('using slot id {id_slot:d}') -def step_id_slot(context, id_slot: int): - context.id_slot = id_slot - - -@step('prompt caching is enabled') -def step_enable_prompt_cache(context): - context.cache_prompt = True - - -@step('continuous batching') -def step_server_continuous_batching(context): - context.server_continuous_batching = True - - -@step('enable embeddings endpoint') -def step_server_embeddings(context): - context.server_embeddings = True - -@step('enable reranking endpoint') -def step_server_reranking(context): - context.server_reranking = True - -@step('prometheus compatible metrics exposed') -def step_server_metrics(context): - context.server_metrics = True - -@step('disable context shifting') -def step_server_disable_ctx_shift(context): - context.disable_ctx_shift = True - -@step("the server is starting") -def step_start_server(context): - start_server_background(context) - attempts = 0 - max_attempts = 20 - if 'GITHUB_ACTIONS' in os.environ: - max_attempts *= 2 - - addrs = socket.getaddrinfo(context.server_fqdn, context.server_port, type=socket.SOCK_STREAM) - family, typ, proto, _, sockaddr = addrs[0] - - while True: - with closing(socket.socket(family, typ, proto)) as sock: - result = sock.connect_ex(sockaddr) - if result == 0: - print("\x1b[33;46mserver started!\x1b[0m") - return - attempts += 1 - if attempts > max_attempts: - assert False, "server not started" - print(f"waiting for server to start, connect error code = {result}...") - time.sleep(0.1) - - -async def wait_for_server_status_with_timeout(context, expecting_status: Literal['healthy', 'ready', 'idle', 'busy'] | str, timeout: int): - match expecting_status: - case 'healthy': - await wait_for_slots_status(context, context.base_url, 200, - timeout=timeout) - - case 'ready' | 'idle': - await wait_for_slots_status(context, context.base_url, 200, - timeout=timeout, - params={'fail_on_no_slot': 1}, - slots_idle=context.n_slots, - slots_processing=0) - case 'busy': - await wait_for_slots_status(context, context.base_url, 503, - params={'fail_on_no_slot': 1}, - slots_idle=0, - slots_processing=context.n_slots) - case _: - assert False, "unknown status" - - -@step("the server is {expecting_status} with timeout {timeout:d} seconds") -@async_run_until_complete -async def step_wait_for_server_status_with_timeout(context, expecting_status: Literal['healthy', 'ready', 'idle', 'busy'] | str, timeout: int): - await wait_for_server_status_with_timeout(context, expecting_status, timeout) - - -@step("the server is {expecting_status}") -@async_run_until_complete -async def step_wait_for_server_status(context, expecting_status: Literal['healthy', 'ready', 'idle', 'busy'] | str): - await wait_for_server_status_with_timeout(context, expecting_status, 30) - - -@step('all slots are {expected_slot_status_string}') -@async_run_until_complete -async def step_all_slots_status(context, expected_slot_status_string: Literal['idle', 'busy'] | str): - match expected_slot_status_string: - case 'idle': - expected_slot_status = 0 - case 'busy': - expected_slot_status = 1 - case _: - assert False, "unknown status" - - expected_slots = [{'id': slot_id, 'state': expected_slot_status} - for slot_id in range(context.n_slots)] - await request_slots_status(context, expected_slots) - - -@step('a completion request with {api_error} api error') -@async_run_until_complete -async def step_request_completion(context, api_error: Literal['raised'] | str): - expect_api_error = api_error == 'raised' or api_error != 'no' - seeds = await completions_seed(context, num_seeds=1) - completion = await request_completion(context.prompts.pop(), - seeds[0] if seeds is not None else seeds, - context.base_url, - debug=context.debug, - n_predict=context.n_predict, - cache_prompt=context.cache_prompt, - id_slot=context.id_slot, - expect_api_error=expect_api_error, - user_api_key=context.user_api_key, - temperature=context.temperature) - context.tasks_result.append(completion) - if context.debug: - print(f"Completion response: {completion}") - if api_error == 'raised': - assert completion == 401, f"completion must be an 401 status code: {completion}" - elif api_error.isdigit(): - api_error_code = int(api_error) - assert completion == api_error_code, f"completion must be an {api_error_code} status code: {completion}" - - -@step('an infill request with {api_error} api error') -@async_run_until_complete -async def step_request_completion(context, api_error: Literal['raised'] | str): - if api_error != 'no': - raise ValueError(f'api_error={api_error} is not yet implemented') - payload = { - "prompt": context.prompts[0], - "input_suffix": context.infill_input_suffix, - "input_prefix": context.infill_input_prefix, - "n_predict": context.n_predict, - "seed": context.seed, - "temperature": context.temperature, - } - if context.infill_input_extra is not None: - payload['input_extra'] = context.infill_input_extra - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with session.post(f'{context.base_url}/infill', - json=payload) as response: - assert response.status == 200 - context.tasks_result = [await response.json()] - - -@step('{predicted_n:d} tokens are predicted matching {re_content}') -def step_n_tokens_predicted_with_content(context, predicted_n, re_content): - context.completion = context.tasks_result.pop() - assert_n_tokens_predicted(context.completion, predicted_n, re_content) - - -@step('{predicted_n:d} tokens are predicted') -def step_n_tokens_predicted(context, predicted_n): - context.completion = context.tasks_result.pop() - assert_n_tokens_predicted(context.completion, predicted_n) - - -@step('all predictions are equal') -@async_run_until_complete -async def step_predictions_equal(context): - n_completions = await gather_tasks_results(context) - assert n_completions >= 2, "need at least 2 completions" - assert_all_predictions_equal(context.tasks_result) - context.tasks_result = [] - - -@step('all predictions are different') -@async_run_until_complete -async def step_predictions_different(context): - n_completions = await gather_tasks_results(context) - assert n_completions >= 2, "need at least 2 completions" - assert_all_predictions_different(context.tasks_result) - context.tasks_result = [] - - -@step('all token probabilities are equal') -@async_run_until_complete -async def step_token_probabilities_equal(context): - n_completions = await gather_tasks_results(context) - assert n_completions >= 2, "need at least 2 completions" - assert_all_token_probabilities_equal(context.tasks_result) - context.tasks_result = [] - - -@step('the completion is truncated') -def step_assert_completion_truncated(context): - step_assert_completion_truncated(context, '') - - -@step('the completion is {truncated} truncated') -def step_assert_completion_truncated(context, truncated): - truncated = truncated != "not" - assert context.completion['truncated'] == truncated, f'{context.completion}' - - -@step('{n_prompt:d} prompt tokens are processed') -def step_impl(context, n_prompt): - assert n_prompt < 0 or n_prompt == context.completion['timings']['prompt_n'], f"n_prompt={context.completion['timings']['prompt_n']}" - - -@step('a user prompt {user_prompt}') -def step_user_prompt(context, user_prompt): - context.prompts.append(user_prompt) - context.n_prompts = len(context.prompts) - - -@step('a system prompt {system_prompt}') -def step_system_prompt(context, system_prompt): - context.system_prompt = system_prompt - - -@step('a model {model}') -def step_model(context, model): - context.model = model - - -@step('{max_tokens:d} max tokens to predict') -def step_max_tokens(context, max_tokens): - context.n_predict = max_tokens - - -@step('a response format {response_format}') -def step_response_format(context, response_format): - context.response_format = json.loads(response_format) - -@step('tools {tools}') -def step_tools(context, tools): - context.tools = json.loads(tools) - - -@step('python tool') -def step_python_tool(context): - if not context.tools: - context.tools = [] - context.tools.append({ - "type": "function", - "function": { - "name": "python", - "description": "Runs code in a Python interpreter and returns the result of the execution after 60 seconds.", - "parameters": { - "type": "object", - "properties": { - "code": { - "type": "string", - "description": "The code to run in the Python interpreter." - } - }, - "required": ["code"] - } - } - }) - - -@step('test tool') -def step_python_tool(context): - if not context.tools: - context.tools = [] - context.tools.append( - { - "type":"function", - "function": { - "name": "test", - "description": "", - "parameters": { - "type": "object", - "properties": {} - } - } - } - ) - -@step('code_interpreter tool') -def step_python_tool(context): - if not context.tools: - context.tools = [] - context.tools.append({ - "type": "code_interpreter", - }) - -@step('a tool choice {tool_choice}') -def step_tool_choice(context, tool_choice): - context.tool_choice = tool_choice - -@step('parallel tool calls is {enable_parallel_tool_calls}') -def step_parallel_tool_calls(context, enable_parallel_tool_calls): - if enable_parallel_tool_calls == 'enabled': - context.parallel_tool_calls = True - elif enable_parallel_tool_calls == 'disabled': - context.parallel_tool_calls = False - elif enable_parallel_tool_calls == '': - context.parallel_tool_calls = None - else: - raise ValueError(f"invalid value for enable_parallel_tool_calls: {enable_parallel_tool_calls}") - -@step('{temperature:f} temperature') -def step_temperature(context, temperature): - context.temperature = temperature - - -@step('streaming is {enable_streaming}') -def step_streaming(context, enable_streaming): - context.enable_streaming = enable_streaming == 'enabled' - - -@step('a user api key {user_api_key}') -def step_user_api_key(context, user_api_key): - context.user_api_key = user_api_key - - -@step('no user api key') -def step_no_user_api_key(context): - context.user_api_key = None - - -@step('a user api key ') -def step_no_user_api_key_space(context): - context.user_api_key = None - - -@step('a server api key {server_api_key}') -def step_server_api_key(context, server_api_key): - context.server_api_key = server_api_key - - -@step('{n_junk:d} as number of junk') -def step_n_junk(context, n_junk): - context.n_junk = n_junk - - -@step('{n_batch:d} as batch size') -def step_n_batch(context, n_batch): - context.n_batch = n_batch - - -@step('{n_ubatch:d} as ubatch size') -def step_n_ubatch(context, n_ubatch): - context.n_ubatch = n_ubatch - - -@step('{seed:d} as seed') -def step_seed(context, seed): - if context.seed is None: - context.seed = [seed] - else: - context.seed.append(seed) - - -@step('BOS token is {bos:d}') -def step_bos_token(context, bos): - context.bos = bos - - -@step('a prefix prompt') -def step_prompt_prefix(context): - context.prompt_prefix = context_text(context) - - -@step('a junk suffix prompt') -def step_prompt_junk_suffix(context): - context.prompt_junk_suffix = context_text(context) - - -@step('a suffix prompt') -def step_prompt_suffix(context): - context.prompt_suffix = context_text(context) - - -@step('{n_ga:d} group attention factor' - ' to extend context size through self-extend') -def step_impl(context, n_ga): - context.n_ga = n_ga - - -@step('{n_ga_w:d} group attention width to extend context size through self-extend') -def step_impl(context, n_ga_w): - context.n_ga_w = n_ga_w - - -@step('a passkey prompt template') -def step_prompt_passkey(context): - context.prompt_passkey = context_text(context) - -@step('a rerank query') -def step_set_rerank_query(context): - context.reranking_query = context_text(context) - context.reranking_documents = [] - -@step('a rerank document') -def step_set_rerank_document(context): - context.reranking_documents.append(context_text(context)) - -@step('{n_prompts:d} fixed prompts') -def step_fixed_prompts(context, n_prompts): - context.prompts.extend([str(0)*(context.n_batch if context.n_batch is not None else 512) for i in range(n_prompts)]) - context.n_prompts = n_prompts - - -@step('a "{passkey}" passkey challenge prompt with the passkey inserted every {i_pos:d} junk') -def step_prompt_passkey(context, passkey, i_pos): - prompt = "" - for i in range(context.n_junk): - if i % context.n_junk == i_pos: - prompt += context.prompt_passkey # the passkey is already substituted - prompt += context.prompt_junk_suffix - if context.debug: - passkey_highlight = "\x1b[33m" + passkey + "\x1b[0m" - print(f"Passkey challenge:\n```{prompt.replace(passkey, passkey_highlight)}```") - context.prompts.append(context.prompt_prefix + prompt + context.prompt_suffix) - context.n_prompts = len(context.prompts) - - -@step('an OAI compatible chat completions request with {api_error} api error') -@async_run_until_complete -async def step_oai_chat_completions(context, api_error): - if context.debug: - print(f"Submitting OAI compatible completions request...") - expect_api_error = api_error == 'raised' - seeds = await completions_seed(context, num_seeds=1) - completion = await oai_chat_completions(context.prompts.pop(), - seeds[0] if seeds else None, - - context.system_prompt - if hasattr(context, 'system_prompt') else None, - - context.base_url, - '/v1/chat', - False, - model=context.model if hasattr(context, 'model') else None, - - n_predict=context.n_predict - if hasattr(context, 'n_predict') else None, - - enable_streaming=context.enable_streaming - if hasattr(context, 'enable_streaming') else None, - - response_format=context.response_format - if hasattr(context, 'response_format') else None, - - tools=context.tools - if hasattr(context, 'tools') else None, - - tool_choice=context.tool_choice, - parallel_tool_calls=context.parallel_tool_calls, - - user_api_key=context.user_api_key - if hasattr(context, 'user_api_key') else None, - - expect_api_error=expect_api_error) - context.tasks_result.append(completion) - if context.debug: - print(f"Completion response: {completion}") - if expect_api_error: - assert completion == 401, f"completion must be an 401 status code: {completion}" - - if context.debug: - print(f"Completion response: {completion}") - - -@step('a prompt') -def step_a_prompt(context): - context.prompts.append(context_text(context)) - context.n_prompts = len(context.prompts) - - -@step('a prompt {prompt}') -def step_a_prompt_prompt(context, prompt): - context.prompts.append(prompt) - context.n_prompts = len(context.prompts) - - -# TODO: allow this to be repeated -@step('an infill input extra {filename} {text}') -def step_infill_input_extra(context, filename, text): - if filename == 'none': - context.infill_input_extra = None - else: - context.infill_input_extra = [{'filename': filename, 'text': text}] - - -@step('an infill input suffix {text}') -def step_infill_input_suffix(context, text): - context.infill_input_suffix = text - - -@step('an infill input prefix {text}') -def step_infill_input_prefix(context, text): - context.infill_input_prefix = text - - -@step('{num_prompts:d} prompts {prompt} with seed {seed:d}') -def step_many_prompts(context, num_prompts, prompt, seed): - if context.seed is None: - context.seed = [] - for _ in range(num_prompts): - context.seed.append(seed) - context.prompts.append(prompt) - context.n_prompts = len(context.prompts) - - -@step('concurrent completion requests') -@async_run_until_complete() -async def step_concurrent_completion_requests(context): - await concurrent_requests( - context, - request_completion, - # prompt is inserted automatically - context.base_url, - debug=context.debug, - prompt_prefix=context.prompt_prefix, - prompt_suffix=context.prompt_suffix, - n_predict=context.n_predict if hasattr(context, 'n_predict') else None, - user_api_key=context.user_api_key if hasattr(context, 'user_api_key') else None, - temperature=context.temperature, - ) - - -@step('concurrent OAI completions requests') -@async_run_until_complete -async def step_oai_chat_completions(context): - await concurrent_requests(context, oai_chat_completions, - # user_prompt is inserted automatically - context.system_prompt, - context.base_url, - '/v1/chat/completions', - True, # async_client - model=context.model - if hasattr(context, 'model') else None, - n_predict=context.n_predict - if hasattr(context, 'n_predict') else None, - enable_streaming=context.enable_streaming - if hasattr(context, 'enable_streaming') else None, - response_format=context.response_format - if hasattr(context, 'response_format') else None, - tools=context.tools - if hasattr(context, 'tools') else None, - tool_choice=context.tool_choice, - parallel_tool_calls=context.parallel_tool_calls, - user_api_key=context.user_api_key - if hasattr(context, 'user_api_key') else None) - - -@step('concurrent OAI completions requests no v1') -@async_run_until_complete -async def step_oai_chat_completions(context): - await concurrent_requests(context, oai_chat_completions, - # user_prompt is inserted automatically - context.system_prompt, - context.base_url, - '/chat/completions', - True, # async_client - model=context.model, - # if hasattr(context, 'model') else None, - n_predict=context.n_predict, - # if hasattr(context, 'n_predict') else None, - enable_streaming=context.enable_streaming - if hasattr(context, 'enable_streaming') else None, - response_format=context.response_format, - # if hasattr(context, 'response_format') else None, - tools=context.tools,# if hasattr(context, 'tools') else None, - tool_choice=context.tool_choice, # if hasattr(context, 'tool_choice') else None, - parallel_tool_calls=context.parallel_tool_calls, - user_api_key=context.user_api_key) - # if hasattr(context, 'user_api_key') else None) - - -@step('all prompts are predicted') -@async_run_until_complete -async def step_all_prompts_are_predicted(context): - await all_prompts_are_predicted(context) - - -@step('all prompts are predicted with {n_expected_predicted:d} tokens') -@async_run_until_complete -async def step_all_prompts_are_predicted_with_n_tokens(context, n_expected_predicted): - await all_prompts_are_predicted(context, n_expected_predicted) - - -async def all_prompts_are_predicted(context, expected_predicted_n=None): - n_completions = await gather_tasks_results(context) - assert n_completions > 0 - for i in range(n_completions): - assert_n_tokens_predicted(context.tasks_result.pop(), expected_predicted_n=expected_predicted_n) - assert len(context.concurrent_tasks) == 0, f"{len(context.concurrent_tasks)} pending requests" - - -@step('tool {expected_name} is called with arguments {expected_arguments}') -@async_run_until_complete -async def step_tool_called(context, expected_name, expected_arguments): - n_completions = await gather_tasks_results(context) - assert n_completions > 0 - - expected_name = expected_name if expected_name else None - expected_arguments = json.loads(expected_arguments) if expected_arguments else None - - for i in range(n_completions): - result = context.tasks_result.pop() - - def check(tool_calls): - if tool_calls is None: - assert expected_name is None and expected_arguments is None, f'expected_name = {expected_name}, expected_arguments = {expected_arguments}, result = {result}' - else: - assert len(tool_calls) == 1, f"tool calls: {tool_calls}" - tool_call = tool_calls[0] - actual_name = tool_call.function.name - actual_arguments = json.loads(tool_call.function.arguments) - assert expected_name == actual_name, f"tool name: {actual_name}, expected: {expected_name}, result = {result}" - assert json.dumps(expected_arguments) == json.dumps(actual_arguments), f"tool arguments: {json.dumps(actual_arguments)}, expected: {json.dumps(expected_arguments)}" - - assert_n_tokens_predicted(result, tool_calls_check=check) - assert len(context.concurrent_tasks) == 0, f"{len(context.concurrent_tasks)} pending requests" - - -@step('receiving the following tool calls: {expected_tool_calls}') -async def step_receiving_tool_calls(context, expected_tool_calls): - tool_caexpected_tool_callslls = json.loads(expected_tool_calls) - n_completions = await gather_tasks_results(context) - assert n_completions > 0 - - for i in range(n_completions): - result = context.tasks_result.pop() - - def check(tool_calls): - assert json.dumps(expected_tool_calls) == json.dumps(tool_calls), f"tool calls: {tool_calls}, expected: {expected_tool_calls}, result = {result}" - - assert_n_tokens_predicted(result, tool_calls_check=check) - assert len(context.concurrent_tasks) == 0, f"{len(context.concurrent_tasks)} pending requests" - - -@step('no tool is called') -@async_run_until_complete -async def step_tool_called(context): - n_completions = await gather_tasks_results(context) - assert n_completions > 0 - - def check(tool_calls): - assert tool_calls is None, f"tool calls: {tool_calls}" - - for i in range(n_completions): - assert_n_tokens_predicted(context.tasks_result.pop(), tool_calls_check=check) - assert len(context.concurrent_tasks) == 0, f"{len(context.concurrent_tasks)} pending requests" - -@step('embeddings are computed for') -@async_run_until_complete -async def step_compute_embedding(context): - context.n_prompts = 1 - context.embeddings = await request_embedding(context_text(context), None, base_url=context.base_url) - - -@step('reranking request') -@async_run_until_complete -async def step_compute_reranking(context): - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with session.post(f'{context.base_url}/reranking', - json={ - "query": context.reranking_query, - "documents": context.reranking_documents, - }) as response: - if response.status == 200: - response_json = await response.json() - context.reranking_results = response_json['results'] - else: - context.reranking_results = response.status - - -@step('all embeddings are the same') -@async_run_until_complete -async def step_all_embeddings_are_the_same(context): - n_embedding_requests = await gather_tasks_results(context) - assert n_embedding_requests > 0 - embeddings = [] - for i in range(n_embedding_requests): - embedding = context.tasks_result.pop().pop() - embeddings.append(embedding) - assert_embeddings(embedding) - n = len(embeddings) - for i in range(n-1): - for j in range(i+1, n): - embedding1 = np.array(embeddings[i]) - embedding2 = np.array(embeddings[j]) - if context.debug: - print(f"embedding1: {embedding1[-8:]}") - print(f"embedding2: {embedding2[-8:]}") - similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2)) - msg = f"Similarity between {i} and {j}: {similarity:.10f}" - if context.debug: - print(f"{msg}") - assert np.isclose(similarity, 1.0, rtol=1e-05, atol=1e-08, equal_nan=False), msg - - -@step('embeddings are generated') -def step_assert_embeddings(context): - assert context.n_prompts == len(context.embeddings), (f"unexpected response:\n" - f"context.n_prompts={context.n_prompts}\n" - f"context.embeddings={context.embeddings}") - for embedding in context.embeddings: - assert_embeddings(embedding) - -@step('embeddings request with {api_error_code:d} api error') -def step_assert_embeddings(context, api_error_code: int): - assert context.embeddings == api_error_code, f"embeddings request must return code {api_error_code}, but got {context.embeddings}" - -@step('an OAI compatible embeddings computation request for') -@async_run_until_complete -async def step_oai_compute_embeddings(context): - context.n_prompts = 1 - context.embeddings = await request_oai_embeddings(context_text(context), None, - base_url=context.base_url, - user_api_key=context.user_api_key, - model=context.model) - - -@step('an OAI compatible embeddings computation request for multiple inputs') -@async_run_until_complete -async def step_oai_compute_embeddings_multiple_inputs(context): - context.embeddings = await request_oai_embeddings(context.prompts, None, - base_url=context.base_url, - user_api_key=context.user_api_key, - model=context.model) - context.prompts.clear() - - -@step('concurrent embedding requests') -@async_run_until_complete() -async def step_concurrent_embedding_requests(context): - await concurrent_requests(context, - request_embedding, - # prompt is inserted automatically - base_url=context.base_url) - - -@step('concurrent OAI embedding requests') -@async_run_until_complete() -async def step_concurrent_oai_embedding_requests(context): - await concurrent_requests(context, - request_oai_embeddings, - # prompt is inserted automatically - base_url=context.base_url, - async_client=True, - model=context.model) - - -@step('all embeddings are generated') -@async_run_until_complete() -async def all_embeddings_are_generated(context): - n_embedding_requests = await gather_tasks_results(context) - assert n_embedding_requests == context.n_prompts - for i in range(n_embedding_requests): - assert_embeddings(context.tasks_result.pop().pop()) - -@step('reranking results are returned') -def reranking_results_are_returned(context): - assert len(context.reranking_results) == len(context.reranking_documents) - -@step('reranking highest score is index {idx_high:d} and lowest score is index {idx_low:d}') -def reranking_results_are_returned(context, idx_high: int, idx_low: int): - max_score, max_idx = 0, 0 - min_score, min_idx = 0, 0 - for res in context.reranking_results: - if max_score < res['relevance_score']: - max_score = res['relevance_score'] - max_idx = res['index'] - if min_score > res['relevance_score']: - min_score = res['relevance_score'] - min_idx = res['index'] - print(context.reranking_results) - assert max_idx == idx_high - assert min_idx == idx_low - -@step('adding special tokens') -def step_tokenize_set_add_special(context): - context.tokenize_add_special = True - - -@step("tokenizing with pieces") -@async_run_until_complete -async def step_tokenize_with_pieces(context): - context.tokenized_text = context_text(context) - async with aiohttp.ClientSession() as session: - tokenize_args = {"content": context.tokenized_text, "with_pieces": True} - if getattr(context, "tokenize_add_special", None) is not None: - tokenize_args["add_special"] = context.tokenize_add_special - - async with session.post( - f"{context.base_url}/tokenize", json=tokenize_args - ) as response: - assert response.status == 200 - tokenize_json = await response.json() - context.tokens_with_pieces = tokenize_json["tokens"] - - -@step("tokens are given with pieces") -@async_run_until_complete -async def step_tokenize_with_pieces(context): - # Verify that the response contains both token IDs and pieces - assert all( - "id" in token and "piece" in token for token in context.tokens_with_pieces - ) - - -@step('tokenizing') -@async_run_until_complete -async def step_tokenize(context): - context.tokenized_text = context_text(context) - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - tokenize_args = { - "content": context.tokenized_text, - } - if getattr(context, 'tokenize_add_special', None) is not None: - tokenize_args['add_special'] = context.tokenize_add_special - async with session.post(f'{context.base_url}/tokenize', - json=tokenize_args) as response: - assert response.status == 200 - tokenize_json = await response.json() - context.tokens = tokenize_json['tokens'] - - -@step('tokens can be detokenized') -@async_run_until_complete -async def step_detokenize(context): - assert len(context.tokens) > 0 - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with session.post(f'{context.base_url}/detokenize', - json={ - "tokens": context.tokens, - }) as response: - assert response.status == 200 - detokenize_json = await response.json() - # SPM tokenizer adds a whitespace prefix: https://github.com/google/sentencepiece/issues/15 - assert context.tokenized_text == detokenize_json['content'].strip() - - -@step('tokens begin with BOS') -def step_strings_for_tokenization(context): - assert context.tokens[0] == context.bos - - -@step('tokens do not begin with BOS') -def step_strings_for_tokenization(context): - assert context.tokens[0] != context.bos - - -@step('first token is removed') -def step_strings_for_tokenization(context): - context.tokens = context.tokens[1:] - - -@step('an OPTIONS request is sent from {origin}') -@async_run_until_complete -async def step_options_request(context, origin): - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - headers = {'Authorization': f'Bearer {context.user_api_key}', 'Origin': origin} - async with session.options(f'{context.base_url}/v1/chat/completions', - headers=headers) as response: - assert response.status == 200 - context.options_response = response - - -@step('CORS header {cors_header} is set to {cors_header_value}') -def step_check_options_header_value(context, cors_header, cors_header_value): - assert context.options_response.headers[cors_header] == cors_header_value - - -@step('prometheus metrics are exposed') -@async_run_until_complete -async def step_prometheus_metrics_exported(context): - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with await session.get(f'{context.base_url}/metrics') as metrics_response: - assert metrics_response.status == 200 - assert metrics_response.headers['Content-Type'] == "text/plain; version=0.0.4" - metrics_raw = await metrics_response.text() - metric_exported = False - if context.debug: - print(f"/metrics answer:\n{metrics_raw}") - context.metrics = {} - for metric in parser.text_string_to_metric_families(metrics_raw): - match metric.name: - case "llamacpp:kv_cache_usage_ratio": - assert len(metric.samples) > 0 - metric_exported = True - context.metrics[metric.name] = metric - assert int(metrics_response.headers["Process-Start-Time-Unix"]) > 0, "no header process start time" - assert metric_exported, "No metrics exported" - - -@step('metric {metric_name} is {metric_value:d}') -def step_assert_metric_value(context, metric_name, metric_value): - if metric_name not in context.metrics: - assert False, f"no metric {metric_name} in {context.metrics.keys()}" - assert context.metrics[metric_name].samples[0].value == metric_value, f"metric: {context.metrics[metric_name]}" - - -@step('available models') -def step_available_models(context): - # openai client always expects an api_key - openai.api_key = context.user_api_key if context.user_api_key is not None else 'nope' - openai.base_url = f'{context.base_url}/v1/' - context.models = openai.models.list().data - - -@step('{n_model:d} models are supported') -def step_supported_models(context, n_model): - if context.debug: - print("server models available:", context.models) - assert len(context.models) == n_model - - -@step('model {i_model:d} is {param} {preposition} {param_value}') -def step_supported_models(context, i_model: int, param: Literal['identified', 'trained'] | str, preposition: str, param_value: str): - assert i_model < len(context.models) - model = context.models[i_model] - - param_value = param_value.split(' ', 1)[0] - match param: - case 'identified': - value = model.id - case 'trained': - value = str(model.meta["n_ctx_train"]) - case _: - assert False, "param {param} not supported" - assert param_value == value, f"model param {param} {value} != {param_value}" - - -async def concurrent_requests(context, f_completion, *args, **kwargs): - context.n_prompts = len(context.prompts) - if context.debug: - print(f"starting {context.n_prompts} concurrent completion requests...") - assert context.n_prompts > 0 - seeds = await completions_seed(context) - assert seeds is not None - for prompt_no in range(context.n_prompts): - shifted_args = [context.prompts.pop(), seeds[prompt_no], *args] - context.concurrent_tasks.append(asyncio.create_task(f_completion(*shifted_args, **kwargs))) - await asyncio.sleep(0.01) - - -@step('the slot {slot_id:d} is saved with filename "{filename}"') -@async_run_until_complete -async def step_save_slot(context, slot_id, filename): - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with session.post(f'{context.base_url}/slots/{slot_id}?action=save', - json={"filename": filename}, - headers={"Content-Type": "application/json"}) as response: - context.response = response - - -@step('the slot {slot_id:d} is restored with filename "{filename}"') -@async_run_until_complete -async def step_restore_slot(context, slot_id, filename): - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with session.post(f'{context.base_url}/slots/{slot_id}?action=restore', - json={"filename": filename}, - headers={"Content-Type": "application/json"}) as response: - context.response = response - - -@step('the slot {slot_id:d} is erased') -@async_run_until_complete -async def step_erase_slot(context, slot_id): - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with session.post(f'{context.base_url}/slots/{slot_id}?action=erase', - headers={"Content-Type": "application/json"}) as response: - context.response = response - - -@step('switch {on_or_off} lora adapter {lora_id:d}') -@async_run_until_complete -async def toggle_lora_adapter(context, on_or_off: str, lora_id: int): - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with session.post(f'{context.base_url}/lora-adapters', - json=[{'id': lora_id, 'scale': 1 if on_or_off == 'on' else 0}], - headers={"Content-Type": "application/json"}) as response: - context.response = response - print([{'id': lora_id, 'scale': 1 if on_or_off == 'on' else 0}]) - - -@step('the server responds with status code {status_code:d}') -def step_server_responds_with_status_code(context, status_code): - assert context.response.status == status_code - - -async def request_completion(prompt, - seed, - base_url, - debug=False, - prompt_prefix=None, - prompt_suffix=None, - n_predict=None, - cache_prompt=False, - id_slot=None, - expect_api_error=None, - user_api_key=None, - temperature=None) -> int | dict[str, Any]: - if debug: - print(f"Sending completion request: {prompt}") - origin = "my.super.domain" - headers = { - 'Origin': origin - } - if user_api_key is not None: - if debug: - print(f"Set user_api_key: {user_api_key}") - headers['Authorization'] = f'Bearer {user_api_key}' - - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with session.post(f'{base_url}/completion', - json={ - "input_prefix": prompt_prefix, - "prompt": prompt, - "input_suffix": prompt_suffix, - "n_predict": n_predict if n_predict is not None else -1, - "cache_prompt": cache_prompt, - "id_slot": id_slot, - "seed": seed if seed is not None else 42, - "temperature": temperature if temperature is not None else 0.8, - "n_probs": 2, - }, - headers=headers) as response: - if expect_api_error is None or not expect_api_error: - assert response.status == 200 - assert response.headers['Access-Control-Allow-Origin'] == origin - return await response.json() - else: - return response.status - - -async def oai_chat_completions(user_prompt, - seed, - system_prompt, - base_url: str, - base_path: str, - async_client, - debug=False, - temperature=None, - model=None, - n_predict=None, - enable_streaming=None, - response_format=None, - tools=None, - tool_choice=None, - parallel_tool_calls=None, - user_api_key=None, - expect_api_error=None) -> int | dict[str, Any]: - if debug: - print(f"Sending OAI Chat completions request: {user_prompt}") - # openai client always expects an api key - user_api_key = user_api_key if user_api_key is not None else 'nope' - seed = seed if seed is not None else 42 - - enable_streaming = enable_streaming if enable_streaming is not None else False - messages = [] - if system_prompt: - messages.append({ - "role": "system", - "content": system_prompt, - }) - if user_prompt: - messages.append({ - "role": "user", - "content": user_prompt, - }) - payload = { - "messages": messages, - "model": model, - "max_tokens": n_predict, - "stream": enable_streaming, - "temperature": temperature if temperature is not None else 0.0, - "seed": seed, - } - if response_format is not None: - payload['response_format'] = response_format - if tools is not None: - payload['tools'] = tools - if tool_choice is not None: - payload['tool_choice'] = tool_choice - if parallel_tool_calls is not None: - payload['parallel_tool_calls'] = parallel_tool_calls - completion_response = { - 'content': '', - 'timings': { - 'predicted_n': 0, - 'prompt_n': 0 - } - } - if async_client: - origin = 'llama.cpp' - headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin} - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with session.post(f'{base_url}{base_path}', - json=payload, - headers=headers) as response: - if enable_streaming: - assert response.status == 200 - assert response.headers['Access-Control-Allow-Origin'] == origin - assert response.headers['Content-Type'] == "text/event-stream" - event_received = True - while event_received: - event_received = False - async for line_in_bytes in response.content: - line = line_in_bytes.decode('utf-8') - line = line.rstrip('\n').rstrip('\r') - if line == '': - continue - event_data = line.split(': ', 1) - assert event_data[0] == 'data', f'Bad event code received: ```{event_data}```' - chunk_raw = event_data[1] - if chunk_raw == '[DONE]': - break - - chunk = json.loads(chunk_raw) - assert len(chunk['choices']) == 1, f"no choices provided, line ```{line}```" - delta = chunk['choices'][0]['delta'] - if 'content' in delta: - completion_response['content'] += delta['content'] - completion_response['timings']['predicted_n'] += 1 - else: - if expect_api_error is None or not expect_api_error: - assert response.status == 200 - assert response.headers['Access-Control-Allow-Origin'] == origin - assert response.headers['Content-Type'] == "application/json; charset=utf-8" - chat_completion_raw = await response.json() - completion_response = { - 'content': chat_completion_raw['choices'][0]['message'], - 'timings': { - 'predicted_n': chat_completion_raw['usage']['completion_tokens'], - 'prompt_n': chat_completion_raw['usage']['prompt_tokens'] - } - } - else: - return response.status - else: - try: - openai.api_key = user_api_key - openai.base_url = f'{base_url}{base_path.removesuffix("chat")}' - assert model is not None - chat_completion = openai.chat.completions.create( - messages=payload['messages'], - model=model, - max_tokens=n_predict, - stream=enable_streaming, - response_format=payload.get('response_format') or openai.NOT_GIVEN, - tools=payload.get('tools') or openai.NOT_GIVEN, - tool_choice=payload.get('tool_choice') or openai.NOT_GIVEN, - parallel_tool_calls=payload.get('parallel_tool_calls', openai.NOT_GIVEN), - seed=seed, - temperature=payload['temperature'] - ) - except openai.AuthenticationError as e: - if expect_api_error is not None and expect_api_error: - return 401 - else: - assert False, f'error raised: {e}' - - if enable_streaming: - chat_completion = cast(openai.Stream[ChatCompletionChunk], chat_completion) - for chunk in chat_completion: - assert len(chunk.choices) == 1 - delta = chunk.choices[0].delta - if delta.content is not None: - completion_response['content'] += delta.content - completion_response['timings']['predicted_n'] += 1 - completion_response['truncated'] = chunk.choices[0].finish_reason != 'stop' - else: - assert len(chat_completion.choices) == 1 - assert chat_completion.usage is not None - completion_response = { - 'content': chat_completion.choices[0].message.content, - 'tool_calls': chat_completion.choices[0].message.tool_calls, - 'timings': { - 'predicted_n': chat_completion.usage.completion_tokens, - 'prompt_n': chat_completion.usage.prompt_tokens - }, - 'truncated': chat_completion.choices[0].finish_reason != 'stop' - } - if debug: - print("OAI response formatted to llama.cpp:", completion_response) - return completion_response - - -async def request_embedding(content, seed, base_url=None) -> list[list[float]] | int: - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with session.post(f'{base_url}/embedding', - json={ - "content": content, - }) as response: - if response.status == 200: - response_json = await response.json() - return [response_json['embedding']] - else: - return response.status - - -async def request_oai_embeddings(input, seed, - base_url=None, user_api_key=None, - model=None, async_client=False) -> list[list[float]]: - # openai client always expects an api_key - user_api_key = user_api_key if user_api_key is not None else 'nope' - if async_client: - origin = 'llama.cpp' - headers=[] - if user_api_key is not None: - headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin} - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with session.post(f'{base_url}/v1/embeddings', - json={ - "input": input, - "model": model, - }, - headers=headers) as response: - assert response.status == 200, f"received status code not expected: {response.status}" - assert response.headers['Access-Control-Allow-Origin'] == origin - assert response.headers['Content-Type'] == "application/json; charset=utf-8" - response_json = await response.json() - assert response_json['model'] == model, f"invalid model received: {response_json['model']}" - assert response_json['object'] == 'list' - if isinstance(input, Sequence): - embeddings = [] - for an_oai_embeddings in response_json['data']: - embeddings.append(an_oai_embeddings['embedding']) - else: - embeddings = [response_json['data']['embedding']] - return embeddings - else: - openai.api_key = user_api_key - openai.base_url = f'{base_url}/v1/' - assert model is not None - oai_embeddings = openai.embeddings.create( - model=model, - input=input, - ) - - return [e.embedding for e in oai_embeddings.data] - - -def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re_content=None, tool_calls_check=None): - content = completion_response['content'] - tool_calls = completion_response.get('tool_calls') - n_predicted = completion_response['timings']['predicted_n'] - assert (content and len(content) > 0) or (tool_calls and len(tool_calls) > 0), "no token predicted" - if re_content is not None: - assert content - p = re.compile(re_content, flags=RegexFlag.IGNORECASE | RegexFlag.MULTILINE | RegexFlag.DOTALL) - matches = p.finditer(content) - last_match = 0 - highlighted = '' - for match in matches: - start, end = match.span() - highlighted += content[last_match: start] - highlighted += '\x1b[33m' - highlighted += content[start: end] - highlighted += '\x1b[0m' - last_match = end - highlighted += content[last_match:] - if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON': - print(f"Checking completion response: {highlighted}") - assert last_match > 0, f'/{re_content}/ must match ```{highlighted}```' - if tool_calls_check: - tool_calls_check(tool_calls) - if expected_predicted_n and expected_predicted_n > 0: - assert n_predicted == expected_predicted_n, (f'invalid number of tokens predicted:' - f' {n_predicted} <> {expected_predicted_n}') - -def assert_all_predictions_equal(completion_responses): - if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON': - for i, response_i in enumerate(completion_responses): - content_i = response_i['content'] - print(f"content {i}: {content_i}") - for i, response_i in enumerate(completion_responses): - content_i = response_i['content'] - for j, response_j in enumerate(completion_responses): - if i == j: - continue - content_j = response_j['content'] - assert content_i == content_j, "contents not equal" - - -def assert_all_predictions_different(completion_responses): - if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON': - for i, response_i in enumerate(completion_responses): - content_i = response_i['content'] - print(f"content {i}: {content_i}") - for i, response_i in enumerate(completion_responses): - content_i = response_i['content'] - for j, response_j in enumerate(completion_responses): - if i == j: - continue - content_j = response_j['content'] - assert content_i != content_j, "contents not different" - - -def assert_all_token_probabilities_equal(completion_responses): - n_predict = len(completion_responses[0]['completion_probabilities']) - if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON': - for pos in range(n_predict): - for i, response_i in enumerate(completion_responses): - probs_i = response_i['completion_probabilities'][pos]['probs'] - print(f"pos {pos}, probs {i}: {probs_i}") - for pos in range(n_predict): - for i, response_i in enumerate(completion_responses): - probs_i = response_i['completion_probabilities'][pos]['probs'] - for j, response_j in enumerate(completion_responses): - if i == j: - continue - probs_j = response_j['completion_probabilities'][pos]['probs'] - assert probs_i == probs_j, "contents not equal" - - -async def gather_tasks_results(context): - n_tasks = len(context.concurrent_tasks) - if context.debug: - print(f"Waiting for all {n_tasks} tasks results...") - for task_no in range(n_tasks): - context.tasks_result.append(await context.concurrent_tasks.pop()) - n_completions = len(context.tasks_result) - return n_completions - - -async def wait_for_slots_status(context, - base_url, - expected_http_status_code, - timeout=3, - params=None, - slots_idle=None, - slots_processing=None): - if context.debug: - print(f"Starting checking for health for expected_http_status_code={expected_http_status_code}") - interval = 0.5 - counter = 0 - if 'GITHUB_ACTIONS' in os.environ: - timeout *= 2 - - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - while True: - headers = {'Authorization': f'Bearer {context.server_api_key}'} - async with await session.get(f'{base_url}/slots', params=params, headers=headers) as slots_response: - status_code = slots_response.status - slots = await slots_response.json() - if context.debug: - print(f"slots responses {slots}\n") - if status_code == 503 and status_code == expected_http_status_code: - return - if status_code == 200 and status_code == expected_http_status_code: - n_slots_idle = sum(1 if slot["state"] == 0 else 0 for slot in slots) - n_slots_processing = sum(1 if slot["state"] != 0 else 0 for slot in slots) - if ((slots_idle is None or slots_idle == n_slots_idle) - and (slots_processing is None or slots_processing == n_slots_processing)): - return - await asyncio.sleep(interval) - - counter += interval - if counter >= timeout: - # Sometimes health requests are triggered after completions are predicted - if expected_http_status_code == 503: - if len(context.tasks_result) == 0: - print("\x1b[5;37;43mWARNING: forcing concurrent tasks," - " busy health check missed, probably too fast inference\x1b[0m\n") - n_completions = await gather_tasks_results(context) - if n_completions > 0: - return - - assert False, f'slots check timeout exceeded {counter}s>={timeout}' - - -def assert_embeddings(embeddings): - assert len(embeddings) > 0 - embeddings_computed = False - for emb in embeddings: - if not isinstance(emb, float): - assert False, f"Bad embeddings: {embeddings}" - if emb != 0: - embeddings_computed = True - assert embeddings_computed, f"Embeddings: {embeddings}" - - -async def request_slots_status(context, expected_slots): - async with aiohttp.ClientSession(timeout=DEFAULT_TIMEOUT_SECONDS) as session: - async with await session.get(f'{context.base_url}/slots') as slots_response: - assert slots_response.status == 200 - slots = await slots_response.json() - assert_slots_status(slots, expected_slots) - - -def assert_slots_status(slots, expected_slots): - assert len(slots) == len(expected_slots) - for slot_id, (expected, slot) in enumerate(zip(expected_slots, slots)): - for key in expected: - assert expected[key] == slot[key], (f"invalid slot {slot_id}" - f" expected[{key}] != slot[{key}]" - f" = {expected[key]} != {slot[key]}") - - -async def completions_seed(context, num_seeds=None): - if hasattr(context, "seed") and context.seed is not None: - assert len(context.seed) == context.n_prompts - if num_seeds is None: - num_seeds = context.n_prompts - assert num_seeds <= context.n_prompts - seeds = context.seed[:num_seeds] - context.seed = context.seed[num_seeds:] if num_seeds < context.n_prompts else None - return seeds - - if hasattr(context, "server_seed") and context.server_seed is not None: - if num_seeds is None: - return [context.server_seed] * context.n_prompts - else: - return [context.server_seed] * num_seeds - return None - - -def context_text(context): - return context.text.replace('\r', '') - - -def start_server_background(context): - if os.name == 'nt': - context.server_path = '../../../build/bin/Release/llama-server.exe' - else: - context.server_path = '../../../build/bin/llama-server' - if 'LLAMA_SERVER_BIN_PATH' in os.environ: - context.server_path = os.environ['LLAMA_SERVER_BIN_PATH'] - server_listen_addr = context.server_fqdn - server_args = [ - '--slots', # requires to get slot status via /slots endpoint - '--host', server_listen_addr, - '--port', context.server_port, - ] - if context.model_file: - server_args.extend(['--model', context.model_file]) - if context.model_url: - server_args.extend(['--model-url', context.model_url]) - if context.model_hf_repo: - server_args.extend(['--hf-repo', context.model_hf_repo]) - if context.model_hf_file: - server_args.extend(['--hf-file', context.model_hf_file]) - if context.n_batch: - server_args.extend(['--batch-size', context.n_batch]) - if context.n_ubatch: - server_args.extend(['--ubatch-size', context.n_ubatch]) - if context.n_threads: - server_args.extend(['--threads', context.threads]) - if context.n_gpu_layer: - server_args.extend(['--n-gpu-layers', context.n_gpu_layer]) - if context.draft is not None: - server_args.extend(['--draft', context.draft]) - if context.server_continuous_batching: - server_args.append('--cont-batching') - if context.server_embeddings: - server_args.append('--embedding') - if context.server_reranking: - server_args.append('--reranking') - if context.server_metrics: - server_args.append('--metrics') - if context.model_alias: - server_args.extend(['--alias', context.model_alias]) - if context.n_ctx: - server_args.extend(['--ctx-size', context.n_ctx]) - if context.n_slots: - server_args.extend(['--parallel', context.n_slots]) - if context.n_server_predict: - server_args.extend(['--n-predict', context.n_server_predict]) - if context.slot_save_path: - server_args.extend(['--slot-save-path', context.slot_save_path]) - if context.server_api_key: - server_args.extend(['--api-key', context.server_api_key]) - if context.n_ga: - server_args.extend(['--grp-attn-n', context.n_ga]) - if context.n_ga_w: - server_args.extend(['--grp-attn-w', context.n_ga_w]) - if context.debug: - server_args.append('--verbose') - if context.use_jinja: - server_args.append('--jinja') - if context.chat_template_file: - server_args.extend(['--chat-template-file', context.chat_template_file]) - if context.lora_file: - server_args.extend(['--lora', context.lora_file]) - if context.disable_ctx_shift: - server_args.extend(['--no-context-shift']) - if context.greedy_sampling: - server_args.extend(['--samplers', 'top-k', '--top-k', '1']) - if not context.warmup: - server_args.extend(['--no-warmup']) - - args = [str(arg) for arg in [context.server_path, *server_args]] - print(f"bench: starting server with: {' '.join(args)}") - - flags = 0 - if 'nt' == os.name: - flags |= subprocess.DETACHED_PROCESS - flags |= subprocess.CREATE_NEW_PROCESS_GROUP - flags |= subprocess.CREATE_NO_WINDOW - - pkwargs = { - 'creationflags': flags, - 'stdout': subprocess.PIPE, - 'stderr': subprocess.PIPE - } - context.server_process = subprocess.Popen( - [str(arg) for arg in [context.server_path, *server_args]], - **pkwargs) # pyright: ignore[reportArgumentType, reportCallIssue] - - def server_log(in_stream, out_stream): - for line in iter(in_stream.readline, b''): - try: - print(line.decode('utf-8'), end='', file=out_stream) - except UnicodeDecodeError: - print(line, end='', file=out_stream) - - thread_stdout = threading.Thread(target=server_log, args=(context.server_process.stdout, sys.stdout)) - thread_stdout.start() - - thread_stderr = threading.Thread(target=server_log, args=(context.server_process.stderr, sys.stderr)) - thread_stderr.start() - - print(f"server pid={context.server_process.pid}, behave pid={os.getpid()}") diff --git a/examples/server/tests/features/wrong_usages.feature b/examples/server/tests/features/wrong_usages.feature deleted file mode 100644 index 61d5f315e..000000000 --- a/examples/server/tests/features/wrong_usages.feature +++ /dev/null @@ -1,25 +0,0 @@ -# run with: ./tests.sh --no-skipped --tags wrong_usage -@wrong_usage -Feature: Wrong usage of llama.cpp server - - #3969 The user must always set --n-predict option - # to cap the number of tokens any completion request can generate - # or pass n_predict/max_tokens in the request. - Scenario: Infinite loop - Given a server listening on localhost:8080 - And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models - And 42 as server seed - And 2048 KV cache size - # Uncomment below to fix the issue - #And 64 server max tokens to predict - Then the server is starting - Then the server is healthy - Given a prompt: - """ - Go to: infinite loop - """ - # Uncomment below to fix the issue - #And 128 max tokens to predict - Given concurrent completion requests - Then the server is idle - Then all prompts are predicted diff --git a/examples/server/tests/requirements.txt b/examples/server/tests/requirements.txt index a1073ba9d..074b9d47b 100644 --- a/examples/server/tests/requirements.txt +++ b/examples/server/tests/requirements.txt @@ -1,7 +1,7 @@ aiohttp~=3.9.3 -behave~=1.2.6 +pytest~=8.3.3 huggingface_hub~=0.23.2 numpy~=1.26.4 -openai~=1.50.2 +openai~=1.55.3 prometheus-client~=0.20.0 requests~=2.32.3 diff --git a/examples/server/tests/tests.sh b/examples/server/tests/tests.sh index 370495afe..1e285dcda 100755 --- a/examples/server/tests/tests.sh +++ b/examples/server/tests/tests.sh @@ -4,8 +4,7 @@ set -eu if [ $# -lt 1 ] then - # Start @llama.cpp scenario - behave --summary --stop --no-capture --exclude 'issues|wrong_usages' --tags llama.cpp,-slow + pytest -v -x else - behave "$@" + pytest "$@" fi diff --git a/examples/server/tests/unit/test_basic.py b/examples/server/tests/unit/test_basic.py new file mode 100644 index 000000000..d82d54a5a --- /dev/null +++ b/examples/server/tests/unit/test_basic.py @@ -0,0 +1,48 @@ +import pytest +from utils import * + +server = ServerPreset.tinyllama2() + + +@pytest.fixture(scope="module", autouse=True) +def create_server(): + global server + server = ServerPreset.tinyllama2() + + +def test_server_start_simple(): + global server + server.start() + res = server.make_request("GET", "/health") + assert res.status_code == 200 + + +def test_server_props(): + global server + server.start() + res = server.make_request("GET", "/props") + assert res.status_code == 200 + assert res.body["total_slots"] == server.n_slots + + +def test_server_models(): + global server + server.start() + res = server.make_request("GET", "/models") + assert res.status_code == 200 + assert len(res.body["data"]) == 1 + assert res.body["data"][0]["id"] == server.model_alias + +def test_load_split_model(): + global server + server.model_hf_repo = "ggml-org/models" + server.model_hf_file = "tinyllamas/split/stories15M-q8_0-00001-of-00003.gguf" + server.model_alias = "tinyllama-split" + server.start() + res = server.make_request("POST", "/completion", data={ + "n_predict": 16, + "prompt": "Hello", + "temperature": 0.0, + }) + assert res.status_code == 200 + assert match_regex("(little|girl)+", res.body["content"]) diff --git a/examples/server/tests/unit/test_chat_completion.py b/examples/server/tests/unit/test_chat_completion.py new file mode 100644 index 000000000..8a439f9ef --- /dev/null +++ b/examples/server/tests/unit/test_chat_completion.py @@ -0,0 +1,165 @@ +import pytest +from openai import OpenAI +from utils import * + +server = ServerPreset.tinyllama2() + + +@pytest.fixture(scope="module", autouse=True) +def create_server(): + global server + server = ServerPreset.tinyllama2() + + +@pytest.mark.parametrize( + "model,system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,truncated", + [ + ("llama-2", "Book", "What is the best book", 8, "(Suddenly)+", 77, 8, False), + ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, False), + ] +) +def test_chat_completion(model, system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, truncated): + global server + server.start() + res = server.make_request("POST", "/chat/completions", data={ + "model": model, + "max_tokens": max_tokens, + "messages": [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt}, + ], + }) + assert res.status_code == 200 + assert res.body["usage"]["prompt_tokens"] == n_prompt + assert res.body["usage"]["completion_tokens"] == n_predicted + choice = res.body["choices"][0] + assert "assistant" == choice["message"]["role"] + assert match_regex(re_content, choice["message"]["content"]) + if truncated: + assert choice["finish_reason"] == "length" + else: + assert choice["finish_reason"] == "stop" + + +@pytest.mark.parametrize( + "model,system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,truncated", + [ + ("llama-2", "Book", "What is the best book", 8, "(Suddenly)+", 77, 8, False), + ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, False), + ] +) +def test_chat_completion_stream(model, system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, truncated): + global server + server.start() + res = server.make_stream_request("POST", "/chat/completions", data={ + "model": model, + "max_tokens": max_tokens, + "messages": [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt}, + ], + "stream": True, + }) + content = "" + for data in res: + choice = data["choices"][0] + if choice["finish_reason"] in ["stop", "length"]: + assert data["usage"]["prompt_tokens"] == n_prompt + assert data["usage"]["completion_tokens"] == n_predicted + assert "content" not in choice["delta"] + assert match_regex(re_content, content) + # FIXME: not sure why this is incorrect in stream mode + # if truncated: + # assert choice["finish_reason"] == "length" + # else: + # assert choice["finish_reason"] == "stop" + else: + assert choice["finish_reason"] is None + content += choice["delta"]["content"] + + +def test_chat_completion_with_openai_library(): + global server + server.start() + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}") + res = client.chat.completions.create( + model="gpt-3.5-turbo-instruct", + messages=[ + {"role": "system", "content": "Book"}, + {"role": "user", "content": "What is the best book"}, + ], + max_tokens=8, + seed=42, + temperature=0.8, + ) + print(res) + assert res.choices[0].finish_reason == "stop" + assert res.choices[0].message.content is not None + assert match_regex("(Suddenly)+", res.choices[0].message.content) + + +@pytest.mark.parametrize("response_format,n_predicted,re_content", [ + ({"type": "json_object", "schema": {"const": "42"}}, 6, "\"42\""), + ({"type": "json_object", "schema": {"items": [{"type": "integer"}]}}, 10, "[ -3000 ]"), + ({"type": "json_object"}, 10, "(\\{|John)+"), + ({"type": "sound"}, 0, None), + # invalid response format (expected to fail) + ({"type": "json_object", "schema": 123}, 0, None), + ({"type": "json_object", "schema": {"type": 123}}, 0, None), + ({"type": "json_object", "schema": {"type": "hiccup"}}, 0, None), +]) +def test_completion_with_response_format(response_format: dict, n_predicted: int, re_content: str | None): + global server + server.start() + res = server.make_request("POST", "/chat/completions", data={ + "max_tokens": n_predicted, + "messages": [ + {"role": "system", "content": "You are a coding assistant."}, + {"role": "user", "content": "Write an example"}, + ], + "response_format": response_format, + }) + if re_content is not None: + assert res.status_code == 200 + choice = res.body["choices"][0] + assert match_regex(re_content, choice["message"]["content"]) + else: + assert res.status_code != 200 + assert "error" in res.body + + +@pytest.mark.parametrize("messages", [ + None, + "string", + [123], + [{}], + [{"role": 123}], + [{"role": "system", "content": 123}], + # [{"content": "hello"}], # TODO: should not be a valid case + [{"role": "system", "content": "test"}, {}], +]) +def test_invalid_chat_completion_req(messages): + global server + server.start() + res = server.make_request("POST", "/chat/completions", data={ + "messages": messages, + }) + assert res.status_code == 400 or res.status_code == 500 + assert "error" in res.body + + +def test_chat_completion_with_timings_per_token(): + global server + server.start() + res = server.make_stream_request("POST", "/chat/completions", data={ + "max_tokens": 10, + "messages": [{"role": "user", "content": "test"}], + "stream": True, + "timings_per_token": True, + }) + for data in res: + assert "timings" in data + assert "prompt_per_second" in data["timings"] + assert "predicted_per_second" in data["timings"] + assert "predicted_n" in data["timings"] + assert data["timings"]["predicted_n"] <= 10 diff --git a/examples/server/tests/unit/test_completion.py b/examples/server/tests/unit/test_completion.py new file mode 100644 index 000000000..2fa30dd03 --- /dev/null +++ b/examples/server/tests/unit/test_completion.py @@ -0,0 +1,223 @@ +import pytest +import time +from utils import * + +server = ServerPreset.tinyllama2() + + +@pytest.fixture(scope="module", autouse=True) +def create_server(): + global server + server = ServerPreset.tinyllama2() + +@pytest.mark.parametrize("prompt,n_predict,re_content,n_prompt,n_predicted,truncated", [ + ("I believe the meaning of life is", 8, "(going|bed)+", 18, 8, False), + ("Write a joke about AI from a very long prompt which will not be truncated", 256, "(princesses|everyone|kids|Anna|forest)+", 46, 64, False), +]) +def test_completion(prompt: str, n_predict: int, re_content: str, n_prompt: int, n_predicted: int, truncated: bool): + global server + server.start() + res = server.make_request("POST", "/completion", data={ + "n_predict": n_predict, + "prompt": prompt, + }) + assert res.status_code == 200 + assert res.body["timings"]["prompt_n"] == n_prompt + assert res.body["timings"]["predicted_n"] == n_predicted + assert res.body["truncated"] == truncated + assert match_regex(re_content, res.body["content"]) + + +@pytest.mark.parametrize("prompt,n_predict,re_content,n_prompt,n_predicted,truncated", [ + ("I believe the meaning of life is", 8, "(going|bed)+", 18, 8, False), + ("Write a joke about AI from a very long prompt which will not be truncated", 256, "(princesses|everyone|kids|Anna|forest)+", 46, 64, False), +]) +def test_completion_stream(prompt: str, n_predict: int, re_content: str, n_prompt: int, n_predicted: int, truncated: bool): + global server + server.start() + res = server.make_stream_request("POST", "/completion", data={ + "n_predict": n_predict, + "prompt": prompt, + "stream": True, + }) + content = "" + for data in res: + if data["stop"]: + assert data["timings"]["prompt_n"] == n_prompt + assert data["timings"]["predicted_n"] == n_predicted + assert data["truncated"] == truncated + assert match_regex(re_content, content) + else: + content += data["content"] + + +@pytest.mark.parametrize("n_slots", [1, 2]) +def test_consistent_result_same_seed(n_slots: int): + global server + server.n_slots = n_slots + server.start() + last_res = None + for _ in range(4): + res = server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "seed": 42, + "temperature": 1.0, + "cache_prompt": False, # TODO: remove this once test_cache_vs_nocache_prompt is fixed + }) + if last_res is not None: + assert res.body["content"] == last_res.body["content"] + last_res = res + + +@pytest.mark.parametrize("n_slots", [1, 2]) +def test_different_result_different_seed(n_slots: int): + global server + server.n_slots = n_slots + server.start() + last_res = None + for seed in range(4): + res = server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "seed": seed, + "temperature": 1.0, + "cache_prompt": False, # TODO: remove this once test_cache_vs_nocache_prompt is fixed + }) + if last_res is not None: + assert res.body["content"] != last_res.body["content"] + last_res = res + + +@pytest.mark.parametrize("n_batch", [16, 32]) +@pytest.mark.parametrize("temperature", [0.0, 1.0]) +def test_consistent_result_different_batch_size(n_batch: int, temperature: float): + global server + server.n_batch = n_batch + server.start() + last_res = None + for _ in range(4): + res = server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "seed": 42, + "temperature": temperature, + "cache_prompt": False, # TODO: remove this once test_cache_vs_nocache_prompt is fixed + }) + if last_res is not None: + assert res.body["content"] == last_res.body["content"] + last_res = res + + +@pytest.mark.skip(reason="This test fails on linux, need to be fixed") +def test_cache_vs_nocache_prompt(): + global server + server.start() + res_cache = server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "seed": 42, + "temperature": 1.0, + "cache_prompt": True, + }) + res_no_cache = server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "seed": 42, + "temperature": 1.0, + "cache_prompt": False, + }) + assert res_cache.body["content"] == res_no_cache.body["content"] + + +def test_completion_with_tokens_input(): + global server + server.temperature = 0.0 + server.start() + prompt_str = "I believe the meaning of life is" + res = server.make_request("POST", "/tokenize", data={ + "content": prompt_str, + "add_special": True, + }) + assert res.status_code == 200 + tokens = res.body["tokens"] + + # single completion + res = server.make_request("POST", "/completion", data={ + "prompt": tokens, + }) + assert res.status_code == 200 + assert type(res.body["content"]) == str + + # batch completion + res = server.make_request("POST", "/completion", data={ + "prompt": [tokens, tokens], + }) + assert res.status_code == 200 + assert type(res.body) == list + assert len(res.body) == 2 + assert res.body[0]["content"] == res.body[1]["content"] + + # mixed string and tokens + res = server.make_request("POST", "/completion", data={ + "prompt": [tokens, prompt_str], + }) + assert res.status_code == 200 + assert type(res.body) == list + assert len(res.body) == 2 + assert res.body[0]["content"] == res.body[1]["content"] + + # mixed string and tokens in one sequence + res = server.make_request("POST", "/completion", data={ + "prompt": [1, 2, 3, 4, 5, 6, prompt_str, 7, 8, 9, 10, prompt_str], + }) + assert res.status_code == 200 + assert type(res.body["content"]) == str + + +@pytest.mark.parametrize("n_slots,n_requests", [ + (1, 3), + (2, 2), + (2, 4), + (4, 2), # some slots must be idle + (4, 6), +]) +def test_completion_parallel_slots(n_slots: int, n_requests: int): + global server + server.n_slots = n_slots + server.temperature = 0.0 + server.start() + + PROMPTS = [ + ("Write a very long book.", "(very|special|big)+"), + ("Write another a poem.", "(small|house)+"), + ("What is LLM?", "(Dad|said)+"), + ("The sky is blue and I love it.", "(climb|leaf)+"), + ("Write another very long music lyrics.", "(friends|step|sky)+"), + ("Write a very long joke.", "(cat|Whiskers)+"), + ] + def check_slots_status(): + should_all_slots_busy = n_requests >= n_slots + time.sleep(0.1) + res = server.make_request("GET", "/slots") + n_busy = sum([1 for slot in res.body if slot["is_processing"]]) + if should_all_slots_busy: + assert n_busy == n_slots + else: + assert n_busy <= n_slots + + tasks = [] + for i in range(n_requests): + prompt, re_content = PROMPTS[i % len(PROMPTS)] + tasks.append((server.make_request, ("POST", "/completion", { + "prompt": prompt, + "seed": 42, + "temperature": 1.0, + }))) + tasks.append((check_slots_status, ())) + results = parallel_function_calls(tasks) + + # check results + for i in range(n_requests): + prompt, re_content = PROMPTS[i % len(PROMPTS)] + res = results[i] + assert res.status_code == 200 + assert type(res.body["content"]) == str + assert len(res.body["content"]) > 10 + # FIXME: the result is not deterministic when using other slot than slot 0 + # assert match_regex(re_content, res.body["content"]) diff --git a/examples/server/tests/unit/test_ctx_shift.py b/examples/server/tests/unit/test_ctx_shift.py new file mode 100644 index 000000000..be93a6d31 --- /dev/null +++ b/examples/server/tests/unit/test_ctx_shift.py @@ -0,0 +1,67 @@ +import pytest +from utils import * + +server = ServerPreset.tinyllama2() + + +LONG_TEXT = """ +Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. +Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. +Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. +Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. +""".strip() + +@pytest.fixture(scope="module", autouse=True) +def create_server(): + global server + server = ServerPreset.tinyllama2() + server.n_ctx = 256 + server.n_slots = 2 + + +def test_ctx_shift_enabled(): + # the prompt is 301 tokens + # the slot context is 256/2 = 128 tokens + # the prompt is truncated to keep the last 109 tokens + # 64 tokens are generated thanks to shifting the context when it gets full + global server + server.start() + res = server.make_request("POST", "/completion", data={ + "n_predict": 64, + "prompt": LONG_TEXT, + }) + assert res.status_code == 200 + assert res.body["timings"]["prompt_n"] == 109 + assert res.body["timings"]["predicted_n"] == 64 + assert res.body["truncated"] is True + + +@pytest.mark.parametrize("n_predict,n_token_output,truncated", [ + (64, 64, False), + (-1, 120, True), +]) +def test_ctx_shift_disabled_short_prompt(n_predict: int, n_token_output: int, truncated: bool): + global server + server.disable_ctx_shift = True + server.n_predict = -1 + server.start() + res = server.make_request("POST", "/completion", data={ + "n_predict": n_predict, + "prompt": "Hi how are you", + }) + assert res.status_code == 200 + assert res.body["timings"]["predicted_n"] == n_token_output + assert res.body["truncated"] == truncated + + +def test_ctx_shift_disabled_long_prompt(): + global server + server.disable_ctx_shift = True + server.start() + res = server.make_request("POST", "/completion", data={ + "n_predict": 64, + "prompt": LONG_TEXT, + }) + assert res.status_code != 200 + assert "error" in res.body + assert "exceeds the available context size" in res.body["error"]["message"] diff --git a/examples/server/tests/unit/test_embedding.py b/examples/server/tests/unit/test_embedding.py new file mode 100644 index 000000000..fc7c20064 --- /dev/null +++ b/examples/server/tests/unit/test_embedding.py @@ -0,0 +1,99 @@ +import pytest +from openai import OpenAI +from utils import * + +server = ServerPreset.bert_bge_small() + +EPSILON = 1e-3 + +@pytest.fixture(scope="module", autouse=True) +def create_server(): + global server + server = ServerPreset.bert_bge_small() + + +def test_embedding_single(): + global server + server.start() + res = server.make_request("POST", "/embeddings", data={ + "input": "I believe the meaning of life is", + }) + assert res.status_code == 200 + assert len(res.body['data']) == 1 + assert 'embedding' in res.body['data'][0] + assert len(res.body['data'][0]['embedding']) > 1 + + # make sure embedding vector is normalized + assert abs(sum([x ** 2 for x in res.body['data'][0]['embedding']]) - 1) < EPSILON + + +def test_embedding_multiple(): + global server + server.start() + res = server.make_request("POST", "/embeddings", data={ + "input": [ + "I believe the meaning of life is", + "Write a joke about AI from a very long prompt which will not be truncated", + "This is a test", + "This is another test", + ], + }) + assert res.status_code == 200 + assert len(res.body['data']) == 4 + for d in res.body['data']: + assert 'embedding' in d + assert len(d['embedding']) > 1 + + +def test_embedding_openai_library_single(): + global server + server.start() + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}") + res = client.embeddings.create(model="text-embedding-3-small", input="I believe the meaning of life is") + assert len(res.data) == 1 + assert len(res.data[0].embedding) > 1 + + +def test_embedding_openai_library_multiple(): + global server + server.start() + client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}") + res = client.embeddings.create(model="text-embedding-3-small", input=[ + "I believe the meaning of life is", + "Write a joke about AI from a very long prompt which will not be truncated", + "This is a test", + "This is another test", + ]) + assert len(res.data) == 4 + for d in res.data: + assert len(d.embedding) > 1 + + +def test_embedding_error_prompt_too_long(): + global server + server.start() + res = server.make_request("POST", "/embeddings", data={ + "input": "This is a test " * 512, + }) + assert res.status_code != 200 + assert "too large" in res.body["error"]["message"] + + +def test_same_prompt_give_same_result(): + server.start() + res = server.make_request("POST", "/embeddings", data={ + "input": [ + "I believe the meaning of life is", + "I believe the meaning of life is", + "I believe the meaning of life is", + "I believe the meaning of life is", + "I believe the meaning of life is", + ], + }) + assert res.status_code == 200 + assert len(res.body['data']) == 5 + for i in range(1, len(res.body['data'])): + v0 = res.body['data'][0]['embedding'] + vi = res.body['data'][i]['embedding'] + for x, y in zip(v0, vi): + assert abs(x - y) < EPSILON diff --git a/examples/server/tests/unit/test_infill.py b/examples/server/tests/unit/test_infill.py new file mode 100644 index 000000000..6a6d40a1c --- /dev/null +++ b/examples/server/tests/unit/test_infill.py @@ -0,0 +1,57 @@ +import pytest +from utils import * + +server = ServerPreset.tinyllama_infill() + +@pytest.fixture(scope="module", autouse=True) +def create_server(): + global server + server = ServerPreset.tinyllama_infill() + + +def test_infill_without_input_extra(): + global server + server.start() + res = server.make_request("POST", "/infill", data={ + "prompt": "Complete this", + "input_prefix": "#include \n#include \"llama.h\"\n\nint main() {\n int n_threads = llama_", + "input_suffix": "}\n", + }) + assert res.status_code == 200 + assert match_regex("(One|day|she|saw|big|scary|bird)+", res.body["content"]) + + +def test_infill_with_input_extra(): + global server + server.start() + res = server.make_request("POST", "/infill", data={ + "prompt": "Complete this", + "input_extra": [{ + "filename": "llama.h", + "text": "LLAMA_API int32_t llama_n_threads();\n" + }], + "input_prefix": "#include \n#include \"llama.h\"\n\nint main() {\n int n_threads = llama_", + "input_suffix": "}\n", + }) + assert res.status_code == 200 + assert match_regex("(cuts|Jimmy|mom|came|into|the|room)+", res.body["content"]) + + +@pytest.mark.parametrize("input_extra", [ + {}, + {"filename": "ok"}, + {"filename": 123}, + {"filename": 123, "text": "abc"}, + {"filename": 123, "text": 456}, +]) +def test_invalid_input_extra_req(input_extra): + global server + server.start() + res = server.make_request("POST", "/infill", data={ + "prompt": "Complete this", + "input_extra": [input_extra], + "input_prefix": "#include \n#include \"llama.h\"\n\nint main() {\n int n_threads = llama_", + "input_suffix": "}\n", + }) + assert res.status_code == 400 + assert "error" in res.body diff --git a/examples/server/tests/unit/test_lora.py b/examples/server/tests/unit/test_lora.py new file mode 100644 index 000000000..749615449 --- /dev/null +++ b/examples/server/tests/unit/test_lora.py @@ -0,0 +1,42 @@ +import pytest +import os +from utils import * + +server = ServerPreset.stories15m_moe() + +LORA_FILE_URL = "https://huggingface.co/ggml-org/stories15M_MOE/resolve/main/moe_shakespeare15M.gguf" + +@pytest.fixture(scope="module", autouse=True) +def create_server(): + global server + server = ServerPreset.stories15m_moe() + # download lora file if needed + file_name = LORA_FILE_URL.split('/').pop() + lora_file = f'../../../{file_name}' + if not os.path.exists(lora_file): + print(f"Downloading {LORA_FILE_URL} to {lora_file}") + with open(lora_file, 'wb') as f: + f.write(requests.get(LORA_FILE_URL).content) + print(f"Done downloading lora file") + server.lora_files = [lora_file] + + +@pytest.mark.parametrize("scale,re_content", [ + # without applying lora, the model should behave like a bedtime story generator + (0.0, "(little|girl|three|years|old)+"), + # with lora, the model should behave like a Shakespearean text generator + (1.0, "(eye|love|glass|sun)+"), +]) +def test_lora(scale: float, re_content: str): + global server + server.start() + res_lora_control = server.make_request("POST", "/lora-adapters", data=[ + {"id": 0, "scale": scale} + ]) + assert res_lora_control.status_code == 200 + res = server.make_request("POST", "/completion", data={ + "prompt": "Look in thy glass", + }) + assert res.status_code == 200 + assert match_regex(re_content, res.body["content"]) + diff --git a/examples/server/tests/unit/test_rerank.py b/examples/server/tests/unit/test_rerank.py new file mode 100644 index 000000000..189bc4c96 --- /dev/null +++ b/examples/server/tests/unit/test_rerank.py @@ -0,0 +1,55 @@ +import pytest +from utils import * + +server = ServerPreset.jina_reranker_tiny() + + +@pytest.fixture(scope="module", autouse=True) +def create_server(): + global server + server = ServerPreset.jina_reranker_tiny() + + +def test_rerank(): + global server + server.start() + res = server.make_request("POST", "/rerank", data={ + "query": "Machine learning is", + "documents": [ + "A machine is a physical system that uses power to apply forces and control movement to perform an action. The term is commonly applied to artificial devices, such as those employing engines or motors, but also to natural biological macromolecules, such as molecular machines.", + "Learning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. The ability to learn is possessed by humans, non-human animals, and some machines; there is also evidence for some kind of learning in certain plants.", + "Machine learning is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions.", + "Paris, capitale de la France, est une grande ville européenne et un centre mondial de l'art, de la mode, de la gastronomie et de la culture. Son paysage urbain du XIXe siècle est traversé par de larges boulevards et la Seine." + ] + }) + assert res.status_code == 200 + assert len(res.body["results"]) == 4 + + most_relevant = res.body["results"][0] + least_relevant = res.body["results"][0] + for doc in res.body["results"]: + if doc["relevance_score"] > most_relevant["relevance_score"]: + most_relevant = doc + if doc["relevance_score"] < least_relevant["relevance_score"]: + least_relevant = doc + + assert most_relevant["relevance_score"] > least_relevant["relevance_score"] + assert most_relevant["index"] == 2 + assert least_relevant["index"] == 3 + + +@pytest.mark.parametrize("documents", [ + [], + None, + 123, + [1, 2, 3], +]) +def test_invalid_rerank_req(documents): + global server + server.start() + res = server.make_request("POST", "/rerank", data={ + "query": "Machine learning is", + "documents": documents, + }) + assert res.status_code == 400 + assert "error" in res.body diff --git a/examples/server/tests/unit/test_security.py b/examples/server/tests/unit/test_security.py new file mode 100644 index 000000000..620b25376 --- /dev/null +++ b/examples/server/tests/unit/test_security.py @@ -0,0 +1,83 @@ +import pytest +from openai import OpenAI +from utils import * + +server = ServerPreset.tinyllama2() + +TEST_API_KEY = "sk-this-is-the-secret-key" + +@pytest.fixture(scope="module", autouse=True) +def create_server(): + global server + server = ServerPreset.tinyllama2() + server.api_key = TEST_API_KEY + + +@pytest.mark.parametrize("endpoint", ["/health", "/models"]) +def test_access_public_endpoint(endpoint: str): + global server + server.start() + res = server.make_request("GET", endpoint) + assert res.status_code == 200 + assert "error" not in res.body + + +@pytest.mark.parametrize("api_key", [None, "invalid-key"]) +def test_incorrect_api_key(api_key: str): + global server + server.start() + res = server.make_request("POST", "/completions", data={ + "prompt": "I believe the meaning of life is", + }, headers={ + "Authorization": f"Bearer {api_key}" if api_key else None, + }) + assert res.status_code == 401 + assert "error" in res.body + assert res.body["error"]["type"] == "authentication_error" + + +def test_correct_api_key(): + global server + server.start() + res = server.make_request("POST", "/completions", data={ + "prompt": "I believe the meaning of life is", + }, headers={ + "Authorization": f"Bearer {TEST_API_KEY}", + }) + assert res.status_code == 200 + assert "error" not in res.body + assert "content" in res.body + + +def test_openai_library_correct_api_key(): + global server + server.start() + client = OpenAI(api_key=TEST_API_KEY, base_url=f"http://{server.server_host}:{server.server_port}") + res = client.chat.completions.create( + model="gpt-3.5-turbo", + messages=[ + {"role": "system", "content": "You are a chatbot."}, + {"role": "user", "content": "What is the meaning of life?"}, + ], + ) + assert len(res.choices) == 1 + + +@pytest.mark.parametrize("origin,cors_header,cors_header_value", [ + ("localhost", "Access-Control-Allow-Origin", "localhost"), + ("web.mydomain.fr", "Access-Control-Allow-Origin", "web.mydomain.fr"), + ("origin", "Access-Control-Allow-Credentials", "true"), + ("web.mydomain.fr", "Access-Control-Allow-Methods", "GET, POST"), + ("web.mydomain.fr", "Access-Control-Allow-Headers", "*"), +]) +def test_cors_options(origin: str, cors_header: str, cors_header_value: str): + global server + server.start() + res = server.make_request("OPTIONS", "/completions", headers={ + "Origin": origin, + "Access-Control-Request-Method": "POST", + "Access-Control-Request-Headers": "Authorization", + }) + assert res.status_code == 200 + assert cors_header in res.headers + assert res.headers[cors_header] == cors_header_value diff --git a/examples/server/tests/unit/test_slot_save.py b/examples/server/tests/unit/test_slot_save.py new file mode 100644 index 000000000..38704f5ec --- /dev/null +++ b/examples/server/tests/unit/test_slot_save.py @@ -0,0 +1,98 @@ +import pytest +from utils import * + +server = ServerPreset.tinyllama2() + +@pytest.fixture(scope="module", autouse=True) +def create_server(): + global server + server = ServerPreset.tinyllama2() + server.slot_save_path = "./tmp" + server.temperature = 0.0 + + +def test_slot_save_restore(): + global server + server.start() + + # First prompt in slot 1 should be fully processed + res = server.make_request("POST", "/completion", data={ + "prompt": "What is the capital of France?", + "id_slot": 1, + "cache_prompt": True, + }) + assert res.status_code == 200 + assert match_regex("(Whiskers|Flana)+", res.body["content"]) + assert res.body["timings"]["prompt_n"] == 21 # all tokens are processed + + # Save state of slot 1 + res = server.make_request("POST", "/slots/1?action=save", data={ + "filename": "slot1.bin", + }) + assert res.status_code == 200 + assert res.body["n_saved"] == 84 + + # Since we have cache, this should only process the last tokens + res = server.make_request("POST", "/completion", data={ + "prompt": "What is the capital of Germany?", + "id_slot": 1, + "cache_prompt": True, + }) + assert res.status_code == 200 + assert match_regex("(Jack|said)+", res.body["content"]) + assert res.body["timings"]["prompt_n"] == 6 # only different part is processed + + # Loading the saved cache into slot 0 + res = server.make_request("POST", "/slots/0?action=restore", data={ + "filename": "slot1.bin", + }) + assert res.status_code == 200 + assert res.body["n_restored"] == 84 + + # Since we have cache, slot 0 should only process the last tokens + res = server.make_request("POST", "/completion", data={ + "prompt": "What is the capital of Germany?", + "id_slot": 0, + "cache_prompt": True, + }) + assert res.status_code == 200 + assert match_regex("(Jack|said)+", res.body["content"]) + assert res.body["timings"]["prompt_n"] == 6 # only different part is processed + + # For verification that slot 1 was not corrupted during slot 0 load, same thing should work + res = server.make_request("POST", "/completion", data={ + "prompt": "What is the capital of Germany?", + "id_slot": 1, + "cache_prompt": True, + }) + assert res.status_code == 200 + assert match_regex("(Jack|said)+", res.body["content"]) + assert res.body["timings"]["prompt_n"] == 1 + + +def test_slot_erase(): + global server + server.start() + + res = server.make_request("POST", "/completion", data={ + "prompt": "What is the capital of France?", + "id_slot": 1, + "cache_prompt": True, + }) + assert res.status_code == 200 + assert match_regex("(Whiskers|Flana)+", res.body["content"]) + assert res.body["timings"]["prompt_n"] == 21 # all tokens are processed + + # erase slot 1 + res = server.make_request("POST", "/slots/1?action=erase") + assert res.status_code == 200 + + # re-run the same prompt, it should process all tokens again + res = server.make_request("POST", "/completion", data={ + "prompt": "What is the capital of France?", + "id_slot": 1, + "cache_prompt": True, + }) + assert res.status_code == 200 + assert match_regex("(Whiskers|Flana)+", res.body["content"]) + assert res.body["timings"]["prompt_n"] == 21 # all tokens are processed diff --git a/examples/server/tests/unit/test_speculative.py b/examples/server/tests/unit/test_speculative.py new file mode 100644 index 000000000..3bb5733cb --- /dev/null +++ b/examples/server/tests/unit/test_speculative.py @@ -0,0 +1,134 @@ +import pytest +from utils import * + +# We use a F16 MOE gguf as main model, and q4_0 as draft model + +server = ServerPreset.stories15m_moe() + +MODEL_DRAFT_FILE_URL = "https://huggingface.co/ggml-org/models/resolve/main/tinyllamas/stories15M-q4_0.gguf" + +def create_server(): + global server + server = ServerPreset.stories15m_moe() + # download draft model file if needed + file_name = MODEL_DRAFT_FILE_URL.split('/').pop() + model_draft_file = f'../../../{file_name}' + if not os.path.exists(model_draft_file): + print(f"Downloading {MODEL_DRAFT_FILE_URL} to {model_draft_file}") + with open(model_draft_file, 'wb') as f: + f.write(requests.get(MODEL_DRAFT_FILE_URL).content) + print(f"Done downloading draft model file") + # set default values + server.model_draft = model_draft_file + server.draft_min = 4 + server.draft_max = 8 + + +@pytest.fixture(scope="module", autouse=True) +def fixture_create_server(): + return create_server() + + +def test_with_and_without_draft(): + global server + server.model_draft = None # disable draft model + server.start() + res = server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "temperature": 0.0, + "top_k": 1, + }) + assert res.status_code == 200 + content_no_draft = res.body["content"] + server.stop() + + # create new server with draft model + create_server() + server.start() + res = server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "temperature": 0.0, + "top_k": 1, + }) + assert res.status_code == 200 + content_draft = res.body["content"] + + assert content_no_draft == content_draft + + +def test_different_draft_min_draft_max(): + global server + test_values = [ + (1, 2), + (1, 4), + (4, 8), + (4, 12), + (8, 16), + ] + last_content = None + for draft_min, draft_max in test_values: + server.stop() + server.draft_min = draft_min + server.draft_max = draft_max + server.start() + res = server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "temperature": 0.0, + "top_k": 1, + }) + assert res.status_code == 200 + if last_content is not None: + assert last_content == res.body["content"] + last_content = res.body["content"] + + +def test_slot_ctx_not_exceeded(): + global server + server.n_ctx = 64 + server.start() + res = server.make_request("POST", "/completion", data={ + "prompt": "Hello " * 56, + "temperature": 0.0, + "top_k": 1, + "speculative.p_min": 0.0, + }) + assert res.status_code == 200 + assert len(res.body["content"]) > 0 + + +def test_with_ctx_shift(): + global server + server.n_ctx = 64 + server.start() + res = server.make_request("POST", "/completion", data={ + "prompt": "Hello " * 56, + "temperature": 0.0, + "top_k": 1, + "n_predict": 64, + "speculative.p_min": 0.0, + }) + assert res.status_code == 200 + assert len(res.body["content"]) > 0 + assert res.body["tokens_predicted"] == 64 + assert res.body["truncated"] == True + + +@pytest.mark.parametrize("n_slots,n_requests", [ + (1, 2), + (2, 2), +]) +def test_multi_requests_parallel(n_slots: int, n_requests: int): + global server + server.n_slots = n_slots + server.start() + tasks = [] + for _ in range(n_requests): + tasks.append((server.make_request, ("POST", "/completion", { + "prompt": "I believe the meaning of life is", + "temperature": 0.0, + "top_k": 1, + }))) + results = parallel_function_calls(tasks) + for res in results: + assert res.status_code == 200 + assert match_regex("(wise|kind|owl|answer)+", res.body["content"]) diff --git a/examples/server/tests/unit/test_tokenize.py b/examples/server/tests/unit/test_tokenize.py new file mode 100644 index 000000000..382457c9d --- /dev/null +++ b/examples/server/tests/unit/test_tokenize.py @@ -0,0 +1,59 @@ +import pytest +from utils import * + +server = ServerPreset.tinyllama2() + + +@pytest.fixture(scope="module", autouse=True) +def create_server(): + global server + server = ServerPreset.tinyllama2() + + +def test_tokenize_detokenize(): + global server + server.start() + # tokenize + content = "What is the capital of France ?" + res_tok = server.make_request("POST", "/tokenize", data={ + "content": content + }) + assert res_tok.status_code == 200 + assert len(res_tok.body["tokens"]) > 5 + # detokenize + res_detok = server.make_request("POST", "/detokenize", data={ + "tokens": res_tok.body["tokens"], + }) + assert res_detok.status_code == 200 + assert res_detok.body["content"].strip() == content + + +def test_tokenize_with_bos(): + global server + server.start() + # tokenize + content = "What is the capital of France ?" + bosId = 1 + res_tok = server.make_request("POST", "/tokenize", data={ + "content": content, + "add_special": True, + }) + assert res_tok.status_code == 200 + assert res_tok.body["tokens"][0] == bosId + + +def test_tokenize_with_pieces(): + global server + server.start() + # tokenize + content = "This is a test string with unicode 媽 and emoji 🤗" + res_tok = server.make_request("POST", "/tokenize", data={ + "content": content, + "with_pieces": True, + }) + assert res_tok.status_code == 200 + for token in res_tok.body["tokens"]: + assert "id" in token + assert token["id"] > 0 + assert "piece" in token + assert len(token["piece"]) > 0 diff --git a/examples/server/tests/utils.py b/examples/server/tests/utils.py new file mode 100644 index 000000000..e17a05ff6 --- /dev/null +++ b/examples/server/tests/utils.py @@ -0,0 +1,371 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +# type: ignore[reportUnusedImport] + +import subprocess +import os +import re +import json +import sys +import requests +import time +from concurrent.futures import ThreadPoolExecutor, as_completed +from typing import ( + Any, + Callable, + ContextManager, + Iterable, + Iterator, + List, + Literal, + Tuple, + Set, +) +from re import RegexFlag + + +class ServerResponse: + headers: dict + status_code: int + body: dict | Any + + +class ServerProcess: + # default options + debug: bool = False + server_port: int = 8080 + server_host: str = "127.0.0.1" + model_hf_repo: str = "ggml-org/models" + model_hf_file: str = "tinyllamas/stories260K.gguf" + model_alias: str = "tinyllama-2" + temperature: float = 0.8 + seed: int = 42 + + # custom options + model_alias: str | None = None + model_url: str | None = None + model_file: str | None = None + model_draft: str | None = None + n_threads: int | None = None + n_gpu_layer: int | None = None + n_batch: int | None = None + n_ubatch: int | None = None + n_ctx: int | None = None + n_ga: int | None = None + n_ga_w: int | None = None + n_predict: int | None = None + n_prompts: int | None = 0 + slot_save_path: str | None = None + id_slot: int | None = None + cache_prompt: bool | None = None + n_slots: int | None = None + server_continuous_batching: bool | None = False + server_embeddings: bool | None = False + server_reranking: bool | None = False + server_metrics: bool | None = False + draft: int | None = None + api_key: str | None = None + response_format: str | None = None + lora_files: List[str] | None = None + disable_ctx_shift: int | None = False + draft_min: int | None = None + draft_max: int | None = None + + # session variables + process: subprocess.Popen | None = None + + def __init__(self): + if "N_GPU_LAYERS" in os.environ: + self.n_gpu_layer = int(os.environ["N_GPU_LAYERS"]) + if "DEBUG" in os.environ: + self.debug = True + if "PORT" in os.environ: + self.server_port = int(os.environ["PORT"]) + + def start(self, timeout_seconds: int = 10) -> None: + if "LLAMA_SERVER_BIN_PATH" in os.environ: + server_path = os.environ["LLAMA_SERVER_BIN_PATH"] + elif os.name == "nt": + server_path = "../../../build/bin/Release/llama-server.exe" + else: + server_path = "../../../build/bin/llama-server" + server_args = [ + "--slots", # requires to get slot status via /slots endpoint + "--host", + self.server_host, + "--port", + self.server_port, + "--temp", + self.temperature, + "--seed", + self.seed, + ] + if self.model_file: + server_args.extend(["--model", self.model_file]) + if self.model_url: + server_args.extend(["--model-url", self.model_url]) + if self.model_draft: + server_args.extend(["--model-draft", self.model_draft]) + if self.model_hf_repo: + server_args.extend(["--hf-repo", self.model_hf_repo]) + if self.model_hf_file: + server_args.extend(["--hf-file", self.model_hf_file]) + if self.n_batch: + server_args.extend(["--batch-size", self.n_batch]) + if self.n_ubatch: + server_args.extend(["--ubatch-size", self.n_ubatch]) + if self.n_threads: + server_args.extend(["--threads", self.n_threads]) + if self.n_gpu_layer: + server_args.extend(["--n-gpu-layers", self.n_gpu_layer]) + if self.draft is not None: + server_args.extend(["--draft", self.draft]) + if self.server_continuous_batching: + server_args.append("--cont-batching") + if self.server_embeddings: + server_args.append("--embedding") + if self.server_reranking: + server_args.append("--reranking") + if self.server_metrics: + server_args.append("--metrics") + if self.model_alias: + server_args.extend(["--alias", self.model_alias]) + if self.n_ctx: + server_args.extend(["--ctx-size", self.n_ctx]) + if self.n_slots: + server_args.extend(["--parallel", self.n_slots]) + if self.n_predict: + server_args.extend(["--n-predict", self.n_predict]) + if self.slot_save_path: + server_args.extend(["--slot-save-path", self.slot_save_path]) + if self.n_ga: + server_args.extend(["--grp-attn-n", self.n_ga]) + if self.n_ga_w: + server_args.extend(["--grp-attn-w", self.n_ga_w]) + if self.debug: + server_args.append("--verbose") + if self.lora_files: + for lora_file in self.lora_files: + server_args.extend(["--lora", lora_file]) + if self.disable_ctx_shift: + server_args.extend(["--no-context-shift"]) + if self.api_key: + server_args.extend(["--api-key", self.api_key]) + if self.draft_max: + server_args.extend(["--draft-max", self.draft_max]) + if self.draft_min: + server_args.extend(["--draft-min", self.draft_min]) + + args = [str(arg) for arg in [server_path, *server_args]] + print(f"bench: starting server with: {' '.join(args)}") + + flags = 0 + if "nt" == os.name: + flags |= subprocess.DETACHED_PROCESS + flags |= subprocess.CREATE_NEW_PROCESS_GROUP + flags |= subprocess.CREATE_NO_WINDOW + + self.process = subprocess.Popen( + [str(arg) for arg in [server_path, *server_args]], + creationflags=flags, + stdout=sys.stdout, + stderr=sys.stdout, + env={**os.environ, "LLAMA_CACHE": "tmp"}, + ) + server_instances.add(self) + + print(f"server pid={self.process.pid}, pytest pid={os.getpid()}") + + # wait for server to start + start_time = time.time() + while time.time() - start_time < timeout_seconds: + try: + response = self.make_request("GET", "/slots", headers={ + "Authorization": f"Bearer {self.api_key}" if self.api_key else None + }) + if response.status_code == 200: + self.ready = True + return # server is ready + except Exception as e: + pass + print(f"Waiting for server to start...") + time.sleep(0.5) + raise TimeoutError(f"Server did not start within {timeout_seconds} seconds") + + def stop(self) -> None: + if self in server_instances: + server_instances.remove(self) + if self.process: + print(f"Stopping server with pid={self.process.pid}") + self.process.kill() + self.process = None + + def make_request( + self, + method: str, + path: str, + data: dict | Any | None = None, + headers: dict | None = None, + ) -> ServerResponse: + url = f"http://{self.server_host}:{self.server_port}{path}" + parse_body = False + if method == "GET": + response = requests.get(url, headers=headers) + parse_body = True + elif method == "POST": + response = requests.post(url, headers=headers, json=data) + parse_body = True + elif method == "OPTIONS": + response = requests.options(url, headers=headers) + else: + raise ValueError(f"Unimplemented method: {method}") + result = ServerResponse() + result.headers = dict(response.headers) + result.status_code = response.status_code + result.body = response.json() if parse_body else None + print("Response from server", result.body) + return result + + def make_stream_request( + self, + method: str, + path: str, + data: dict | None = None, + headers: dict | None = None, + ) -> Iterator[dict]: + url = f"http://{self.server_host}:{self.server_port}{path}" + if method == "POST": + response = requests.post(url, headers=headers, json=data, stream=True) + else: + raise ValueError(f"Unimplemented method: {method}") + for line_bytes in response.iter_lines(): + line = line_bytes.decode("utf-8") + if '[DONE]' in line: + break + elif line.startswith('data: '): + data = json.loads(line[6:]) + print("Partial response from server", data) + yield data + + +server_instances: Set[ServerProcess] = set() + + +class ServerPreset: + @staticmethod + def tinyllama2() -> ServerProcess: + server = ServerProcess() + server.model_hf_repo = "ggml-org/models" + server.model_hf_file = "tinyllamas/stories260K.gguf" + server.model_alias = "tinyllama-2" + server.n_ctx = 256 + server.n_batch = 32 + server.n_slots = 2 + server.n_predict = 64 + server.seed = 42 + return server + + @staticmethod + def bert_bge_small() -> ServerProcess: + server = ServerProcess() + server.model_hf_repo = "ggml-org/models" + server.model_hf_file = "bert-bge-small/ggml-model-f16.gguf" + server.model_alias = "bert-bge-small" + server.n_ctx = 512 + server.n_batch = 128 + server.n_ubatch = 128 + server.n_slots = 2 + server.seed = 42 + server.server_embeddings = True + return server + + @staticmethod + def tinyllama_infill() -> ServerProcess: + server = ServerProcess() + server.model_hf_repo = "ggml-org/models" + server.model_hf_file = "tinyllamas/stories260K-infill.gguf" + server.model_alias = "tinyllama-infill" + server.n_ctx = 2048 + server.n_batch = 1024 + server.n_slots = 1 + server.n_predict = 64 + server.temperature = 0.0 + server.seed = 42 + return server + + @staticmethod + def stories15m_moe() -> ServerProcess: + server = ServerProcess() + server.model_hf_repo = "ggml-org/stories15M_MOE" + server.model_hf_file = "stories15M_MOE-F16.gguf" + server.model_alias = "stories15m-moe" + server.n_ctx = 2048 + server.n_batch = 1024 + server.n_slots = 1 + server.n_predict = 64 + server.temperature = 0.0 + server.seed = 42 + return server + + @staticmethod + def jina_reranker_tiny() -> ServerProcess: + server = ServerProcess() + server.model_hf_repo = "ggml-org/models" + server.model_hf_file = "jina-reranker-v1-tiny-en/ggml-model-f16.gguf" + server.model_alias = "jina-reranker" + server.n_ctx = 512 + server.n_batch = 512 + server.n_slots = 1 + server.seed = 42 + server.server_reranking = True + return server + + +def parallel_function_calls(function_list: List[Tuple[Callable[..., Any], Tuple[Any, ...]]]) -> List[Any]: + """ + Run multiple functions in parallel and return results in the same order as calls. Equivalent to Promise.all in JS. + + Example usage: + + results = parallel_function_calls([ + (func1, (arg1, arg2)), + (func2, (arg3, arg4)), + ]) + """ + results = [None] * len(function_list) + exceptions = [] + + def worker(index, func, args): + try: + result = func(*args) + results[index] = result + except Exception as e: + exceptions.append((index, str(e))) + + with ThreadPoolExecutor() as executor: + futures = [] + for i, (func, args) in enumerate(function_list): + future = executor.submit(worker, i, func, args) + futures.append(future) + + # Wait for all futures to complete + for future in as_completed(futures): + pass + + # Check if there were any exceptions + if exceptions: + print("Exceptions occurred:") + for index, error in exceptions: + print(f"Function at index {index}: {error}") + + return results + + +def match_regex(regex: str, text: str) -> bool: + return ( + re.compile( + regex, flags=RegexFlag.IGNORECASE | RegexFlag.MULTILINE | RegexFlag.DOTALL + ).search(text) + is not None + ) diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index 30c7483a9..ac0843695 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -26,7 +26,6 @@ #define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613" using json = nlohmann::ordered_json; -using llama_tokens = std::vector; #define SLT_INF(slot, fmt, ...) LOG_INF("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__) #define SLT_WRN(slot, fmt, ...) LOG_WRN("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, (slot).id_task, __VA_ARGS__) @@ -429,21 +428,11 @@ static std::string gen_chatcmplid() { // other common utils // -static size_t longest_common_prefix(const std::vector & a, const std::vector & b) { - size_t i; - for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {} - - return i; +static bool ends_with(const std::string & str, const std::string & suffix) { + return str.size() >= suffix.size() && 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix); } -static size_t longest_common_prefix(const std::string & a, const std::string & b) { - size_t i; - for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {} - - return i; -} - -static size_t find_partial_stop_string(const std::string & stop, const std::string & text) { +static size_t find_partial_stop_string(const std::string &stop, const std::string &text) { if (!text.empty() && !stop.empty()) { auto it = std::find(stop.rbegin(), stop.rend(), text.back()); while (it != stop.rend()) { @@ -741,6 +730,10 @@ static json format_final_response_oaicompat(const json & request, const json & r res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array()); } + if (result.contains("timings")) { + res.push_back({"timings", json_value(result, "timings", json::object())}); + } + return res; } @@ -831,6 +824,11 @@ static std::vector format_partial_response_oaicompat(const json & result, {"model", modelname}, {"object", "chat.completion.chunk"} }; + + if (result.contains("timings")) { + ret.push_back({"timings", json_value(result, "timings", json::object())}); + } + if (!finish_reason.empty()) { int num_tokens_predicted = json_value(result, "tokens_predicted", 0); int num_prompt_tokens = json_value(result, "tokens_evaluated", 0); diff --git a/examples/server/webui/index.html b/examples/server/webui/index.html new file mode 100644 index 000000000..c7e18b45e --- /dev/null +++ b/examples/server/webui/index.html @@ -0,0 +1,268 @@ + + + + + + + 🦙 llama.cpp - chat + + + +
+
+ + + +
+ +
+
+

Conversations

+ + + +
+ + +
+ + New conversation +
+
+ {{ conv.messages[0].content }} +
+
+ Conversations are saved to browser's localStorage +
+
+
+ + +
+ +
+ + + +
llama.cpp
+ + +
+
+ + + + +
+ + + +
+
+ + + +
+
    +
  • + +
    • +
  • + +
    • +
+
+
+
+ + +
+
+ + {{ messages.length === 0 ? 'Send a message to start' : '' }} +
+
+
+
+ + + + +
+
+ + +
+ + + + + +
+
+ + +
+
+ + +
+
+
+ + +
+ + + +
+
+ +
+ + + + +
+

Settings

+
+

Settings below are saved in browser's localStorage

+ + + + + +
+ Other sampler settings +
+ + + + +
+
+ +
+ Penalties settings +
+ +
+
+ +
+ Advanced config +
+ +
+
+
+ + +
+ + + +
+
+ + +
+ + + + + + + + diff --git a/examples/server/webui/package-lock.json b/examples/server/webui/package-lock.json new file mode 100644 index 000000000..6b93090f0 --- /dev/null +++ b/examples/server/webui/package-lock.json @@ -0,0 +1,2783 @@ +{ + "name": "webui", + "version": "0.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "webui", + "version": "0.0.0", + "dependencies": { + "autoprefixer": "^10.4.20", + "daisyui": "^4.12.14", + "markdown-it": "^14.1.0", + "postcss": "^8.4.49", + "tailwindcss": "^3.4.15", + "vite-plugin-singlefile": "^2.0.3", + "vue": "^3.5.13" + }, + "devDependencies": { + "vite": "^5.4.10" + } + }, + "node_modules/@alloc/quick-lru": { + "version": "5.2.0", + "resolved": "https://registry.npmjs.org/@alloc/quick-lru/-/quick-lru-5.2.0.tgz", + "integrity": "sha512-UrcABB+4bUrFABwbluTIBErXwvbsU/V7TZWfmbgJfbkwiBuziS9gxdODUyuiecfdGQ85jglMW6juS3+z5TsKLw==", + "license": "MIT", + "engines": { + "node": ">=10" + }, + "funding": { + "url": 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"tailwindcss": "^3.4.15", + "vite-plugin-singlefile": "^2.0.3", + "vue": "^3.5.13" + } +} diff --git a/examples/server/webui/postcss.config.js b/examples/server/webui/postcss.config.js new file mode 100644 index 000000000..2e7af2b7f --- /dev/null +++ b/examples/server/webui/postcss.config.js @@ -0,0 +1,6 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} diff --git a/examples/server/webui/src/completion.js b/examples/server/webui/src/completion.js new file mode 100644 index 000000000..54a0f22f5 --- /dev/null +++ b/examples/server/webui/src/completion.js @@ -0,0 +1,225 @@ +const paramDefaults = { + stream: true, + temperature: 0.2, +}; + +let generation_settings = null; + +export class CompletionError extends Error { + constructor(message, name, data) { + super(message); + this.name = name; + } +}; + +// Completes the prompt as a generator. Recommended for most use cases. +// +// Example: +// +// import { llama } from '/completion.js' +// +// const request = llama("Tell me a joke", {n_predict: 800}) +// for await (const chunk of request) { +// document.write(chunk.data.content) +// } +// +export async function* llama(prompt, params = {}, config = {}) { + let controller = config.controller; + const api_url = config.api_url?.replace(/\/+$/, '') || ""; + + if (!controller) { + controller = new AbortController(); + } + + const completionParams = { ...paramDefaults, ...params, prompt }; + + const response = await fetch(`${api_url}${config.endpoint || '/completion'}`, { + method: 'POST', + body: JSON.stringify(completionParams), + headers: { + 'Connection': 'keep-alive', + 'Content-Type': 'application/json', + 'Accept': 'text/event-stream', + ...(params.api_key ? {'Authorization': `Bearer ${params.api_key}`} : {}) + }, + signal: controller.signal, + }); + + const status = response.status; + if (status !== 200) { + try { + const body = await response.json(); + if (body && body.error && body.error.message) { + throw new CompletionError(body.error.message, 'ServerError'); + } + } catch (err) { + throw new CompletionError(err.message, 'ServerError'); + } + } + + const reader = response.body.getReader(); + const decoder = new TextDecoder(); + + let content = ""; + let leftover = ""; // Buffer for partially read lines + + try { + let cont = true; + + while (cont) { + const result = await reader.read(); + if (result.done) { + break; + } + + // Add any leftover data to the current chunk of data + const text = leftover + decoder.decode(result.value); + + // Check if the last character is a line break + const endsWithLineBreak = text.endsWith('\n'); + + // Split the text into lines + let lines = text.split('\n'); + + // If the text doesn't end with a line break, then the last line is incomplete + // Store it in leftover to be added to the next chunk of data + if (!endsWithLineBreak) { + leftover = lines.pop(); + } else { + leftover = ""; // Reset leftover if we have a line break at the end + } + + // Parse all sse events and add them to result + const regex = /^(\S+):\s(.*)$/gm; + for (const line of lines) { + const match = regex.exec(line); + if (match) { + result[match[1]] = match[2]; + if (result.data === '[DONE]') { + cont = false; + break; + } + + // since we know this is llama.cpp, let's just decode the json in data + if (result.data) { + result.data = JSON.parse(result.data); + content += result.data.content; + + // yield + yield result; + + // if we got a stop token from server, we will break here + if (result.data.stop) { + if (result.data.generation_settings) { + generation_settings = result.data.generation_settings; + } + cont = false; + break; + } + } + if (result.error) { + try { + result.error = JSON.parse(result.error); + if (result.error.message.includes('slot unavailable')) { + // Throw an error to be caught by upstream callers + throw new Error('slot unavailable'); + } else { + console.error(`llama.cpp error [${result.error.code} - ${result.error.type}]: ${result.error.message}`); + } + } catch(e) { + console.error(`llama.cpp error ${result.error}`) + } + } + } + } + } + } catch (e) { + if (e.name !== 'AbortError') { + console.error("llama error: ", e); + } + throw e; + } + finally { + controller.abort(); + } + + return content; +} + +// Call llama, return an event target that you can subscribe to +// +// Example: +// +// import { llamaEventTarget } from '/completion.js' +// +// const conn = llamaEventTarget(prompt) +// conn.addEventListener("message", (chunk) => { +// document.write(chunk.detail.content) +// }) +// +export const llamaEventTarget = (prompt, params = {}, config = {}) => { + const eventTarget = new EventTarget(); + (async () => { + let content = ""; + for await (const chunk of llama(prompt, params, config)) { + if (chunk.data) { + content += chunk.data.content; + eventTarget.dispatchEvent(new CustomEvent("message", { detail: chunk.data })); + } + if (chunk.data.generation_settings) { + eventTarget.dispatchEvent(new CustomEvent("generation_settings", { detail: chunk.data.generation_settings })); + } + if (chunk.data.timings) { + eventTarget.dispatchEvent(new CustomEvent("timings", { detail: chunk.data.timings })); + } + } + eventTarget.dispatchEvent(new CustomEvent("done", { detail: { content } })); + })(); + return eventTarget; +} + +// Call llama, return a promise that resolves to the completed text. This does not support streaming +// +// Example: +// +// llamaPromise(prompt).then((content) => { +// document.write(content) +// }) +// +// or +// +// const content = await llamaPromise(prompt) +// document.write(content) +// +export const llamaPromise = (prompt, params = {}, config = {}) => { + return new Promise(async (resolve, reject) => { + let content = ""; + try { + for await (const chunk of llama(prompt, params, config)) { + content += chunk.data.content; + } + resolve(content); + } catch (error) { + reject(error); + } + }); +}; + +/** + * (deprecated) + */ +export const llamaComplete = async (params, controller, callback) => { + for await (const chunk of llama(params.prompt, params, { controller })) { + callback(chunk); + } +} + +// Get the model info from the server. This is useful for getting the context window and so on. +export const llamaModelInfo = async (config = {}) => { + if (!generation_settings) { + const api_url = config.api_url?.replace(/\/+$/, '') || ""; + const props = await fetch(`${api_url}/props`).then(r => r.json()); + generation_settings = props.default_generation_settings; + } + return generation_settings; +} diff --git a/examples/server/webui/src/main.js b/examples/server/webui/src/main.js new file mode 100644 index 000000000..9b5b12329 --- /dev/null +++ b/examples/server/webui/src/main.js @@ -0,0 +1,456 @@ +import './styles.css'; +import { createApp, defineComponent, shallowRef, computed, h } from 'vue/dist/vue.esm-bundler.js'; +import { llama } from './completion.js'; +import MarkdownIt from 'markdown-it'; + +// utility functions +const isString = (x) => !!x.toLowerCase; +const isNumeric = (n) => !isString(n) && !isNaN(n); +const escapeAttr = (str) => str.replace(/>/g, '>').replace(/"/g, '"'); +const copyStr = (str) => navigator.clipboard.writeText(str); + +// constants +const BASE_URL = localStorage.getItem('base') // for debugging + || (new URL('.', document.baseURI).href).toString(); // for production +const CONFIG_DEFAULT = { + // Note: in order not to introduce breaking changes, please keep the same data type (number, string, etc) if you want to change the default value. Do not use null or undefined for default value. + apiKey: '', + systemMessage: 'You are a helpful assistant.', + // make sure these default values are in sync with `common.h` + samplers: 'dkypmxt', + temperature: 0.8, + dynatemp_range: 0.0, + dynatemp_exponent: 1.0, + top_k: 40, + top_p: 0.95, + min_p: 0.05, + xtc_probability: 0.0, + xtc_threshold: 0.1, + typical_p: 1.0, + repeat_last_n: 64, + repeat_penalty: 1.0, + presence_penalty: 0.0, + frequency_penalty: 0.0, + dry_multiplier: 0.0, + dry_base: 1.75, + dry_allowed_length: 2, + dry_penalty_last_n: -1, + max_tokens: -1, + custom: '', // custom json-stringified object +}; +const CONFIG_INFO = { + apiKey: 'Set the API Key if you are using --api-key option for the server.', + systemMessage: 'The starting message that defines how model should behave.', + samplers: 'The order at which samplers are applied, in simplified way. Default is "dkypmxt": dry->top_k->typ_p->top_p->min_p->xtc->temperature', + temperature: 'Controls the randomness of the generated text by affecting the probability distribution of the output tokens. Higher = more random, lower = more focused.', + dynatemp_range: 'Addon for the temperature sampler. The added value to the range of dynamic temperature, which adjusts probabilities by entropy of tokens.', + dynatemp_exponent: 'Addon for the temperature sampler. Smoothes out the probability redistribution based on the most probable token.', + top_k: 'Keeps only k top tokens.', + top_p: 'Limits tokens to those that together have a cumulative probability of at least p', + min_p: 'Limits tokens based on the minimum probability for a token to be considered, relative to the probability of the most likely token.', + xtc_probability: 'XTC sampler cuts out top tokens; this parameter controls the chance of cutting tokens at all. 0 disables XTC.', + xtc_threshold: 'XTC sampler cuts out top tokens; this parameter controls the token probability that is required to cut that token.', + typical_p: 'Sorts and limits tokens based on the difference between log-probability and entropy.', + repeat_last_n: 'Last n tokens to consider for penalizing repetition', + repeat_penalty: 'Controls the repetition of token sequences in the generated text', + presence_penalty: 'Limits tokens based on whether they appear in the output or not.', + frequency_penalty: 'Limits tokens based on how often they appear in the output.', + dry_multiplier: 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the DRY sampling multiplier.', + dry_base: 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the DRY sampling base value.', + dry_allowed_length: 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the allowed length for DRY sampling.', + dry_penalty_last_n: 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets DRY penalty for the last n tokens.', + max_tokens: 'The maximum number of token per output.', + custom: '', // custom json-stringified object +}; +// config keys having numeric value (i.e. temperature, top_k, top_p, etc) +const CONFIG_NUMERIC_KEYS = Object.entries(CONFIG_DEFAULT).filter(e => isNumeric(e[1])).map(e => e[0]); +// list of themes supported by daisyui +const THEMES = ['light', 'dark', 'cupcake', 'bumblebee', 'emerald', 'corporate', 'synthwave', 'retro', 'cyberpunk', 'valentine', 'halloween', 'garden', 'forest', 'aqua', 'lofi', 'pastel', 'fantasy', 'wireframe', 'black', 'luxury', 'dracula', 'cmyk', 'autumn', 'business', 'acid', 'lemonade', 'night', 'coffee', 'winter', 'dim', 'nord', 'sunset']; + +// markdown support +const VueMarkdown = defineComponent( + (props) => { + const md = shallowRef(new MarkdownIt({ breaks: true })); + const origFenchRenderer = md.value.renderer.rules.fence; + md.value.renderer.rules.fence = (tokens, idx, ...args) => { + const content = tokens[idx].content; + const origRendered = origFenchRenderer(tokens, idx, ...args); + return `
+ + ${origRendered} +
`; + }; + window.copyStr = copyStr; + const content = computed(() => md.value.render(props.source)); + return () => h("div", { innerHTML: content.value }); + }, + { props: ["source"] } +); + +// input field to be used by settings modal +const SettingsModalShortInput = defineComponent({ + template: document.getElementById('settings-modal-short-input').innerHTML, + props: { + label: { type: String, required: false }, + configKey: String, + configDefault: Object, + configInfo: Object, + modelValue: [Object, String, Number], + }, +}); + +// coversations is stored in localStorage +// format: { [convId]: { id: string, lastModified: number, messages: [...] } } +// convId is a string prefixed with 'conv-' +const StorageUtils = { + // manage conversations + getAllConversations() { + const res = []; + for (const key in localStorage) { + if (key.startsWith('conv-')) { + res.push(JSON.parse(localStorage.getItem(key))); + } + } + res.sort((a, b) => b.lastModified - a.lastModified); + return res; + }, + // can return null if convId does not exist + getOneConversation(convId) { + return JSON.parse(localStorage.getItem(convId) || 'null'); + }, + // if convId does not exist, create one + appendMsg(convId, msg) { + if (msg.content === null) return; + const conv = StorageUtils.getOneConversation(convId) || { + id: convId, + lastModified: Date.now(), + messages: [], + }; + conv.messages.push(msg); + conv.lastModified = Date.now(); + localStorage.setItem(convId, JSON.stringify(conv)); + }, + getNewConvId() { + return `conv-${Date.now()}`; + }, + remove(convId) { + localStorage.removeItem(convId); + }, + filterAndKeepMsgs(convId, predicate) { + const conv = StorageUtils.getOneConversation(convId); + if (!conv) return; + conv.messages = conv.messages.filter(predicate); + conv.lastModified = Date.now(); + localStorage.setItem(convId, JSON.stringify(conv)); + }, + popMsg(convId) { + const conv = StorageUtils.getOneConversation(convId); + if (!conv) return; + const msg = conv.messages.pop(); + conv.lastModified = Date.now(); + if (conv.messages.length === 0) { + StorageUtils.remove(convId); + } else { + localStorage.setItem(convId, JSON.stringify(conv)); + } + return msg; + }, + + // manage config + getConfig() { + const savedVal = JSON.parse(localStorage.getItem('config') || '{}'); + // to prevent breaking changes in the future, we always provide default value for missing keys + return { + ...CONFIG_DEFAULT, + ...savedVal, + }; + }, + setConfig(config) { + localStorage.setItem('config', JSON.stringify(config)); + }, + getTheme() { + return localStorage.getItem('theme') || 'auto'; + }, + setTheme(theme) { + if (theme === 'auto') { + localStorage.removeItem('theme'); + } else { + localStorage.setItem('theme', theme); + } + }, +}; + +// scroll to bottom of chat messages +// if requiresNearBottom is true, only auto-scroll if user is near bottom +const chatScrollToBottom = (requiresNearBottom) => { + const msgListElem = document.getElementById('messages-list'); + const spaceToBottom = msgListElem.scrollHeight - msgListElem.scrollTop - msgListElem.clientHeight; + if (!requiresNearBottom || (spaceToBottom < 100)) { + setTimeout(() => msgListElem.scrollTo({ top: msgListElem.scrollHeight }), 1); + } +}; + +const mainApp = createApp({ + components: { + VueMarkdown, + SettingsModalShortInput, + }, + data() { + return { + conversations: StorageUtils.getAllConversations(), + messages: [], // { id: number, role: 'user' | 'assistant', content: string } + viewingConvId: StorageUtils.getNewConvId(), + inputMsg: '', + isGenerating: false, + pendingMsg: null, // the on-going message from assistant + stopGeneration: () => {}, + selectedTheme: StorageUtils.getTheme(), + config: StorageUtils.getConfig(), + showConfigDialog: false, + editingMsg: null, + // const + themes: THEMES, + configDefault: {...CONFIG_DEFAULT}, + configInfo: {...CONFIG_INFO}, + } + }, + computed: {}, + mounted() { + document.getElementById('app').classList.remove('opacity-0'); // show app + // scroll to the bottom when the pending message height is updated + const pendingMsgElem = document.getElementById('pending-msg'); + const resizeObserver = new ResizeObserver(() => { + if (this.isGenerating) chatScrollToBottom(true); + }); + resizeObserver.observe(pendingMsgElem); + }, + methods: { + hideSidebar() { + document.getElementById('toggle-drawer').checked = false; + }, + setSelectedTheme(theme) { + this.selectedTheme = theme; + StorageUtils.setTheme(theme); + }, + newConversation() { + if (this.isGenerating) return; + this.viewingConvId = StorageUtils.getNewConvId(); + this.editingMsg = null; + this.fetchMessages(); + chatScrollToBottom(); + this.hideSidebar(); + }, + setViewingConv(convId) { + if (this.isGenerating) return; + this.viewingConvId = convId; + this.editingMsg = null; + this.fetchMessages(); + chatScrollToBottom(); + this.hideSidebar(); + }, + deleteConv(convId) { + if (this.isGenerating) return; + if (window.confirm('Are you sure to delete this conversation?')) { + StorageUtils.remove(convId); + if (this.viewingConvId === convId) { + this.viewingConvId = StorageUtils.getNewConvId(); + this.editingMsg = null; + } + this.fetchConversation(); + this.fetchMessages(); + } + }, + downloadConv(convId) { + const conversation = StorageUtils.getOneConversation(convId); + if (!conversation) { + alert('Conversation not found.'); + return; + } + const conversationJson = JSON.stringify(conversation, null, 2); + const blob = new Blob([conversationJson], { type: 'application/json' }); + const url = URL.createObjectURL(blob); + const a = document.createElement('a'); + a.href = url; + a.download = `conversation_${convId}.json`; + document.body.appendChild(a); + a.click(); + document.body.removeChild(a); + URL.revokeObjectURL(url); + }, + async sendMessage() { + if (!this.inputMsg) return; + const currConvId = this.viewingConvId; + + StorageUtils.appendMsg(currConvId, { + id: Date.now(), + role: 'user', + content: this.inputMsg, + }); + this.fetchConversation(); + this.fetchMessages(); + this.inputMsg = ''; + this.editingMsg = null; + this.generateMessage(currConvId); + chatScrollToBottom(); + }, + async generateMessage(currConvId) { + if (this.isGenerating) return; + this.pendingMsg = { id: Date.now()+1, role: 'assistant', content: null }; + this.isGenerating = true; + this.editingMsg = null; + + try { + const abortController = new AbortController(); + this.stopGeneration = () => abortController.abort(); + const params = { + messages: [ + { role: 'system', content: this.config.systemMessage }, + ...this.messages, + ], + stream: true, + cache_prompt: true, + samplers: this.config.samplers, + temperature: this.config.temperature, + dynatemp_range: this.config.dynatemp_range, + dynatemp_exponent: this.config.dynatemp_exponent, + top_k: this.config.top_k, + top_p: this.config.top_p, + min_p: this.config.min_p, + typical_p: this.config.typical_p, + xtc_probability: this.config.xtc_probability, + xtc_threshold: this.config.xtc_threshold, + repeat_last_n: this.config.repeat_last_n, + repeat_penalty: this.config.repeat_penalty, + presence_penalty: this.config.presence_penalty, + frequency_penalty: this.config.frequency_penalty, + dry_multiplier: this.config.dry_multiplier, + dry_base: this.config.dry_base, + dry_allowed_length: this.config.dry_allowed_length, + dry_penalty_last_n: this.config.dry_penalty_last_n, + max_tokens: this.config.max_tokens, + ...(this.config.custom.length ? JSON.parse(this.config.custom) : {}), + ...(this.config.apiKey ? { api_key: this.config.apiKey } : {}), + }; + const config = { + controller: abortController, + api_url: BASE_URL, + endpoint: '/chat/completions', + }; + for await (const chunk of llama(prompt, params, config)) { + const stop = chunk.data.stop; + const addedContent = chunk.data.choices[0].delta.content; + const lastContent = this.pendingMsg.content || ''; + if (addedContent) { + this.pendingMsg = { + id: this.pendingMsg.id, + role: 'assistant', + content: lastContent + addedContent, + }; + } + } + + StorageUtils.appendMsg(currConvId, this.pendingMsg); + this.fetchConversation(); + this.fetchMessages(); + setTimeout(() => document.getElementById('msg-input').focus(), 1); + } catch (error) { + if (error.name === 'AbortError') { + // user stopped the generation via stopGeneration() function + StorageUtils.appendMsg(currConvId, this.pendingMsg); + this.fetchConversation(); + this.fetchMessages(); + } else { + console.error(error); + alert(error); + // pop last user message + const lastUserMsg = StorageUtils.popMsg(currConvId); + this.inputMsg = lastUserMsg ? lastUserMsg.content : ''; + } + } + + this.pendingMsg = null; + this.isGenerating = false; + this.stopGeneration = () => {}; + this.fetchMessages(); + chatScrollToBottom(); + }, + + // message actions + regenerateMsg(msg) { + if (this.isGenerating) return; + // TODO: somehow keep old history (like how ChatGPT has different "tree"). This can be done by adding "sub-conversations" with "subconv-" prefix, and new message will have a list of subconvIds + const currConvId = this.viewingConvId; + StorageUtils.filterAndKeepMsgs(currConvId, (m) => m.id < msg.id); + this.fetchConversation(); + this.fetchMessages(); + this.generateMessage(currConvId); + }, + copyMsg(msg) { + copyStr(msg.content); + }, + editUserMsgAndRegenerate(msg) { + if (this.isGenerating) return; + const currConvId = this.viewingConvId; + const newContent = msg.content; + this.editingMsg = null; + StorageUtils.filterAndKeepMsgs(currConvId, (m) => m.id < msg.id); + StorageUtils.appendMsg(currConvId, { + id: Date.now(), + role: 'user', + content: newContent, + }); + this.fetchConversation(); + this.fetchMessages(); + this.generateMessage(currConvId); + }, + + // settings dialog methods + closeAndSaveConfigDialog() { + try { + if (this.config.custom.length) JSON.parse(this.config.custom); + } catch (error) { + alert('Invalid JSON for custom config. Please either fix it or leave it empty.'); + return; + } + for (const key of CONFIG_NUMERIC_KEYS) { + if (isNaN(this.config[key]) || this.config[key].toString().trim().length === 0) { + alert(`Invalid number for ${key} (expected an integer or a float)`); + return; + } + this.config[key] = parseFloat(this.config[key]); + } + this.showConfigDialog = false; + StorageUtils.setConfig(this.config); + }, + closeAndDiscardConfigDialog() { + this.showConfigDialog = false; + this.config = StorageUtils.getConfig(); + }, + resetConfigDialog() { + if (window.confirm('Are you sure to reset all settings?')) { + this.config = {...CONFIG_DEFAULT}; + } + }, + + // sync state functions + fetchConversation() { + this.conversations = StorageUtils.getAllConversations(); + }, + fetchMessages() { + this.messages = StorageUtils.getOneConversation(this.viewingConvId)?.messages ?? []; + }, + }, +}); +mainApp.config.errorHandler = alert; +try { + mainApp.mount('#app'); +} catch (err) { + console.error(err); + document.getElementById('app').innerHTML = `
+ Failed to start app. Please try clearing localStorage and try again.
+
+ +
`; +} diff --git a/examples/server/webui/src/styles.css b/examples/server/webui/src/styles.css new file mode 100644 index 000000000..67d35b99e --- /dev/null +++ b/examples/server/webui/src/styles.css @@ -0,0 +1,26 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; + +.markdown { + h1, h2, h3, h4, h5, h6, ul, ol, li { all: revert; } + pre { + @apply whitespace-pre-wrap rounded-lg p-2; + border: 1px solid currentColor; + } + /* TODO: fix markdown table */ +} + +.show-on-hover { + @apply md:opacity-0 md:group-hover:opacity-100; +} +.btn-mini { + @apply cursor-pointer hover:shadow-md; +} +.chat-screen { max-width: 900px; } + +.chat-bubble-base-300 { + --tw-bg-opacity: 1; + --tw-text-opacity: 1; + @apply bg-base-300 text-base-content; +} diff --git a/examples/server/webui/tailwind.config.js b/examples/server/webui/tailwind.config.js new file mode 100644 index 000000000..c43066a19 --- /dev/null +++ b/examples/server/webui/tailwind.config.js @@ -0,0 +1,16 @@ +/** @type {import('tailwindcss').Config} */ +export default { + content: [ + "./index.html", + "./src/**/*.{js,ts,jsx,tsx}", + ], + theme: { + extend: {}, + }, + plugins: [ + require('daisyui'), + ], + daisyui: { + themes: ['light', 'dark', 'cupcake', 'bumblebee', 'emerald', 'corporate', 'synthwave', 'retro', 'cyberpunk', 'valentine', 'halloween', 'garden', 'forest', 'aqua', 'lofi', 'pastel', 'fantasy', 'wireframe', 'black', 'luxury', 'dracula', 'cmyk', 'autumn', 'business', 'acid', 'lemonade', 'night', 'coffee', 'winter', 'dim', 'nord', 'sunset'], + } +} diff --git a/examples/server/webui/vite.config.js b/examples/server/webui/vite.config.js new file mode 100644 index 000000000..789bf9cbb --- /dev/null +++ b/examples/server/webui/vite.config.js @@ -0,0 +1,36 @@ + +import { viteSingleFile } from 'vite-plugin-singlefile'; +import path from 'path'; +import fs from 'fs'; + +const GUIDE_FOR_FRONTEND = ` + +`.trim(); + +export default { + plugins: [ + viteSingleFile(), + (function llamaCppPlugin() { + let config; + return { + name: 'llamacpp:build', + apply: 'build', + async configResolved(_config) { + config = _config; + }, + writeBundle() { + const outputIndexHtml = path.join(config.build.outDir, 'index.html'); + const content = fs.readFileSync(outputIndexHtml, 'utf-8'); + + const targetOutputFile = path.join(config.build.outDir, '../../public/index.html'); + fs.writeFileSync(targetOutputFile, GUIDE_FOR_FRONTEND + '\n' + content); + } + } + })(), + ], +}; diff --git a/examples/simple-chat/CMakeLists.txt b/examples/simple-chat/CMakeLists.txt new file mode 100644 index 000000000..567f7fbbb --- /dev/null +++ b/examples/simple-chat/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET llama-simple-chat) +add_executable(${TARGET} simple-chat.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/simple-chat/README.md b/examples/simple-chat/README.md new file mode 100644 index 000000000..f0099ce3d --- /dev/null +++ b/examples/simple-chat/README.md @@ -0,0 +1,7 @@ +# llama.cpp/example/simple-chat + +The purpose of this example is to demonstrate a minimal usage of llama.cpp to create a simple chat program using the chat template from the GGUF file. + +```bash +./llama-simple-chat -m Meta-Llama-3.1-8B-Instruct.gguf -c 2048 +... diff --git a/examples/simple-chat/simple-chat.cpp b/examples/simple-chat/simple-chat.cpp new file mode 100644 index 000000000..7f4da666b --- /dev/null +++ b/examples/simple-chat/simple-chat.cpp @@ -0,0 +1,200 @@ +#include "llama.h" +#include +#include +#include +#include +#include + +static void print_usage(int, char ** argv) { + printf("\nexample usage:\n"); + printf("\n %s -m model.gguf [-c context_size] [-ngl n_gpu_layers]\n", argv[0]); + printf("\n"); +} + +int main(int argc, char ** argv) { + std::string model_path; + int ngl = 99; + int n_ctx = 2048; + + // parse command line arguments + for (int i = 1; i < argc; i++) { + try { + if (strcmp(argv[i], "-m") == 0) { + if (i + 1 < argc) { + model_path = argv[++i]; + } else { + print_usage(argc, argv); + return 1; + } + } else if (strcmp(argv[i], "-c") == 0) { + if (i + 1 < argc) { + n_ctx = std::stoi(argv[++i]); + } else { + print_usage(argc, argv); + return 1; + } + } else if (strcmp(argv[i], "-ngl") == 0) { + if (i + 1 < argc) { + ngl = std::stoi(argv[++i]); + } else { + print_usage(argc, argv); + return 1; + } + } else { + print_usage(argc, argv); + return 1; + } + } catch (std::exception & e) { + fprintf(stderr, "error: %s\n", e.what()); + print_usage(argc, argv); + return 1; + } + } + if (model_path.empty()) { + print_usage(argc, argv); + return 1; + } + + // only print errors + llama_log_set([](enum ggml_log_level level, const char * text, void * /* user_data */) { + if (level >= GGML_LOG_LEVEL_ERROR) { + fprintf(stderr, "%s", text); + } + }, nullptr); + + // load dynamic backends + ggml_backend_load_all(); + + // initialize the model + llama_model_params model_params = llama_model_default_params(); + model_params.n_gpu_layers = ngl; + + llama_model * model = llama_load_model_from_file(model_path.c_str(), model_params); + if (!model) { + fprintf(stderr , "%s: error: unable to load model\n" , __func__); + return 1; + } + + // initialize the context + llama_context_params ctx_params = llama_context_default_params(); + ctx_params.n_ctx = n_ctx; + ctx_params.n_batch = n_ctx; + + llama_context * ctx = llama_new_context_with_model(model, ctx_params); + if (!ctx) { + fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__); + return 1; + } + + // initialize the sampler + llama_sampler * smpl = llama_sampler_chain_init(llama_sampler_chain_default_params()); + llama_sampler_chain_add(smpl, llama_sampler_init_min_p(0.05f, 1)); + llama_sampler_chain_add(smpl, llama_sampler_init_temp(0.8f)); + llama_sampler_chain_add(smpl, llama_sampler_init_dist(LLAMA_DEFAULT_SEED)); + + // helper function to evaluate a prompt and generate a response + auto generate = [&](const std::string & prompt) { + std::string response; + + // tokenize the prompt + const int n_prompt_tokens = -llama_tokenize(model, prompt.c_str(), prompt.size(), NULL, 0, true, true); + std::vector prompt_tokens(n_prompt_tokens); + if (llama_tokenize(model, prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), llama_get_kv_cache_used_cells(ctx) == 0, true) < 0) { + GGML_ABORT("failed to tokenize the prompt\n"); + } + + // prepare a batch for the prompt + llama_batch batch = llama_batch_get_one(prompt_tokens.data(), prompt_tokens.size()); + llama_token new_token_id; + while (true) { + // check if we have enough space in the context to evaluate this batch + int n_ctx = llama_n_ctx(ctx); + int n_ctx_used = llama_get_kv_cache_used_cells(ctx); + if (n_ctx_used + batch.n_tokens > n_ctx) { + printf("\033[0m\n"); + fprintf(stderr, "context size exceeded\n"); + exit(0); + } + + if (llama_decode(ctx, batch)) { + GGML_ABORT("failed to decode\n"); + } + + // sample the next token + new_token_id = llama_sampler_sample(smpl, ctx, -1); + + // is it an end of generation? + if (llama_token_is_eog(model, new_token_id)) { + break; + } + + // convert the token to a string, print it and add it to the response + char buf[256]; + int n = llama_token_to_piece(model, new_token_id, buf, sizeof(buf), 0, true); + if (n < 0) { + GGML_ABORT("failed to convert token to piece\n"); + } + std::string piece(buf, n); + printf("%s", piece.c_str()); + fflush(stdout); + response += piece; + + // prepare the next batch with the sampled token + batch = llama_batch_get_one(&new_token_id, 1); + } + + return response; + }; + + std::vector messages; + std::vector formatted(llama_n_ctx(ctx)); + int prev_len = 0; + while (true) { + // get user input + printf("\033[32m> \033[0m"); + std::string user; + std::getline(std::cin, user); + + if (user.empty()) { + break; + } + + // add the user input to the message list and format it + messages.push_back({"user", strdup(user.c_str())}); + int new_len = llama_chat_apply_template(model, nullptr, messages.data(), messages.size(), true, formatted.data(), formatted.size()); + if (new_len > (int)formatted.size()) { + formatted.resize(new_len); + new_len = llama_chat_apply_template(model, nullptr, messages.data(), messages.size(), true, formatted.data(), formatted.size()); + } + if (new_len < 0) { + fprintf(stderr, "failed to apply the chat template\n"); + return 1; + } + + // remove previous messages to obtain the prompt to generate the response + std::string prompt(formatted.begin() + prev_len, formatted.begin() + new_len); + + // generate a response + printf("\033[33m"); + std::string response = generate(prompt); + printf("\n\033[0m"); + + // add the response to the messages + messages.push_back({"assistant", strdup(response.c_str())}); + prev_len = llama_chat_apply_template(model, nullptr, messages.data(), messages.size(), false, nullptr, 0); + if (prev_len < 0) { + fprintf(stderr, "failed to apply the chat template\n"); + return 1; + } + } + + // free resources + for (auto & msg : messages) { + free(const_cast(msg.content)); + } + llama_sampler_free(smpl); + llama_free(ctx); + llama_free_model(model); + + return 0; +} diff --git a/examples/simple/CMakeLists.txt b/examples/simple/CMakeLists.txt index b63afbb8b..104ecabfd 100644 --- a/examples/simple/CMakeLists.txt +++ b/examples/simple/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-simple) add_executable(${TARGET} simple.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/simple/README.md b/examples/simple/README.md index 0ff342535..937008b24 100644 --- a/examples/simple/README.md +++ b/examples/simple/README.md @@ -3,7 +3,7 @@ The purpose of this example is to demonstrate a minimal usage of llama.cpp for generating text with a given prompt. ```bash -./llama-simple -m ./models/llama-7b-v2/ggml-model-f16.gguf -p "Hello my name is" +./llama-simple -m ./models/llama-7b-v2/ggml-model-f16.gguf "Hello my name is" ... diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp index 59760fe95..3288c0250 100644 --- a/examples/simple/simple.cpp +++ b/examples/simple/simple.cpp @@ -74,6 +74,10 @@ int main(int argc, char ** argv) { } } + // load dynamic backends + + ggml_backend_load_all(); + // initialize the model llama_model_params model_params = llama_model_default_params(); diff --git a/examples/speculative-simple/CMakeLists.txt b/examples/speculative-simple/CMakeLists.txt new file mode 100644 index 000000000..aeaea74fc --- /dev/null +++ b/examples/speculative-simple/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET llama-speculative-simple) +add_executable(${TARGET} speculative-simple.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/speculative-simple/README.md b/examples/speculative-simple/README.md new file mode 100644 index 000000000..e3a6c6b4a --- /dev/null +++ b/examples/speculative-simple/README.md @@ -0,0 +1,12 @@ +# llama.cpp/examples/speculative-simple + +Demonstration of basic greedy speculative decoding + +```bash +./bin/llama-speculative-simple \ + -m ../models/qwen2.5-32b-coder-instruct/ggml-model-q8_0.gguf \ + -md ../models/qwen2.5-1.5b-coder-instruct/ggml-model-q4_0.gguf \ + -f test.txt -c 0 -ngl 99 --color \ + --sampling-seq k --top-k 1 -fa --temp 0.0 \ + -ngld 99 --draft-max 16 --draft-min 5 --draft-p-min 0.9 +``` diff --git a/examples/speculative-simple/speculative-simple.cpp b/examples/speculative-simple/speculative-simple.cpp new file mode 100644 index 000000000..8ca84f7af --- /dev/null +++ b/examples/speculative-simple/speculative-simple.cpp @@ -0,0 +1,265 @@ +#include "arg.h" +#include "common.h" +#include "sampling.h" +#include "speculative.h" +#include "log.h" +#include "llama.h" + +#include +#include +#include +#include + +int main(int argc, char ** argv) { + common_params params; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_SPECULATIVE)) { + return 1; + } + + if (params.n_predict < -1) { + LOG_ERR("%s: --n-predict must be >= -1\n", __func__); + return 1; + } + + common_init(); + + if (params.speculative.model.empty()) { + LOG_ERR("%s: --model-draft is required\n", __func__); + return 1; + } + + // init llama.cpp + llama_backend_init(); + llama_numa_init(params.numa); + + llama_model * model_tgt = NULL; + llama_model * model_dft = NULL; + + llama_context * ctx_tgt = NULL; + llama_context * ctx_dft = NULL; + + // load the target model + common_init_result llama_init_tgt = common_init_from_params(params); + + model_tgt = llama_init_tgt.model; + ctx_tgt = llama_init_tgt.context; + + // load the draft model + params.devices = params.speculative.devices; + params.model = params.speculative.model; + params.n_ctx = params.speculative.n_ctx; + params.n_batch = params.speculative.n_ctx > 0 ? params.speculative.n_ctx : params.n_batch; + params.n_gpu_layers = params.speculative.n_gpu_layers; + + if (params.speculative.cpuparams.n_threads > 0) { + params.cpuparams.n_threads = params.speculative.cpuparams.n_threads; + } + + params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads; + common_init_result llama_init_dft = common_init_from_params(params); + + model_dft = llama_init_dft.model; + ctx_dft = llama_init_dft.context; + + if (!common_speculative_are_compatible(ctx_tgt, ctx_dft)) { + return 1; + } + + // Tokenize the prompt + std::vector inp; + inp = common_tokenize(ctx_tgt, params.prompt, true, true); + + if (llama_n_ctx(ctx_tgt) < (uint32_t) inp.size()) { + LOG_ERR("%s: the prompt exceeds the context size (%d tokens, ctx %d)\n", __func__, (int) inp.size(), llama_n_ctx(ctx_tgt)); + + return 1; + } + + if (llama_n_batch(ctx_tgt) < (uint32_t) inp.size()) { + LOG_ERR("%s: the prompt exceeds the batch size (%d tokens, batch %d)\n", __func__, (int) inp.size(), llama_n_batch(ctx_tgt)); + + return 1; + } + + LOG("\n\n"); + + for (auto id : inp) { + LOG("%s", common_token_to_piece(ctx_tgt, id).c_str()); + } + + // how many tokens to draft each time + int n_draft = params.speculative.n_max; + int n_draft_min = params.speculative.n_min; + + float p_min = params.speculative.p_min; + + int n_predict = 0; + int n_drafted = 0; + int n_accept = 0; + + // used to determine end of generation + bool has_eos = false; + + // ================================================ + // everything until here is standard initialization + // the relevant stuff for speculative decoding starts here + + const auto t_enc_start = ggml_time_us(); + + // target model sampling context + struct common_sampler * smpl = common_sampler_init(model_tgt, params.sampling); + + // eval the prompt + llama_decode(ctx_tgt, llama_batch_get_one(inp.data(), inp.size() - 1)); + + // note: keep the last token separate! + llama_token id_last = inp.back(); + + // all tokens currently in the target context + llama_tokens prompt_tgt(inp.begin(), inp.end() - 1); + prompt_tgt.reserve(llama_n_ctx(ctx_tgt)); + + int n_past = inp.size() - 1; + + // init the speculator + struct common_speculative_params params_spec; + params_spec.n_draft = n_draft; + params_spec.n_reuse = llama_n_ctx(ctx_dft) - n_draft; + params_spec.p_min = p_min; + + struct common_speculative * spec = common_speculative_init(ctx_dft); + + llama_batch batch_tgt = llama_batch_init(llama_n_batch(ctx_tgt), 0, 1); + + const auto t_enc_end = ggml_time_us(); + + const auto t_dec_start = ggml_time_us(); + + while (true) { + // optionally, generate draft tokens that can be appended to the target batch + // + // this is the most important part of the speculation. the more probable tokens that are provided here + // the better the performance will be. in theory, this computation can be performed asynchronously and even + // offloaded to a remote device. it doesn't even have to be based on an LLM. instead, it can provide tokens + // from a cache or lookup tables. + // + llama_tokens draft = common_speculative_gen_draft(spec, params_spec, prompt_tgt, id_last); + + //LOG_DBG("draft: %s\n", string_from(ctx_dft, draft).c_str()); + + // always have a token to evaluate from before - id_last + common_batch_clear(batch_tgt); + common_batch_add (batch_tgt, id_last, n_past++, { 0 }, true); + + // evaluate the target model on [id_last, draft0, draft1, ..., draftN-1] + { + // do not waste time on small drafts + if (draft.size() < (size_t) n_draft_min) { + draft.clear(); + } + + for (size_t i = 0; i < draft.size(); ++i) { + common_batch_add(batch_tgt, draft[i], n_past + i, { 0 }, true); + } + + //LOG_DBG("target batch: %s\n", string_from(ctx_tgt, batch_tgt).c_str()); + + llama_decode(ctx_tgt, batch_tgt); + } + + // sample from the full target batch and return the accepted tokens based on the target sampler + // + // for each token to be accepted, the sampler would have to sample that same token + // in such cases, instead of decoding the sampled token as we normally do, we simply continue with the + // available logits from the batch and sample the next token until we run out of logits or the sampler + // disagrees with the draft + // + const auto ids = common_sampler_sample_and_accept_n(smpl, ctx_tgt, draft); + + //LOG_DBG("ids: %s\n", string_from(ctx_tgt, ids).c_str()); + + GGML_ASSERT(ids.size() > 0); // there will always be at least one accepted token + + n_past += ids.size() - 1; + n_drafted += draft.size(); // note: we ignore the discarded small drafts + n_accept += ids.size() - 1; + n_predict += ids.size(); + + // process the accepted tokens and update contexts + // + // this is the standard token post-processing that we normally do + // in this case, we do it for a group of accepted tokens at once + // + for (size_t i = 0; i < ids.size(); ++i) { + prompt_tgt.push_back(id_last); + + id_last = ids[i]; + + if (llama_token_is_eog(model_tgt, id_last)) { + has_eos = true; + break; + } + + const std::string token_str = common_token_to_piece(ctx_tgt, id_last); + + if (params.use_color && i + 1 < ids.size()) { + LOG("\u001b[%dm%s\u001b[37m", (36 - 0 % 6), token_str.c_str()); + } else { + LOG("%s", token_str.c_str()); + } + } + + LOG_DBG("accepted %d/%d draft tokens, the last target token is: (%d)\n", (int) ids.size() - 1, (int) draft.size(), id_last); + + { + LOG_DBG("clear kv cache from any extra tokens, n_past = %d\n", n_past); + + llama_kv_cache_seq_rm(ctx_tgt, 0, n_past, -1); + } + + if ((params.n_predict >= 0 && n_predict > params.n_predict) || has_eos) { + break; + } + } + + auto t_dec_end = ggml_time_us(); + + const int n_input = inp.size(); + + LOG("\n\n"); + + LOG_INF("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input, (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f)); + LOG_INF("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f)); + + LOG_INF("\n"); + LOG_INF("n_draft = %d\n", n_draft); + LOG_INF("n_predict = %d\n", n_predict); + LOG_INF("n_drafted = %d\n", n_drafted); + LOG_INF("n_accept = %d\n", n_accept); + LOG_INF("accept = %.3f%%\n", 100.0f * n_accept / n_drafted); + + LOG_INF("\n"); + LOG_INF("draft:\n\n"); + + llama_perf_context_print(ctx_dft); + + LOG_INF("\n"); + LOG_INF("target:\n\n"); + common_perf_print(ctx_tgt, smpl); + + common_sampler_free(smpl); + common_speculative_free(spec); + + llama_free(ctx_tgt); + llama_free_model(model_tgt); + + llama_free(ctx_dft); + llama_free_model(model_dft); + + llama_backend_free(); + + LOG("\n\n"); + + return 0; +} diff --git a/examples/speculative/CMakeLists.txt b/examples/speculative/CMakeLists.txt index aa208e7aa..c84196bd9 100644 --- a/examples/speculative/CMakeLists.txt +++ b/examples/speculative/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-speculative) add_executable(${TARGET} speculative.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index a40e755a2..d4ad9751e 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -12,7 +12,7 @@ #include #include -#define SPEC_VOCAB_MAX_SIZE_DIFFERENCE 100 +#define SPEC_VOCAB_MAX_SIZE_DIFFERENCE 128 #define SPEC_VOCAB_CHECK_START_TOKEN_ID 5 struct seq_draft { @@ -33,7 +33,7 @@ int main(int argc, char ** argv) { common_params params; // needed to get candidate probs even for temp <= 0.0 - params.sparams.n_probs = 128; + params.sampling.n_probs = 128; if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_SPECULATIVE)) { return 1; @@ -46,7 +46,7 @@ int main(int argc, char ** argv) { common_init(); - if (params.model_draft.empty()) { + if (params.speculative.model.empty()) { LOG_ERR("%s: --model-draft is required\n", __func__); return 1; } @@ -55,9 +55,9 @@ int main(int argc, char ** argv) { const int n_seq_dft = params.n_parallel; // probability threshold for splitting a draft branch (only for n_seq_dft > 1) - const float p_split = params.p_split; + const float p_draft_split = params.speculative.p_split; - std::default_random_engine rng(params.sparams.seed == LLAMA_DEFAULT_SEED ? std::random_device()() : params.sparams.seed); + std::default_random_engine rng(params.sampling.seed == LLAMA_DEFAULT_SEED ? std::random_device()() : params.sampling.seed); std::uniform_real_distribution<> u_dist; // init llama.cpp @@ -76,13 +76,14 @@ int main(int argc, char ** argv) { ctx_tgt = llama_init_tgt.context; // load the draft model - params.model = params.model_draft; - params.n_gpu_layers = params.n_gpu_layers_draft; - if (params.draft_cpuparams.n_threads > 0) { - params.cpuparams.n_threads = params.draft_cpuparams.n_threads; + params.devices = params.speculative.devices; + params.model = params.speculative.model; + params.n_gpu_layers = params.speculative.n_gpu_layers; + if (params.speculative.cpuparams.n_threads > 0) { + params.cpuparams.n_threads = params.speculative.cpuparams.n_threads; } - params.cpuparams_batch.n_threads = params.draft_cpuparams_batch.n_threads; + params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads; common_init_result llama_init_dft = common_init_from_params(params); model_dft = llama_init_dft.model; ctx_dft = llama_init_dft.context; @@ -170,7 +171,7 @@ int main(int argc, char ** argv) { //GGML_ASSERT(n_vocab == llama_n_vocab(model_dft)); // how many tokens to draft each time - int n_draft = params.n_draft; + int n_draft = params.speculative.n_max; int n_predict = 0; int n_drafted = 0; @@ -183,14 +184,14 @@ int main(int argc, char ** argv) { bool has_eos = false; // target model sampling context (reuse the llama_context's sampling instance) - struct common_sampler * smpl = common_sampler_init(model_tgt, params.sparams); + struct common_sampler * smpl = common_sampler_init(model_tgt, params.sampling); // draft sequence data std::vector drafts(n_seq_dft); for (int s = 0; s < n_seq_dft; ++s) { // allocate llama_sampler for each draft sequence - drafts[s].smpl = common_sampler_init(model_dft, params.sparams); + drafts[s].smpl = common_sampler_init(model_dft, params.sampling); } llama_batch batch_dft = llama_batch_init(llama_n_batch(ctx_dft), 0, 1); @@ -230,7 +231,7 @@ int main(int argc, char ** argv) { // for stochastic sampling, attempt to match the token with the drafted tokens { bool accept = false; - if (params.sparams.temp > 0) { + if (params.sampling.temp > 0) { // stochastic verification common_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft], true); @@ -267,11 +268,12 @@ int main(int argc, char ** argv) { for (size_t i = 0; i < dist_tgt.size; i++) { if (dist_tgt.data[i].id == drafts[s].tokens[i_dft]) { p_tgt = dist_tgt.data[i].p; + break; } + } + for (size_t i = 0; i < dist_dft.size; i++) { if (dist_dft.data[i].id == drafts[s].tokens[i_dft]) { p_dft = dist_dft.data[i].p; - } - if (p_tgt && p_dft) { break; } } @@ -493,7 +495,7 @@ int main(int argc, char ** argv) { // attempt to split the branch if the probability is high enough for (int f = 1; f < 8; ++f) { - if (n_seq_cur < n_seq_dft && cur_p->data[f].p > p_split) { + if (n_seq_cur < n_seq_dft && cur_p->data[f].p > p_draft_split) { LOG_DBG("splitting seq %3d into %3d\n", s, n_seq_cur); llama_kv_cache_seq_rm(ctx_dft, n_seq_cur, -1, -1); diff --git a/examples/tokenize/CMakeLists.txt b/examples/tokenize/CMakeLists.txt index b704dcae1..1690b53e5 100644 --- a/examples/tokenize/CMakeLists.txt +++ b/examples/tokenize/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-tokenize) add_executable(${TARGET} tokenize.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/flake.lock b/flake.lock index 732c7539c..d114f4422 100644 --- a/flake.lock +++ b/flake.lock @@ -5,11 +5,11 @@ "nixpkgs-lib": "nixpkgs-lib" }, "locked": { - "lastModified": 1727826117, - "narHash": "sha256-K5ZLCyfO/Zj9mPFldf3iwS6oZStJcU4tSpiXTMYaaL0=", + "lastModified": 1730504689, + "narHash": "sha256-hgmguH29K2fvs9szpq2r3pz2/8cJd2LPS+b4tfNFCwE=", "owner": "hercules-ci", "repo": "flake-parts", - "rev": "3d04084d54bedc3d6b8b736c70ef449225c361b1", + "rev": "506278e768c2a08bec68eb62932193e341f55c90", "type": "github" }, "original": { @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1729665710, - "narHash": "sha256-AlcmCXJZPIlO5dmFzV3V2XF6x/OpNWUV8Y/FMPGd8Z4=", + "lastModified": 1732014248, + "narHash": "sha256-y/MEyuJ5oBWrWAic/14LaIr/u5E0wRVzyYsouYY3W6w=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "2768c7d042a37de65bb1b5b3268fc987e534c49d", + "rev": "23e89b7da85c3640bbc2173fe04f4bd114342367", "type": "github" }, "original": { @@ -36,14 +36,14 @@ }, "nixpkgs-lib": { "locked": { - "lastModified": 1727825735, - "narHash": "sha256-0xHYkMkeLVQAMa7gvkddbPqpxph+hDzdu1XdGPJR+Os=", + "lastModified": 1730504152, + "narHash": "sha256-lXvH/vOfb4aGYyvFmZK/HlsNsr/0CVWlwYvo2rxJk3s=", "type": "tarball", - "url": "https://github.com/NixOS/nixpkgs/archive/fb192fec7cc7a4c26d51779e9bab07ce6fa5597a.tar.gz" + "url": "https://github.com/NixOS/nixpkgs/archive/cc2f28000298e1269cea6612cd06ec9979dd5d7f.tar.gz" }, "original": { "type": "tarball", - "url": "https://github.com/NixOS/nixpkgs/archive/fb192fec7cc7a4c26d51779e9bab07ce6fa5597a.tar.gz" + "url": "https://github.com/NixOS/nixpkgs/archive/cc2f28000298e1269cea6612cd06ec9979dd5d7f.tar.gz" } }, "root": { diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt index cfa6e3f70..1b3d98967 100644 --- a/ggml/CMakeLists.txt +++ b/ggml/CMakeLists.txt @@ -33,6 +33,7 @@ else() endif() option(BUILD_SHARED_LIBS "ggml: build shared libraries" ${BUILD_SHARED_LIBS_DEFAULT}) +option(GGML_BACKEND_DL "ggml: build backends as dynamic libraries (requires BUILD_SHARED_LIBS)" OFF) # # option list @@ -91,31 +92,38 @@ else() set(INS_ENB ON) endif() -option(GGML_CPU_HBM "ggml: use memkind for CPU HBM" OFF) - -option(GGML_AVX "ggml: enable AVX" ${INS_ENB}) -option(GGML_AVX2 "ggml: enable AVX2" ${INS_ENB}) -option(GGML_AVX512 "ggml: enable AVX512" OFF) -option(GGML_AVX512_VBMI "ggml: enable AVX512-VBMI" OFF) -option(GGML_AVX512_VNNI "ggml: enable AVX512-VNNI" OFF) -option(GGML_AVX512_BF16 "ggml: enable AVX512-BF16" OFF) -option(GGML_AMX_TILE "ggml: enable AMX-TILE" OFF) -option(GGML_AMX_INT8 "ggml: enable AMX-INT8" OFF) -option(GGML_AMX_BF16 "ggml: enable AMX-BF16" OFF) -option(GGML_FMA "ggml: enable FMA" ${INS_ENB}) +option(GGML_CPU_HBM "ggml: use memkind for CPU HBM" OFF) +option(GGML_CPU_AARCH64 "ggml: use runtime weight conversion of Q4_0 to Q4_X_X" ON) +option(GGML_AVX "ggml: enable AVX" ${INS_ENB}) +option(GGML_AVX_VNNI "ggml: enable AVX-VNNI" OFF) +option(GGML_AVX2 "ggml: enable AVX2" ${INS_ENB}) +option(GGML_AVX512 "ggml: enable AVX512F" OFF) +option(GGML_AVX512_VBMI "ggml: enable AVX512-VBMI" OFF) +option(GGML_AVX512_VNNI "ggml: enable AVX512-VNNI" OFF) +option(GGML_AVX512_BF16 "ggml: enable AVX512-BF16" OFF) if (NOT MSVC) - option(GGML_F16C "ggml: enable F16C" ${INS_ENB}) # in MSVC F16C is implied with AVX2/AVX512 + # in MSVC F16C and FMA is implied with AVX2/AVX512 + option(GGML_FMA "ggml: enable FMA" ${INS_ENB}) + option(GGML_F16C "ggml: enable F16C" ${INS_ENB}) + # MSVC does not seem to support AMX + option(GGML_AMX_TILE "ggml: enable AMX-TILE" OFF) + option(GGML_AMX_INT8 "ggml: enable AMX-INT8" OFF) + option(GGML_AMX_BF16 "ggml: enable AMX-BF16" OFF) endif() -option(GGML_LASX "ggml: enable lasx" ON) -option(GGML_LSX "ggml: enable lsx" ON) -option(GGML_SVE "ggml: enable SVE" OFF) +option(GGML_LASX "ggml: enable lasx" ON) +option(GGML_LSX "ggml: enable lsx" ON) +option(GGML_RVV "ggml: enable rvv" ON) +option(GGML_SVE "ggml: enable SVE" OFF) +option(GGML_CPU_ALL_VARIANTS "ggml: build all variants of the CPU backend (requires GGML_BACKEND_DL)" OFF) + if (WIN32) - set(GGML_WIN_VER "0x602" CACHE STRING "ggml: Windows Version") + set(GGML_WIN_VER "0x602" CACHE STRING "ggml: Windows version") endif() # ggml core set(GGML_SCHED_MAX_COPIES "4" CACHE STRING "ggml: max input copies for pipeline parallelism") +option(GGML_CPU "ggml: enable CPU backend" ON) # 3rd party libs / backends option(GGML_ACCELERATE "ggml: enable Accelerate framework" ON) @@ -126,14 +134,9 @@ option(GGML_LLAMAFILE "ggml: use LLAMAFILE" option(GGML_CUDA "ggml: use CUDA" OFF) option(GGML_MUSA "ggml: use MUSA" OFF) -option(GGML_CUDA_FORCE_DMMV "ggml: use dmmv instead of mmvq CUDA kernels" OFF) option(GGML_CUDA_FORCE_MMQ "ggml: use mmq kernels instead of cuBLAS" OFF) option(GGML_CUDA_FORCE_CUBLAS "ggml: always use cuBLAS instead of mmq kernels" OFF) -set (GGML_CUDA_DMMV_X "32" CACHE STRING "ggml: x stride for dmmv CUDA kernels") -set (GGML_CUDA_MMV_Y "1" CACHE STRING "ggml: y block size for mmv CUDA kernels") option(GGML_CUDA_F16 "ggml: use 16 bit floats for some calculations" OFF) -set (GGML_CUDA_KQUANTS_ITER "2" CACHE STRING - "ggml: iters./thread per block for Q2_K/Q6_K") set (GGML_CUDA_PEER_MAX_BATCH_SIZE "128" CACHE STRING "ggml: max. batch size for using peer access") option(GGML_CUDA_NO_PEER_COPY "ggml: do not use peer to peer copies" OFF) @@ -141,7 +144,7 @@ option(GGML_CUDA_NO_VMM "ggml: do not try to use CUDA VMM" option(GGML_CUDA_FA_ALL_QUANTS "ggml: compile all quants for FlashAttention" OFF) option(GGML_CUDA_GRAPHS "ggml: use CUDA graphs (llama.cpp only)" ${GGML_CUDA_GRAPHS_DEFAULT}) -option(GGML_HIPBLAS "ggml: use hipBLAS" OFF) +option(GGML_HIP "ggml: use HIP" OFF) option(GGML_HIP_UMA "ggml: use HIP unified memory architecture" OFF) option(GGML_VULKAN "ggml: use Vulkan" OFF) option(GGML_VULKAN_CHECK_RESULTS "ggml: run Vulkan op checks" OFF) @@ -153,6 +156,7 @@ option(GGML_VULKAN_VALIDATE "ggml: enable Vulkan validation" option(GGML_VULKAN_RUN_TESTS "ggml: run Vulkan tests" OFF) option(GGML_KOMPUTE "ggml: use Kompute" OFF) option(GGML_METAL "ggml: use Metal" ${GGML_METAL_DEFAULT}) +option(GGML_METAL_USE_BF16 "ggml: use bfloat if available" OFF) option(GGML_METAL_NDEBUG "ggml: disable Metal debugging" OFF) option(GGML_METAL_SHADER_DEBUG "ggml: compile Metal with -fno-fast-math" OFF) option(GGML_METAL_EMBED_LIBRARY "ggml: embed Metal library" ${GGML_METAL}) @@ -161,11 +165,12 @@ set (GGML_METAL_MACOSX_VERSION_MIN "" CACHE STRING set (GGML_METAL_STD "" CACHE STRING "ggml: metal standard version (-std flag)") option(GGML_OPENMP "ggml: use OpenMP" ON) option(GGML_RPC "ggml: use RPC" OFF) -option(GGML_AMX "ggml: use AMX" OFF) option(GGML_SYCL "ggml: use SYCL" OFF) option(GGML_SYCL_F16 "ggml: use 16 bit floats for sycl calculations" OFF) set (GGML_SYCL_TARGET "INTEL" CACHE STRING "ggml: sycl target device") +set (GGML_SYCL_DEVICE_ARCH "" CACHE STRING + "ggml: sycl device architecture") # extra artifacts option(GGML_BUILD_TESTS "ggml: build tests" ${GGML_STANDALONE}) @@ -178,11 +183,7 @@ option(GGML_BUILD_EXAMPLES "ggml: build examples" ${GGML_STANDALONE}) set(CMAKE_C_STANDARD 11) set(CMAKE_C_STANDARD_REQUIRED true) -if (GGML_SYCL) - set(CMAKE_CXX_STANDARD 17) -else() - set(CMAKE_CXX_STANDARD 11) -endif() +set(CMAKE_CXX_STANDARD 17) set(CMAKE_CXX_STANDARD_REQUIRED true) set(THREADS_PREFER_PTHREAD_FLAG ON) @@ -218,13 +219,14 @@ include(CMakePackageConfigHelpers) # all public headers set(GGML_PUBLIC_HEADERS include/ggml.h + include/ggml-cpu.h include/ggml-alloc.h include/ggml-backend.h include/ggml-blas.h include/ggml-cann.h include/ggml-cuda.h - include/ggml.h include/ggml-kompute.h + include/ggml-opt.h include/ggml-metal.h include/ggml-rpc.h include/ggml-sycl.h @@ -234,15 +236,14 @@ set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}") #if (GGML_METAL) # set_target_properties(ggml PROPERTIES RESOURCE "${CMAKE_CURRENT_SOURCE_DIR}/src/ggml-metal.metal") #endif() -install(TARGETS ggml PUBLIC_HEADER) - -if (BUILD_SHARED_LIBS) - install(TARGETS ggml LIBRARY) -endif() +install(TARGETS ggml LIBRARY PUBLIC_HEADER) +install(TARGETS ggml-base LIBRARY) +# FIXME: this should be done in the backend cmake files if (GGML_METAL) + # FIXME: does this need to be installed with GGML_METAL_EMBED_LIBRARY? install( - FILES src/ggml-metal.metal + FILES src/ggml-metal/ggml-metal.metal PERMISSIONS OWNER_READ OWNER_WRITE diff --git a/ggml/include/ggml-amx.h b/ggml/include/ggml-amx.h deleted file mode 100644 index 22b3f70f4..000000000 --- a/ggml/include/ggml-amx.h +++ /dev/null @@ -1,25 +0,0 @@ -#pragma once - -#include "ggml.h" -#include "ggml-backend.h" - - -#ifdef __cplusplus -extern "C" { -#endif - -// buffer_type API -GGML_API ggml_backend_buffer_type_t ggml_backend_amx_buffer_type(void); - -GGML_API bool ggml_backend_is_amx(ggml_backend_t backend); - -// backend API -GGML_API ggml_backend_t ggml_backend_amx_init(void); - -GGML_API void ggml_backend_amx_set_n_threads(ggml_backend_t backend_amx, int n_threads); - -GGML_API ggml_backend_reg_t ggml_backend_amx_reg(void); - -#ifdef __cplusplus -} -#endif diff --git a/ggml/include/ggml-backend.h b/ggml/include/ggml-backend.h index c11eb4183..19881a505 100644 --- a/ggml/include/ggml-backend.h +++ b/ggml/include/ggml-backend.h @@ -3,6 +3,20 @@ #include "ggml.h" #include "ggml-alloc.h" +#ifdef GGML_BACKEND_SHARED +# if defined(_WIN32) && !defined(__MINGW32__) +# ifdef GGML_BACKEND_BUILD +# define GGML_BACKEND_API __declspec(dllexport) extern +# else +# define GGML_BACKEND_API __declspec(dllimport) extern +# endif +# else +# define GGML_BACKEND_API __attribute__ ((visibility ("default"))) extern +# endif +#else +# define GGML_BACKEND_API extern +#endif + #ifdef __cplusplus extern "C" { #endif @@ -72,7 +86,7 @@ extern "C" { GGML_API void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); GGML_API void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); - // "offset" refers to the offset of the tensor data for setting/getting data + // "offset" refers to the offset in tensor->data for setting/getting data GGML_API void ggml_backend_tensor_set( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); GGML_API void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); GGML_API void ggml_backend_tensor_memset( struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size); @@ -176,6 +190,14 @@ extern "C" { typedef void (*ggml_backend_set_n_threads_t)(ggml_backend_t backend, int n_threads); // Get additional buffer types provided by the device (returns a NULL-terminated array) typedef ggml_backend_buffer_type_t * (*ggml_backend_dev_get_extra_bufts_t)(ggml_backend_dev_t device); + // Set the abort callback for the backend + typedef void (*ggml_backend_set_abort_callback_t)(ggml_backend_t backend, ggml_abort_callback abort_callback, void * abort_callback_data); + // Get a list of feature flags supported by the backend (returns a NULL-terminated array) + struct ggml_backend_feature { + const char * name; + const char * value; + }; + typedef struct ggml_backend_feature * (*ggml_backend_get_features_t)(ggml_backend_reg_t reg); // // Backend registry @@ -200,6 +222,13 @@ extern "C" { // = ggml_backend_dev_init(ggml_backend_dev_by_type(GPU) OR ggml_backend_dev_by_type(CPU), NULL) GGML_API ggml_backend_t ggml_backend_init_best(void); + // Load a backend from a dynamic library and register it + GGML_API ggml_backend_reg_t ggml_backend_load(const char * path); + // Unload a backend if loaded dynamically and unregister it + GGML_API void ggml_backend_unload(ggml_backend_reg_t reg); + // Load all known backends from dynamic libraries + GGML_API void ggml_backend_load_all(void); + // // Backend scheduler // @@ -228,14 +257,20 @@ extern "C" { ggml_backend_sched_reserve(sched, reserve_graph); // compute - graph = build_graph(sched); - ggml_backend_sched_graph_compute(sched, graph); + graph = build_graph(sched); // the graph and its tensors are single-use in terms of allocation, multi-use in terms of computation + for (int i = 0; i < 10; ++i) { + ggml_backend_sched_graph_compute(sched, graph); // on the first iteration the graph is allocated automatically + } // if there are graph inputs: - ggml_backend_sched_reset(sched); - ggml_backend_sched_alloc_graph(sched, graph); - ggml_backend_tensor_set(input_tensor, ...); - ggml_backend_sched_graph_compute(sched, graph); + graph = build_graph(sched); // get a new graph that is not allocated (the metadata for the old graph is freed once ggml_free is called) + ggml_backend_sched_reset(sched); // clear the allocation of the previous graph + ggml_backend_sched_alloc_graph(sched, graph); // explicitly allocate the new graph but do not execute it + ggml_backend_tensor_set(input_tensor, ...); // copy data to the newly allocated graph tensors + ggml_backend_sched_graph_compute(sched, graph); // execute the graph + + // as an alternative to the above it is also possible to assign the inputs to a dedicated context and + // allocate them statically via ggml_backend_alloc_ctx_tensors } */ @@ -250,7 +285,7 @@ extern "C" { // typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data); - // Initialize a backend scheduler + // Initialize a backend scheduler, backends with low index are given priority over backends with high index GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel); GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched); @@ -275,7 +310,9 @@ extern "C" { GGML_API enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph); GGML_API void ggml_backend_sched_synchronize(ggml_backend_sched_t sched); - // Reset all assignments and allocators - must be called before changing the node backends + // Reset all assignments and allocators - must be called before changing the node backends or allocating a new graph. + // This in effect deallocates all tensors that were previously allocated and leaves them with dangling pointers. + // The correct way to use this API is to discard the deallocated tensors and create new ones. GGML_API void ggml_backend_sched_reset(ggml_backend_sched_t sched); // Set a callback to be called for each resulting node during graph compute @@ -305,27 +342,10 @@ extern "C" { GGML_API void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr); GGML_API void ggml_backend_view_init(struct ggml_tensor * tensor); - // - // CPU backend - // - - GGML_API ggml_backend_t ggml_backend_cpu_init(void); - - GGML_API bool ggml_backend_is_cpu (ggml_backend_t backend); - GGML_API void ggml_backend_cpu_set_n_threads (ggml_backend_t backend_cpu, int n_threads); - GGML_API void ggml_backend_cpu_set_threadpool (ggml_backend_t backend_cpu, ggml_threadpool_t threadpool); - GGML_API void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data); - - // Create a backend buffer from an existing pointer + // CPU buffer types are always available GGML_API ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size); GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void); - GGML_API ggml_backend_reg_t ggml_backend_cpu_reg(void); - -#ifdef GGML_USE_CPU_HBM - GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void); -#endif - #ifdef __cplusplus } #endif diff --git a/ggml/include/ggml-blas.h b/ggml/include/ggml-blas.h index 25b2e637f..87a81b363 100644 --- a/ggml/include/ggml-blas.h +++ b/ggml/include/ggml-blas.h @@ -9,15 +9,15 @@ extern "C" { #endif // backend API -GGML_API ggml_backend_t ggml_backend_blas_init(void); +GGML_BACKEND_API ggml_backend_t ggml_backend_blas_init(void); -GGML_API bool ggml_backend_is_blas(ggml_backend_t backend); +GGML_BACKEND_API bool ggml_backend_is_blas(ggml_backend_t backend); // number of threads used for conversion to float // for openblas and blis, this will also set the number of threads used for blas operations -GGML_API void ggml_backend_blas_set_n_threads(ggml_backend_t backend_blas, int n_threads); +GGML_BACKEND_API void ggml_backend_blas_set_n_threads(ggml_backend_t backend_blas, int n_threads); -GGML_API ggml_backend_reg_t ggml_backend_blas_reg(void); +GGML_BACKEND_API ggml_backend_reg_t ggml_backend_blas_reg(void); #ifdef __cplusplus diff --git a/ggml/include/ggml-cann.h b/ggml/include/ggml-cann.h index 528975493..b469e228d 100644 --- a/ggml/include/ggml-cann.h +++ b/ggml/include/ggml-cann.h @@ -34,7 +34,7 @@ extern "C" { */ #define GGML_CANN_MAX_DEVICES 16 -GGML_API ggml_backend_reg_t ggml_backend_cann_reg(void); +GGML_BACKEND_API ggml_backend_reg_t ggml_backend_cann_reg(void); /** * @brief Initializes the CANN backend for a specified device. @@ -46,7 +46,7 @@ GGML_API ggml_backend_reg_t ggml_backend_cann_reg(void); * @param device The index of the device to initialize. * @return A pointer to the initialized backend instance, or nullptr on failure. */ -GGML_API ggml_backend_t ggml_backend_cann_init(int32_t device); +GGML_BACKEND_API ggml_backend_t ggml_backend_cann_init(int32_t device); /** * @brief Checks if a given backend is a CANN backend. @@ -57,7 +57,7 @@ GGML_API ggml_backend_t ggml_backend_cann_init(int32_t device); * @param backend The backend instance to check. * @return True if the backend is a CANN backend, false otherwise. */ -GGML_API bool ggml_backend_is_cann(ggml_backend_t backend); +GGML_BACKEND_API bool ggml_backend_is_cann(ggml_backend_t backend); /** * @brief Retrieves the CANN buffer type for a specified device. @@ -69,7 +69,7 @@ GGML_API bool ggml_backend_is_cann(ggml_backend_t backend); * @return A pointer to the buffer type interface for the specified device, or * nullptr if the device index is out of range. */ -GGML_API ggml_backend_buffer_type_t +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cann_buffer_type(int32_t device); /** @@ -80,14 +80,14 @@ ggml_backend_cann_buffer_type(int32_t device); * * @return The number of CANN devices available. */ -GGML_API int32_t ggml_backend_cann_get_device_count(void); +GGML_BACKEND_API int32_t ggml_backend_cann_get_device_count(void); /** * @brief pinned host buffer for use with the CPU backend for faster copies between CPU and NPU. * * @return A pointer to the host buffer type interface. */ -GGML_API ggml_backend_buffer_type_t ggml_backend_cann_host_buffer_type(void); +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cann_host_buffer_type(void); /** * @brief Retrieves the description of a specific CANN device. @@ -99,7 +99,7 @@ GGML_API ggml_backend_buffer_type_t ggml_backend_cann_host_buffer_type(void); * @param description Pointer to a buffer where the description will be written. * @param description_size Size of the description buffer. */ -GGML_API void ggml_backend_cann_get_device_description( +GGML_BACKEND_API void ggml_backend_cann_get_device_description( int32_t device, char* description, size_t description_size); /** @@ -114,7 +114,7 @@ GGML_API void ggml_backend_cann_get_device_description( * @param total Pointer to a variable where the total memory size will be * stored. */ -GGML_API void ggml_backend_cann_get_device_memory(int32_t device, +GGML_BACKEND_API void ggml_backend_cann_get_device_memory(int32_t device, size_t* free, size_t* total); diff --git a/ggml/include/ggml-cpp.h b/ggml/include/ggml-cpp.h new file mode 100644 index 000000000..219361af4 --- /dev/null +++ b/ggml/include/ggml-cpp.h @@ -0,0 +1,38 @@ +#pragma once + +#ifndef __cplusplus +#error "This header is for C++ only" +#endif + +#include "ggml.h" +#include "ggml-alloc.h" +#include "ggml-backend.h" +#include + +// Smart pointers for ggml types + +// ggml + +struct ggml_context_deleter { void operator()(ggml_context * ctx) { ggml_free(ctx); } }; +struct gguf_context_deleter { void operator()(gguf_context * ctx) { gguf_free(ctx); } }; + +typedef std::unique_ptr ggml_context_ptr; +typedef std::unique_ptr gguf_context_ptr; + +// ggml-alloc + +struct ggml_gallocr_deleter { void operator()(ggml_gallocr_t galloc) { ggml_gallocr_free(galloc); } }; + +typedef std::unique_ptr ggml_gallocr_ptr; + +// ggml-backend + +struct ggml_backend_deleter { void operator()(ggml_backend_t backend) { ggml_backend_free(backend); } }; +struct ggml_backend_buffer_deleter { void operator()(ggml_backend_buffer_t buffer) { ggml_backend_buffer_free(buffer); } }; +struct ggml_backend_event_deleter { void operator()(ggml_backend_event_t event) { ggml_backend_event_free(event); } }; +struct ggml_backend_sched_deleter { void operator()(ggml_backend_sched_t sched) { ggml_backend_sched_free(sched); } }; + +typedef std::unique_ptr ggml_backend_ptr; +typedef std::unique_ptr ggml_backend_buffer_ptr; +typedef std::unique_ptr ggml_backend_event_ptr; +typedef std::unique_ptr ggml_backend_sched_ptr; diff --git a/ggml/include/ggml-cpu.h b/ggml/include/ggml-cpu.h new file mode 100644 index 000000000..e14ea9ea5 --- /dev/null +++ b/ggml/include/ggml-cpu.h @@ -0,0 +1,152 @@ +#pragma once + +#include "ggml.h" +#include "ggml-backend.h" + +#ifdef __cplusplus +extern "C" { +#endif + + // the compute plan that needs to be prepared for ggml_graph_compute() + // since https://github.com/ggerganov/ggml/issues/287 + struct ggml_cplan { + size_t work_size; // size of work buffer, calculated by `ggml_graph_plan()` + uint8_t * work_data; // work buffer, to be allocated by caller before calling to `ggml_graph_compute()` + + int n_threads; + struct ggml_threadpool * threadpool; + + // abort ggml_graph_compute when true + ggml_abort_callback abort_callback; + void * abort_callback_data; + }; + + // numa strategies + enum ggml_numa_strategy { + GGML_NUMA_STRATEGY_DISABLED = 0, + GGML_NUMA_STRATEGY_DISTRIBUTE = 1, + GGML_NUMA_STRATEGY_ISOLATE = 2, + GGML_NUMA_STRATEGY_NUMACTL = 3, + GGML_NUMA_STRATEGY_MIRROR = 4, + GGML_NUMA_STRATEGY_COUNT + }; + + GGML_BACKEND_API void ggml_numa_init(enum ggml_numa_strategy numa); // call once for better performance on NUMA systems + GGML_BACKEND_API bool ggml_is_numa(void); // true if init detected that system has >1 NUMA node + + GGML_BACKEND_API struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value); + GGML_BACKEND_API struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value); + + GGML_BACKEND_API struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value); + GGML_BACKEND_API struct ggml_tensor * ggml_set_f32 (struct ggml_tensor * tensor, float value); + + GGML_BACKEND_API int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i); + GGML_BACKEND_API void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value); + + GGML_BACKEND_API int32_t ggml_get_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3); + GGML_BACKEND_API void ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, int32_t value); + + GGML_BACKEND_API float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i); + GGML_BACKEND_API void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value); + + GGML_BACKEND_API float ggml_get_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3); + GGML_BACKEND_API void ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, float value); + + GGML_BACKEND_API struct ggml_threadpool * ggml_threadpool_new (struct ggml_threadpool_params * params); + GGML_BACKEND_API void ggml_threadpool_free (struct ggml_threadpool * threadpool); + GGML_BACKEND_API int ggml_threadpool_get_n_threads (struct ggml_threadpool * threadpool); + GGML_BACKEND_API void ggml_threadpool_pause (struct ggml_threadpool * threadpool); + GGML_BACKEND_API void ggml_threadpool_resume (struct ggml_threadpool * threadpool); + + // ggml_graph_plan() has to be called before ggml_graph_compute() + // when plan.work_size > 0, caller must allocate memory for plan.work_data + GGML_BACKEND_API struct ggml_cplan ggml_graph_plan( + const struct ggml_cgraph * cgraph, + int n_threads, /* = GGML_DEFAULT_N_THREADS */ + struct ggml_threadpool * threadpool /* = NULL */ ); + GGML_BACKEND_API enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan); + + // same as ggml_graph_compute() but the work data is allocated as a part of the context + // note: the drawback of this API is that you must have ensured that the context has enough memory for the work data + GGML_BACKEND_API enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads); + + // + // system info + // + + // x86 + GGML_BACKEND_API int ggml_cpu_has_sse3 (void); + GGML_BACKEND_API int ggml_cpu_has_ssse3 (void); + GGML_BACKEND_API int ggml_cpu_has_avx (void); + GGML_BACKEND_API int ggml_cpu_has_avx_vnni (void); + GGML_BACKEND_API int ggml_cpu_has_avx2 (void); + GGML_BACKEND_API int ggml_cpu_has_f16c (void); + GGML_BACKEND_API int ggml_cpu_has_fma (void); + GGML_BACKEND_API int ggml_cpu_has_avx512 (void); + GGML_BACKEND_API int ggml_cpu_has_avx512_vbmi(void); + GGML_BACKEND_API int ggml_cpu_has_avx512_vnni(void); + GGML_BACKEND_API int ggml_cpu_has_avx512_bf16(void); + GGML_BACKEND_API int ggml_cpu_has_amx_int8 (void); + // ARM + GGML_BACKEND_API int ggml_cpu_has_neon (void); + GGML_BACKEND_API int ggml_cpu_has_arm_fma (void); + GGML_BACKEND_API int ggml_cpu_has_fp16_va (void); + GGML_BACKEND_API int ggml_cpu_has_dotprod (void); + GGML_BACKEND_API int ggml_cpu_has_matmul_int8(void); + GGML_BACKEND_API int ggml_cpu_has_sve (void); + GGML_BACKEND_API int ggml_cpu_get_sve_cnt (void); // sve vector length in bytes + // other + GGML_BACKEND_API int ggml_cpu_has_riscv_v (void); + GGML_BACKEND_API int ggml_cpu_has_vsx (void); + GGML_BACKEND_API int ggml_cpu_has_wasm_simd (void); + GGML_BACKEND_API int ggml_cpu_has_llamafile (void); + + // Internal types and functions exposed for tests and benchmarks + + typedef void (*ggml_from_float_to_mat_t) + (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nr, int64_t k, int64_t bs); + typedef void (*ggml_vec_dot_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, size_t bx, + const void * GGML_RESTRICT y, size_t by, int nrc); + typedef void (*ggml_gemv_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, + const void * GGML_RESTRICT y, int nr, int nc); + typedef void (*ggml_gemm_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, + const void * GGML_RESTRICT y, int nr, int nc); + + struct ggml_type_traits_cpu { + ggml_from_float_t from_float; + ggml_from_float_to_mat_t from_float_to_mat; + ggml_vec_dot_t vec_dot; + enum ggml_type vec_dot_type; + int64_t nrows; // number of rows to process simultaneously + int64_t ncols; // number of columns to process simultaneously + ggml_gemv_t gemv; + ggml_gemm_t gemm; + }; + + GGML_BACKEND_API const struct ggml_type_traits_cpu * ggml_get_type_traits_cpu(enum ggml_type type); + + GGML_BACKEND_API void ggml_cpu_init(void); + + // + // CPU backend + // + + GGML_BACKEND_API ggml_backend_t ggml_backend_cpu_init(void); + + GGML_BACKEND_API bool ggml_backend_is_cpu (ggml_backend_t backend); + GGML_BACKEND_API void ggml_backend_cpu_set_n_threads (ggml_backend_t backend_cpu, int n_threads); + GGML_BACKEND_API void ggml_backend_cpu_set_threadpool (ggml_backend_t backend_cpu, ggml_threadpool_t threadpool); + GGML_BACKEND_API void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data); + + GGML_BACKEND_API ggml_backend_reg_t ggml_backend_cpu_reg(void); + +#ifdef GGML_USE_CPU_HBM + GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void); +#endif + + GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cpu_aarch64_buffer_type(void); + GGML_BACKEND_API bool ggml_backend_cpu_buft_is_aarch64(ggml_backend_buffer_type_t buft); + +#ifdef __cplusplus +} +#endif diff --git a/ggml/include/ggml-cuda.h b/ggml/include/ggml-cuda.h index 305d0b636..22ad2c009 100644 --- a/ggml/include/ggml-cuda.h +++ b/ggml/include/ggml-cuda.h @@ -7,7 +7,7 @@ extern "C" { #endif -#ifdef GGML_USE_HIPBLAS +#ifdef GGML_USE_HIP #define GGML_CUDA_NAME "ROCm" #define GGML_CUBLAS_NAME "hipBLAS" #elif defined(GGML_USE_MUSA) @@ -20,27 +20,27 @@ extern "C" { #define GGML_CUDA_MAX_DEVICES 16 // backend API -GGML_API ggml_backend_t ggml_backend_cuda_init(int device); +GGML_BACKEND_API ggml_backend_t ggml_backend_cuda_init(int device); -GGML_API bool ggml_backend_is_cuda(ggml_backend_t backend); +GGML_BACKEND_API bool ggml_backend_is_cuda(ggml_backend_t backend); // device buffer -GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device); +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device); // split tensor buffer that splits matrices by rows across multiple devices -GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(int main_device, const float * tensor_split); +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(int main_device, const float * tensor_split); // pinned host buffer for use with the CPU backend for faster copies between CPU and GPU -GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void); +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void); -GGML_API int ggml_backend_cuda_get_device_count(void); -GGML_API void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size); -GGML_API void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total); +GGML_BACKEND_API int ggml_backend_cuda_get_device_count(void); +GGML_BACKEND_API void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size); +GGML_BACKEND_API void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total); -GGML_API bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size); -GGML_API void ggml_backend_cuda_unregister_host_buffer(void * buffer); +GGML_BACKEND_API bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size); +GGML_BACKEND_API void ggml_backend_cuda_unregister_host_buffer(void * buffer); -GGML_API ggml_backend_reg_t ggml_backend_cuda_reg(void); +GGML_BACKEND_API ggml_backend_reg_t ggml_backend_cuda_reg(void); #ifdef __cplusplus } diff --git a/ggml/include/ggml-kompute.h b/ggml/include/ggml-kompute.h index c0c43521b..154aa56a7 100644 --- a/ggml/include/ggml-kompute.h +++ b/ggml/include/ggml-kompute.h @@ -37,13 +37,13 @@ struct ggml_vk_device ggml_vk_current_device(void); // forward declaration typedef struct ggml_backend * ggml_backend_t; -GGML_API ggml_backend_t ggml_backend_kompute_init(int device); +GGML_BACKEND_API ggml_backend_t ggml_backend_kompute_init(int device); -GGML_API bool ggml_backend_is_kompute(ggml_backend_t backend); +GGML_BACKEND_API bool ggml_backend_is_kompute(ggml_backend_t backend); -GGML_API ggml_backend_buffer_type_t ggml_backend_kompute_buffer_type(int device); +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_kompute_buffer_type(int device); -GGML_API ggml_backend_reg_t ggml_backend_kompute_reg(void); +GGML_BACKEND_API ggml_backend_reg_t ggml_backend_kompute_reg(void); #ifdef __cplusplus } diff --git a/ggml/include/ggml-metal.h b/ggml/include/ggml-metal.h index b8d3f678b..669c1f84a 100644 --- a/ggml/include/ggml-metal.h +++ b/ggml/include/ggml-metal.h @@ -39,27 +39,27 @@ extern "C" { // user-code should use only these functions // -GGML_API ggml_backend_t ggml_backend_metal_init(void); +GGML_BACKEND_API ggml_backend_t ggml_backend_metal_init(void); -GGML_API bool ggml_backend_is_metal(ggml_backend_t backend); +GGML_BACKEND_API bool ggml_backend_is_metal(ggml_backend_t backend); GGML_DEPRECATED( - GGML_API ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size), + GGML_BACKEND_API ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size), "obsoleted by the new device interface - https://github.com/ggerganov/llama.cpp/pull/9713"); -GGML_API void ggml_backend_metal_set_abort_callback(ggml_backend_t backend, ggml_abort_callback abort_callback, void * user_data); +GGML_BACKEND_API void ggml_backend_metal_set_abort_callback(ggml_backend_t backend, ggml_abort_callback abort_callback, void * user_data); -GGML_API ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void); +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void); // helper to check if the device supports a specific family // ideally, the user code should be doing these checks // ref: https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf -GGML_API bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family); +GGML_BACKEND_API bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family); // capture all command buffers committed the next time `ggml_backend_graph_compute` is called -GGML_API void ggml_backend_metal_capture_next_compute(ggml_backend_t backend); +GGML_BACKEND_API void ggml_backend_metal_capture_next_compute(ggml_backend_t backend); -GGML_API ggml_backend_reg_t ggml_backend_metal_reg(void); +GGML_BACKEND_API ggml_backend_reg_t ggml_backend_metal_reg(void); #ifdef __cplusplus } diff --git a/ggml/include/ggml-opt.h b/ggml/include/ggml-opt.h new file mode 100644 index 000000000..eb5eab9de --- /dev/null +++ b/ggml/include/ggml-opt.h @@ -0,0 +1,216 @@ +// This file contains functionality for training models using GGML. +// It is not strictly needed vs. just vanilla GGML but it provides a more high-level interface for common needs such as datasets. +// At the bottom of this file especially there are relatively high-level functions that are suitable use or adaptation in user code. +// +// Module maintainer: Johannes Gäßler (@JohannesGaessler, johannesg@5d6.de) + +#pragma once + +#include "ggml.h" +#include "ggml-backend.h" + +#include + +#ifdef __cplusplus +extern "C" { +#endif + + struct ggml_opt_dataset; + struct ggml_opt_context; + struct ggml_opt_result; + + typedef struct ggml_opt_dataset * ggml_opt_dataset_t; + typedef struct ggml_opt_context * ggml_opt_context_t; + typedef struct ggml_opt_result * ggml_opt_result_t; + + // ====== Loss ====== + + // built-in loss types, i.e. the built-in quantities minimized by the optimizer + // custom loss types can be defined via mean or sum which simply reduce the outputs for all datapoints to a single value + enum ggml_opt_loss_type { + GGML_OPT_LOSS_TYPE_MEAN, + GGML_OPT_LOSS_TYPE_SUM, + GGML_OPT_LOSS_TYPE_CROSS_ENTROPY, + GGML_OPT_LOSS_TYPE_MEAN_SQUARED_ERROR, + }; + + // ====== Dataset ====== + + GGML_API ggml_opt_dataset_t ggml_opt_dataset_init( + int64_t ne_datapoint, // number of elements per datapoint + int64_t ne_label, // number of elements per label + int64_t ndata, // total number of datapoints/labels + int64_t ndata_shard); // number of datapoints/labels per shard (unit at which the dataset is shuffled/copied) + GGML_API void ggml_opt_dataset_free(ggml_opt_dataset_t dataset); + + // get underlying tensors that store the data + GGML_API struct ggml_tensor * ggml_opt_dataset_data (ggml_opt_dataset_t dataset); // shape = [ne_datapoint, ndata] + GGML_API struct ggml_tensor * ggml_opt_dataset_labels(ggml_opt_dataset_t dataset); // shape = [nd_label, ndata] + + // shuffle idata first datapoints from dataset with RNG from opt_ctx, shuffle all datapoints if idata is negative + GGML_API void ggml_opt_dataset_shuffle(ggml_opt_context_t opt_ctx, ggml_opt_dataset_t dataset, int64_t idata); + + // get batch at position ibatch from dataset and copy the data to data_batch and labels_batch + GGML_API void ggml_opt_dataset_get_batch( + ggml_opt_dataset_t dataset, + struct ggml_tensor * data_batch, // shape = [ne_datapoint, ndata_batch] + struct ggml_tensor * labels_batch, // shape = [ne_label, ndata_batch] + int64_t ibatch); + + // ====== Model / Context ====== + + enum ggml_opt_build_type { + GGML_OPT_BUILD_TYPE_FORWARD, + GGML_OPT_BUILD_TYPE_GRAD, + GGML_OPT_BUILD_TYPE_OPT, + }; + + // parameters that control which optimizer is used and how said optimizer tries to find the minimal loss + struct ggml_opt_optimizer_params { + // AdamW optimizer parameters + struct { + float alpha; // learning rate + float beta1; + float beta2; + float eps; // epsilon for numerical stability + float wd; // weight decay for AdamW, use 0.0f to disable + } adamw; + }; + + // callback to calculate optimizer parameters prior to a backward pass + // userdata can be used to pass arbitrary data + typedef struct ggml_opt_optimizer_params (*ggml_opt_get_optimizer_params)(void * userdata); + + // returns the default optimizer params (constant) + // userdata is not used + GGML_API struct ggml_opt_optimizer_params ggml_opt_get_default_optimizer_params(void * userdata); + + // parameters for initializing a new optimization context + struct ggml_opt_params { + ggml_backend_sched_t backend_sched; // defines which backends are used to construct the compute graphs + + struct ggml_context * ctx_compute; // created in user code, holds non-static tensors + + // the forward graph is defined by inputs and outputs + // those tensors and all tensors inbetween are not intended to be reusable between multiple optimization contexts + struct ggml_tensor * inputs; + struct ggml_tensor * outputs; + + enum ggml_opt_loss_type loss_type; + enum ggml_opt_build_type build_type; + + int32_t opt_period; // after how many gradient accumulation steps an optimizer step should be done + + ggml_opt_get_optimizer_params get_opt_pars; // callback for calculating optimizer parameters + void * get_opt_pars_ud; // userdata for calculating optimizer parameters + }; + + // get parameters for an optimization context with defaults set where possible + // parameters for which no sensible defaults exist are supplied as arguments to this function + GGML_API ggml_opt_params ggml_opt_default_params( + ggml_backend_sched_t backend_sched, + struct ggml_context * ctx_compute, + struct ggml_tensor * inputs, + struct ggml_tensor * outputs, + enum ggml_opt_loss_type loss_type); + + GGML_API ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params); + GGML_API void ggml_opt_free(ggml_opt_context_t opt_ctx); + + // set gradients to zero, initilize loss, and optionally reset the optimizer + GGML_API void ggml_opt_reset(ggml_opt_context_t opt_ctx, bool optimizer); + + // get underlying tensors that store data + GGML_API struct ggml_tensor * ggml_opt_inputs( ggml_opt_context_t opt_ctx); // forward graph input tensor + GGML_API struct ggml_tensor * ggml_opt_outputs( ggml_opt_context_t opt_ctx); // forward graph output tensor + GGML_API struct ggml_tensor * ggml_opt_labels( ggml_opt_context_t opt_ctx); // labels to compare outputs against + GGML_API struct ggml_tensor * ggml_opt_loss( ggml_opt_context_t opt_ctx); // scalar tensor that contains the loss + GGML_API struct ggml_tensor * ggml_opt_pred( ggml_opt_context_t opt_ctx); // predictions made by outputs + GGML_API struct ggml_tensor * ggml_opt_ncorrect(ggml_opt_context_t opt_ctx); // number of matching predictions between outputs and labels + + GGML_API struct ggml_tensor * ggml_opt_grad_acc(ggml_opt_context_t opt_ctx, struct ggml_tensor * node); + + // ====== Optimization Result ====== + + GGML_API ggml_opt_result_t ggml_opt_result_init(); + GGML_API void ggml_opt_result_free(ggml_opt_result_t result); + GGML_API void ggml_opt_result_reset(ggml_opt_result_t result); + + // get data from result, uncertainties are optional and can be ignored by passing NULL + GGML_API void ggml_opt_result_ndata( ggml_opt_result_t result, int64_t * ndata); // writes 1 value, number of datapoints + GGML_API void ggml_opt_result_loss( ggml_opt_result_t result, double * loss, double * unc); // writes 1 value + GGML_API void ggml_opt_result_pred( ggml_opt_result_t result, int32_t * pred); // writes ndata values + GGML_API void ggml_opt_result_accuracy(ggml_opt_result_t result, double * accuracy, double * unc); // writes 1 value + + // ====== Computation ====== + + // do forward pass, increment result if not NULL + GGML_API void ggml_opt_forward(ggml_opt_context_t opt_ctx, ggml_opt_result_t result); + + // do forward pass, increment result if not NULL, do backward pass + GGML_API void ggml_opt_forward_backward(ggml_opt_context_t opt_ctx, ggml_opt_result_t result); + + // ############################################################################ + // ## The high-level functions start here. They do not depend on any private ## + // ## functions or structs and can be copied to and adapted for user code. ## + // ############################################################################ + + // ====== Intended Usage ====== + // + // 1. Select the appropriate loss for your problem. + // 2. Create a dataset and set the data for the "data" tensor. Also set the "labels" tensor if your loss needs them. + // Setting the shard size to 1 will be fine, it's the granularity with which data is shuffled/loaded (bigger values are faster). + // 3. Create a GGML graph for your model with no_alloc == true. Use two separate contexts for the tensors. + // The first context should contain the model parameters and inputs and be allocated statically in user code. + // The second context should contain all other tensors and will be (re)allocated automatically. + // Due to this automated allocation the data of the second context is not defined when accessed in user code. + // Note that the second dimension of the inputs/outputs are interpreted as the number of datapoints in those tensors. + // 4. Call ggml_opt_fit. If you need more control you can use ggml_opt_epoch instead. + + // signature for a callback while evaluating opt_ctx on dataset, called after an evaluation + typedef void (*ggml_opt_epoch_callback)( + bool train, // true after training evaluation, false after validation evaluation + ggml_opt_context_t opt_ctx, + ggml_opt_dataset_t dataset, + ggml_opt_result_t result, // result associated with the dataset subsection + int64_t ibatch, // number of batches that have been evaluated so far + int64_t ibatch_max, // total number of batches in this dataset subsection + int64_t t_start_us); // time at which the evaluation on the dataset subsection was started + + // do training on front of dataset, do evaluation only on back of dataset + GGML_API void ggml_opt_epoch( + ggml_opt_context_t opt_ctx, + ggml_opt_dataset_t dataset, + ggml_opt_result_t result_train, // result to increment during training, ignored if NULL + ggml_opt_result_t result_eval, // result to increment during evaluation, ignored if NULL + int64_t idata_split, // data index at which to split training and evaluation + ggml_opt_epoch_callback callback_train, + ggml_opt_epoch_callback callback_eval); + + // callback that prints a progress bar on stderr + GGML_API void ggml_opt_epoch_callback_progress_bar( + bool train, + ggml_opt_context_t opt_ctx, + ggml_opt_dataset_t dataset, + ggml_opt_result_t result, + int64_t ibatch, + int64_t ibatch_max, + int64_t t_start_us); + + // fit model defined by inputs and outputs to dataset + GGML_API void ggml_opt_fit( + ggml_backend_sched_t backend_sched, // backend scheduler for constructing the compute graphs + ggml_context * ctx_compute, // context with temporarily allocated tensors to calculate the outputs + ggml_tensor * inputs, // input tensor with shape [ne_datapoint, ndata_batch] + ggml_tensor * outputs, // output tensor, must have shape [ne_label, ndata_batch] if labels are used + ggml_opt_dataset_t dataset, // dataset with data and optionally also labels + enum ggml_opt_loss_type loss_type, // loss to minimize + ggml_opt_get_optimizer_params get_opt_pars, // callback to get optimizer params, userdata is pointer to epoch (of type int64_t) + int64_t nepoch, // how many times the dataset should be iterated over + int64_t nbatch_logical, // datapoints optimizer step, must be a multiple of ndata_batch in inputs/outputs + float val_split, // fraction of the dataset to use for validation, must be in [0.0f, 1.0f) + bool silent); // whether or not info prints to stderr should be suppressed + +#ifdef __cplusplus +} +#endif diff --git a/ggml/include/ggml-rpc.h b/ggml/include/ggml-rpc.h index d57967368..ade6c3b0e 100644 --- a/ggml/include/ggml-rpc.h +++ b/ggml/include/ggml-rpc.h @@ -10,18 +10,18 @@ extern "C" { #define GGML_RPC_MAX_SERVERS 16 // backend API -GGML_API ggml_backend_t ggml_backend_rpc_init(const char * endpoint); -GGML_API bool ggml_backend_is_rpc(ggml_backend_t backend); +GGML_BACKEND_API ggml_backend_t ggml_backend_rpc_init(const char * endpoint); +GGML_BACKEND_API bool ggml_backend_is_rpc(ggml_backend_t backend); -GGML_API ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint); +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint); -GGML_API void ggml_backend_rpc_get_device_memory(const char * endpoint, size_t * free, size_t * total); +GGML_BACKEND_API void ggml_backend_rpc_get_device_memory(const char * endpoint, size_t * free, size_t * total); -GGML_API void ggml_backend_rpc_start_server(ggml_backend_t backend, const char * endpoint, size_t free_mem, size_t total_mem); +GGML_BACKEND_API void ggml_backend_rpc_start_server(ggml_backend_t backend, const char * endpoint, size_t free_mem, size_t total_mem); -GGML_API ggml_backend_reg_t ggml_backend_rpc_reg(void); +GGML_BACKEND_API ggml_backend_reg_t ggml_backend_rpc_reg(void); -GGML_API ggml_backend_dev_t ggml_backend_rpc_add_device(const char * endpoint); +GGML_BACKEND_API ggml_backend_dev_t ggml_backend_rpc_add_device(const char * endpoint); #ifdef __cplusplus } diff --git a/ggml/include/ggml-sycl.h b/ggml/include/ggml-sycl.h index af521f599..5ce349a88 100644 --- a/ggml/include/ggml-sycl.h +++ b/ggml/include/ggml-sycl.h @@ -17,32 +17,32 @@ extern "C" { #endif // backend API -GGML_API ggml_backend_t ggml_backend_sycl_init(int device); +GGML_BACKEND_API ggml_backend_t ggml_backend_sycl_init(int device); -GGML_API bool ggml_backend_is_sycl(ggml_backend_t backend); +GGML_BACKEND_API bool ggml_backend_is_sycl(ggml_backend_t backend); // devide buffer -GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device); +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device); // split tensor buffer that splits matrices by rows across multiple devices -GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split); +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split); // pinned host buffer for use with the CPU backend for faster copies between CPU and GPU -GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type(void); +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type(void); -GGML_API void ggml_backend_sycl_print_sycl_devices(void); -GGML_API void ggml_backend_sycl_get_gpu_list(int *id_list, int max_len); -GGML_API void ggml_backend_sycl_get_device_description(int device, +GGML_BACKEND_API void ggml_backend_sycl_print_sycl_devices(void); +GGML_BACKEND_API void ggml_backend_sycl_get_gpu_list(int *id_list, int max_len); +GGML_BACKEND_API void ggml_backend_sycl_get_device_description(int device, char *description, size_t description_size); -GGML_API int ggml_backend_sycl_get_device_count(); -GGML_API void ggml_backend_sycl_get_device_memory(int device, size_t *free, size_t *total); +GGML_BACKEND_API int ggml_backend_sycl_get_device_count(); +GGML_BACKEND_API void ggml_backend_sycl_get_device_memory(int device, size_t *free, size_t *total); // SYCL doesn't support registering host memory, keep here for reference -// GGML_API bool ggml_backend_sycl_register_host_buffer(void * buffer, size_t size); -// GGML_API void ggml_backend_sycl_unregister_host_buffer(void * buffer); +// GGML_BACKEND_API bool ggml_backend_sycl_register_host_buffer(void * buffer, size_t size); +// GGML_BACKEND_API void ggml_backend_sycl_unregister_host_buffer(void * buffer); -GGML_API ggml_backend_reg_t ggml_backend_sycl_reg(void); +GGML_BACKEND_API ggml_backend_reg_t ggml_backend_sycl_reg(void); #ifdef __cplusplus } diff --git a/ggml/include/ggml-vulkan.h b/ggml/include/ggml-vulkan.h index c03bbfe5e..53cdba072 100644 --- a/ggml/include/ggml-vulkan.h +++ b/ggml/include/ggml-vulkan.h @@ -10,21 +10,21 @@ extern "C" { #define GGML_VK_NAME "Vulkan" #define GGML_VK_MAX_DEVICES 16 -GGML_API void ggml_vk_instance_init(void); +GGML_BACKEND_API void ggml_vk_instance_init(void); // backend API -GGML_API ggml_backend_t ggml_backend_vk_init(size_t dev_num); +GGML_BACKEND_API ggml_backend_t ggml_backend_vk_init(size_t dev_num); -GGML_API bool ggml_backend_is_vk(ggml_backend_t backend); -GGML_API int ggml_backend_vk_get_device_count(void); -GGML_API void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size); -GGML_API void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total); +GGML_BACKEND_API bool ggml_backend_is_vk(ggml_backend_t backend); +GGML_BACKEND_API int ggml_backend_vk_get_device_count(void); +GGML_BACKEND_API void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size); +GGML_BACKEND_API void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total); -GGML_API ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num); +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num); // pinned host buffer for use with the CPU backend for faster copies between CPU and GPU -GGML_API ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type(void); +GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type(void); -GGML_API ggml_backend_reg_t ggml_backend_vk_reg(void); +GGML_BACKEND_API ggml_backend_reg_t ggml_backend_vk_reg(void); #ifdef __cplusplus } diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index de3c706fc..1c8cc11b6 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -176,15 +176,15 @@ #ifdef GGML_SHARED # if defined(_WIN32) && !defined(__MINGW32__) # ifdef GGML_BUILD -# define GGML_API __declspec(dllexport) +# define GGML_API __declspec(dllexport) extern # else -# define GGML_API __declspec(dllimport) +# define GGML_API __declspec(dllimport) extern # endif # else -# define GGML_API __attribute__ ((visibility ("default"))) +# define GGML_API __attribute__ ((visibility ("default"))) extern # endif #else -# define GGML_API +# define GGML_API extern #endif // TODO: support for clang @@ -217,7 +217,6 @@ #define GGML_MAX_DIMS 4 #define GGML_MAX_PARAMS 2048 -#define GGML_MAX_CONTEXTS 64 #define GGML_MAX_SRC 10 #define GGML_MAX_N_THREADS 512 #define GGML_MAX_OP_PARAMS 64 @@ -390,6 +389,9 @@ extern "C" { GGML_TYPE_Q4_0_8_8 = 33, GGML_TYPE_TQ1_0 = 34, GGML_TYPE_TQ2_0 = 35, + GGML_TYPE_IQ4_NL_4_4 = 36, + // GGML_TYPE_IQ4_NL_4_8 = 37, + // GGML_TYPE_IQ4_NL_8_8 = 38, GGML_TYPE_COUNT, }; @@ -497,6 +499,7 @@ extern "C" { GGML_OP_POOL_2D_BACK, GGML_OP_UPSCALE, // nearest interpolate GGML_OP_PAD, + GGML_OP_PAD_REFLECT_1D, GGML_OP_ARANGE, GGML_OP_TIMESTEP_EMBEDDING, GGML_OP_ARGSORT, @@ -510,7 +513,7 @@ extern "C" { GGML_OP_WIN_UNPART, GGML_OP_GET_REL_POS, GGML_OP_ADD_REL_POS, - GGML_OP_RWKV_WKV, + GGML_OP_RWKV_WKV6, GGML_OP_UNARY, @@ -559,10 +562,10 @@ extern "C" { enum ggml_log_level { GGML_LOG_LEVEL_NONE = 0, - GGML_LOG_LEVEL_INFO = 1, - GGML_LOG_LEVEL_WARN = 2, - GGML_LOG_LEVEL_ERROR = 3, - GGML_LOG_LEVEL_DEBUG = 4, + GGML_LOG_LEVEL_DEBUG = 1, + GGML_LOG_LEVEL_INFO = 2, + GGML_LOG_LEVEL_WARN = 3, + GGML_LOG_LEVEL_ERROR = 4, GGML_LOG_LEVEL_CONT = 5, // continue previous log }; @@ -574,6 +577,13 @@ extern "C" { GGML_TENSOR_FLAG_LOSS = 8, // ...defines loss for numerical optimization (multiple loss tensors add up) }; + struct ggml_init_params { + // memory pool + size_t mem_size; // bytes + void * mem_buffer; // if NULL, memory will be allocated internally + bool no_alloc; // don't allocate memory for the tensor data + }; + // n-dimensional tensor struct ggml_tensor { enum ggml_type type; @@ -596,7 +606,6 @@ extern "C" { int32_t flags; - struct ggml_tensor * grad; struct ggml_tensor * src[GGML_MAX_SRC]; // source tensor and offset for views @@ -609,7 +618,7 @@ extern "C" { void * extra; // extra things e.g. for ggml-cuda.cu - // char padding[4]; + char padding[8]; }; static const size_t GGML_TENSOR_SIZE = sizeof(struct ggml_tensor); @@ -619,66 +628,6 @@ extern "C" { // If it returns true, the computation is aborted typedef bool (*ggml_abort_callback)(void * data); - // Scheduling priorities - enum ggml_sched_priority { - GGML_SCHED_PRIO_NORMAL, - GGML_SCHED_PRIO_MEDIUM, - GGML_SCHED_PRIO_HIGH, - GGML_SCHED_PRIO_REALTIME - }; - - // Threadpool params - // Use ggml_threadpool_params_default() or ggml_threadpool_params_init() to populate the defaults - struct ggml_threadpool_params { - bool cpumask[GGML_MAX_N_THREADS]; // mask of cpu cores (all-zeros means use default affinity settings) - int n_threads; // number of threads - enum ggml_sched_priority prio; // thread priority - uint32_t poll; // polling level (0 - no polling, 100 - aggressive polling) - bool strict_cpu; // strict cpu placement - bool paused; // start in paused state - }; - - struct ggml_threadpool; // forward declaration, see ggml.c - - typedef struct ggml_threadpool * ggml_threadpool_t; - - // the compute plan that needs to be prepared for ggml_graph_compute() - // since https://github.com/ggerganov/ggml/issues/287 - struct ggml_cplan { - size_t work_size; // size of work buffer, calculated by `ggml_graph_plan()` - uint8_t * work_data; // work buffer, to be allocated by caller before calling to `ggml_graph_compute()` - - int n_threads; - struct ggml_threadpool * threadpool; - - // abort ggml_graph_compute when true - ggml_abort_callback abort_callback; - void * abort_callback_data; - }; - - // scratch buffer - struct ggml_scratch { - size_t offs; - size_t size; - void * data; - }; - - struct ggml_init_params { - // memory pool - size_t mem_size; // bytes - void * mem_buffer; // if NULL, memory will be allocated internally - bool no_alloc; // don't allocate memory for the tensor data - }; - - // numa strategies - enum ggml_numa_strategy { - GGML_NUMA_STRATEGY_DISABLED = 0, - GGML_NUMA_STRATEGY_DISTRIBUTE = 1, - GGML_NUMA_STRATEGY_ISOLATE = 2, - GGML_NUMA_STRATEGY_NUMACTL = 3, - GGML_NUMA_STRATEGY_MIRROR = 4, - GGML_NUMA_STRATEGY_COUNT - }; // // GUID @@ -701,9 +650,6 @@ extern "C" { // accepts a UTF-8 path, even on Windows GGML_API FILE * ggml_fopen(const char * fname, const char * mode); - GGML_API void ggml_numa_init(enum ggml_numa_strategy numa); // call once for better performance on NUMA systems - GGML_API bool ggml_is_numa(void); // true if init detected that system has >1 NUMA node - GGML_API void ggml_print_object (const struct ggml_object * obj); GGML_API void ggml_print_objects(const struct ggml_context * ctx); @@ -760,12 +706,12 @@ extern "C" { // main - GGML_API struct ggml_context * ggml_init(struct ggml_init_params params); - GGML_API void ggml_free(struct ggml_context * ctx); + GGML_API struct ggml_context * ggml_init (struct ggml_init_params params); + GGML_API void ggml_reset(struct ggml_context * ctx); + GGML_API void ggml_free (struct ggml_context * ctx); GGML_API size_t ggml_used_mem(const struct ggml_context * ctx); - GGML_API size_t ggml_set_scratch (struct ggml_context * ctx, struct ggml_scratch scratch); GGML_API bool ggml_get_no_alloc(struct ggml_context * ctx); GGML_API void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc); @@ -805,8 +751,7 @@ extern "C" { int64_t ne2, int64_t ne3); - GGML_API struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value); - GGML_API struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value); + GGML_API void * ggml_new_buffer(struct ggml_context * ctx, size_t nbytes); GGML_API struct ggml_tensor * ggml_dup_tensor (struct ggml_context * ctx, const struct ggml_tensor * src); GGML_API struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, struct ggml_tensor * src); @@ -816,35 +761,25 @@ extern "C" { GGML_API struct ggml_tensor * ggml_get_next_tensor (const struct ggml_context * ctx, struct ggml_tensor * tensor); GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name); - GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor); - GGML_API struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value); - GGML_API struct ggml_tensor * ggml_set_f32 (struct ggml_tensor * tensor, float value); - // Converts a flat index into coordinates - GGML_API void ggml_unravel_index(const struct ggml_tensor * tensor, int64_t i, int64_t * i0, int64_t * i1, int64_t * i2, int64_t * i3); + GGML_API void ggml_unravel_index(const struct ggml_tensor * tensor, int64_t i, int64_t * i0, int64_t * i1, int64_t * i2, int64_t * i3); - GGML_API int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i); - GGML_API void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value); - - GGML_API int32_t ggml_get_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3); - GGML_API void ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, int32_t value); - - GGML_API float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i); - GGML_API void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value); - - GGML_API float ggml_get_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3); - GGML_API void ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, float value); + GGML_API enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor); GGML_API void * ggml_get_data (const struct ggml_tensor * tensor); GGML_API float * ggml_get_data_f32(const struct ggml_tensor * tensor); - GGML_API enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor); - GGML_API const char * ggml_get_name (const struct ggml_tensor * tensor); GGML_API struct ggml_tensor * ggml_set_name ( struct ggml_tensor * tensor, const char * name); GGML_ATTRIBUTE_FORMAT(2, 3) GGML_API struct ggml_tensor * ggml_format_name( struct ggml_tensor * tensor, const char * fmt, ...); + // Tensor flags + GGML_API void ggml_set_input(struct ggml_tensor * tensor); + GGML_API void ggml_set_output(struct ggml_tensor * tensor); + GGML_API void ggml_set_param(struct ggml_context * ctx, struct ggml_tensor * tensor); + GGML_API void ggml_set_loss(struct ggml_tensor * tensor); + // // operations on tensors with backpropagation // @@ -1558,7 +1493,7 @@ extern "C" { "use ggml_rope_ext_inplace instead"); // compute correction dims for YaRN RoPE scaling - void ggml_rope_yarn_corr_dims( + GGML_API void ggml_rope_yarn_corr_dims( int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2]); // rotary position embedding backward, i.e compute dx from dy @@ -1761,6 +1696,13 @@ extern "C" { int p2, int p3); + // pad each dimension with reflection: [a, b, c, d] -> [b, a, b, c, d, c] + GGML_API struct ggml_tensor * ggml_pad_reflect_1d( + struct ggml_context * ctx, + struct ggml_tensor * a, + int p0, + int p1); + // Ref: https://github.com/CompVis/stable-diffusion/blob/main/ldm/modules/diffusionmodules/util.py#L151 // timesteps: [N,] // return: [N, dim] @@ -1814,6 +1756,9 @@ extern "C" { struct ggml_tensor * a, enum ggml_prec prec); + GGML_API enum ggml_prec ggml_flash_attn_ext_get_prec( + const struct ggml_tensor * a); + // TODO: needs to be adapted to ggml_flash_attn_ext GGML_API struct ggml_tensor * ggml_flash_attn_back( struct ggml_context * ctx, @@ -1887,7 +1832,7 @@ extern "C" { struct ggml_tensor * pw, struct ggml_tensor * ph); - GGML_API struct ggml_tensor * ggml_rwkv_wkv( + GGML_API struct ggml_tensor * ggml_rwkv_wkv6( struct ggml_context * ctx, struct ggml_tensor * k, struct ggml_tensor * v, @@ -2050,31 +1995,20 @@ extern "C" { struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * grad, - float alpha, - float beta1, - float beta2, - float eps, - float wd); // weight decay + struct ggml_tensor * m, + struct ggml_tensor * v, + struct ggml_tensor * adamw_params); // parameters such a the learning rate // // automatic differentiation // - GGML_API void ggml_set_param(struct ggml_context * ctx, struct ggml_tensor * tensor); - GGML_API void ggml_set_loss(struct ggml_tensor * tensor); - - GGML_API void ggml_build_forward_expand (struct ggml_cgraph * cgraph, struct ggml_tensor * tensor); - GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool accumulate); - - GGML_API void ggml_build_opt_adamw( - struct ggml_context * ctx, - struct ggml_cgraph * gf, - struct ggml_cgraph * gb, - float alpha, - float beta1, - float beta2, - float eps, - float wd); // weight decay + GGML_API void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor); + GGML_API void ggml_build_backward_expand( + struct ggml_context * ctx_static, // context for static gradients (loss + gradient accumulation) + struct ggml_context * ctx_compute, // context for gradient computation + struct ggml_cgraph * cgraph, + bool accumulate); // whether or not gradients should be accumulated, requires static allocation of tensors in ctx_static // graph allocation in a context GGML_API struct ggml_cgraph * ggml_new_graph (struct ggml_context * ctx); // size = GGML_DEFAULT_GRAPH_SIZE, grads = false @@ -2094,28 +2028,9 @@ extern "C" { GGML_API size_t ggml_graph_overhead(void); GGML_API size_t ggml_graph_overhead_custom(size_t size, bool grads); - GGML_API struct ggml_threadpool_params ggml_threadpool_params_default(int n_threads); - GGML_API void ggml_threadpool_params_init (struct ggml_threadpool_params * p, int n_threads); - GGML_API bool ggml_threadpool_params_match (const struct ggml_threadpool_params * p0, const struct ggml_threadpool_params * p1); - GGML_API struct ggml_threadpool * ggml_threadpool_new (struct ggml_threadpool_params * params); - GGML_API void ggml_threadpool_free (struct ggml_threadpool * threadpool); - GGML_API int ggml_threadpool_get_n_threads(struct ggml_threadpool * threadpool); - GGML_API void ggml_threadpool_pause (struct ggml_threadpool * threadpool); - GGML_API void ggml_threadpool_resume (struct ggml_threadpool * threadpool); - - // ggml_graph_plan() has to be called before ggml_graph_compute() - // when plan.work_size > 0, caller must allocate memory for plan.work_data - GGML_API struct ggml_cplan ggml_graph_plan( - const struct ggml_cgraph * cgraph, - int n_threads, /* = GGML_DEFAULT_N_THREADS */ - struct ggml_threadpool * threadpool /* = NULL */ ); - GGML_API enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan); - - // same as ggml_graph_compute() but the work data is allocated as a part of the context - // note: the drawback of this API is that you must have ensured that the context has enough memory for the work data - GGML_API enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads); - - GGML_API struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name); + GGML_API struct ggml_tensor * ggml_graph_get_tensor (const struct ggml_cgraph * cgraph, const char * name); + GGML_API struct ggml_tensor * ggml_graph_get_grad (const struct ggml_cgraph * cgraph, const struct ggml_tensor * node); + GGML_API struct ggml_tensor * ggml_graph_get_grad_acc(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node); GGML_API void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname); GGML_API struct ggml_cgraph * ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval); @@ -2126,201 +2041,14 @@ extern "C" { // dump the graph into a file using the dot format GGML_API void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * gf, const char * filename); - // build gradient checkpointing backward graph gb for gf using provided checkpoints - // gb_tmp will contain original backward graph with rewritten backward process nodes, - // but without the second forward pass nodes. - GGML_API void ggml_build_backward_gradient_checkpointing( - struct ggml_context * ctx, - struct ggml_cgraph * gf, - struct ggml_cgraph * gb, - struct ggml_cgraph * gb_tmp, - struct ggml_tensor * * checkpoints, - int n_checkpoints); - // - // optimization - // - - // optimization methods - enum ggml_opt_type { - GGML_OPT_TYPE_ADAM, - GGML_OPT_TYPE_LBFGS, - }; - - // linesearch methods - enum ggml_linesearch { - GGML_LINESEARCH_DEFAULT = 1, - - GGML_LINESEARCH_BACKTRACKING_ARMIJO = 0, - GGML_LINESEARCH_BACKTRACKING_WOLFE = 1, - GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE = 2, - }; - - // optimization return values - enum ggml_opt_result { - GGML_OPT_RESULT_OK = 0, - GGML_OPT_RESULT_DID_NOT_CONVERGE, - GGML_OPT_RESULT_NO_CONTEXT, - GGML_OPT_RESULT_INVALID_WOLFE, - GGML_OPT_RESULT_FAIL, - GGML_OPT_RESULT_CANCEL, - - GGML_LINESEARCH_FAIL = -128, - GGML_LINESEARCH_MINIMUM_STEP, - GGML_LINESEARCH_MAXIMUM_STEP, - GGML_LINESEARCH_MAXIMUM_ITERATIONS, - GGML_LINESEARCH_INVALID_PARAMETERS, - }; - - typedef void (*ggml_opt_callback)(void * data, int accum_step, float * sched, bool * cancel); + // TODO these functions were sandwiched in the old optimization interface, is there a better place for them? typedef void (*ggml_log_callback)(enum ggml_log_level level, const char * text, void * user_data); // Set callback for all future logging events. // If this is not called, or NULL is supplied, everything is output on stderr. GGML_API void ggml_log_set(ggml_log_callback log_callback, void * user_data); - // optimization parameters - // - // see ggml.c (ggml_opt_default_params) for default values - // - struct ggml_opt_params { - enum ggml_opt_type type; - - size_t graph_size; - - int n_threads; - - // delta-based convergence test - // - // if past == 0 - disabled - // if past > 0: - // stop if |f(x) - f(x_past)| < delta * max(1, |f(x)|) - // - int past; - float delta; - - // maximum number of iterations without improvement - // - // if 0 - disabled - // if > 0: - // assume convergence if no cost improvement in this number of iterations - // - int max_no_improvement; - - bool print_forward_graph; - bool print_backward_graph; - - int n_gradient_accumulation; - - // ADAM parameters - struct { - int n_iter; - - float sched; // schedule multiplier (fixed, decay or warmup) - float decay; // weight decay for AdamW, use 0.0f to disable - int decay_min_ndim; // minimum number of tensor dimension to apply weight decay - float alpha; // learning rate - float beta1; - float beta2; - float eps; // epsilon for numerical stability - float eps_f; // epsilon for convergence test - float eps_g; // epsilon for convergence test - float gclip; // gradient clipping - } adam; - - // LBFGS parameters - struct { - int m; // number of corrections to approximate the inv. Hessian - int n_iter; - int max_linesearch; - - float eps; // convergence tolerance - float ftol; // line search tolerance - float wolfe; - float min_step; - float max_step; - - enum ggml_linesearch linesearch; - } lbfgs; - }; - - struct ggml_opt_context { - struct ggml_context * ctx; - struct ggml_opt_params params; - - int iter; - int64_t nx; // number of parameter elements - - bool just_initialized; - - float loss_before; - float loss_after; - - struct { - struct ggml_tensor * g; // current gradient - struct ggml_tensor * m; // first moment - struct ggml_tensor * v; // second moment - struct ggml_tensor * pf; // past function values - float fx_best; - float fx_prev; - int n_no_improvement; - } adam; - - struct { - struct ggml_tensor * x; // current parameters - struct ggml_tensor * xp; // previous parameters - struct ggml_tensor * g; // current gradient - struct ggml_tensor * gp; // previous gradient - struct ggml_tensor * d; // search direction - struct ggml_tensor * pf; // past function values - struct ggml_tensor * lmal; // the L-BFGS memory alpha - struct ggml_tensor * lmys; // the L-BFGS memory ys - struct ggml_tensor * lms; // the L-BFGS memory s - struct ggml_tensor * lmy; // the L-BFGS memory y - float fx_best; - float step; - int j; - int k; - int end; - int n_no_improvement; - } lbfgs; - }; - - GGML_API struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type); - - // optimize the function defined by the tensor f - GGML_API enum ggml_opt_result ggml_opt( - struct ggml_context * ctx, - struct ggml_opt_params params, - struct ggml_tensor * f); - - // initialize optimizer context - GGML_API void ggml_opt_init( - struct ggml_context * ctx, - struct ggml_opt_context * opt, - struct ggml_opt_params params, - int64_t nx); - - // continue optimizing the function defined by the tensor f - GGML_API enum ggml_opt_result ggml_opt_resume( - struct ggml_context * ctx, - struct ggml_opt_context * opt, - struct ggml_tensor * f); - - // continue optimizing the function defined by the tensor f - GGML_API enum ggml_opt_result ggml_opt_resume_g( - struct ggml_context * ctx, - struct ggml_opt_context * opt, - struct ggml_tensor * f, - struct ggml_cgraph * gf, - struct ggml_cgraph * gb, - ggml_opt_callback callback, - void * callback_data); - - // - // tensor flags - // - GGML_API void ggml_set_input(struct ggml_tensor * tensor); - GGML_API void ggml_set_output(struct ggml_tensor * tensor); + GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor); // // quantization @@ -2477,48 +2205,6 @@ extern "C" { GGML_API size_t gguf_get_meta_size(const struct gguf_context * ctx); GGML_API void gguf_get_meta_data(const struct gguf_context * ctx, void * data); - // - // system info - // - - GGML_API int ggml_cpu_has_avx (void); - GGML_API int ggml_cpu_has_avx_vnni (void); - GGML_API int ggml_cpu_has_avx2 (void); - GGML_API int ggml_cpu_has_avx512 (void); - GGML_API int ggml_cpu_has_avx512_vbmi(void); - GGML_API int ggml_cpu_has_avx512_vnni(void); - GGML_API int ggml_cpu_has_avx512_bf16(void); - GGML_API int ggml_cpu_has_amx_int8 (void); - GGML_API int ggml_cpu_has_fma (void); - GGML_API int ggml_cpu_has_neon (void); - GGML_API int ggml_cpu_has_sve (void); - GGML_API int ggml_cpu_has_arm_fma (void); - GGML_API int ggml_cpu_has_metal (void); - GGML_API int ggml_cpu_has_f16c (void); - GGML_API int ggml_cpu_has_fp16_va (void); - GGML_API int ggml_cpu_has_wasm_simd (void); - GGML_API int ggml_cpu_has_blas (void); - GGML_API int ggml_cpu_has_cuda (void); - GGML_API int ggml_cpu_has_vulkan (void); - GGML_API int ggml_cpu_has_kompute (void); - GGML_API int ggml_cpu_has_gpublas (void); - GGML_API int ggml_cpu_has_sse3 (void); - GGML_API int ggml_cpu_has_ssse3 (void); - GGML_API int ggml_cpu_has_riscv_v (void); - GGML_API int ggml_cpu_has_sycl (void); - GGML_API int ggml_cpu_has_rpc (void); - GGML_API int ggml_cpu_has_vsx (void); - GGML_API int ggml_cpu_has_matmul_int8(void); - GGML_API int ggml_cpu_has_cann (void); - GGML_API int ggml_cpu_has_llamafile (void); - - // get the sve vector length in bytes - GGML_API int ggml_cpu_get_sve_cnt(void); - - // - // Internal types and functions exposed for tests and benchmarks - // - #ifdef __cplusplus // restrict not standard in C++ #define GGML_RESTRICT @@ -2527,14 +2213,6 @@ extern "C" { #endif typedef void (*ggml_to_float_t) (const void * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); typedef void (*ggml_from_float_t)(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); - typedef void (*ggml_from_float_to_mat_t) - (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nr, int64_t k, int64_t bs); - typedef void (*ggml_vec_dot_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, size_t bx, - const void * GGML_RESTRICT y, size_t by, int nrc); - typedef void (*ggml_gemv_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, - const void * GGML_RESTRICT y, int nr, int nc); - typedef void (*ggml_gemm_t) (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, - const void * GGML_RESTRICT y, int nr, int nc); struct ggml_type_traits { const char * type_name; @@ -2543,19 +2221,42 @@ extern "C" { size_t type_size; bool is_quantized; ggml_to_float_t to_float; - ggml_from_float_t from_float; ggml_from_float_t from_float_ref; - ggml_from_float_to_mat_t from_float_to_mat; - ggml_vec_dot_t vec_dot; - enum ggml_type vec_dot_type; - int64_t nrows; // number of rows to process simultaneously - int64_t ncols; // number of columns to process simultaneously - ggml_gemv_t gemv; - ggml_gemm_t gemm; }; GGML_API const struct ggml_type_traits * ggml_get_type_traits(enum ggml_type type); + // ggml threadpool + // TODO: currently, only a few functions are in the base ggml API, while the rest are in the CPU backend + // the goal should be to create an API that other backends can use move everything to the ggml base + + // scheduling priorities + enum ggml_sched_priority { + GGML_SCHED_PRIO_NORMAL, + GGML_SCHED_PRIO_MEDIUM, + GGML_SCHED_PRIO_HIGH, + GGML_SCHED_PRIO_REALTIME + }; + + // threadpool params + // Use ggml_threadpool_params_default() or ggml_threadpool_params_init() to populate the defaults + struct ggml_threadpool_params { + bool cpumask[GGML_MAX_N_THREADS]; // mask of cpu cores (all-zeros means use default affinity settings) + int n_threads; // number of threads + enum ggml_sched_priority prio; // thread priority + uint32_t poll; // polling level (0 - no polling, 100 - aggressive polling) + bool strict_cpu; // strict cpu placement + bool paused; // start in paused state + }; + + struct ggml_threadpool; // forward declaration, see ggml.c + + typedef struct ggml_threadpool * ggml_threadpool_t; + + GGML_API struct ggml_threadpool_params ggml_threadpool_params_default(int n_threads); + GGML_API void ggml_threadpool_params_init (struct ggml_threadpool_params * p, int n_threads); + GGML_API bool ggml_threadpool_params_match (const struct ggml_threadpool_params * p0, const struct ggml_threadpool_params * p1); + #ifdef __cplusplus } #endif diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt index aa405e4d0..f07533fdb 100644 --- a/ggml/src/CMakeLists.txt +++ b/ggml/src/CMakeLists.txt @@ -1,7 +1,5 @@ include(CheckCXXCompilerFlag) -unset(GGML_CDEF_PUBLIC) - add_compile_definitions(GGML_SCHED_MAX_COPIES=${GGML_SCHED_MAX_COPIES}) # enable libstdc++ assertions for debug builds @@ -26,924 +24,7 @@ if (NOT MSVC) endif() endif() -unset(GGML_EXTRA_LIBS_PRIVATE) -unset(GGML_EXTRA_LIBS_PUBLIC) - -if (APPLE AND GGML_ACCELERATE) - find_library(ACCELERATE_FRAMEWORK Accelerate) - if (ACCELERATE_FRAMEWORK) - message(STATUS "Accelerate framework found") - - add_compile_definitions(GGML_USE_ACCELERATE) - add_compile_definitions(ACCELERATE_NEW_LAPACK) - add_compile_definitions(ACCELERATE_LAPACK_ILP64) - - list(APPEND GGML_EXTRA_LIBS_PRIVATE ${ACCELERATE_FRAMEWORK}) - else() - message(WARNING "Accelerate framework not found") - endif() -endif() - -if (GGML_METAL) - find_library(FOUNDATION_LIBRARY Foundation REQUIRED) - find_library(METAL_FRAMEWORK Metal REQUIRED) - find_library(METALKIT_FRAMEWORK MetalKit REQUIRED) - - message(STATUS "Metal framework found") - set(GGML_HEADERS_METAL ../include/ggml-metal.h) - set(GGML_SOURCES_METAL ggml-metal.m) - - list(APPEND GGML_CDEF_PUBLIC GGML_USE_METAL) - if (GGML_METAL_NDEBUG) - add_compile_definitions(GGML_METAL_NDEBUG) - endif() - - # copy ggml-common.h and ggml-metal.metal to bin directory - configure_file(ggml-common.h ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h COPYONLY) - configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY) - - if (GGML_METAL_EMBED_LIBRARY) - enable_language(ASM) - - add_compile_definitions(GGML_METAL_EMBED_LIBRARY) - - set(METALLIB_COMMON "${CMAKE_CURRENT_SOURCE_DIR}/ggml-common.h") - set(METALLIB_SOURCE "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal") - - file(MAKE_DIRECTORY "${CMAKE_BINARY_DIR}/autogenerated") - - # merge ggml-common.h and ggml-metal.metal into a single file - set(METALLIB_EMBED_ASM "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.s") - set(METALLIB_SOURCE_EMBED "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.metal") - - add_custom_command( - OUTPUT ${METALLIB_EMBED_ASM} - COMMAND echo "Embedding Metal library" - COMMAND sed -e '/\#include \"ggml-common.h\"/r ${METALLIB_COMMON}' -e '/\#include \"ggml-common.h\"/d' < ${METALLIB_SOURCE} > ${METALLIB_SOURCE_EMBED} - COMMAND echo ".section __DATA,__ggml_metallib" > ${METALLIB_EMBED_ASM} - COMMAND echo ".globl _ggml_metallib_start" >> ${METALLIB_EMBED_ASM} - COMMAND echo "_ggml_metallib_start:" >> ${METALLIB_EMBED_ASM} - COMMAND echo ".incbin \\\"${METALLIB_SOURCE_EMBED}\\\"" >> ${METALLIB_EMBED_ASM} - COMMAND echo ".globl _ggml_metallib_end" >> ${METALLIB_EMBED_ASM} - COMMAND echo "_ggml_metallib_end:" >> ${METALLIB_EMBED_ASM} - DEPENDS ggml-metal.metal ggml-common.h - COMMENT "Generate assembly for embedded Metal library" - ) - - list(APPEND GGML_SOURCES_METAL ${METALLIB_EMBED_ASM}) - else() - if (GGML_METAL_SHADER_DEBUG) - # custom command to do the following: - # xcrun -sdk macosx metal -fno-fast-math -c ggml-metal.metal -o ggml-metal.air - # xcrun -sdk macosx metallib ggml-metal.air -o default.metallib - # - # note: this is the only way I found to disable fast-math in Metal. it's ugly, but at least it works - # disabling fast math is needed in order to pass tests/test-backend-ops - # note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1 - # note: unfortunately, we have to call it default.metallib instead of ggml.metallib - # ref: https://github.com/ggerganov/whisper.cpp/issues/1720 - set(XC_FLAGS -fno-fast-math -fno-inline -g) - else() - set(XC_FLAGS -O3) - endif() - - # Append macOS metal versioning flags - if (GGML_METAL_MACOSX_VERSION_MIN) - message(STATUS "Adding -mmacosx-version-min=${GGML_METAL_MACOSX_VERSION_MIN} flag to metal compilation") - list (APPEND XC_FLAGS -mmacosx-version-min=${GGML_METAL_MACOSX_VERSION_MIN}) - endif() - - if (GGML_METAL_STD) - message(STATUS "Adding -std=${GGML_METAL_STD} flag to metal compilation") - list (APPEND XC_FLAGS -std=${GGML_METAL_STD}) - endif() - - add_custom_command( - OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib - COMMAND xcrun -sdk macosx metal ${XC_FLAGS} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air - COMMAND xcrun -sdk macosx metallib ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib - COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air - COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h - COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal - DEPENDS ggml-metal.metal ggml-common.h - COMMENT "Compiling Metal kernels" - ) - - add_custom_target( - ggml-metal ALL - DEPENDS ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib - ) - endif() # GGML_METAL_EMBED_LIBRARY - - list(APPEND GGML_EXTRA_LIBS_PRIVATE - ${FOUNDATION_LIBRARY} - ${METAL_FRAMEWORK} - ${METALKIT_FRAMEWORK} - ) -endif() - -if (GGML_MUSA) - set(CMAKE_C_COMPILER clang) - set(CMAKE_C_EXTENSIONS OFF) - set(CMAKE_CXX_COMPILER clang++) - set(CMAKE_CXX_EXTENSIONS OFF) - - set(GGML_CUDA ON) - - list(APPEND GGML_CDEF_PUBLIC GGML_USE_MUSA) -endif() - -if (GGML_OPENMP) - find_package(OpenMP) - if (OpenMP_FOUND) - message(STATUS "OpenMP found") - - add_compile_definitions(GGML_USE_OPENMP) - - list(APPEND GGML_EXTRA_LIBS_PRIVATE OpenMP::OpenMP_C OpenMP::OpenMP_CXX) - - if (GGML_MUSA) - list(APPEND GGML_EXTRA_INCLUDES "/usr/lib/llvm-14/lib/clang/14.0.0/include") - list(APPEND GGML_EXTRA_LIBS_PRIVATE "/usr/lib/llvm-14/lib/libomp.so") - endif() - else() - message(WARNING "OpenMP not found") - endif() -endif() - -if (GGML_BLAS) - if (GGML_STATIC) - set(BLA_STATIC ON) - endif() - #if (CMAKE_VERSION VERSION_GREATER_EQUAL 3.22) - # set(BLA_SIZEOF_INTEGER 8) - #endif() - - set(BLA_VENDOR ${GGML_BLAS_VENDOR}) - find_package(BLAS) - - if (BLAS_FOUND) - message(STATUS "BLAS found, Libraries: ${BLAS_LIBRARIES}") - - if (("${BLAS_INCLUDE_DIRS}" STREQUAL "") AND NOT (${GGML_BLAS_VENDOR} MATCHES "Apple")) - # BLAS_INCLUDE_DIRS is missing in FindBLAS.cmake. - # see https://gitlab.kitware.com/cmake/cmake/-/issues/20268 - find_package(PkgConfig REQUIRED) - if (${GGML_BLAS_VENDOR} MATCHES "Generic") - pkg_check_modules(DepBLAS blas) - elseif (${GGML_BLAS_VENDOR} MATCHES "OpenBLAS") - # As of openblas v0.3.22, the 64-bit is named openblas64.pc - pkg_check_modules(DepBLAS openblas64) - if (NOT DepBLAS_FOUND) - pkg_check_modules(DepBLAS openblas) - endif() - elseif (${GGML_BLAS_VENDOR} MATCHES "FLAME") - add_compile_definitions(GGML_BLAS_USE_BLIS) - pkg_check_modules(DepBLAS blis) - elseif (${GGML_BLAS_VENDOR} MATCHES "ATLAS") - pkg_check_modules(DepBLAS blas-atlas) - elseif (${GGML_BLAS_VENDOR} MATCHES "FlexiBLAS") - pkg_check_modules(DepBLAS flexiblas_api) - elseif (${GGML_BLAS_VENDOR} MATCHES "Intel") - add_compile_definitions(GGML_BLAS_USE_MKL) - # all Intel* libraries share the same include path - pkg_check_modules(DepBLAS mkl-sdl) - elseif (${GGML_BLAS_VENDOR} MATCHES "NVHPC") - # this doesn't provide pkg-config - # suggest to assign BLAS_INCLUDE_DIRS on your own - if ("${NVHPC_VERSION}" STREQUAL "") - message(WARNING "Better to set NVHPC_VERSION") - else() - set(DepBLAS_FOUND ON) - set(DepBLAS_INCLUDE_DIRS "/opt/nvidia/hpc_sdk/${CMAKE_SYSTEM_NAME}_${CMAKE_SYSTEM_PROCESSOR}/${NVHPC_VERSION}/math_libs/include") - endif() - endif() - if (DepBLAS_FOUND) - set(BLAS_INCLUDE_DIRS ${DepBLAS_INCLUDE_DIRS}) - else() - message(WARNING "BLAS_INCLUDE_DIRS neither been provided nor been automatically" - " detected by pkgconfig, trying to find cblas.h from possible paths...") - find_path(BLAS_INCLUDE_DIRS - NAMES cblas.h - HINTS - /usr/include - /usr/local/include - /usr/include/openblas - /opt/homebrew/opt/openblas/include - /usr/local/opt/openblas/include - /usr/include/x86_64-linux-gnu/openblas/include - ) - endif() - endif() - - message(STATUS "BLAS found, Includes: ${BLAS_INCLUDE_DIRS}") - - add_compile_options(${BLAS_LINKER_FLAGS}) - - list(APPEND GGML_CDEF_PUBLIC GGML_USE_BLAS) - - if (${BLAS_INCLUDE_DIRS} MATCHES "mkl" AND (${GGML_BLAS_VENDOR} MATCHES "Generic" OR ${GGML_BLAS_VENDOR} MATCHES "Intel")) - add_compile_definitions(GGML_BLAS_USE_MKL) - endif() - - set(GGML_HEADERS_BLAS ../include/ggml-blas.h) - set(GGML_SOURCES_BLAS ggml-blas.cpp) - - list(APPEND GGML_EXTRA_LIBS_PRIVATE ${BLAS_LIBRARIES}) - list(APPEND GGML_EXTRA_INCLUDES ${BLAS_INCLUDE_DIRS}) - else() - message(WARNING "BLAS not found, please refer to " - "https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors" - " to set correct GGML_BLAS_VENDOR") - endif() -endif() - -if (GGML_LLAMAFILE) - message(STATUS "Using llamafile") - - add_compile_definitions(GGML_USE_LLAMAFILE) - - set(GGML_HEADERS_LLAMAFILE llamafile/sgemm.h) - set(GGML_SOURCES_LLAMAFILE llamafile/sgemm.cpp) -endif() - -if (GGML_AMX) - if (CMAKE_COMPILER_IS_GNUCC AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 11.0) - else() - set(GGML_AMX OFF) - message(WARNING "AMX requires gcc version > 11.0. Turning off GGML_AMX.") - endif() - - if (GGML_AMX) - message(STATUS "Using AMX") - - list(APPEND GGML_CDEF_PUBLIC GGML_USE_AMX) - - file(GLOB GGML_HEADERS_AMX "ggml-amx/*.h") - list(APPEND GGML_HEADERS_AMX "../include/ggml-amx.h") - - file(GLOB GGML_SOURCES_AMX "ggml-amx/*.cpp") - list(APPEND GGML_SOURCES_AMX "ggml-amx.cpp") - endif() -endif() - -if (GGML_CUDA) - cmake_minimum_required(VERSION 3.18) # for CMAKE_CUDA_ARCHITECTURES - - if (GGML_MUSA) - list(APPEND CMAKE_MODULE_PATH "/usr/local/musa/cmake/") - find_package(MUSAToolkit) - set(CUDAToolkit_FOUND ${MUSAToolkit_FOUND}) - else() - find_package(CUDAToolkit) - endif() - - if (CUDAToolkit_FOUND) - message(STATUS "CUDA found") - - if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES) - # 52 == lowest CUDA 12 standard - # 60 == FP16 CUDA intrinsics - # 61 == integer CUDA intrinsics - # 70 == compute capability at which unrolling a loop in mul_mat_q kernels is faster - if (GGML_CUDA_F16 OR GGML_CUDA_DMMV_F16) - set(CMAKE_CUDA_ARCHITECTURES "60;61;70;75") - else() - set(CMAKE_CUDA_ARCHITECTURES "52;61;70;75") - #set(CMAKE_CUDA_ARCHITECTURES "OFF") # use this to compile much faster, but only F16 models work - endif() - endif() - message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}") - - if (GGML_MUSA) - set(CMAKE_CUDA_COMPILER ${MUSAToolkit_MCC_EXECUTABLE}) - else() - enable_language(CUDA) - endif() - - file(GLOB GGML_HEADERS_CUDA "ggml-cuda/*.cuh") - list(APPEND GGML_HEADERS_CUDA "../include/ggml-cuda.h") - - file(GLOB GGML_SOURCES_CUDA "ggml-cuda/*.cu") - list(APPEND GGML_SOURCES_CUDA "ggml-cuda.cu") - file(GLOB SRCS "ggml-cuda/template-instances/fattn-wmma*.cu") - list(APPEND GGML_SOURCES_CUDA ${SRCS}) - file(GLOB SRCS "ggml-cuda/template-instances/mmq*.cu") - list(APPEND GGML_SOURCES_CUDA ${SRCS}) - - if (GGML_CUDA_FA_ALL_QUANTS) - file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*.cu") - list(APPEND GGML_SOURCES_CUDA ${SRCS}) - add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS) - else() - file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu") - list(APPEND GGML_SOURCES_CUDA ${SRCS}) - file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu") - list(APPEND GGML_SOURCES_CUDA ${SRCS}) - file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*f16-f16.cu") - list(APPEND GGML_SOURCES_CUDA ${SRCS}) - endif() - - list(APPEND GGML_CDEF_PUBLIC GGML_USE_CUDA) - - add_compile_definitions(GGML_CUDA_DMMV_X=${GGML_CUDA_DMMV_X}) - add_compile_definitions(GGML_CUDA_MMV_Y=${GGML_CUDA_MMV_Y}) - add_compile_definitions(K_QUANTS_PER_ITERATION=${GGML_CUDA_KQUANTS_ITER}) - add_compile_definitions(GGML_CUDA_PEER_MAX_BATCH_SIZE=${GGML_CUDA_PEER_MAX_BATCH_SIZE}) - - if (GGML_CUDA_GRAPHS) - add_compile_definitions(GGML_CUDA_USE_GRAPHS) - endif() - - if (GGML_CUDA_FORCE_DMMV) - add_compile_definitions(GGML_CUDA_FORCE_DMMV) - endif() - - if (GGML_CUDA_FORCE_MMQ) - add_compile_definitions(GGML_CUDA_FORCE_MMQ) - endif() - - if (GGML_CUDA_FORCE_CUBLAS) - add_compile_definitions(GGML_CUDA_FORCE_CUBLAS) - endif() - - if (GGML_CUDA_NO_VMM) - add_compile_definitions(GGML_CUDA_NO_VMM) - endif() - - if (DEFINED GGML_CUDA_DMMV_Y) - add_compile_definitions(GGML_CUDA_MMV_Y=${GGML_CUDA_DMMV_Y}) # for backwards compatibility - endif() - - if (GGML_CUDA_F16 OR GGML_CUDA_DMMV_F16) - add_compile_definitions(GGML_CUDA_F16) - endif() - - if (GGML_CUDA_NO_PEER_COPY) - add_compile_definitions(GGML_CUDA_NO_PEER_COPY) - endif() - - if (GGML_MUSA) - set_source_files_properties(${GGML_SOURCES_CUDA} PROPERTIES LANGUAGE CXX) - foreach(SOURCE ${GGML_SOURCES_CUDA}) - set_property(SOURCE ${SOURCE} PROPERTY COMPILE_FLAGS "-x musa -mtgpu --cuda-gpu-arch=mp_21 --cuda-gpu-arch=mp_22") - endforeach() - endif() - - if (GGML_STATIC) - if (WIN32) - # As of 12.3.1 CUDA Toolkit for Windows does not offer a static cublas library - list(APPEND GGML_EXTRA_LIBS_PRIVATE CUDA::cudart_static CUDA::cublas CUDA::cublasLt) - else () - if (GGML_MUSA) - list(APPEND GGML_EXTRA_LIBS_PRIVATE MUSA::musart_static MUSA::mublas_static) - else() - list(APPEND GGML_EXTRA_LIBS_PRIVATE CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static) - endif() - endif() - else() - if (GGML_MUSA) - list(APPEND GGML_EXTRA_LIBS_PRIVATE MUSA::musart MUSA::mublas) - else() - list(APPEND GGML_EXTRA_LIBS_PRIVATE CUDA::cudart CUDA::cublas CUDA::cublasLt) - endif() - endif() - - if (GGML_CUDA_NO_VMM) - # No VMM requested, no need to link directly with the cuda driver lib (libcuda.so) - else() - if (GGML_MUSA) - list(APPEND GGML_EXTRA_LIBS_PRIVATE MUSA::musa_driver) # required by muDeviceGetAttribute(), muMemGetAllocationGranularity(...), ... - else() - list(APPEND GGML_EXTRA_LIBS_PRIVATE CUDA::cuda_driver) # required by cuDeviceGetAttribute(), cuMemGetAllocationGranularity(...), ... - endif() - endif() - else() - message(WARNING "CUDA not found") - endif() -endif() - -if (GGML_HIPBLAS) - if (NOT EXISTS $ENV{ROCM_PATH}) - if (NOT EXISTS /opt/rocm) - set(ROCM_PATH /usr) - else() - set(ROCM_PATH /opt/rocm) - endif() - else() - set(ROCM_PATH $ENV{ROCM_PATH}) - endif() - - list(APPEND CMAKE_PREFIX_PATH ${ROCM_PATH}) - list(APPEND CMAKE_PREFIX_PATH "${ROCM_PATH}/lib64/cmake") - - # CMake on Windows doesn't support the HIP language yet - if (WIN32) - set(CXX_IS_HIPCC TRUE) - else() - string(REGEX MATCH "hipcc(\.bat)?$" CXX_IS_HIPCC "${CMAKE_CXX_COMPILER}") - endif() - - if (CXX_IS_HIPCC) - if (LINUX) - if (NOT ${CMAKE_CXX_COMPILER_ID} MATCHES "Clang") - message(WARNING "Only LLVM is supported for HIP, hint: CXX=/opt/rocm/llvm/bin/clang++") - endif() - - message(WARNING "Setting hipcc as the C++ compiler is legacy behavior." - " Prefer setting the HIP compiler directly. See README for details.") - endif() - else() - # Forward AMDGPU_TARGETS to CMAKE_HIP_ARCHITECTURES. - if (AMDGPU_TARGETS AND NOT CMAKE_HIP_ARCHITECTURES) - set(CMAKE_HIP_ARCHITECTURES ${AMDGPU_TARGETS}) - endif() - cmake_minimum_required(VERSION 3.21) - enable_language(HIP) - endif() - - find_package(hip REQUIRED) - find_package(hipblas REQUIRED) - find_package(rocblas REQUIRED) - - message(STATUS "HIP and hipBLAS found") - - file(GLOB GGML_HEADERS_ROCM "ggml-cuda/*.cuh") - list(APPEND GGML_HEADERS_ROCM "../include/ggml-cuda.h") - - file(GLOB GGML_SOURCES_ROCM "ggml-cuda/*.cu") - list(APPEND GGML_SOURCES_ROCM "ggml-cuda.cu") - file(GLOB SRCS "ggml-cuda/template-instances/fattn-wmma*.cu") - list(APPEND GGML_SOURCES_ROCM ${SRCS}) - file(GLOB SRCS "ggml-cuda/template-instances/mmq*.cu") - list(APPEND GGML_SOURCES_ROCM ${SRCS}) - - if (GGML_CUDA_FA_ALL_QUANTS) - file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*.cu") - list(APPEND GGML_SOURCES_ROCM ${SRCS}) - add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS) - else() - file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu") - list(APPEND GGML_SOURCES_ROCM ${SRCS}) - file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu") - list(APPEND GGML_SOURCES_ROCM ${SRCS}) - file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*f16-f16.cu") - list(APPEND GGML_SOURCES_ROCM ${SRCS}) - endif() - - list(APPEND GGML_CDEF_PUBLIC GGML_USE_CUDA) - - add_compile_definitions(GGML_USE_HIPBLAS) - add_compile_definitions(GGML_CUDA_DMMV_X=${GGML_CUDA_DMMV_X}) - add_compile_definitions(GGML_CUDA_MMV_Y=${GGML_CUDA_MMV_Y}) - add_compile_definitions(K_QUANTS_PER_ITERATION=${GGML_CUDA_KQUANTS_ITER}) - - if (GGML_HIP_UMA) - add_compile_definitions(GGML_HIP_UMA) - endif() - - if (GGML_CUDA_FORCE_DMMV) - add_compile_definitions(GGML_CUDA_FORCE_DMMV) - endif() - - if (GGML_CUDA_FORCE_MMQ) - add_compile_definitions(GGML_CUDA_FORCE_MMQ) - endif() - - if (GGML_CUDA_FORCE_CUBLAS) - add_compile_definitions(GGML_CUDA_FORCE_CUBLAS) - endif() - - if (GGML_CUDA_NO_PEER_COPY) - add_compile_definitions(GGML_CUDA_NO_PEER_COPY) - endif() - - if (CXX_IS_HIPCC) - set_source_files_properties(${GGML_SOURCES_ROCM} PROPERTIES LANGUAGE CXX) - list(APPEND GGML_EXTRA_LIBS_PRIVATE hip::device) - else() - set_source_files_properties(${GGML_SOURCES_ROCM} PROPERTIES LANGUAGE HIP) - endif() - - if (GGML_STATIC) - message(FATAL_ERROR "Static linking not supported for HIP/ROCm") - endif() - - list(APPEND GGML_EXTRA_LIBS_PUBLIC hip::host roc::rocblas roc::hipblas) -endif() - -if (GGML_SYCL) - if (NOT GGML_SYCL_TARGET MATCHES "^(INTEL|NVIDIA|AMD)$") - message(FATAL_ERROR "Invalid backend chosen, supported options are INTEL, NVIDIA, or AMD") - endif() - - check_cxx_compiler_flag("-fsycl" SUPPORTS_SYCL) - - if (DEFINED ENV{ONEAPI_ROOT}) - message(STATUS "Using oneAPI Release SYCL compiler (icpx).") - elseif(SUPPORTS_SYCL) - message(WARNING "Using open-source SYCL compiler (clang++). Didn't detect ENV {ONEAPI_ROOT}. - If you expected the oneAPI Release compiler, please install oneAPI & source it, like: - source /opt/intel/oneapi/setvars.sh") - else() - message(FATAL_ERROR, "C++ compiler lacks SYCL support.") - endif() - message(STATUS "SYCL found") - #todo: AOT - - list(APPEND GGML_CDEF_PUBLIC GGML_USE_SYCL) - - if (GGML_SYCL_F16) - if (GGML_SYCL_TARGET STREQUAL "AMD") - message(WARNING "AMD target does not entirely support FP16 in the SYCL backend.") - endif() - add_compile_definitions(GGML_SYCL_F16) - endif() - - if (GGML_CUDA_FORCE_MMQ) - add_compile_definitions(GGML_SYCL_FORCE_MMQ) - endif() - - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing -fsycl") - - if (GGML_SYCL_TARGET STREQUAL "NVIDIA") - add_compile_definitions(GGML_SYCL_WARP_SIZE=32) - elseif (GGML_SYCL_TARGET STREQUAL "AMD") - # INFO: Allowed Sub_group_sizes are not consistent through all - # hip targets. For example, 64 is used for certain models, but the backend - # does not support it. - # Target archs tested working: gfx1030, gfx1031, (Only tested sub_group_size = 32) - add_compile_definitions(GGML_SYCL_WARP_SIZE=32) - else() - add_compile_definitions(GGML_SYCL_WARP_SIZE=16) - endif() - - file(GLOB GGML_HEADERS_SYCL "ggml-sycl/*.hpp") - list(APPEND GGML_HEADERS_SYCL "../include/ggml-sycl.h") - - file(GLOB GGML_SOURCES_SYCL "ggml-sycl/*.cpp") - list(APPEND GGML_SOURCES_SYCL "ggml-sycl.cpp") - - find_package(DNNL) - message("-- DNNL found:" ${DNNL_FOUND}) - - if (GGML_SYCL_TARGET STREQUAL "INTEL") - add_compile_definitions(GGML_SYCL_DNNL=${DNNL_FOUND}) - else() - add_compile_definitions(GGML_SYCL_DNNL=0) - endif() - - if (${DNNL_FOUND} AND GGML_SYCL_TARGET STREQUAL "INTEL") - list(APPEND GGML_EXTRA_LIBS_PRIVATE DNNL::dnnl) - endif() - - if (WIN32) - find_package(IntelSYCL REQUIRED) - find_package(MKL REQUIRED) - list(APPEND GGML_EXTRA_LIBS_PRIVATE IntelSYCL::SYCL_CXX MKL::MKL MKL::MKL_SYCL) - else() - if (GGML_SYCL_TARGET STREQUAL "INTEL") - list(APPEND GGML_EXTRA_LIBS_PRIVATE sycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread) - elseif (GGML_SYCL_TARGET STREQUAL "NVIDIA") - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=nvptx64-nvidia-cuda") - list(APPEND GGML_EXTRA_LIBS_PRIVATE sycl pthread m dl onemkl) - elseif (GGML_SYCL_TARGET STREQUAL "AMD") - if (GGML_SYCL_HIP_TARGET STREQUAL "") - message(ERROR "Can't enable SYCL hip backend, GGML_SYCL_HIP_TARGET has not been set.") - endif() - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=amdgcn-amd-amdhsa -Xsycl-target-backend --offload-arch=${GGML_SYCL_HIP_TARGET}") - list(APPEND GGML_EXTRA_LIBS_PRIVATE sycl pthread m dl onemkl) - endif() - endif() -endif() - -if (GGML_RPC) - message(STATUS "RPC found") - - list(APPEND GGML_CDEF_PUBLIC GGML_USE_RPC) - - if (WIN32) - list(APPEND GGML_EXTRA_LIBS_PRIVATE ws2_32) - endif() - - set(GGML_HEADERS_RPC ../include/ggml-rpc.h) - set(GGML_SOURCES_RPC ggml-rpc.cpp) -endif() - -if (GGML_VULKAN) - find_package(Vulkan COMPONENTS glslc REQUIRED) - - if (Vulkan_FOUND) - message(STATUS "Vulkan found") - - list(APPEND GGML_CDEF_PUBLIC GGML_USE_VULKAN) - - # Workaround to the "can't dereference invalidated vector iterator" bug in clang-cl debug build - # Posssibly relevant: https://stackoverflow.com/questions/74748276/visual-studio-no-displays-the-correct-length-of-stdvector - if (MSVC AND CMAKE_CXX_COMPILER_ID STREQUAL "Clang") - add_compile_definitions(_ITERATOR_DEBUG_LEVEL=0) - endif() - - if (GGML_VULKAN_CHECK_RESULTS) - add_compile_definitions(GGML_VULKAN_CHECK_RESULTS) - endif() - - if (GGML_VULKAN_DEBUG) - add_compile_definitions(GGML_VULKAN_DEBUG) - endif() - - if (GGML_VULKAN_MEMORY_DEBUG) - add_compile_definitions(GGML_VULKAN_MEMORY_DEBUG) - endif() - - if (GGML_VULKAN_SHADER_DEBUG_INFO) - add_compile_definitions(GGML_VULKAN_SHADER_DEBUG_INFO) - endif() - - if (GGML_VULKAN_PERF) - add_compile_definitions(GGML_VULKAN_PERF) - endif() - - if (GGML_VULKAN_VALIDATE) - add_compile_definitions(GGML_VULKAN_VALIDATE) - endif() - - if (GGML_VULKAN_RUN_TESTS) - add_compile_definitions(GGML_VULKAN_RUN_TESTS) - endif() - - add_subdirectory(vulkan-shaders) - - set (_ggml_vk_genshaders_cmd vulkan-shaders-gen) - set (_ggml_vk_header ${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan-shaders.hpp) - set (_ggml_vk_source ${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan-shaders.cpp) - set (_ggml_vk_input_dir ${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders) - set (_ggml_vk_output_dir ${CMAKE_CURRENT_BINARY_DIR}/vulkan-shaders.spv) - - file(GLOB _ggml_vk_shader_deps "${_ggml_vk_input_dir}/*.comp") - - add_custom_command( - OUTPUT ${_ggml_vk_header} - ${_ggml_vk_source} - - COMMAND ${_ggml_vk_genshaders_cmd} - --glslc ${Vulkan_GLSLC_EXECUTABLE} - --input-dir ${_ggml_vk_input_dir} - --output-dir ${_ggml_vk_output_dir} - --target-hpp ${_ggml_vk_header} - --target-cpp ${_ggml_vk_source} - --no-clean - - DEPENDS ${_ggml_vk_shader_deps} - COMMENT "Generate vulkan shaders" - ) - - set(GGML_HEADERS_VULKAN ${CMAKE_CURRENT_SOURCE_DIR}/../include/ggml-vulkan.h ${_ggml_vk_header}) - set(GGML_SOURCES_VULKAN ggml-vulkan.cpp ${_ggml_vk_source}) - - list(APPEND GGML_EXTRA_LIBS_PRIVATE Vulkan::Vulkan) - list(APPEND GGML_EXTRA_INCLUDES ${CMAKE_CURRENT_BINARY_DIR}) - else() - message(WARNING "Vulkan not found") - endif() -endif() - -if (GGML_KOMPUTE) - add_compile_definitions(VULKAN_HPP_DISPATCH_LOADER_DYNAMIC=1) - - find_package(Vulkan COMPONENTS glslc REQUIRED) - find_program(glslc_executable NAMES glslc HINTS Vulkan::glslc) - - if (NOT glslc_executable) - message(FATAL_ERROR "glslc not found") - endif() - - function(compile_shader) - set(options) - set(oneValueArgs) - set(multiValueArgs SOURCES) - cmake_parse_arguments(compile_shader "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) - foreach(source ${compile_shader_SOURCES}) - get_filename_component(filename ${source} NAME) - set(spv_file ${filename}.spv) - add_custom_command( - OUTPUT ${spv_file} - DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/${source} - ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/common.comp - ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_getrows.comp - ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_mul_mv_q_n_pre.comp - ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_mul_mv_q_n.comp - COMMAND ${glslc_executable} --target-env=vulkan1.2 -o ${spv_file} ${CMAKE_CURRENT_SOURCE_DIR}/${source} - COMMENT "Compiling ${source} to ${spv_file}" - ) - - get_filename_component(RAW_FILE_NAME ${spv_file} NAME) - set(FILE_NAME "shader${RAW_FILE_NAME}") - string(REPLACE ".comp.spv" ".h" HEADER_FILE ${FILE_NAME}) - string(TOUPPER ${HEADER_FILE} HEADER_FILE_DEFINE) - string(REPLACE "." "_" HEADER_FILE_DEFINE "${HEADER_FILE_DEFINE}") - set(OUTPUT_HEADER_FILE "${HEADER_FILE}") - message(STATUS "${HEADER_FILE} generating ${HEADER_FILE_DEFINE}") - if(CMAKE_GENERATOR MATCHES "Visual Studio") - add_custom_command( - OUTPUT ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "/*THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT*/" > ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo \"\#ifndef ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_BINARY_DIR}/bin/$/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} - DEPENDS ${spv_file} xxd - COMMENT "Converting to hpp: ${FILE_NAME} ${CMAKE_BINARY_DIR}/bin/$/xxd" - ) - else() - add_custom_command( - OUTPUT ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "/*THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT*/" > ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo \"\#ifndef ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_BINARY_DIR}/bin/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE} - COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} - DEPENDS ${spv_file} xxd - COMMENT "Converting to hpp: ${FILE_NAME} ${CMAKE_BINARY_DIR}/bin/xxd" - ) - endif() - endforeach() - endfunction() - - if (EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/kompute/CMakeLists.txt") - message(STATUS "Kompute found") - set(KOMPUTE_OPT_LOG_LEVEL Error CACHE STRING "Kompute log level") - add_subdirectory(kompute) - - # Compile our shaders - compile_shader(SOURCES - kompute-shaders/op_scale.comp - kompute-shaders/op_scale_8.comp - kompute-shaders/op_add.comp - kompute-shaders/op_addrow.comp - kompute-shaders/op_mul.comp - kompute-shaders/op_silu.comp - kompute-shaders/op_relu.comp - kompute-shaders/op_gelu.comp - kompute-shaders/op_softmax.comp - kompute-shaders/op_norm.comp - kompute-shaders/op_rmsnorm.comp - kompute-shaders/op_diagmask.comp - kompute-shaders/op_mul_mat_mat_f32.comp - kompute-shaders/op_mul_mat_f16.comp - kompute-shaders/op_mul_mat_q8_0.comp - kompute-shaders/op_mul_mat_q4_0.comp - kompute-shaders/op_mul_mat_q4_1.comp - kompute-shaders/op_mul_mat_q6_k.comp - kompute-shaders/op_getrows_f32.comp - kompute-shaders/op_getrows_f16.comp - kompute-shaders/op_getrows_q4_0.comp - kompute-shaders/op_getrows_q4_1.comp - kompute-shaders/op_getrows_q6_k.comp - kompute-shaders/op_rope_f16.comp - kompute-shaders/op_rope_f32.comp - kompute-shaders/op_cpy_f16_f16.comp - kompute-shaders/op_cpy_f16_f32.comp - kompute-shaders/op_cpy_f32_f16.comp - kompute-shaders/op_cpy_f32_f32.comp - ) - - # Create a custom target for our generated shaders - add_custom_target(generated_shaders DEPENDS - shaderop_scale.h - shaderop_scale_8.h - shaderop_add.h - shaderop_addrow.h - shaderop_mul.h - shaderop_silu.h - shaderop_relu.h - shaderop_gelu.h - shaderop_softmax.h - shaderop_norm.h - shaderop_rmsnorm.h - shaderop_diagmask.h - shaderop_mul_mat_mat_f32.h - shaderop_mul_mat_f16.h - shaderop_mul_mat_q8_0.h - shaderop_mul_mat_q4_0.h - shaderop_mul_mat_q4_1.h - shaderop_mul_mat_q6_k.h - shaderop_getrows_f32.h - shaderop_getrows_f16.h - shaderop_getrows_q4_0.h - shaderop_getrows_q4_1.h - shaderop_getrows_q6_k.h - shaderop_rope_f16.h - shaderop_rope_f32.h - shaderop_cpy_f16_f16.h - shaderop_cpy_f16_f32.h - shaderop_cpy_f32_f16.h - shaderop_cpy_f32_f32.h - ) - - # Create a custom command that depends on the generated_shaders - add_custom_command( - OUTPUT ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp - COMMAND ${CMAKE_COMMAND} -E touch ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp - DEPENDS generated_shaders - COMMENT "Ensuring shaders are generated before compiling ggml-kompute.cpp" - ) - - # Add the stamp to the main sources to ensure dependency tracking - set(GGML_SOURCES_KOMPUTE ggml-kompute.cpp ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp) - set(GGML_HEADERS_KOMPUTE ../include/ggml-kompute.h ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp) - - list(APPEND GGML_CDEF_PUBLIC GGML_USE_KOMPUTE) - - list(APPEND GGML_EXTRA_LIBS_PRIVATE kompute) - list(APPEND GGML_EXTRA_INCLUDES ${CMAKE_CURRENT_BINARY_DIR}) - else() - message(WARNING "Kompute not found") - endif() -endif() - -if (GGML_CPU_HBM) - find_library(memkind memkind REQUIRED) - - message(STATUS "Using memkind for CPU HBM") - - add_compile_definitions(GGML_USE_CPU_HBM) - - target_link_libraries(ggml PUBLIC memkind) -endif() - -if (GGML_CANN) - if ("cann${CANN_INSTALL_DIR}" STREQUAL "cann" AND DEFINED ENV{ASCEND_TOOLKIT_HOME}) - set(CANN_INSTALL_DIR $ENV{ASCEND_TOOLKIT_HOME}) - message(STATUS "CANN: updated CANN_INSTALL_DIR from ASCEND_TOOLKIT_HOME=$ENV{ASCEND_TOOLKIT_HOME}") - endif() - - if (CANN_INSTALL_DIR) - # Only Support Linux. - if (GGML_CANN) - if (NOT UNIX) - set(GGML_CANN OFF) - message(WARNING "CANN: CANN toolkit supports unix but not ${CMAKE_SYSTEM_NAME}. Turning off GGML_CANN") - endif() - endif() - - # Supported platforms: x86-64, arm64 - if (GGML_CANN) - if (CMAKE_SYSTEM_PROCESSOR STREQUAL "aarch64") - elseif (CMAKE_SYSTEM_PROCESSOR STREQUAL "x86_64" OR CMAKE_SYSTEM_PROCESSOR STREQUAL "amd64") - else() - set(GGML_CANN OFF) - message(WARNING "CANN: CANN toolkit supports x86-64 and arm64 but not ${CMAKE_SYSTEM_PROCESSOR}. Turning off GGML_CANN") - endif() - endif() - - # Set header and libs - if(GGML_CANN) - set(CANN_INCLUDE_DIRS - ${CANN_INSTALL_DIR}/include - ${CANN_INSTALL_DIR}/include/aclnn - ${CANN_INSTALL_DIR}/acllib/include - ) - - add_subdirectory(ggml-cann/kernels) - list(APPEND CANN_LIBRARIES - ascendcl - nnopbase - opapi - acl_op_compiler - ascendc_kernels - ) - - set(GGML_HEADERS_CANN "../include/ggml-cann.h") - file(GLOB GGML_SOURCES_CANN "ggml-cann/*.cpp") - list(APPEND GGML_SOURCES_CANN "ggml-cann.cpp") - - message(STATUS "CANN: CANN_INCLUDE_DIRS = ${CANN_INCLUDE_DIRS}") - message(STATUS "CANN: CANN_LIBRARIES = ${CANN_LIBRARIES}") - - list(APPEND GGML_EXTRA_LIBS_PRIVATE ${CANN_LIBRARIES} ) - list(APPEND GGML_EXTRA_INCLUDES ${CANN_INCLUDE_DIRS}) - list(APPEND GGML_EXTRA_LIBDIRS ${CANN_INSTALL_DIR}/lib64) - - list(APPEND GGML_CDEF_PUBLIC GGML_USE_CANN) - endif() - else() - set(GGML_CANN OFF) - message(WARNING "CANN: Can't find CANN_INSTALL_DIR, do you forget to source set_var.sh. Turning off GGML_CANN") - endif() - - if(NOT GGML_CANN) - message(WARNING "CANN: GGML_CANN is turned OFF, see above for details.") - endif() -endif() - -function(get_flags CCID CCVER) +function(ggml_get_flags CCID CCVER) set(C_FLAGS "") set(CXX_FLAGS "") @@ -961,11 +42,6 @@ function(get_flags CCID CCVER) set(C_FLAGS -Wdouble-promotion) set(CXX_FLAGS -Wno-array-bounds) - if (NOT GGML_MUSA) - if (CCVER VERSION_GREATER_EQUAL 7.1.0) - list(APPEND CXX_FLAGS -Wno-format-truncation) - endif() - endif() if (CCVER VERSION_GREATER_EQUAL 8.1.0) list(APPEND CXX_FLAGS -Wextra-semi) endif() @@ -994,7 +70,7 @@ if (GGML_ALL_WARNINGS) list(APPEND C_FLAGS ${WARNING_FLAGS}) list(APPEND CXX_FLAGS ${WARNING_FLAGS}) - get_flags(${CMAKE_CXX_COMPILER_ID} ${CMAKE_CXX_COMPILER_VERSION}) + ggml_get_flags(${CMAKE_CXX_COMPILER_ID} ${CMAKE_CXX_COMPILER_VERSION}) add_compile_options("$<$:${C_FLAGS};${GF_C_FLAGS}>" "$<$:${CXX_FLAGS};${GF_CXX_FLAGS}>") @@ -1005,54 +81,6 @@ if (GGML_ALL_WARNINGS) endif() endif() -set(CUDA_CXX_FLAGS "") - -if (GGML_CUDA) - set(CUDA_FLAGS -use_fast_math) - - if (GGML_FATAL_WARNINGS) - list(APPEND CUDA_FLAGS -Werror all-warnings) - endif() - - if (GGML_ALL_WARNINGS AND NOT MSVC) - set(NVCC_CMD ${CMAKE_CUDA_COMPILER} .c) - if (NOT CMAKE_CUDA_HOST_COMPILER STREQUAL "") - list(APPEND NVCC_CMD -ccbin ${CMAKE_CUDA_HOST_COMPILER}) - endif() - - execute_process( - COMMAND ${NVCC_CMD} -Xcompiler --version - OUTPUT_VARIABLE CUDA_CCFULLVER - ERROR_QUIET - ) - - if (NOT CUDA_CCFULLVER MATCHES clang) - set(CUDA_CCID "GNU") - execute_process( - COMMAND ${NVCC_CMD} -Xcompiler "-dumpfullversion -dumpversion" - OUTPUT_VARIABLE CUDA_CCVER - ERROR_QUIET - ) - else() - if (CUDA_CCFULLVER MATCHES Apple) - set(CUDA_CCID "AppleClang") - else() - set(CUDA_CCID "Clang") - endif() - string(REGEX REPLACE "^.* version ([0-9.]*).*$" "\\1" CUDA_CCVER ${CUDA_CCFULLVER}) - endif() - - message("-- CUDA host compiler is ${CUDA_CCID} ${CUDA_CCVER}") - - get_flags(${CUDA_CCID} ${CUDA_CCVER}) - list(APPEND CUDA_CXX_FLAGS ${CXX_FLAGS} ${GF_CXX_FLAGS}) # This is passed to -Xcompiler later - endif() - - if (NOT MSVC) - list(APPEND CUDA_CXX_FLAGS -Wno-pedantic) - endif() -endif() - if (GGML_LTO) include(CheckIPOSupported) check_ipo_supported(RESULT result OUTPUT output) @@ -1110,189 +138,6 @@ if (NOT MSVC) endif() endif() -set(ARCH_FLAGS "") - -if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR - CMAKE_GENERATOR_PLATFORM_LWR STREQUAL "arm64" OR - (NOT CMAKE_OSX_ARCHITECTURES AND - NOT CMAKE_GENERATOR_PLATFORM_LWR AND - CMAKE_SYSTEM_PROCESSOR MATCHES "^(aarch64|arm.*|ARM64)$")) - - message(STATUS "ARM detected") - - if (MSVC) - add_compile_definitions(__aarch64__) # MSVC defines _M_ARM64 instead - add_compile_definitions(__ARM_NEON) - add_compile_definitions(__ARM_FEATURE_FMA) - - set(CMAKE_REQUIRED_FLAGS_PREV ${CMAKE_REQUIRED_FLAGS}) - string(JOIN " " CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS} "/arch:armv8.2") - - check_cxx_source_compiles("#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_DOTPROD) - if (GGML_COMPILER_SUPPORT_DOTPROD) - add_compile_definitions(__ARM_FEATURE_DOTPROD) - endif () - - check_cxx_source_compiles("#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vmlaq_f32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_MATMUL_INT8) - - if (GGML_COMPILER_SUPPORT_MATMUL_INT8) - add_compile_definitions(__ARM_FEATURE_MATMUL_INT8) - endif () - - check_cxx_source_compiles("#include \nint main() { float16_t _a; float16x8_t _s = vdupq_n_f16(_a); return 0; }" GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC) - if (GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC) - add_compile_definitions(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) - endif () - - set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_PREV}) - else() - check_cxx_compiler_flag(-mfp16-format=ieee COMPILER_SUPPORTS_FP16_FORMAT_I3E) - if (NOT "${COMPILER_SUPPORTS_FP16_FORMAT_I3E}" STREQUAL "") - list(APPEND ARCH_FLAGS -mfp16-format=ieee) - endif() - if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv6") - # Raspberry Pi 1, Zero - list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access) - endif() - if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv7") - if ("${CMAKE_SYSTEM_NAME}" STREQUAL "Android") - # Android armeabi-v7a - list(APPEND ARCH_FLAGS -mfpu=neon-vfpv4 -mno-unaligned-access -funsafe-math-optimizations) - else() - # Raspberry Pi 2 - list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access -funsafe-math-optimizations) - endif() - endif() - if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv8") - # Android arm64-v8a - # Raspberry Pi 3, 4, Zero 2 (32-bit) - list(APPEND ARCH_FLAGS -mno-unaligned-access) - endif() - if (GGML_SVE) - list(APPEND ARCH_FLAGS -march=armv8.6-a+sve) - endif() - endif() -elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR - (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND - CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64)$")) - message(STATUS "x86 detected") - if (MSVC) - # instruction set detection for MSVC only - if (GGML_NATIVE) - # TODO: improve, should not reference files from the parent folder - include(../cmake/FindSIMD.cmake) - endif () - if (GGML_AVX512) - list(APPEND ARCH_FLAGS /arch:AVX512) - # MSVC has no compile-time flags enabling specific - # AVX512 extensions, neither it defines the - # macros corresponding to the extensions. - # Do it manually. - if (GGML_AVX512_VBMI) - add_compile_definitions($<$:__AVX512VBMI__>) - add_compile_definitions($<$:__AVX512VBMI__>) - endif() - if (GGML_AVX512_VNNI) - add_compile_definitions($<$:__AVX512VNNI__>) - add_compile_definitions($<$:__AVX512VNNI__>) - endif() - if (GGML_AVX512_BF16) - add_compile_definitions($<$:__AVX512BF16__>) - add_compile_definitions($<$:__AVX512BF16__>) - endif() - if (GGML_AMX_TILE) - add_compile_definitions($<$:__AMX_TILE__>) - add_compile_definitions($<$:__AMX_TILE__>) - endif() - if (GGML_AMX_INT8) - add_compile_definitions($<$:__AMX_INT8__>) - add_compile_definitions($<$:__AMX_INT8__>) - endif() - if (GGML_AMX_BF16) - add_compile_definitions($<$:__AMX_BF16__>) - add_compile_definitions($<$:__AMX_BF16__>) - endif() - elseif (GGML_AVX2) - list(APPEND ARCH_FLAGS /arch:AVX2) - elseif (GGML_AVX) - list(APPEND ARCH_FLAGS /arch:AVX) - endif() - else() - if (GGML_NATIVE) - list(APPEND ARCH_FLAGS -march=native) - endif() - if (GGML_F16C) - list(APPEND ARCH_FLAGS -mf16c) - endif() - if (GGML_FMA) - list(APPEND ARCH_FLAGS -mfma) - endif() - if (GGML_AVX) - list(APPEND ARCH_FLAGS -mavx) - endif() - if (GGML_AVX2) - list(APPEND ARCH_FLAGS -mavx2) - endif() - if (GGML_AVX512) - list(APPEND ARCH_FLAGS -mavx512f) - list(APPEND ARCH_FLAGS -mavx512dq) - list(APPEND ARCH_FLAGS -mavx512bw) - endif() - if (GGML_AVX512_VBMI) - list(APPEND ARCH_FLAGS -mavx512vbmi) - endif() - if (GGML_AVX512_VNNI) - list(APPEND ARCH_FLAGS -mavx512vnni) - endif() - if (GGML_AVX512_BF16) - list(APPEND ARCH_FLAGS -mavx512bf16) - endif() - if (GGML_AMX_TILE) - list(APPEND ARCH_FLAGS -mamx-tile) - endif() - if (GGML_AMX_INT8) - list(APPEND ARCH_FLAGS -mamx-int8) - endif() - if (GGML_AMX_BF16) - list(APPEND ARCH_FLAGS -mamx-bf16) - endif() - endif() -elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64") - message(STATUS "PowerPC detected") - if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64le") - list(APPEND ARCH_FLAGS -mcpu=powerpc64le) - else() - list(APPEND ARCH_FLAGS -mcpu=native -mtune=native) - #TODO: Add targets for Power8/Power9 (Altivec/VSX) and Power10(MMA) and query for big endian systems (ppc64/le/be) - endif() -elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64") - message(STATUS "loongarch64 detected") - - list(APPEND ARCH_FLAGS -march=loongarch64) - if (GGML_LASX) - list(APPEND ARCH_FLAGS -mlasx) - endif() - if (GGML_LSX) - list(APPEND ARCH_FLAGS -mlsx) - endif() -else() - message(STATUS "Unknown architecture") -endif() - -add_compile_options("$<$:${ARCH_FLAGS}>") -add_compile_options("$<$:${ARCH_FLAGS}>") - -if (GGML_CUDA) - list(APPEND CUDA_CXX_FLAGS ${ARCH_FLAGS}) - list(JOIN CUDA_CXX_FLAGS " " CUDA_CXX_FLAGS_JOINED) # pass host compiler flags as a single argument - - if (NOT CUDA_CXX_FLAGS_JOINED STREQUAL "") - list(APPEND CUDA_FLAGS -Xcompiler ${CUDA_CXX_FLAGS_JOINED}) - endif() - - add_compile_options("$<$:${CUDA_FLAGS}>") -endif() - if (MINGW) # Target Windows 8 for PrefetchVirtualMemory add_compile_definitions(_WIN32_WINNT=${GGML_WIN_VER}) @@ -1306,14 +151,14 @@ endif() # CLOCK_MONOTONIC came in POSIX.1-2001 / SUSv3 as optional # posix_memalign came in POSIX.1-2001 / SUSv3 # M_PI is an XSI extension since POSIX.1-2001 / SUSv3, came in XPG1 (1985) -add_compile_definitions(_XOPEN_SOURCE=600) # Somehow in OpenBSD whenever POSIX conformance is specified # some string functions rely on locale_t availability, # which was introduced in POSIX.1-2008, forcing us to go higher if (CMAKE_SYSTEM_NAME MATCHES "OpenBSD") - remove_definitions(-D_XOPEN_SOURCE=600) add_compile_definitions(_XOPEN_SOURCE=700) +else() + add_compile_definitions(_XOPEN_SOURCE=600) endif() # Data types, macros and functions related to controlling CPU affinity and @@ -1356,64 +201,143 @@ if (WIN32) endif() endif() -# -# libraries -# - # ggml -add_library(ggml +if (GGML_BACKEND_DL AND NOT BUILD_SHARED_LIBS) + message(FATAL_ERROR "GGML_BACKEND_DL requires BUILD_SHARED_LIBS") +endif() + +add_library(ggml-base ../include/ggml.h ../include/ggml-alloc.h ../include/ggml-backend.h + ../include/ggml-cpp.h + ../include/ggml-opt.h ggml.c ggml-alloc.c ggml-backend.cpp + ggml-opt.cpp + ggml-threading.cpp + ggml-threading.h ggml-quants.c ggml-quants.h - ${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA} - ${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL} - ${GGML_SOURCES_RPC} ${GGML_HEADERS_RPC} - ${GGML_SOURCES_EXTRA} ${GGML_HEADERS_EXTRA} - ${GGML_SOURCES_SYCL} ${GGML_HEADERS_SYCL} - ${GGML_SOURCES_KOMPUTE} ${GGML_HEADERS_KOMPUTE} - ${GGML_SOURCES_VULKAN} ${GGML_HEADERS_VULKAN} - ${GGML_SOURCES_ROCM} ${GGML_HEADERS_ROCM} - ${GGML_SOURCES_BLAS} ${GGML_HEADERS_BLAS} - ${GGML_SOURCES_LLAMAFILE} ${GGML_HEADERS_LLAMAFILE} - ${GGML_SOURCES_AMX} ${GGML_HEADERS_AMX} - ${GGML_SOURCES_CANN} ${GGML_HEADERS_CANN} - ggml-aarch64.c ggml-aarch64.h - ) + ggml-aarch64.c + ggml-aarch64.h) -if (EMSCRIPTEN) - set_target_properties(ggml PROPERTIES COMPILE_FLAGS "-msimd128") +target_include_directories(ggml-base PRIVATE .) + +add_library(ggml + ggml-backend-reg.cpp) + +target_link_libraries(ggml PUBLIC ggml-base) + +if (CMAKE_SYSTEM_NAME MATCHES "Linux") + target_link_libraries(ggml PRIVATE dl) endif() -target_compile_definitions(ggml PUBLIC ${GGML_CDEF_PUBLIC}) -target_include_directories(ggml PUBLIC ../include) -target_include_directories(ggml PRIVATE . ${GGML_EXTRA_INCLUDES}) -target_link_directories (ggml PRIVATE ${GGML_EXTRA_LIBDIRS}) -target_compile_features (ggml PRIVATE c_std_11) # don't bump +function(ggml_add_backend_library backend) + if (GGML_BACKEND_DL) + add_library(${backend} MODULE ${ARGN}) + # write the shared library to the output directory + set_target_properties(${backend} PROPERTIES LIBRARY_OUTPUT_DIRECTORY ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}) + target_compile_definitions(${backend} PRIVATE GGML_BACKEND_DL) + else() + add_library(${backend} ${ARGN}) + target_link_libraries(ggml PUBLIC ${backend}) + install(TARGETS ${backend} LIBRARY) + endif() -list(APPEND GGML_EXTRA_LIBS_PRIVATE Threads::Threads) + target_link_libraries(${backend} PRIVATE ggml-base) + target_include_directories(${backend} PRIVATE ..) + + if (${BUILD_SHARED_LIBS}) + target_compile_definitions(${backend} PRIVATE GGML_BACKEND_BUILD) + target_compile_definitions(${backend} PUBLIC GGML_BACKEND_SHARED) + endif() +endfunction() + +function(ggml_add_backend backend) + string(TOUPPER "GGML_${backend}" backend_id) + if (${backend_id}) + string(TOLOWER "ggml-${backend}" backend_target) + add_subdirectory(${backend_target}) + message(STATUS "Including ${backend} backend") + if (NOT GGML_BACKEND_DL) + string(TOUPPER "GGML_USE_${backend}" backend_use) + target_compile_definitions(ggml PUBLIC ${backend_use}) + endif() + endif() +endfunction() + +function(ggml_add_cpu_backend_variant tag_name) + set(GGML_CPU_TAG_NAME ${tag_name}) + # other: OPENMP LLAMAFILE CPU_HBM + foreach (feat NATIVE + AVX AVX2 AVX_VNNI FMA F16C + AVX512 AVX512_VBMI AVX512_VNNI AVX512_BF16 + AMX_TILE AMX_INT8 AMX_BF16) + set(GGML_${feat} OFF) + endforeach() + + foreach (feat ${ARGN}) + set(GGML_${feat} ON) + endforeach() + + ggml_add_cpu_backend_variant_impl(${tag_name}) +endfunction() + +ggml_add_backend(CPU) + +if (GGML_CPU_ALL_VARIANTS) + if (NOT GGML_BACKEND_DL) + message(FATAL_ERROR "GGML_CPU_ALL_VARIANTS requires GGML_BACKEND_DL") + endif() + ggml_add_cpu_backend_variant(sandybridge AVX) + ggml_add_cpu_backend_variant(haswell AVX F16C AVX2 FMA) + ggml_add_cpu_backend_variant(skylakex AVX F16C AVX2 FMA AVX512) + ggml_add_cpu_backend_variant(icelake AVX F16C AVX2 FMA AVX512 AVX512_VBMI AVX512_VNNI) + if (NOT MSVC) + # MSVC doesn't support AVX-VNNI or AMX + ggml_add_cpu_backend_variant(alderlake AVX F16C AVX2 FMA AVX_VNNI) + ggml_add_cpu_backend_variant(sapphirerapids AVX F16C AVX2 FMA AVX512 AVX512_VBMI AVX512_VNNI AVX512_BF16 AMX_TILE AMX_INT8) + endif() +else () + ggml_add_cpu_backend_variant_impl("") +endif() + +ggml_add_backend(BLAS) +ggml_add_backend(CANN) +ggml_add_backend(CUDA) +ggml_add_backend(HIP) +ggml_add_backend(Kompute) +ggml_add_backend(METAL) +ggml_add_backend(MUSA) +ggml_add_backend(RPC) +ggml_add_backend(SYCL) +ggml_add_backend(Vulkan) + +foreach (target ggml-base ggml) + target_include_directories(${target} PUBLIC $ $) + target_compile_features (${target} PRIVATE c_std_11 cxx_std_17) # don't bump +endforeach() + +target_link_libraries(ggml-base PRIVATE Threads::Threads) find_library(MATH_LIBRARY m) if (MATH_LIBRARY) - if (NOT WIN32 OR NOT GGML_SYCL) - list(APPEND GGML_EXTRA_LIBS_PRIVATE m) + if (NOT WIN32 OR NOT DEFINED ENV{ONEAPI_ROOT}) + target_link_libraries(ggml-base PRIVATE m) endif() endif() if (CMAKE_SYSTEM_NAME MATCHES "Android") - list(APPEND GGML_EXTRA_LIBS_PRIVATE dl) # Must be linked explicitly + target_link_libraries(ggml-base PRIVATE dl) endif() -list(REMOVE_DUPLICATES GGML_EXTRA_LIBS_PRIVATE) -list(REMOVE_DUPLICATES GGML_EXTRA_LIBS_PUBLIC) -target_link_libraries(ggml PRIVATE ${GGML_EXTRA_LIBS_PRIVATE} PUBLIC ${GGML_EXTRA_LIBS_PUBLIC}) - if (BUILD_SHARED_LIBS) - set_target_properties(ggml PROPERTIES POSITION_INDEPENDENT_CODE ON) - target_compile_definitions(ggml PRIVATE GGML_SHARED GGML_BUILD) + foreach (target ggml-base ggml) + set_target_properties(${target} PROPERTIES POSITION_INDEPENDENT_CODE ON) + target_compile_definitions(${target} PRIVATE GGML_BUILD) + target_compile_definitions(${target} PUBLIC GGML_SHARED) + endforeach() endif() diff --git a/ggml/src/ggml-aarch64.c b/ggml/src/ggml-aarch64.c index eb30f8944..013912051 100644 --- a/ggml/src/ggml-aarch64.c +++ b/ggml/src/ggml-aarch64.c @@ -1,204 +1,49 @@ -// SPDX-FileCopyrightText: Copyright 2024 Arm Limited and/or its affiliates -// SPDX-License-Identifier: MIT -// - -#define GGML_COMMON_IMPL_C +#define GGML_COMMON_DECL_C #include "ggml-common.h" -#include "ggml-quants.h" -#include "ggml-impl.h" -#include "ggml-cpu-impl.h" - -#include -#include -#include -#include -#include // for qsort -#include // for GGML_ASSERT - #include "ggml-aarch64.h" - -#if defined(__GNUC__) -#pragma GCC diagnostic ignored "-Woverlength-strings" -#elif defined(_MSC_VER) -#pragma warning(disable: 4244 4267) // possible loss of data -#endif +#include "ggml-impl.h" +#include "ggml-quants.h" +#include #define UNUSED GGML_UNUSED -// Functions to create the interleaved data layout formats - -// interleave 4 block_q4_0s in blocks of blck_size_interleave -// returns an interleaved block_q4_0x4 -// in the interleaved block_q4_0x4, place deltas for 4 block_q4_0 blocks -// first, then interleave quants from 4 block_q4_0s in blocks of blck_size_interleave -// -// - in : an array of block_q4_0 pointers -// - blck_size_interleave : the block_q4_0 quants bytes are interleaved in blocks of -// blck_size_interleave bytes -// - xor_mask : the mask to convert the nibbles in block_q4_0 quants bytes -// from bias offset form to pure sign form (this saves subtract -// operations durin unpacking) -// -#if defined(__AVX__) -#if defined(__F16C__) -#if defined(__AVX512F__) -#define GGML_F32Cx8x2_LOAD(x, y) _mm512_cvtph_ps(_mm256_set_m128i(_mm_loadu_si128((const __m128i *)(y)), _mm_loadu_si128((const __m128i *)(x)))) -#define GGML_F32Cx16_REPEAT_LOAD(x) _mm512_cvtph_ps(_mm256_set_m128i(x, x)) -#endif -// the _mm256_cvt intrinsics require F16C -#define GGML_F32Cx8_LOAD(x) _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)(x))) -#define GGML_F32Cx8_REPEAT_LOAD(x, loadMask) _mm256_cvtph_ps(_mm_shuffle_epi32(_mm_maskload_epi32((int const*)(x), loadMask), 68)) -#define GGML_F32Cx8_REARRANGE_LOAD(x, arrangeMask) _mm256_cvtph_ps(_mm_shuffle_epi8(_mm_loadu_si128((const __m128i *) x), arrangeMask)) -#else -#if defined(__AVX512F__) -static inline __m512 __avx512_f32cx8x2_load(ggml_fp16_t *x, ggml_fp16_t *y) { - float tmp[16]; - - for (int i = 0; i < 8; i++) { - tmp[i] = GGML_FP16_TO_FP32(x[i]); - } - - for (int i = 0; i < 8; i++) { - tmp[i + 8] = GGML_FP16_TO_FP32(y[i]); - } - - return _mm512_loadu_ps(tmp); -} -static inline __m512 __avx512_repeat_f32cx16_load(__m128i x) { - float tmp[16]; - uint16_t tmphalf[8]; - _mm_storeu_si128((__m128i*)tmphalf, x); - - for (int i = 0; i < 4; i++) { - tmp[i] = GGML_FP16_TO_FP32(tmphalf[i]); - tmp[i + 4] = GGML_FP16_TO_FP32(tmphalf[i]); - tmp[i + 8] = GGML_FP16_TO_FP32(tmphalf[i]); - tmp[i + 12] = GGML_FP16_TO_FP32(tmphalf[i]); - } - - return _mm512_loadu_ps(tmp); -} -#endif -static inline __m256 __avx_f32cx8_load(ggml_fp16_t *x) { - float tmp[8]; - - for (int i = 0; i < 8; i++) { - tmp[i] = GGML_FP16_TO_FP32(x[i]); - } - - return _mm256_loadu_ps(tmp); -} -static inline __m256 __avx_repeat_f32cx8_load(ggml_fp16_t *x) { - float tmp[8]; - - for (int i = 0; i < 4; i++) { - tmp[i] = GGML_FP16_TO_FP32(x[i]); - tmp[i + 4] = GGML_FP16_TO_FP32(x[i]); - } - - return _mm256_loadu_ps(tmp); -} -static inline __m256 __avx_rearranged_f32cx8_load(ggml_fp16_t *x, __m128i arrangeMask) { - uint16_t tmphalf[8]; - float tmp[8]; - - _mm_storeu_si128((__m128i*)tmphalf, _mm_shuffle_epi8(_mm_loadu_si128((const __m128i *) x), arrangeMask)); - for (int i = 0; i < 8; i++) { - tmp[i] = GGML_FP16_TO_FP32(tmphalf[i]); - } - - return _mm256_loadu_ps(tmp); -} - -#define GGML_F32Cx8_LOAD(x) __avx_f32cx8_load(x) -#define GGML_F32Cx8_REPEAT_LOAD(x, loadMask) __avx_repeat_f32cx8_load(x) -#define GGML_F32Cx8_REARRANGE_LOAD(x, arrangeMask) __avx_rearranged_f32cx8_load(x, arrangeMask) -#if defined(__AVX512F__) -#define GGML_F32Cx8x2_LOAD(x, y) __avx512_f32cx8x2_load(x, y) -#define GGML_F32Cx16_REPEAT_LOAD(x) __avx512_repeat_f32cx16_load(x) -#endif -#endif -#endif - - -#if defined(__AVX2__) || defined(__AVX512F__) -#if defined(__AVX512F__) -// add int16_t pairwise and return as 512 bit int vector -static inline __m512i sum_i16_pairs_int_32x16(const __m512i x) { - const __m512i ones = _mm512_set1_epi16(1); - return _mm512_madd_epi16(ones, x); -} - -static inline __m512i mul_sum_us8_pairs_int32x16(const __m512i ax, const __m512i sy) { -#if defined(__AVXVNNI__) || (defined(__AVX512VNNI__) && defined(__AVX512VL__)) - const __m512i zero = _mm512_setzero_si512(); - return _mm512_dpbusd_epi32(zero, ax, sy); -#else - // Perform multiplication and create 16-bit values - const __m512i dot = _mm512_maddubs_epi16(ax, sy); - return sum_i16_pairs_int_32x16(dot); -#endif -} - -// multiply int8_t, add results pairwise twice and return as 512 bit int vector -static inline __m512i mul_sum_i8_pairs_int32x16(const __m512i x, const __m512i y) { - const __m512i zero = _mm512_setzero_si512(); - // Get absolute values of x vectors - const __m512i ax = _mm512_abs_epi8(x); - // Sign the values of the y vectors - __mmask64 blt0 = _mm512_movepi8_mask(x); - const __m512i sy = _mm512_mask_sub_epi8(y, blt0, zero, y); - return mul_sum_us8_pairs_int32x16(ax, sy); -} -#endif - -// add int16_t pairwise and return as 256 bit int vector -static inline __m256i sum_i16_pairs_int32x8(const __m256i x) { - const __m256i ones = _mm256_set1_epi16(1); - return _mm256_madd_epi16(ones, x); -} - -static inline __m256i mul_sum_us8_pairs_int32x8(const __m256i ax, const __m256i sy) { -#if defined(__AVXVNNI__) || (defined(__AVX512VNNI__) && defined(__AVX512VL__)) - const __m256i zero = _mm256_setzero_si256(); - return _mm256_dpbusd_epi32(zero, ax, sy); -#else - // Perform multiplication and create 16-bit values - const __m256i dot = _mm256_maddubs_epi16(ax, sy); - return sum_i16_pairs_int32x8(dot); -#endif -} - -// Integer variant of the function defined in ggml-quants.c -// multiply int8_t, add results pairwise twice and return as 256 bit int vector -static inline __m256i mul_sum_i8_pairs_int32x8(const __m256i x, const __m256i y) { -#if __AVXVNNIINT8__ - const __m256i zero = _mm256_setzero_si256(); - return _mm256_dpbssd_epi32(zero, x, y); -#else - // Get absolute values of x vectors - const __m256i ax = _mm256_sign_epi8(x, x); - // Sign the values of the y vectors - const __m256i sy = _mm256_sign_epi8(y, x); - return mul_sum_us8_pairs_int32x8(ax, sy); -#endif -} -#endif - -static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int blck_size_interleave, unsigned int xor_mask) { +static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int blck_size_interleave) { block_q4_0x4 out; for (int i = 0; i < 4; i++) { out.d[i] = in[i].d; } - for (int i = 0; i < QK4_0 * 2; i++) { - int src_offset = (i / (4 * blck_size_interleave)) * blck_size_interleave; - int src_id = (i % (4 * blck_size_interleave)) / blck_size_interleave; - src_offset += (i % blck_size_interleave); + const int end = QK4_0 * 2 / blck_size_interleave; - out.qs[i] = in[src_id].qs[src_offset] ^ xor_mask; + if (blck_size_interleave == 8) { + const uint64_t xor_mask = 0x8888888888888888ULL; + for (int i = 0; i < end; ++i) { + int src_id = i % 4; + int src_offset = (i / 4) * blck_size_interleave; + int dst_offset = i * blck_size_interleave; + + uint64_t elems; + // Using memcpy to avoid unaligned memory accesses + memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint64_t)); + elems ^= xor_mask; + memcpy(&out.qs[dst_offset], &elems, sizeof(uint64_t)); + } + } else if (blck_size_interleave == 4) { + const uint32_t xor_mask = 0x88888888; + for (int i = 0; i < end; ++i) { + int src_id = i % 4; + int src_offset = (i / 4) * blck_size_interleave; + int dst_offset = i * blck_size_interleave; + + uint32_t elems; + memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint32_t)); + elems ^= xor_mask; + memcpy(&out.qs[dst_offset], &elems, sizeof(uint32_t)); + } + } else { + GGML_ASSERT(false); } return out; @@ -208,345 +53,30 @@ static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int blck_size_in // returns an interleaved block_q4_0x8 // in the interleaved block_q4_0x8, place deltas for 8 block_q4_0 blocks // first, then interleave quants from 8 block_q4_0s in blocks of blck_size_interleave -static block_q4_0x8 make_block_q4_0x8(block_q4_0 * in, unsigned int blck_size_interleave, unsigned int xor_mask) { +static block_q4_0x8 make_block_q4_0x8(block_q4_0 * in, unsigned int blck_size_interleave) { block_q4_0x8 out; for (int i = 0; i < 8; i++) { out.d[i] = in[i].d; } - for (int i = 0; i < QK4_0 * 4; i++) { - int src_offset = (i / (8 * blck_size_interleave)) * blck_size_interleave; - int src_id = (i % (8 * blck_size_interleave)) / blck_size_interleave; - src_offset += (i % blck_size_interleave); + const int end = QK4_0 * 4 / blck_size_interleave; + const uint64_t xor_mask = 0x8888888888888888ULL; - out.qs[i] = in[src_id].qs[src_offset] ^ xor_mask; + for (int i = 0; i < end; ++i) { + int src_id = i % 8; + int src_offset = (i / 8) * blck_size_interleave; + int dst_offset = i * blck_size_interleave; + + uint64_t elems; + memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint64_t)); + elems ^= xor_mask; + memcpy(&out.qs[dst_offset], &elems, sizeof(uint64_t)); } return out; } -void quantize_q8_0_4x4(const float * restrict x, void * restrict vy, int64_t k) { - assert(QK8_0 == 32); - assert(k % QK8_0 == 0); - const int nb = k / QK8_0; - - block_q8_0x4 * restrict y = (block_q8_0x4 *) vy; - -#if defined(__ARM_NEON) - float32x4_t srcv[4][8]; - float id[4]; - - for (int i = 0; i < nb; i++) { - float32x4_t asrcv[8]; - float32x4_t amaxv[8]; - - for (int row_iter = 0; row_iter < 4; row_iter++) { - for (int j = 0; j < 8; j++) srcv[row_iter][j] = vld1q_f32(x + row_iter * k + i * 32 + 4 * j); - for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[row_iter][j]); - - for (int j = 0; j < 4; j++) amaxv[2 * j] = vmaxq_f32(asrcv[2 * j], asrcv[2 * j + 1]); - for (int j = 0; j < 2; j++) amaxv[4 * j] = vmaxq_f32(amaxv[4 * j], amaxv[4 * j + 2]); - for (int j = 0; j < 1; j++) amaxv[8 * j] = vmaxq_f32(amaxv[8 * j], amaxv[8 * j + 4]); - - const float amax = vmaxvq_f32(amaxv[0]); - - const float d = amax / ((1 << 7) - 1); - id[row_iter] = d ? 1.0f / d : 0.0f; - - y[i].d[row_iter] = GGML_FP32_TO_FP16(d); - } - - for (int j = 0; j < 8; j++) { - float32x4_t v = vmulq_n_f32(srcv[0][j], id[0]); - int32x4_t vi = vcvtnq_s32_f32(v); - y[i].qs[16 * j + 0] = vgetq_lane_s32(vi, 0); - y[i].qs[16 * j + 1] = vgetq_lane_s32(vi, 1); - y[i].qs[16 * j + 2] = vgetq_lane_s32(vi, 2); - y[i].qs[16 * j + 3] = vgetq_lane_s32(vi, 3); - - v = vmulq_n_f32(srcv[1][j], id[1]); - vi = vcvtnq_s32_f32(v); - y[i].qs[16 * j + 4] = vgetq_lane_s32(vi, 0); - y[i].qs[16 * j + 5] = vgetq_lane_s32(vi, 1); - y[i].qs[16 * j + 6] = vgetq_lane_s32(vi, 2); - y[i].qs[16 * j + 7] = vgetq_lane_s32(vi, 3); - - v = vmulq_n_f32(srcv[2][j], id[2]); - vi = vcvtnq_s32_f32(v); - y[i].qs[16 * j + 8] = vgetq_lane_s32(vi, 0); - y[i].qs[16 * j + 9] = vgetq_lane_s32(vi, 1); - y[i].qs[16 * j + 10] = vgetq_lane_s32(vi, 2); - y[i].qs[16 * j + 11] = vgetq_lane_s32(vi, 3); - - v = vmulq_n_f32(srcv[3][j], id[3]); - vi = vcvtnq_s32_f32(v); - y[i].qs[16 * j + 12] = vgetq_lane_s32(vi, 0); - y[i].qs[16 * j + 13] = vgetq_lane_s32(vi, 1); - y[i].qs[16 * j + 14] = vgetq_lane_s32(vi, 2); - y[i].qs[16 * j + 15] = vgetq_lane_s32(vi, 3); - } - } -#else - // scalar - const int blck_size_interleave = 4; - float srcv[4][QK8_0]; - float id[4]; - - for (int i = 0; i < nb; i++) { - for (int row_iter = 0; row_iter < 4; row_iter++) { - float amax = 0.0f; // absolute max - - for (int j = 0; j < QK8_0; j++) { - srcv[row_iter][j] = x[row_iter * k + i * QK8_0 + j]; - amax = MAX(amax, fabsf(srcv[row_iter][j])); - } - - const float d = amax / ((1 << 7) - 1); - id[row_iter] = d ? 1.0f / d : 0.0f; - - y[i].d[row_iter] = GGML_FP32_TO_FP16(d); - } - - for (int j = 0; j < QK8_0 * 4; j++) { - int src_offset = (j / (4 * blck_size_interleave)) * blck_size_interleave; - int src_id = (j % (4 * blck_size_interleave)) / blck_size_interleave; - src_offset += (j % blck_size_interleave); - - float x0 = srcv[src_id][src_offset] * id[src_id]; - y[i].qs[j] = roundf(x0); - } - } -#endif -} - -void quantize_q8_0_4x8(const float * restrict x, void * restrict vy, int64_t k) { - assert(QK8_0 == 32); - assert(k % QK8_0 == 0); - const int nb = k / QK8_0; - - block_q8_0x4 * restrict y = (block_q8_0x4 *) vy; - -#if defined(__ARM_NEON) - float32x4_t srcv[4][8]; - float id[4]; - - for (int i = 0; i < nb; i++) { - float32x4_t asrcv[8]; - float32x4_t amaxv[8]; - - for (int row_iter = 0; row_iter < 4; row_iter++) { - for (int j = 0; j < 8; j++) srcv[row_iter][j] = vld1q_f32(x + row_iter * k + i * 32 + 4 * j); - for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[row_iter][j]); - - for (int j = 0; j < 4; j++) amaxv[2 * j] = vmaxq_f32(asrcv[2 * j], asrcv[2 * j + 1]); - for (int j = 0; j < 2; j++) amaxv[4 * j] = vmaxq_f32(amaxv[4 * j], amaxv[4 * j + 2]); - for (int j = 0; j < 1; j++) amaxv[8 * j] = vmaxq_f32(amaxv[8 * j], amaxv[8 * j + 4]); - - const float amax = vmaxvq_f32(amaxv[0]); - - const float d = amax / ((1 << 7) - 1); - id[row_iter] = d ? 1.0f / d : 0.0f; - - y[i].d[row_iter] = GGML_FP32_TO_FP16(d); - } - - for (int j = 0; j < 4; j++) { - float32x4_t v = vmulq_n_f32(srcv[0][2 * j], id[0]); - int32x4_t vi = vcvtnq_s32_f32(v); - y[i].qs[32 * j + 0] = vgetq_lane_s32(vi, 0); - y[i].qs[32 * j + 1] = vgetq_lane_s32(vi, 1); - y[i].qs[32 * j + 2] = vgetq_lane_s32(vi, 2); - y[i].qs[32 * j + 3] = vgetq_lane_s32(vi, 3); - v = vmulq_n_f32(srcv[0][2 * j + 1], id[0]); - vi = vcvtnq_s32_f32(v); - y[i].qs[32 * j + 4] = vgetq_lane_s32(vi, 0); - y[i].qs[32 * j + 5] = vgetq_lane_s32(vi, 1); - y[i].qs[32 * j + 6] = vgetq_lane_s32(vi, 2); - y[i].qs[32 * j + 7] = vgetq_lane_s32(vi, 3); - - v = vmulq_n_f32(srcv[1][2 * j], id[1]); - vi = vcvtnq_s32_f32(v); - y[i].qs[32 * j + 8] = vgetq_lane_s32(vi, 0); - y[i].qs[32 * j + 9] = vgetq_lane_s32(vi, 1); - y[i].qs[32 * j + 10] = vgetq_lane_s32(vi, 2); - y[i].qs[32 * j + 11] = vgetq_lane_s32(vi, 3); - v = vmulq_n_f32(srcv[1][2 * j + 1], id[1]); - vi = vcvtnq_s32_f32(v); - y[i].qs[32 * j + 12] = vgetq_lane_s32(vi, 0); - y[i].qs[32 * j + 13] = vgetq_lane_s32(vi, 1); - y[i].qs[32 * j + 14] = vgetq_lane_s32(vi, 2); - y[i].qs[32 * j + 15] = vgetq_lane_s32(vi, 3); - - v = vmulq_n_f32(srcv[2][2 * j], id[2]); - vi = vcvtnq_s32_f32(v); - y[i].qs[32 * j + 16] = vgetq_lane_s32(vi, 0); - y[i].qs[32 * j + 17] = vgetq_lane_s32(vi, 1); - y[i].qs[32 * j + 18] = vgetq_lane_s32(vi, 2); - y[i].qs[32 * j + 19] = vgetq_lane_s32(vi, 3); - v = vmulq_n_f32(srcv[2][2 * j + 1], id[2]); - vi = vcvtnq_s32_f32(v); - y[i].qs[32 * j + 20] = vgetq_lane_s32(vi, 0); - y[i].qs[32 * j + 21] = vgetq_lane_s32(vi, 1); - y[i].qs[32 * j + 22] = vgetq_lane_s32(vi, 2); - y[i].qs[32 * j + 23] = vgetq_lane_s32(vi, 3); - - v = vmulq_n_f32(srcv[3][2 * j], id[3]); - vi = vcvtnq_s32_f32(v); - y[i].qs[32 * j + 24] = vgetq_lane_s32(vi, 0); - y[i].qs[32 * j + 25] = vgetq_lane_s32(vi, 1); - y[i].qs[32 * j + 26] = vgetq_lane_s32(vi, 2); - y[i].qs[32 * j + 27] = vgetq_lane_s32(vi, 3); - v = vmulq_n_f32(srcv[3][2 * j + 1], id[3]); - vi = vcvtnq_s32_f32(v); - y[i].qs[32 * j + 28] = vgetq_lane_s32(vi, 0); - y[i].qs[32 * j + 29] = vgetq_lane_s32(vi, 1); - y[i].qs[32 * j + 30] = vgetq_lane_s32(vi, 2); - y[i].qs[32 * j + 31] = vgetq_lane_s32(vi, 3); - } - } -#elif defined(__AVX2__) || defined(__AVX__) - float id[4]; - __m256 srcv[4][4]; - __m256 idvec[4]; - - for (int i = 0; i < nb; i++) { - for (int row_iter = 0; row_iter < 4; row_iter++) { - // Load elements into 4 AVX vectors - __m256 v0 = _mm256_loadu_ps( x + row_iter * k + i * 32 ); - __m256 v1 = _mm256_loadu_ps( x + row_iter * k + i * 32 + 8 ); - __m256 v2 = _mm256_loadu_ps( x + row_iter * k + i * 32 + 16 ); - __m256 v3 = _mm256_loadu_ps( x + row_iter * k + i * 32 + 24 ); - - // Compute max(abs(e)) for the block - const __m256 signBit = _mm256_set1_ps( -0.0f ); - __m256 maxAbs = _mm256_andnot_ps( signBit, v0 ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) ); - - __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) ); - max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) ); - max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) ); - const float maxScalar = _mm_cvtss_f32( max4 ); - - // Divided by 127.f to mirror results in quantize_row_q8_0 - const float d = maxScalar / 127.f; - id[row_iter] = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f; //d ? 1.0f / d : 0.0f; - - // Store the scale for the individual block - y[i].d[row_iter] = GGML_FP32_TO_FP16(d); - - // Store the values in blocks of eight values - Aim is to use these later for block interleaving - srcv[row_iter][0] = v0; - srcv[row_iter][1] = v1; - srcv[row_iter][2] = v2; - srcv[row_iter][3] = v3; - idvec[row_iter] = _mm256_set1_ps(id[row_iter]); - } - - // The loop iterates four times - The aim is to get 4 corresponding chunks of eight bytes from the original weight blocks that are interleaved - for (int j = 0; j < 4; j++) { - // Apply the multiplier - __m256 v0 = _mm256_mul_ps(srcv[0][j], idvec[0]); - __m256 v1 = _mm256_mul_ps(srcv[1][j], idvec[1]); - __m256 v2 = _mm256_mul_ps(srcv[2][j], idvec[2]); - __m256 v3 = _mm256_mul_ps(srcv[3][j], idvec[3]); - - // Round to nearest integer - v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST ); - v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST ); - v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST ); - v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST ); - - // Convert floats to integers - __m256i i0 = _mm256_cvtps_epi32( v0 ); - __m256i i1 = _mm256_cvtps_epi32( v1 ); - __m256i i2 = _mm256_cvtps_epi32( v2 ); - __m256i i3 = _mm256_cvtps_epi32( v3 ); - -#if defined(__AVX2__) - // Convert int32 to int16 - i0 = _mm256_packs_epi32( i0, i1 ); - i2 = _mm256_packs_epi32( i2, i3 ); - // Convert int16 to int8 - i0 = _mm256_packs_epi16( i0, i2 ); - - // Permute and store the quantized weights in the required order after the pack instruction - const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 ); - i0 = _mm256_permutevar8x32_epi32( i0, perm ); - - _mm256_storeu_si256((__m256i *)(y[i].qs + 32 * j), i0); -#else - // Since we don't have in AVX some necessary functions, - // we split the registers in half and call AVX2 analogs from SSE - __m128i ni0 = _mm256_castsi256_si128( i0 ); - __m128i ni1 = _mm256_extractf128_si256( i0, 1); - __m128i ni2 = _mm256_castsi256_si128( i1 ); - __m128i ni3 = _mm256_extractf128_si256( i1, 1); - __m128i ni4 = _mm256_castsi256_si128( i2 ); - __m128i ni5 = _mm256_extractf128_si256( i2, 1); - __m128i ni6 = _mm256_castsi256_si128( i3 ); - __m128i ni7 = _mm256_extractf128_si256( i3, 1); - - // Convert int32 to int16 - ni0 = _mm_packs_epi32( ni0, ni1 ); - ni2 = _mm_packs_epi32( ni2, ni3 ); - ni4 = _mm_packs_epi32( ni4, ni5 ); - ni6 = _mm_packs_epi32( ni6, ni7 ); - // Convert int16 to int8 - ni0 = _mm_packs_epi16( ni0, ni2 ); - ni4 = _mm_packs_epi16( ni4, ni6 ); - _mm_storeu_si128((__m128i *)(y[i].qs + 32 * j), ni0); - _mm_storeu_si128((__m128i *)(y[i].qs + 32 * j + 16), ni4); -#endif - } - } -#else - // scalar - const int blck_size_interleave = 8; - float srcv[4][QK8_0]; - float id[4]; - - for (int i = 0; i < nb; i++) { - for (int row_iter = 0; row_iter < 4; row_iter++) { - float amax = 0.0f; // absolute max - - for (int j = 0; j < QK8_0; j++) { - srcv[row_iter][j] = x[row_iter * k + i * QK8_0 + j]; - amax = MAX(amax, fabsf(srcv[row_iter][j])); - } - - const float d = amax / ((1 << 7) - 1); - id[row_iter] = d ? 1.0f / d : 0.0f; - - y[i].d[row_iter] = GGML_FP32_TO_FP16(d); - } - - for (int j = 0; j < QK8_0 * 4; j++) { - int src_offset = (j / (4 * blck_size_interleave)) * blck_size_interleave; - int src_id = (j % (4 * blck_size_interleave)) / blck_size_interleave; - src_offset += (j % blck_size_interleave); - - float x0 = srcv[src_id][src_offset] * id[src_id]; - y[i].qs[j] = roundf(x0); - } - } -#endif -} - -void quantize_mat_q8_0(const float * restrict x, void * restrict vy, int64_t nrow, int64_t n_per_row, int64_t blck_size_interleave) { - assert(nrow == 4); - UNUSED(nrow); - if (blck_size_interleave == 4) { - quantize_q8_0_4x4(x, vy, n_per_row); - } else if (blck_size_interleave == 8) { - quantize_q8_0_4x8(x, vy, n_per_row); - } else { - assert(false); - } -} - static size_t quantize_q4_0_nr_bl(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, int nrows_interleaved, int blck_size_interleave) { assert(n_per_row % QK4_0 == 0); const int nb = n_per_row / QK4_0; @@ -570,11 +100,11 @@ static size_t quantize_q4_0_nr_bl(const float * restrict src, void * restrict ds } if (nrows_interleaved == 8) { - *(block_q4_0x8 *) out_ptr = make_block_q4_0x8(dst_tmp, blck_size_interleave, 0x88); + *(block_q4_0x8 *) out_ptr = make_block_q4_0x8(dst_tmp, blck_size_interleave); out_ptr = (block_q4_0x8 *) out_ptr + 1; } else if (nrows_interleaved == 4) { - *(block_q4_0x4 *) out_ptr = make_block_q4_0x4(dst_tmp, blck_size_interleave, 0x88); + *(block_q4_0x4 *) out_ptr = make_block_q4_0x4(dst_tmp, blck_size_interleave); out_ptr = (block_q4_0x4 *) out_ptr + 1; } } @@ -597,2881 +127,3 @@ size_t quantize_q4_0_8x8(const float * restrict src, void * restrict dst, int64_ UNUSED(quant_weights); return quantize_q4_0_nr_bl(src, dst, nrow, n_per_row, 8, 8); } - -void ggml_gemv_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { - const int qk = QK8_0; - const int nb = n / qk; - const int ncols_interleaved = 4; - const int blocklen = 4; - - assert (n % qk == 0); - assert (nc % ncols_interleaved == 0); - - UNUSED(s); - UNUSED(bs); - UNUSED(vx); - UNUSED(vy); - UNUSED(nr); - UNUSED(nc); - UNUSED(nb); - UNUSED(ncols_interleaved); - UNUSED(blocklen); - -#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) - if (ggml_cpu_has_neon()) { - const void * b_ptr = vx; - const void * a_ptr = vy; - float * res_ptr = s; - - __asm__ __volatile__( - "movi v31.16b, #0x4\n" - "movi v30.16b, #0xf0\n" - "add %x[b_ptr], %x[b_ptr], #0x8\n" - "1:" // Column loop - "add x22, %x[a_ptr], #0x2\n" - "movi v29.16b, #0x0\n" - "mov x21, %x[nb]\n" - "2:" // Block loop - "ldr q28, [%x[b_ptr], #0x0]\n" - "ldr q27, [x22, #0x0]\n" - "movi v26.4s, #0x0\n" - "sub x20, x22, #0x2\n" - "ldr q25, [x22, #0x10]\n" - "ldr q24, [%x[b_ptr], #0x10]\n" - "sub x21, x21, #0x1\n" - "add x22, x22, #0x22\n" - "ldr q23, [%x[b_ptr], #0x20]\n" - "ldr q22, [%x[b_ptr], #0x30]\n" - "ld1r { v21.8h }, [x20]\n" - "ldr q20, [%x[b_ptr], #-0x8]\n" - "sshl v16.16b, v28.16b, v31.16b\n" - "and v28.16b, v28.16b, v30.16b\n" - "sshl v19.16b, v24.16b, v31.16b\n" - "and v24.16b, v24.16b, v30.16b\n" - "add %x[b_ptr], %x[b_ptr], #0x48\n" - "sshl v18.16b, v23.16b, v31.16b\n" - "and v23.16b, v23.16b, v30.16b\n" - ".inst 0x4f9be21a // sdot v26.4s, v16.16b, v27.4b[0]\n" - "sshl v17.16b, v22.16b, v31.16b\n" - "and v22.16b, v22.16b, v30.16b\n" - "fcvtl v21.4s, v21.4h\n" - "fcvtl v16.4s, v20.4h\n" - ".inst 0x4f99e39a // sdot v26.4s, v28.16b, v25.4b[0]\n" - "fmul v16.4s, v16.4s, v21.4s\n" - ".inst 0x4fbbe27a // sdot v26.4s, v19.16b, v27.4b[1]\n" - ".inst 0x4fb9e31a // sdot v26.4s, v24.16b, v25.4b[1]\n" - ".inst 0x4f9bea5a // sdot v26.4s, v18.16b, v27.4b[2]\n" - ".inst 0x4f99eafa // sdot v26.4s, v23.16b, v25.4b[2]\n" - ".inst 0x4fbbea3a // sdot v26.4s, v17.16b, v27.4b[3]\n" - ".inst 0x4fb9eada // sdot v26.4s, v22.16b, v25.4b[3]\n" - "scvtf v26.4s, v26.4s, #0x4\n" - "fmla v29.4s, v26.4s, v16.4s\n" - "cbnz x21, 2b\n" - "sub %x[nc], %x[nc], #0x4\n" - "str q29, [%x[res_ptr], #0x0]\n" - "add %x[res_ptr], %x[res_ptr], #0x10\n" - "cbnz %x[nc], 1b\n" - : [b_ptr] "+&r" (b_ptr), [res_ptr] "+&r" (res_ptr), [nc] "+&r" (nc) - : [a_ptr] "r" (a_ptr), [nb] "r" (nb) - : "memory", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x20", "x21", "x22" - ); - return; - } -#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) - float sumf[4]; - int sumi; - - const block_q8_0 * a_ptr = (const block_q8_0 *) vy; - for (int x = 0; x < nc / ncols_interleaved; x++) { - const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb); - - for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0; - for (int l = 0; l < nb; l++) { - for (int k = 0; k < (qk / (2 * blocklen)); k++) { - for (int j = 0; j < ncols_interleaved; j++) { - sumi = 0; - for (int i = 0; i < blocklen; ++i) { - const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); - const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); - sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4; - } - sumf[j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d); - } - } - } - for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j]; - } -} - -void ggml_gemv_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { - const int qk = QK8_0; - const int nb = n / qk; - const int ncols_interleaved = 4; - const int blocklen = 8; - - assert (n % qk == 0); - assert (nc % ncols_interleaved == 0); - - UNUSED(s); - UNUSED(bs); - UNUSED(vx); - UNUSED(vy); - UNUSED(nr); - UNUSED(nc); - UNUSED(nb); - UNUSED(ncols_interleaved); - UNUSED(blocklen); - -#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) - if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) { - const void * b_ptr = vx; - const void * a_ptr = vy; - float * res_ptr = s; - - __asm__ __volatile__( - "movi v2.16b, #0x4\n" - "movi v1.16b, #0xf0\n" - "add %x[b_ptr], %x[b_ptr], #0x8\n" - "1:" // Column loop - "add x23, %x[a_ptr], #0x2\n" - "movi v0.16b, #0x0\n" - "mov x22, %x[nb]\n" - "2:" // Block loop - "ldr q31, [%x[b_ptr], #0x0]\n" - "ldr q30, [%x[b_ptr], #0x10]\n" - "mov x21, x23\n" - "movi v29.4s, #0x0\n" - "ldr q28, [%x[b_ptr], #0x20]\n" - "ldr q27, [%x[b_ptr], #0x30]\n" - "movi v26.4s, #0x0\n" - "sub x20, x23, #0x2\n" - "ld1r { v25.8h }, [x20]\n" - "ldr q24, [%x[b_ptr], #-0x8]\n" - "sub x22, x22, #0x1\n" - "add x23, x23, #0x22\n" - "ld1r { v23.2d }, [x21], #0x8\n" - "sshl v22.16b, v31.16b, v2.16b\n" - "sshl v16.16b, v30.16b, v2.16b\n" - "add %x[b_ptr], %x[b_ptr], #0x48\n" - "ld1r { v21.2d }, [x21], #0x8\n" - "sshl v20.16b, v28.16b, v2.16b\n" - "sshl v19.16b, v27.16b, v2.16b\n" - "ld1r { v18.2d }, [x21], #0x8\n" - "ld1r { v17.2d }, [x21], #0x8\n" - "and v31.16b, v31.16b, v1.16b\n" - "and v30.16b, v30.16b, v1.16b\n" - ".inst 0x4e9796dd // sdot v29.4s, v22.16b, v23.16b\n" - ".inst 0x4e97961a // sdot v26.4s, v16.16b, v23.16b\n" - "and v28.16b, v28.16b, v1.16b\n" - "and v27.16b, v27.16b, v1.16b\n" - "fcvtl v25.4s, v25.4h\n" - "fcvtl v16.4s, v24.4h\n" - ".inst 0x4e95969d // sdot v29.4s, v20.16b, v21.16b\n" - ".inst 0x4e95967a // sdot v26.4s, v19.16b, v21.16b\n" - "fmul v16.4s, v16.4s, v25.4s\n" - ".inst 0x4e9297fd // sdot v29.4s, v31.16b, v18.16b\n" - ".inst 0x4e9297da // sdot v26.4s, v30.16b, v18.16b\n" - ".inst 0x4e91979d // sdot v29.4s, v28.16b, v17.16b\n" - ".inst 0x4e91977a // sdot v26.4s, v27.16b, v17.16b\n" - "addp v29.4s, v29.4s, v26.4s\n" - "scvtf v29.4s, v29.4s, #0x4\n" - "fmla v0.4s, v29.4s, v16.4s\n" - "cbnz x22, 2b\n" - "sub %x[nc], %x[nc], #0x4\n" - "str q0, [%x[res_ptr], #0x0]\n" - "add %x[res_ptr], %x[res_ptr], #0x10\n" - "cbnz %x[nc], 1b\n" - : [b_ptr] "+&r" (b_ptr), [res_ptr] "+&r" (res_ptr), [nc] "+&r" (nc) - : [a_ptr] "r" (a_ptr), [nb] "r" (nb) - : "memory", "v0", "v1", "v2", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x20", "x21", "x22", "x23" - ); - return; - } -#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) - float sumf[4]; - int sumi; - - const block_q8_0 * a_ptr = (const block_q8_0 *) vy; - for (int x = 0; x < nc / ncols_interleaved; x++) { - const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb); - - for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0; - for (int l = 0; l < nb; l++) { - for (int k = 0; k < (qk / (2 * blocklen)); k++) { - for (int j = 0; j < ncols_interleaved; j++) { - sumi = 0; - for (int i = 0; i < blocklen; ++i) { - const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); - const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); - sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4; - } - sumf[j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d); - } - } - } - for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j]; - } -} - -void ggml_gemv_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { - const int qk = QK8_0; - const int nb = n / qk; - const int ncols_interleaved = 8; - const int blocklen = 8; - - assert (n % qk == 0); - assert (nc % ncols_interleaved == 0); - - UNUSED(s); - UNUSED(bs); - UNUSED(vx); - UNUSED(vy); - UNUSED(nr); - UNUSED(nc); - UNUSED(nb); - UNUSED(ncols_interleaved); - UNUSED(blocklen); - -#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) -#if defined(__ARM_FEATURE_SVE) - if (ggml_cpu_has_sve() && ggml_cpu_get_sve_cnt() == QK8_0) { - const void * b_ptr = vx; - const void * a_ptr = vy; - float * res_ptr = s; - - __asm__ __volatile__( - "ptrue p0.b\n" - "add %x[b_ptr], %x[b_ptr], #0x10\n" - "1:" // Column loop - "add x22, %x[a_ptr], #0x2\n" - "mov z31.b, #0x0\n" - "mov x21, %x[nb]\n" - "2:" // Block loop - "ld1b { z30.b }, p0/Z, [%x[b_ptr]]\n" - "ld1b { z29.b }, p0/Z, [%x[b_ptr], #1, MUL VL]\n" - "mov z28.s, #0x0\n" - "mov z27.s, #0x0\n" - "ld1rd { z26.d }, p0/Z, [x22]\n" - "ld1b { z25.b }, p0/Z, [%x[b_ptr], #2, MUL VL]\n" - "sub x20, x22, #0x2\n" - "sub x21, x21, #0x1\n" - "ld1b { z24.b }, p0/Z, [%x[b_ptr], #3, MUL VL]\n" - "ld1rd { z23.d }, p0/Z, [x22, #8]\n" - "lsl z22.b, z30.b, #0x4\n" - "lsl z16.b, z29.b, #0x4\n" - "and z30.b, z30.b, #0xf0\n" - "and z29.b, z29.b, #0xf0\n" - "ld1rd { z21.d }, p0/Z, [x22, #16]\n" - "ld1rd { z20.d }, p0/Z, [x22, #24]\n" - "lsl z19.b, z25.b, #0x4\n" - "and z25.b, z25.b, #0xf0\n" - "ld1rh { z17.h }, p0/Z, [x20]\n" - "ld1h { z18.s }, p0/Z, [%x[b_ptr], #-1, MUL VL]\n" - "sdot z28.s, z22.b, z26.b\n" - "sdot z27.s, z16.b, z26.b\n" - "lsl z16.b, z24.b, #0x4\n" - "add x22, x22, #0x22\n" - "and z24.b, z24.b, #0xf0\n" - "add %x[b_ptr], %x[b_ptr], #0x90\n" - "fcvt z17.s, p0/m, z17.h\n" - "fcvt z18.s, p0/m, z18.h\n" - "sdot z28.s, z19.b, z23.b\n" - "sdot z27.s, z16.b, z23.b\n" - "fmul z18.s, z18.s, z17.s\n" - "sdot z28.s, z30.b, z21.b\n" - "sdot z27.s, z29.b, z21.b\n" - "sdot z28.s, z25.b, z20.b\n" - "sdot z27.s, z24.b, z20.b\n" - "uzp1 z17.s, z28.s, z27.s\n" - "uzp2 z16.s, z28.s, z27.s\n" - "add z17.s, z17.s, z16.s\n" - "asr z17.s, z17.s, #0x4\n" - "scvtf z17.s, p0/m, z17.s\n" - "fmla z31.s, p0/M, z17.s, z18.s\n" - "cbnz x21, 2b\n" - "sub %x[nc], %x[nc], #0x8\n" - "st1w { z31.s }, p0, [%x[res_ptr]]\n" - "add %x[res_ptr], %x[res_ptr], #0x20\n" - "cbnz %x[nc], 1b\n" - : [b_ptr] "+&r" (b_ptr), [res_ptr] "+&r" (res_ptr), [nc] "+&r" (nc) - : [a_ptr] "r" (a_ptr), [nb] "r" (nb) - : "memory", "p0", "x20", "x21", "x22", "z16", "z17", "z18", "z19", "z20", "z21", "z22", "z23", "z24", "z25", "z26", "z27", "z28", "z29", "z30", "z31" - ); - return; - } -#endif // #if defined(__ARM_FEATURE_SVE) -#elif defined(__AVX2__) - // Lookup table to convert signed nibbles to signed bytes - __m256i signextendlut = _mm256_castsi128_si256(_mm_set_epi8(-1, -2, -3, -4, -5, -6, -7, -8, 7, 6, 5, 4, 3, 2, 1, 0)); - signextendlut = _mm256_permute2f128_si256(signextendlut, signextendlut, 0); - __m128i changemask = _mm_set_epi8(15, 14, 7, 6, 13, 12, 5, 4, 11, 10, 3, 2, 9, 8, 1, 0); - __m256i finalpermutemask = _mm256_set_epi32(7, 5, 3, 1, 6, 4, 2, 0); - - // Permute mask used for easier vector processing at later stages - const __m256i m4b = _mm256_set1_epi8(0x0F); - - int64_t b_nb = n / QK4_0; - - const block_q4_0x8 * b_ptr_start = (const block_q4_0x8 *)vx; - const block_q8_0 * a_ptr_start = (const block_q8_0 *)vy; - - // Process Q8_0 blocks one by one - for (int64_t y = 0; y < nr; y++) { - - // Pointers to LHS blocks of block_q8_0 format - const block_q8_0 * a_ptr = a_ptr_start + (y * nb); - - // Take group of eight block_q4_0x8 structures at each pass of the loop and perform dot product operation - for (int64_t x = 0; x < nc / 8; x++) { - - // Pointers to RHS blocks - const block_q4_0x8 * b_ptr = b_ptr_start + (x * b_nb); - - // Master FP accumulator - __m256 acc_row = _mm256_setzero_ps(); - - for (int64_t b = 0; b < nb; b++) { - // Load 8 blocks of Q4_0 interleaved as 8 bytes (B0 - B7) - const __m256i rhs_raw_vec_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs)); - const __m256i rhs_raw_vec_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs) + 1); - const __m256i rhs_raw_vec_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs) + 2); - const __m256i rhs_raw_vec_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs) + 3); - - // 4-bit -> 8-bit - Sign is maintained - const __m256i rhs_vec_0123_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_vec_0123_0, m4b)); // B0(0-7) B1(0-7) B2(0-7) B3(0-7) - const __m256i rhs_vec_4567_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_vec_4567_0, m4b)); // B4(0-7) B5(0-7) B6(0-7) B7(0-7) - const __m256i rhs_vec_0123_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_vec_0123_1, m4b)); // B0(8-15) B1(8-15) B2(8-15) B3(8-15) - const __m256i rhs_vec_4567_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_vec_4567_1, m4b)); // B0(8-15) B1(8-15) B2(8-15) B3(8-15) - - const __m256i rhs_vec_0123_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_vec_0123_0, 4), m4b)); // B0(16-23) B1(16-23) B2(16-23) B3(16-23) - const __m256i rhs_vec_4567_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_vec_4567_0, 4), m4b)); // B4(16-23) B5(16-23) B6(16-23) B7(16-23) - const __m256i rhs_vec_0123_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_vec_0123_1, 4), m4b)); // B0(24-31) B1(24-31) B2(24-31) B3(24-31) - const __m256i rhs_vec_4567_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_vec_4567_1, 4), m4b)); // B4(24-31) B5(24-31) B6(24-31) B7(24-31) - - // Load the scale values for the 8 blocks interleaved in block_q4_0x8 - const __m256 col_scale_f32 = GGML_F32Cx8_REARRANGE_LOAD(b_ptr[b].d, changemask); - - // Load and convert to FP32 scale from block_q8_0 - const __m256 row_scale_f32 = _mm256_set1_ps(GGML_FP16_TO_FP32(a_ptr[b].d)); - - // Load the block values in block_q8_0 in batches of 16 bytes and replicate the same across 256 bit vector - __m256i lhs_vec_0 = _mm256_castsi128_si256(_mm_loadu_si128((const __m128i *)a_ptr[b].qs)); - __m256i lhs_vec_1 = _mm256_castsi128_si256(_mm_loadu_si128((const __m128i *)(a_ptr[b].qs + 16))); - - lhs_vec_0 = _mm256_permute2f128_si256(lhs_vec_0, lhs_vec_0, 0); // A0 (0-15) A0(0-15) - lhs_vec_1 = _mm256_permute2f128_si256(lhs_vec_1, lhs_vec_1, 0); // A0 (16-31) A0(16-31)) - - __m256i iacc = _mm256_setzero_si256(); - - // Dot product done within 32 bit lanes and accumulated in the same vector - // B0(0-3) B4(0-3) B1(0-3) B5(0-3) B2(0-3) B6(0-3) B3(0-3) B7(0-3) with A0(0-3) - // B0(4-7) B4(4-7) B1(4-7) B5(4-7) B2(4-7) B6(4-7) B3(4-7) B7(4-7) with A0(4-7) - // ........................................................................... - // B0(28-31) B4(28-31) B1(28-31) B5(28-31) B2(28-31) B6(28-31) B3(28-31) B7(28-31) with A0(28-31) - - iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(rhs_vec_0123_0 ,_mm256_shuffle_epi32(rhs_vec_4567_0, 177), 170), _mm256_shuffle_epi32(lhs_vec_0, 0))); - iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(_mm256_shuffle_epi32(rhs_vec_0123_0, 177) ,rhs_vec_4567_0, 170), _mm256_shuffle_epi32(lhs_vec_0, 85))); - - iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(rhs_vec_0123_1 ,_mm256_shuffle_epi32(rhs_vec_4567_1, 177), 170), _mm256_shuffle_epi32(lhs_vec_0, 170))); - iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(_mm256_shuffle_epi32(rhs_vec_0123_1, 177) ,rhs_vec_4567_1, 170), _mm256_shuffle_epi32(lhs_vec_0, 255))); - - iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(rhs_vec_0123_2 ,_mm256_shuffle_epi32(rhs_vec_4567_2, 177), 170), _mm256_shuffle_epi32(lhs_vec_1, 0))); - iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(_mm256_shuffle_epi32(rhs_vec_0123_2, 177) ,rhs_vec_4567_2, 170), _mm256_shuffle_epi32(lhs_vec_1, 85))); - - iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(rhs_vec_0123_3 ,_mm256_shuffle_epi32(rhs_vec_4567_3, 177), 170), _mm256_shuffle_epi32(lhs_vec_1, 170))); - iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(_mm256_shuffle_epi32(rhs_vec_0123_3, 177) ,rhs_vec_4567_3, 170), _mm256_shuffle_epi32(lhs_vec_1, 255))); - - // Accumulated values multipled with appropriate scales - acc_row = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc), _mm256_mul_ps(col_scale_f32, row_scale_f32), acc_row); - } - - // Accumulated output values permuted so as to be stored in appropriate order post accumulation - acc_row = _mm256_permutevar8x32_ps(acc_row, finalpermutemask); - _mm256_storeu_ps(s + (y * nr + x * 8), acc_row); - } - } - return; -#elif defined(__riscv_v_intrinsic) - if (__riscv_vlenb() >= QK4_0) { - const size_t vl = QK4_0; - - const block_q8_0 * a_ptr = (const block_q8_0 *) vy; - for (int x = 0; x < nc / ncols_interleaved; x++) { - const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb); - - vfloat32m1_t sumf = __riscv_vfmv_v_f_f32m1(0.0, vl / 4); - for (int l = 0; l < nb; l++) { - const int64_t a0 = *(const int64_t *)&a_ptr[l].qs[0]; - const int64_t a1 = *(const int64_t *)&a_ptr[l].qs[8]; - const int64_t a2 = *(const int64_t *)&a_ptr[l].qs[16]; - const int64_t a3 = *(const int64_t *)&a_ptr[l].qs[24]; - __asm__ __volatile__("" ::: "memory"); // prevent gcc from emitting fused vlse64, violating alignment - const vint8m2_t lhs_0_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(a0, vl / 4)); - const vint8m2_t lhs_1_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(a1, vl / 4)); - const vint8m2_t lhs_2_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(a2, vl / 4)); - const vint8m2_t lhs_3_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(a3, vl / 4)); - - const vint8m4_t rhs_raw_vec = __riscv_vle8_v_i8m4((const int8_t *)b_ptr[l].qs, vl * 4); - const vint8m4_t rhs_vec_lo = __riscv_vsra_vx_i8m4(__riscv_vsll_vx_i8m4(rhs_raw_vec, 4, vl * 4), 4, vl * 4); - const vint8m4_t rhs_vec_hi = __riscv_vsra_vx_i8m4(rhs_raw_vec, 4, vl * 4); - const vint8m2_t rhs_vec_lo_0 = __riscv_vget_v_i8m4_i8m2(rhs_vec_lo, 0); - const vint8m2_t rhs_vec_lo_1 = __riscv_vget_v_i8m4_i8m2(rhs_vec_lo, 1); - const vint8m2_t rhs_vec_hi_0 = __riscv_vget_v_i8m4_i8m2(rhs_vec_hi, 0); - const vint8m2_t rhs_vec_hi_1 = __riscv_vget_v_i8m4_i8m2(rhs_vec_hi, 1); - - const vint16m4_t sumi_lo_0 = __riscv_vwmul_vv_i16m4(rhs_vec_lo_0, lhs_0_8, vl * 2); - const vint16m4_t sumi_lo_1 = __riscv_vwmacc_vv_i16m4(sumi_lo_0, rhs_vec_lo_1, lhs_1_8, vl * 2); - const vint16m4_t sumi_hi_0 = __riscv_vwmacc_vv_i16m4(sumi_lo_1, rhs_vec_hi_0, lhs_2_8, vl * 2); - const vint16m4_t sumi_hi_m = __riscv_vwmacc_vv_i16m4(sumi_hi_0, rhs_vec_hi_1, lhs_3_8, vl * 2); - - const vuint32m4_t sumi_i32 = __riscv_vreinterpret_v_i32m4_u32m4(__riscv_vreinterpret_v_i16m4_i32m4(sumi_hi_m)); - const vuint16m2_t sumi_h2_0 = __riscv_vnsrl_wx_u16m2(sumi_i32, 0, vl); - const vuint16m2_t sumi_h2_1 = __riscv_vnsrl_wx_u16m2(sumi_i32, 16, vl); - const vuint16m2_t sumi_h2 = __riscv_vadd_vv_u16m2(sumi_h2_0, sumi_h2_1, vl); - const vuint32m2_t sumi_h2_i32 = __riscv_vreinterpret_v_u16m2_u32m2(sumi_h2); - const vuint16m1_t sumi_h4_0 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 0, vl / 2); - const vuint16m1_t sumi_h4_1 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 16, vl / 2); - const vuint16m1_t sumi_h4 = __riscv_vadd_vv_u16m1(sumi_h4_0, sumi_h4_1, vl / 2); - const vuint32m1_t sumi_h4_i32 = __riscv_vreinterpret_v_u16m1_u32m1(sumi_h4); - const vint16mf2_t sumi_h8_0 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 0, vl / 4)); - const vint16mf2_t sumi_h8_1 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 16, vl / 4)); - const vint32m1_t sumi_h8 = __riscv_vwadd_vv_i32m1(sumi_h8_0, sumi_h8_1, vl / 4); - const vfloat32m1_t facc = __riscv_vfcvt_f_x_v_f32m1(sumi_h8, vl / 4); - - // vector version needs Zvfhmin extension - const float a_scale = GGML_FP16_TO_FP32(a_ptr[l].d); - const float b_scales[8] = { - GGML_FP16_TO_FP32(b_ptr[l].d[0]), - GGML_FP16_TO_FP32(b_ptr[l].d[1]), - GGML_FP16_TO_FP32(b_ptr[l].d[2]), - GGML_FP16_TO_FP32(b_ptr[l].d[3]), - GGML_FP16_TO_FP32(b_ptr[l].d[4]), - GGML_FP16_TO_FP32(b_ptr[l].d[5]), - GGML_FP16_TO_FP32(b_ptr[l].d[6]), - GGML_FP16_TO_FP32(b_ptr[l].d[7]) - }; - const vfloat32m1_t b_scales_vec = __riscv_vle32_v_f32m1(b_scales, vl / 4); - const vfloat32m1_t tmp1 = __riscv_vfmul_vf_f32m1(facc, a_scale, vl / 4); - sumf = __riscv_vfmacc_vv_f32m1(sumf, tmp1, b_scales_vec, vl / 4); - } - __riscv_vse32_v_f32m1(s + x * ncols_interleaved, sumf, vl / 4); - } - return; - } -#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) - { - float sumf[8]; - int sumi; - - const block_q8_0 * a_ptr = (const block_q8_0 *) vy; - for (int x = 0; x < nc / ncols_interleaved; x++) { - const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb); - - for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0; - for (int l = 0; l < nb; l++) { - for (int k = 0; k < (qk / (2 * blocklen)); k++) { - for (int j = 0; j < ncols_interleaved; j++) { - sumi = 0; - for (int i = 0; i < blocklen; ++i) { - const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); - const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); - sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4; - } - sumf[j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d); - } - } - } - for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j]; - } - } -} - -void ggml_gemm_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { - const int qk = QK8_0; - const int nb = n / qk; - const int ncols_interleaved = 4; - const int blocklen = 4; - - assert (n % qk == 0); - assert (nr % 4 == 0); - assert (nc % ncols_interleaved == 0); - - UNUSED(s); - UNUSED(bs); - UNUSED(vx); - UNUSED(vy); - UNUSED(nr); - UNUSED(nc); - UNUSED(nb); - UNUSED(ncols_interleaved); - UNUSED(blocklen); - -#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) - if (ggml_cpu_has_neon()) { - const void * b_ptr = vx; - const void * a_ptr = vy; - float * res_ptr = s; - size_t res_stride = bs * sizeof(float); - - __asm__ __volatile__( - "mov x10, %x[nr]\n" - "mov x9, #0x88\n" - "cmp x10, #0x10\n" - "mul x9, %x[nb], x9\n" - "blt 4f\n" - "1:" // Row loop - "add x28, %x[b_ptr], #0x8\n" - "mov x27, %x[nc]\n" - "add x26, %x[res_ptr], %x[res_stride], LSL #4\n" - "2:" // Column loop - "add x25, %x[a_ptr], #0x8\n" - "movi v15.16b, #0x0\n" - "movi v19.16b, #0x0\n" - "mov x24, %x[nb]\n" - "add x23, x25, x9\n" - "movi v18.16b, #0x0\n" - "movi v14.16b, #0x0\n" - "add x22, x23, x9\n" - "movi v11.16b, #0x0\n" - "movi v13.16b, #0x0\n" - "add x21, x22, x9\n" - "movi v23.16b, #0x0\n" - "movi v16.16b, #0x0\n" - "movi v25.16b, #0x0\n" - "movi v7.16b, #0x0\n" - "movi v0.16b, #0x0\n" - "movi v4.16b, #0x0\n" - "movi v5.16b, #0x0\n" - "movi v21.16b, #0x0\n" - "movi v8.16b, #0x0\n" - "movi v1.16b, #0x0\n" - "3:" // Block loop - "ldr q3, [x28, #0x0]\n" - "ldr q31, [x25, #0x0]\n" - "movi v28.16b, #0x4\n" - "movi v10.4s, #0x0\n" - "ldr q22, [x28, #0x10]\n" - "ldr q6, [x25, #0x10]\n" - "movi v29.4s, #0x0\n" - "movi v9.4s, #0x0\n" - "ldr q27, [x28, #0x20]\n" - "ldr q30, [x28, #0x30]\n" - "movi v20.4s, #0x0\n" - "movi v24.16b, #0xf0\n" - "ldr d2, [x25, #-0x8]\n" - "ldr d26, [x23, #-0x8]\n" - "sshl v12.16b, v3.16b, v28.16b\n" - "sub x20, x28, #0x8\n" - "ldr d17, [x20, #0x0]\n" - "and v3.16b, v3.16b, v24.16b\n" - "subs x24, x24, #0x1\n" - "add x28, x28, #0x48\n" - ".inst 0x4f9fe18a // sdot v10.4s, v12.16b, v31.4b[0]\n" - ".inst 0x4fbfe19d // sdot v29.4s, v12.16b, v31.4b[1]\n" - ".inst 0x4f9fe989 // sdot v9.4s, v12.16b, v31.4b[2]\n" - ".inst 0x4fbfe994 // sdot v20.4s, v12.16b, v31.4b[3]\n" - "sshl v31.16b, v22.16b, v28.16b\n" - "and v22.16b, v22.16b, v24.16b\n" - "fcvtl v17.4s, v17.4h\n" - "fcvtl v2.4s, v2.4h\n" - "fcvtl v26.4s, v26.4h\n" - ".inst 0x4f86e3ea // sdot v10.4s, v31.16b, v6.4b[0]\n" - ".inst 0x4fa6e3fd // sdot v29.4s, v31.16b, v6.4b[1]\n" - ".inst 0x4f86ebe9 // sdot v9.4s, v31.16b, v6.4b[2]\n" - ".inst 0x4fa6ebf4 // sdot v20.4s, v31.16b, v6.4b[3]\n" - "sshl v6.16b, v27.16b, v28.16b\n" - "sshl v28.16b, v30.16b, v28.16b\n" - "and v27.16b, v27.16b, v24.16b\n" - "and v30.16b, v30.16b, v24.16b\n" - "ldr q24, [x25, #0x20]\n" - ".inst 0x4f98e0ca // sdot v10.4s, v6.16b, v24.4b[0]\n" - ".inst 0x4fb8e0dd // sdot v29.4s, v6.16b, v24.4b[1]\n" - ".inst 0x4f98e8c9 // sdot v9.4s, v6.16b, v24.4b[2]\n" - ".inst 0x4fb8e8d4 // sdot v20.4s, v6.16b, v24.4b[3]\n" - "ldr q24, [x25, #0x30]\n" - ".inst 0x4f98e38a // sdot v10.4s, v28.16b, v24.4b[0]\n" - ".inst 0x4fb8e39d // sdot v29.4s, v28.16b, v24.4b[1]\n" - ".inst 0x4f98eb89 // sdot v9.4s, v28.16b, v24.4b[2]\n" - ".inst 0x4fb8eb94 // sdot v20.4s, v28.16b, v24.4b[3]\n" - "ldr q24, [x25, #0x40]\n" - ".inst 0x4f98e06a // sdot v10.4s, v3.16b, v24.4b[0]\n" - ".inst 0x4fb8e07d // sdot v29.4s, v3.16b, v24.4b[1]\n" - ".inst 0x4f98e869 // sdot v9.4s, v3.16b, v24.4b[2]\n" - ".inst 0x4fb8e874 // sdot v20.4s, v3.16b, v24.4b[3]\n" - "ldr q24, [x25, #0x50]\n" - ".inst 0x4f98e2ca // sdot v10.4s, v22.16b, v24.4b[0]\n" - ".inst 0x4fb8e2dd // sdot v29.4s, v22.16b, v24.4b[1]\n" - ".inst 0x4f98eac9 // sdot v9.4s, v22.16b, v24.4b[2]\n" - ".inst 0x4fb8ead4 // sdot v20.4s, v22.16b, v24.4b[3]\n" - "ldr q24, [x25, #0x60]\n" - ".inst 0x4f98e36a // sdot v10.4s, v27.16b, v24.4b[0]\n" - ".inst 0x4fb8e37d // sdot v29.4s, v27.16b, v24.4b[1]\n" - ".inst 0x4f98eb69 // sdot v9.4s, v27.16b, v24.4b[2]\n" - ".inst 0x4fb8eb74 // sdot v20.4s, v27.16b, v24.4b[3]\n" - "ldr q24, [x25, #0x70]\n" - "add x25, x25, #0x88\n" - ".inst 0x4f98e3ca // sdot v10.4s, v30.16b, v24.4b[0]\n" - ".inst 0x4fb8e3dd // sdot v29.4s, v30.16b, v24.4b[1]\n" - ".inst 0x4f98ebc9 // sdot v9.4s, v30.16b, v24.4b[2]\n" - ".inst 0x4fb8ebd4 // sdot v20.4s, v30.16b, v24.4b[3]\n" - "fmul v24.4s, v17.4s, v2.s[0]\n" - "scvtf v10.4s, v10.4s, #0x4\n" - "scvtf v29.4s, v29.4s, #0x4\n" - "scvtf v9.4s, v9.4s, #0x4\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "fmla v15.4s, v10.4s, v24.4s\n" - "ldr q24, [x23, #0x0]\n" - "fmul v10.4s, v17.4s, v2.s[1]\n" - "fmla v19.4s, v29.4s, v10.4s\n" - "ldr q10, [x23, #0x10]\n" - "fmul v29.4s, v17.4s, v2.s[2]\n" - "fmul v2.4s, v17.4s, v2.s[3]\n" - "fmla v18.4s, v9.4s, v29.4s\n" - "movi v9.4s, #0x0\n" - "movi v29.4s, #0x0\n" - ".inst 0x4f98e189 // sdot v9.4s, v12.16b, v24.4b[0]\n" - ".inst 0x4fb8e19d // sdot v29.4s, v12.16b, v24.4b[1]\n" - "fmla v14.4s, v20.4s, v2.4s\n" - "movi v20.4s, #0x0\n" - "movi v2.4s, #0x0\n" - ".inst 0x4f98e994 // sdot v20.4s, v12.16b, v24.4b[2]\n" - ".inst 0x4fb8e982 // sdot v2.4s, v12.16b, v24.4b[3]\n" - "ldr q24, [x23, #0x20]\n" - ".inst 0x4f8ae3e9 // sdot v9.4s, v31.16b, v10.4b[0]\n" - ".inst 0x4faae3fd // sdot v29.4s, v31.16b, v10.4b[1]\n" - ".inst 0x4f8aebf4 // sdot v20.4s, v31.16b, v10.4b[2]\n" - ".inst 0x4faaebe2 // sdot v2.4s, v31.16b, v10.4b[3]\n" - "ldr q10, [x23, #0x30]\n" - ".inst 0x4f98e0c9 // sdot v9.4s, v6.16b, v24.4b[0]\n" - ".inst 0x4fb8e0dd // sdot v29.4s, v6.16b, v24.4b[1]\n" - ".inst 0x4f98e8d4 // sdot v20.4s, v6.16b, v24.4b[2]\n" - ".inst 0x4fb8e8c2 // sdot v2.4s, v6.16b, v24.4b[3]\n" - "ldr q24, [x23, #0x40]\n" - ".inst 0x4f8ae389 // sdot v9.4s, v28.16b, v10.4b[0]\n" - ".inst 0x4faae39d // sdot v29.4s, v28.16b, v10.4b[1]\n" - ".inst 0x4f8aeb94 // sdot v20.4s, v28.16b, v10.4b[2]\n" - ".inst 0x4faaeb82 // sdot v2.4s, v28.16b, v10.4b[3]\n" - "ldr q10, [x23, #0x50]\n" - ".inst 0x4f98e069 // sdot v9.4s, v3.16b, v24.4b[0]\n" - ".inst 0x4fb8e07d // sdot v29.4s, v3.16b, v24.4b[1]\n" - ".inst 0x4f98e874 // sdot v20.4s, v3.16b, v24.4b[2]\n" - ".inst 0x4fb8e862 // sdot v2.4s, v3.16b, v24.4b[3]\n" - "ldr q24, [x23, #0x60]\n" - ".inst 0x4f8ae2c9 // sdot v9.4s, v22.16b, v10.4b[0]\n" - ".inst 0x4faae2dd // sdot v29.4s, v22.16b, v10.4b[1]\n" - ".inst 0x4f8aead4 // sdot v20.4s, v22.16b, v10.4b[2]\n" - ".inst 0x4faaeac2 // sdot v2.4s, v22.16b, v10.4b[3]\n" - "ldr q10, [x23, #0x70]\n" - "add x23, x23, #0x88\n" - ".inst 0x4f98e369 // sdot v9.4s, v27.16b, v24.4b[0]\n" - ".inst 0x4fb8e37d // sdot v29.4s, v27.16b, v24.4b[1]\n" - ".inst 0x4f98eb74 // sdot v20.4s, v27.16b, v24.4b[2]\n" - ".inst 0x4fb8eb62 // sdot v2.4s, v27.16b, v24.4b[3]\n" - "ldr q24, [x22, #0x0]\n" - ".inst 0x4f8ae3c9 // sdot v9.4s, v30.16b, v10.4b[0]\n" - ".inst 0x4faae3dd // sdot v29.4s, v30.16b, v10.4b[1]\n" - ".inst 0x4f8aebd4 // sdot v20.4s, v30.16b, v10.4b[2]\n" - ".inst 0x4faaebc2 // sdot v2.4s, v30.16b, v10.4b[3]\n" - "fmul v10.4s, v17.4s, v26.s[0]\n" - "scvtf v9.4s, v9.4s, #0x4\n" - "scvtf v29.4s, v29.4s, #0x4\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "scvtf v2.4s, v2.4s, #0x4\n" - "fmla v11.4s, v9.4s, v10.4s\n" - "ldr q9, [x22, #0x10]\n" - "fmul v10.4s, v17.4s, v26.s[1]\n" - "fmla v13.4s, v29.4s, v10.4s\n" - "ldr d29, [x22, #-0x8]\n" - "fmul v10.4s, v17.4s, v26.s[2]\n" - "fmul v26.4s, v17.4s, v26.s[3]\n" - "fcvtl v29.4s, v29.4h\n" - "fmla v23.4s, v20.4s, v10.4s\n" - "movi v20.4s, #0x0\n" - "movi v10.4s, #0x0\n" - "fmla v16.4s, v2.4s, v26.4s\n" - "movi v26.4s, #0x0\n" - "movi v2.4s, #0x0\n" - ".inst 0x4f98e194 // sdot v20.4s, v12.16b, v24.4b[0]\n" - ".inst 0x4fb8e18a // sdot v10.4s, v12.16b, v24.4b[1]\n" - ".inst 0x4f98e99a // sdot v26.4s, v12.16b, v24.4b[2]\n" - ".inst 0x4fb8e982 // sdot v2.4s, v12.16b, v24.4b[3]\n" - "ldr q24, [x22, #0x20]\n" - ".inst 0x4f89e3f4 // sdot v20.4s, v31.16b, v9.4b[0]\n" - ".inst 0x4fa9e3ea // sdot v10.4s, v31.16b, v9.4b[1]\n" - ".inst 0x4f89ebfa // sdot v26.4s, v31.16b, v9.4b[2]\n" - ".inst 0x4fa9ebe2 // sdot v2.4s, v31.16b, v9.4b[3]\n" - "ldr q9, [x22, #0x30]\n" - ".inst 0x4f98e0d4 // sdot v20.4s, v6.16b, v24.4b[0]\n" - ".inst 0x4fb8e0ca // sdot v10.4s, v6.16b, v24.4b[1]\n" - ".inst 0x4f98e8da // sdot v26.4s, v6.16b, v24.4b[2]\n" - ".inst 0x4fb8e8c2 // sdot v2.4s, v6.16b, v24.4b[3]\n" - "ldr q24, [x22, #0x40]\n" - ".inst 0x4f89e394 // sdot v20.4s, v28.16b, v9.4b[0]\n" - ".inst 0x4fa9e38a // sdot v10.4s, v28.16b, v9.4b[1]\n" - ".inst 0x4f89eb9a // sdot v26.4s, v28.16b, v9.4b[2]\n" - ".inst 0x4fa9eb82 // sdot v2.4s, v28.16b, v9.4b[3]\n" - "ldr q9, [x22, #0x50]\n" - ".inst 0x4f98e074 // sdot v20.4s, v3.16b, v24.4b[0]\n" - ".inst 0x4fb8e06a // sdot v10.4s, v3.16b, v24.4b[1]\n" - ".inst 0x4f98e87a // sdot v26.4s, v3.16b, v24.4b[2]\n" - ".inst 0x4fb8e862 // sdot v2.4s, v3.16b, v24.4b[3]\n" - "ldr q24, [x22, #0x60]\n" - ".inst 0x4f89e2d4 // sdot v20.4s, v22.16b, v9.4b[0]\n" - ".inst 0x4fa9e2ca // sdot v10.4s, v22.16b, v9.4b[1]\n" - ".inst 0x4f89eada // sdot v26.4s, v22.16b, v9.4b[2]\n" - ".inst 0x4fa9eac2 // sdot v2.4s, v22.16b, v9.4b[3]\n" - "ldr q9, [x22, #0x70]\n" - "add x22, x22, #0x88\n" - ".inst 0x4f98e374 // sdot v20.4s, v27.16b, v24.4b[0]\n" - ".inst 0x4fb8e36a // sdot v10.4s, v27.16b, v24.4b[1]\n" - ".inst 0x4f98eb7a // sdot v26.4s, v27.16b, v24.4b[2]\n" - ".inst 0x4fb8eb62 // sdot v2.4s, v27.16b, v24.4b[3]\n" - "ldr q24, [x21, #0x0]\n" - ".inst 0x4f89e3d4 // sdot v20.4s, v30.16b, v9.4b[0]\n" - ".inst 0x4fa9e3ca // sdot v10.4s, v30.16b, v9.4b[1]\n" - ".inst 0x4f89ebda // sdot v26.4s, v30.16b, v9.4b[2]\n" - ".inst 0x4fa9ebc2 // sdot v2.4s, v30.16b, v9.4b[3]\n" - "fmul v9.4s, v17.4s, v29.s[0]\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "scvtf v10.4s, v10.4s, #0x4\n" - "scvtf v26.4s, v26.4s, #0x4\n" - "scvtf v2.4s, v2.4s, #0x4\n" - "fmla v25.4s, v20.4s, v9.4s\n" - "ldr q9, [x21, #0x10]\n" - "fmul v20.4s, v17.4s, v29.s[1]\n" - "fmla v7.4s, v10.4s, v20.4s\n" - "ldr d20, [x21, #-0x8]\n" - "fmul v10.4s, v17.4s, v29.s[2]\n" - "fmul v29.4s, v17.4s, v29.s[3]\n" - "fcvtl v20.4s, v20.4h\n" - "fmla v0.4s, v26.4s, v10.4s\n" - "movi v26.4s, #0x0\n" - "movi v10.4s, #0x0\n" - "fmla v4.4s, v2.4s, v29.4s\n" - "movi v2.4s, #0x0\n" - "movi v29.4s, #0x0\n" - ".inst 0x4f98e19a // sdot v26.4s, v12.16b, v24.4b[0]\n" - ".inst 0x4fb8e18a // sdot v10.4s, v12.16b, v24.4b[1]\n" - ".inst 0x4f98e982 // sdot v2.4s, v12.16b, v24.4b[2]\n" - ".inst 0x4fb8e99d // sdot v29.4s, v12.16b, v24.4b[3]\n" - "ldr q12, [x21, #0x20]\n" - "fmul v24.4s, v17.4s, v20.s[0]\n" - ".inst 0x4f89e3fa // sdot v26.4s, v31.16b, v9.4b[0]\n" - ".inst 0x4fa9e3ea // sdot v10.4s, v31.16b, v9.4b[1]\n" - ".inst 0x4f89ebe2 // sdot v2.4s, v31.16b, v9.4b[2]\n" - ".inst 0x4fa9ebfd // sdot v29.4s, v31.16b, v9.4b[3]\n" - "ldr q9, [x21, #0x30]\n" - "fmul v31.4s, v17.4s, v20.s[1]\n" - ".inst 0x4f8ce0da // sdot v26.4s, v6.16b, v12.4b[0]\n" - ".inst 0x4face0ca // sdot v10.4s, v6.16b, v12.4b[1]\n" - ".inst 0x4f8ce8c2 // sdot v2.4s, v6.16b, v12.4b[2]\n" - ".inst 0x4face8dd // sdot v29.4s, v6.16b, v12.4b[3]\n" - "ldr q12, [x21, #0x40]\n" - "fmul v6.4s, v17.4s, v20.s[2]\n" - "fmul v20.4s, v17.4s, v20.s[3]\n" - ".inst 0x4f89e39a // sdot v26.4s, v28.16b, v9.4b[0]\n" - ".inst 0x4fa9e38a // sdot v10.4s, v28.16b, v9.4b[1]\n" - ".inst 0x4f89eb82 // sdot v2.4s, v28.16b, v9.4b[2]\n" - ".inst 0x4fa9eb9d // sdot v29.4s, v28.16b, v9.4b[3]\n" - "ldr q9, [x21, #0x50]\n" - ".inst 0x4f8ce07a // sdot v26.4s, v3.16b, v12.4b[0]\n" - ".inst 0x4face06a // sdot v10.4s, v3.16b, v12.4b[1]\n" - ".inst 0x4f8ce862 // sdot v2.4s, v3.16b, v12.4b[2]\n" - ".inst 0x4face87d // sdot v29.4s, v3.16b, v12.4b[3]\n" - "ldr q12, [x21, #0x60]\n" - ".inst 0x4f89e2da // sdot v26.4s, v22.16b, v9.4b[0]\n" - ".inst 0x4fa9e2ca // sdot v10.4s, v22.16b, v9.4b[1]\n" - ".inst 0x4f89eac2 // sdot v2.4s, v22.16b, v9.4b[2]\n" - ".inst 0x4fa9eadd // sdot v29.4s, v22.16b, v9.4b[3]\n" - "ldr q17, [x21, #0x70]\n" - "add x21, x21, #0x88\n" - ".inst 0x4f8ce37a // sdot v26.4s, v27.16b, v12.4b[0]\n" - ".inst 0x4face36a // sdot v10.4s, v27.16b, v12.4b[1]\n" - ".inst 0x4f8ceb62 // sdot v2.4s, v27.16b, v12.4b[2]\n" - ".inst 0x4faceb7d // sdot v29.4s, v27.16b, v12.4b[3]\n" - ".inst 0x4f91e3da // sdot v26.4s, v30.16b, v17.4b[0]\n" - ".inst 0x4fb1e3ca // sdot v10.4s, v30.16b, v17.4b[1]\n" - ".inst 0x4f91ebc2 // sdot v2.4s, v30.16b, v17.4b[2]\n" - ".inst 0x4fb1ebdd // sdot v29.4s, v30.16b, v17.4b[3]\n" - "scvtf v26.4s, v26.4s, #0x4\n" - "scvtf v10.4s, v10.4s, #0x4\n" - "fmla v5.4s, v26.4s, v24.4s\n" - "scvtf v2.4s, v2.4s, #0x4\n" - "scvtf v29.4s, v29.4s, #0x4\n" - "fmla v21.4s, v10.4s, v31.4s\n" - "fmla v8.4s, v2.4s, v6.4s\n" - "fmla v1.4s, v29.4s, v20.4s\n" - "bgt 3b\n" - "mov x20, %x[res_ptr]\n" - "subs x27, x27, #0x4\n" - "add %x[res_ptr], %x[res_ptr], #0x10\n" - "str q15, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q19, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q18, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q14, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q11, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q13, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q23, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q16, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q25, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q7, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q0, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q4, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q5, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q21, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q8, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q1, [x20, #0x0]\n" - "bne 2b\n" - "mov x20, #0x4\n" - "sub x10, x10, #0x10\n" - "cmp x10, #0x10\n" - "mov %x[res_ptr], x26\n" - "madd %x[a_ptr], x20, x9, %x[a_ptr]\n" - "bge 1b\n" - "4:" // Row loop skip - "cbz x10, 9f\n" - "5:" // Row tail: Row loop - "add x24, %x[b_ptr], #0x8\n" - "mov x23, %x[nc]\n" - "add x22, %x[res_ptr], %x[res_stride], LSL #2\n" - "6:" // Row tail: Column loop - "movi v15.16b, #0x0\n" - "movi v19.16b, #0x0\n" - "add x25, %x[a_ptr], #0x8\n" - "mov x21, %x[nb]\n" - "movi v18.16b, #0x0\n" - "movi v14.16b, #0x0\n" - "7:" // Row tail: Block loop - "ldr q7, [x24, #0x0]\n" - "ldr q5, [x25, #0x0]\n" - "movi v9.16b, #0x4\n" - "movi v4.4s, #0x0\n" - "ldr q3, [x24, #0x10]\n" - "ldr q2, [x25, #0x10]\n" - "movi v1.4s, #0x0\n" - "movi v0.4s, #0x0\n" - "ldr q13, [x24, #0x20]\n" - "ldr q31, [x25, #0x20]\n" - "movi v30.4s, #0x0\n" - "movi v29.16b, #0xf0\n" - "ldr q28, [x24, #0x30]\n" - "ldr q27, [x25, #0x30]\n" - "sshl v20.16b, v7.16b, v9.16b\n" - "sub x20, x24, #0x8\n" - "ldr q26, [x25, #0x40]\n" - "ldr q25, [x25, #0x50]\n" - "sshl v17.16b, v3.16b, v9.16b\n" - "and v7.16b, v7.16b, v29.16b\n" - "ldr q24, [x25, #0x60]\n" - "ldr q16, [x25, #0x70]\n" - "sshl v22.16b, v13.16b, v9.16b\n" - "and v3.16b, v3.16b, v29.16b\n" - "ldr d21, [x20, #0x0]\n" - "ldr d12, [x25, #-0x8]\n" - ".inst 0x4f85e284 // sdot v4.4s, v20.16b, v5.4b[0]\n" - ".inst 0x4fa5e281 // sdot v1.4s, v20.16b, v5.4b[1]\n" - ".inst 0x4f85ea80 // sdot v0.4s, v20.16b, v5.4b[2]\n" - ".inst 0x4fa5ea9e // sdot v30.4s, v20.16b, v5.4b[3]\n" - "sshl v9.16b, v28.16b, v9.16b\n" - "subs x21, x21, #0x1\n" - "and v13.16b, v13.16b, v29.16b\n" - "and v28.16b, v28.16b, v29.16b\n" - "add x25, x25, #0x88\n" - "add x24, x24, #0x48\n" - "fcvtl v21.4s, v21.4h\n" - "fcvtl v12.4s, v12.4h\n" - ".inst 0x4f82e224 // sdot v4.4s, v17.16b, v2.4b[0]\n" - ".inst 0x4fa2e221 // sdot v1.4s, v17.16b, v2.4b[1]\n" - ".inst 0x4f82ea20 // sdot v0.4s, v17.16b, v2.4b[2]\n" - ".inst 0x4fa2ea3e // sdot v30.4s, v17.16b, v2.4b[3]\n" - "fmul v11.4s, v21.4s, v12.s[0]\n" - "fmul v23.4s, v21.4s, v12.s[1]\n" - "fmul v17.4s, v21.4s, v12.s[2]\n" - ".inst 0x4f9fe2c4 // sdot v4.4s, v22.16b, v31.4b[0]\n" - "fmul v6.4s, v21.4s, v12.s[3]\n" - ".inst 0x4fbfe2c1 // sdot v1.4s, v22.16b, v31.4b[1]\n" - ".inst 0x4f9feac0 // sdot v0.4s, v22.16b, v31.4b[2]\n" - ".inst 0x4fbfeade // sdot v30.4s, v22.16b, v31.4b[3]\n" - ".inst 0x4f9be124 // sdot v4.4s, v9.16b, v27.4b[0]\n" - ".inst 0x4fbbe121 // sdot v1.4s, v9.16b, v27.4b[1]\n" - ".inst 0x4f9be920 // sdot v0.4s, v9.16b, v27.4b[2]\n" - ".inst 0x4fbbe93e // sdot v30.4s, v9.16b, v27.4b[3]\n" - ".inst 0x4f9ae0e4 // sdot v4.4s, v7.16b, v26.4b[0]\n" - ".inst 0x4fbae0e1 // sdot v1.4s, v7.16b, v26.4b[1]\n" - ".inst 0x4f9ae8e0 // sdot v0.4s, v7.16b, v26.4b[2]\n" - ".inst 0x4fbae8fe // sdot v30.4s, v7.16b, v26.4b[3]\n" - ".inst 0x4f99e064 // sdot v4.4s, v3.16b, v25.4b[0]\n" - ".inst 0x4fb9e061 // sdot v1.4s, v3.16b, v25.4b[1]\n" - ".inst 0x4f99e860 // sdot v0.4s, v3.16b, v25.4b[2]\n" - ".inst 0x4fb9e87e // sdot v30.4s, v3.16b, v25.4b[3]\n" - ".inst 0x4f98e1a4 // sdot v4.4s, v13.16b, v24.4b[0]\n" - ".inst 0x4fb8e1a1 // sdot v1.4s, v13.16b, v24.4b[1]\n" - ".inst 0x4f98e9a0 // sdot v0.4s, v13.16b, v24.4b[2]\n" - ".inst 0x4fb8e9be // sdot v30.4s, v13.16b, v24.4b[3]\n" - ".inst 0x4f90e384 // sdot v4.4s, v28.16b, v16.4b[0]\n" - ".inst 0x4fb0e381 // sdot v1.4s, v28.16b, v16.4b[1]\n" - ".inst 0x4f90eb80 // sdot v0.4s, v28.16b, v16.4b[2]\n" - ".inst 0x4fb0eb9e // sdot v30.4s, v28.16b, v16.4b[3]\n" - "scvtf v4.4s, v4.4s, #0x4\n" - "scvtf v1.4s, v1.4s, #0x4\n" - "scvtf v0.4s, v0.4s, #0x4\n" - "fmla v15.4s, v4.4s, v11.4s\n" - "scvtf v30.4s, v30.4s, #0x4\n" - "fmla v19.4s, v1.4s, v23.4s\n" - "fmla v18.4s, v0.4s, v17.4s\n" - "fmla v14.4s, v30.4s, v6.4s\n" - "bgt 7b\n" - "mov x20, %x[res_ptr]\n" - "cmp x10, #0x1\n" - "str q15, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "cmp x10, #0x2\n" - "str q19, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "cmp x10, #0x3\n" - "str q18, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "str q14, [x20, #0x0]\n" - "8:" // Row tail: Accumulator store skip - "subs x23, x23, #0x4\n" - "add %x[res_ptr], %x[res_ptr], #0x10\n" - "bne 6b\n" - "subs x10, x10, #0x4\n" - "add %x[a_ptr], %x[a_ptr], x9\n" - "mov %x[res_ptr], x22\n" - "bgt 5b\n" - "9:" // Row tail: Row loop skip - : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr) - : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc) - : "cc", "memory", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x9", "x10", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28" - ); - return; - } -#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) - { - float sumf[4][4]; - int sumi; - - for (int y = 0; y < nr / 4; y++) { - const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); - for (int x = 0; x < nc / ncols_interleaved; x++) { - const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb); - for (int m = 0; m < 4; m++) { - for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0; - } - for (int l = 0; l < nb; l++) { - for (int k = 0; k < (qk / (2 * blocklen)); k++) { - for (int m = 0; m < 4; m++) { - for (int j = 0; j < ncols_interleaved; j++) { - sumi = 0; - for (int i = 0; i < blocklen; ++i) { - const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); - const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); - sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) + - (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4; - } - sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d[m]); - } - } - } - } - for (int m = 0; m < 4; m++) { - for (int j = 0; j < ncols_interleaved; j++) - s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j]; - } - } - } - } -} - -void ggml_gemm_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { - const int qk = QK8_0; - const int nb = n / qk; - const int ncols_interleaved = 4; - const int blocklen = 8; - - assert (n % qk == 0); - assert (nr % 4 == 0); - assert (nc % ncols_interleaved == 0); - - UNUSED(s); - UNUSED(bs); - UNUSED(vx); - UNUSED(vy); - UNUSED(nr); - UNUSED(nc); - UNUSED(nb); - UNUSED(ncols_interleaved); - UNUSED(blocklen); - -#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) - if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) { - const void * b_ptr = vx; - const void * a_ptr = vy; - float * res_ptr = s; - size_t res_stride = bs * sizeof(float); - - __asm__ __volatile__( - "mov x10, %x[nr]\n" - "mov x9, #0x88\n" - "cmp x10, #0x10\n" - "mul x9, %x[nb], x9\n" - "blt 4f\n" - "1:" // Row loop - "add x28, %x[b_ptr], #0x8\n" - "mov x27, %x[nc]\n" - "add x26, %x[res_ptr], %x[res_stride], LSL #4\n" - "2:" // Column loop - "add x25, %x[a_ptr], #0x8\n" - "movi v2.16b, #0x0\n" - "movi v10.16b, #0x0\n" - "mov x24, %x[nb]\n" - "add x23, x25, x9\n" - "movi v12.16b, #0x0\n" - "movi v28.16b, #0x0\n" - "add x22, x23, x9\n" - "movi v11.16b, #0x0\n" - "movi v13.16b, #0x0\n" - "add x21, x22, x9\n" - "movi v22.16b, #0x0\n" - "movi v23.16b, #0x0\n" - "movi v25.16b, #0x0\n" - "movi v5.16b, #0x0\n" - "movi v7.16b, #0x0\n" - "movi v4.16b, #0x0\n" - "movi v6.16b, #0x0\n" - "movi v30.16b, #0x0\n" - "movi v24.16b, #0x0\n" - "movi v14.16b, #0x0\n" - "3:" // Block loop - "ldr q21, [x28, #0x0]\n" - "ldr q16, [x28, #0x10]\n" - "movi v1.16b, #0x4\n" - "movi v19.4s, #0x0\n" - "ldr q27, [x25, #0x0]\n" - "ldr q15, [x25, #0x10]\n" - "movi v26.4s, #0x0\n" - "movi v18.4s, #0x0\n" - "ldr q29, [x28, #0x20]\n" - "ldr q3, [x28, #0x30]\n" - "movi v17.4s, #0x0\n" - "movi v0.16b, #0xf0\n" - "ldr d20, [x25, #-0x8]\n" - "ldr d9, [x23, #-0x8]\n" - "sshl v8.16b, v21.16b, v1.16b\n" - "sshl v31.16b, v16.16b, v1.16b\n" - "and v21.16b, v21.16b, v0.16b\n" - "and v16.16b, v16.16b, v0.16b\n" - "sub x20, x28, #0x8\n" - "subs x24, x24, #0x1\n" - "add x28, x28, #0x48\n" - ".inst 0x4e88a773 // smmla v19.4s, v27.16b, v8.16b\n" - ".inst 0x4e9fa77a // smmla v26.4s, v27.16b, v31.16b\n" - "ldr q27, [x25, #0x20]\n" - ".inst 0x4e88a5f2 // smmla v18.4s, v15.16b, v8.16b\n" - ".inst 0x4e9fa5f1 // smmla v17.4s, v15.16b, v31.16b\n" - "sshl v15.16b, v29.16b, v1.16b\n" - "sshl v1.16b, v3.16b, v1.16b\n" - "and v29.16b, v29.16b, v0.16b\n" - "and v3.16b, v3.16b, v0.16b\n" - "ldr q0, [x25, #0x30]\n" - "fcvtl v20.4s, v20.4h\n" - ".inst 0x4e8fa773 // smmla v19.4s, v27.16b, v15.16b\n" - "fcvtl v9.4s, v9.4h\n" - ".inst 0x4e81a77a // smmla v26.4s, v27.16b, v1.16b\n" - "ldr q27, [x25, #0x40]\n" - ".inst 0x4e8fa412 // smmla v18.4s, v0.16b, v15.16b\n" - ".inst 0x4e81a411 // smmla v17.4s, v0.16b, v1.16b\n" - "ldr q0, [x25, #0x50]\n" - ".inst 0x4e95a773 // smmla v19.4s, v27.16b, v21.16b\n" - ".inst 0x4e90a77a // smmla v26.4s, v27.16b, v16.16b\n" - "ldr q27, [x25, #0x60]\n" - ".inst 0x4e95a412 // smmla v18.4s, v0.16b, v21.16b\n" - ".inst 0x4e90a411 // smmla v17.4s, v0.16b, v16.16b\n" - "ldr q0, [x25, #0x70]\n" - "add x25, x25, #0x88\n" - ".inst 0x4e9da773 // smmla v19.4s, v27.16b, v29.16b\n" - ".inst 0x4e83a77a // smmla v26.4s, v27.16b, v3.16b\n" - "ldr d27, [x20, #0x0]\n" - ".inst 0x4e9da412 // smmla v18.4s, v0.16b, v29.16b\n" - ".inst 0x4e83a411 // smmla v17.4s, v0.16b, v3.16b\n" - "fcvtl v27.4s, v27.4h\n" - "uzp1 v0.2d, v19.2d, v26.2d\n" - "uzp2 v26.2d, v19.2d, v26.2d\n" - "fmul v19.4s, v27.4s, v20.s[0]\n" - "scvtf v0.4s, v0.4s, #0x4\n" - "scvtf v26.4s, v26.4s, #0x4\n" - "fmla v2.4s, v0.4s, v19.4s\n" - "ldr q19, [x23, #0x0]\n" - "uzp1 v0.2d, v18.2d, v17.2d\n" - "uzp2 v18.2d, v18.2d, v17.2d\n" - "fmul v17.4s, v27.4s, v20.s[1]\n" - "scvtf v0.4s, v0.4s, #0x4\n" - "scvtf v18.4s, v18.4s, #0x4\n" - "fmla v10.4s, v26.4s, v17.4s\n" - "ldr q17, [x23, #0x10]\n" - "fmul v26.4s, v27.4s, v20.s[2]\n" - "fmul v20.4s, v27.4s, v20.s[3]\n" - "fmla v12.4s, v0.4s, v26.4s\n" - "ldr d0, [x22, #-0x8]\n" - "ldr d26, [x21, #-0x8]\n" - "fcvtl v0.4s, v0.4h\n" - "fmla v28.4s, v18.4s, v20.4s\n" - "movi v20.4s, #0x0\n" - "movi v18.4s, #0x0\n" - ".inst 0x4e88a674 // smmla v20.4s, v19.16b, v8.16b\n" - ".inst 0x4e9fa672 // smmla v18.4s, v19.16b, v31.16b\n" - "ldr q19, [x23, #0x20]\n" - "fcvtl v26.4s, v26.4h\n" - ".inst 0x4e8fa674 // smmla v20.4s, v19.16b, v15.16b\n" - ".inst 0x4e81a672 // smmla v18.4s, v19.16b, v1.16b\n" - "ldr q19, [x23, #0x40]\n" - ".inst 0x4e95a674 // smmla v20.4s, v19.16b, v21.16b\n" - ".inst 0x4e90a672 // smmla v18.4s, v19.16b, v16.16b\n" - "ldr q19, [x23, #0x60]\n" - ".inst 0x4e9da674 // smmla v20.4s, v19.16b, v29.16b\n" - ".inst 0x4e83a672 // smmla v18.4s, v19.16b, v3.16b\n" - "uzp1 v19.2d, v20.2d, v18.2d\n" - "scvtf v19.4s, v19.4s, #0x4\n" - "uzp2 v20.2d, v20.2d, v18.2d\n" - "fmul v18.4s, v27.4s, v9.s[0]\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "fmla v11.4s, v19.4s, v18.4s\n" - "ldr q18, [x22, #0x0]\n" - "fmul v19.4s, v27.4s, v9.s[1]\n" - "fmla v13.4s, v20.4s, v19.4s\n" - "movi v19.4s, #0x0\n" - "movi v20.4s, #0x0\n" - ".inst 0x4e88a633 // smmla v19.4s, v17.16b, v8.16b\n" - ".inst 0x4e9fa634 // smmla v20.4s, v17.16b, v31.16b\n" - "ldr q17, [x23, #0x30]\n" - ".inst 0x4e8fa633 // smmla v19.4s, v17.16b, v15.16b\n" - ".inst 0x4e81a634 // smmla v20.4s, v17.16b, v1.16b\n" - "ldr q17, [x23, #0x50]\n" - ".inst 0x4e95a633 // smmla v19.4s, v17.16b, v21.16b\n" - ".inst 0x4e90a634 // smmla v20.4s, v17.16b, v16.16b\n" - "ldr q17, [x23, #0x70]\n" - "add x23, x23, #0x88\n" - ".inst 0x4e9da633 // smmla v19.4s, v17.16b, v29.16b\n" - ".inst 0x4e83a634 // smmla v20.4s, v17.16b, v3.16b\n" - "uzp1 v17.2d, v19.2d, v20.2d\n" - "scvtf v17.4s, v17.4s, #0x4\n" - "uzp2 v20.2d, v19.2d, v20.2d\n" - "fmul v19.4s, v27.4s, v9.s[2]\n" - "fmul v9.4s, v27.4s, v9.s[3]\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "fmla v22.4s, v17.4s, v19.4s\n" - "ldr q17, [x22, #0x10]\n" - "movi v19.4s, #0x0\n" - ".inst 0x4e88a653 // smmla v19.4s, v18.16b, v8.16b\n" - "fmla v23.4s, v20.4s, v9.4s\n" - "movi v20.4s, #0x0\n" - "movi v9.4s, #0x0\n" - ".inst 0x4e9fa654 // smmla v20.4s, v18.16b, v31.16b\n" - "ldr q18, [x22, #0x20]\n" - ".inst 0x4e88a629 // smmla v9.4s, v17.16b, v8.16b\n" - ".inst 0x4e8fa653 // smmla v19.4s, v18.16b, v15.16b\n" - ".inst 0x4e81a654 // smmla v20.4s, v18.16b, v1.16b\n" - "ldr q18, [x22, #0x40]\n" - ".inst 0x4e95a653 // smmla v19.4s, v18.16b, v21.16b\n" - ".inst 0x4e90a654 // smmla v20.4s, v18.16b, v16.16b\n" - "ldr q18, [x22, #0x60]\n" - ".inst 0x4e9da653 // smmla v19.4s, v18.16b, v29.16b\n" - ".inst 0x4e83a654 // smmla v20.4s, v18.16b, v3.16b\n" - "movi v18.4s, #0x0\n" - ".inst 0x4e9fa632 // smmla v18.4s, v17.16b, v31.16b\n" - "ldr q17, [x22, #0x30]\n" - ".inst 0x4e8fa629 // smmla v9.4s, v17.16b, v15.16b\n" - ".inst 0x4e81a632 // smmla v18.4s, v17.16b, v1.16b\n" - "ldr q17, [x22, #0x50]\n" - ".inst 0x4e95a629 // smmla v9.4s, v17.16b, v21.16b\n" - ".inst 0x4e90a632 // smmla v18.4s, v17.16b, v16.16b\n" - "ldr q17, [x22, #0x70]\n" - "add x22, x22, #0x88\n" - ".inst 0x4e9da629 // smmla v9.4s, v17.16b, v29.16b\n" - ".inst 0x4e83a632 // smmla v18.4s, v17.16b, v3.16b\n" - "uzp1 v17.2d, v19.2d, v20.2d\n" - "uzp2 v20.2d, v19.2d, v20.2d\n" - "fmul v19.4s, v27.4s, v0.s[0]\n" - "scvtf v17.4s, v17.4s, #0x4\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "fmla v25.4s, v17.4s, v19.4s\n" - "ldr q19, [x21, #0x0]\n" - "fmul v17.4s, v27.4s, v0.s[1]\n" - "fmla v5.4s, v20.4s, v17.4s\n" - "ldr q17, [x21, #0x10]\n" - "uzp1 v20.2d, v9.2d, v18.2d\n" - "uzp2 v9.2d, v9.2d, v18.2d\n" - "fmul v18.4s, v27.4s, v0.s[2]\n" - "fmul v0.4s, v27.4s, v0.s[3]\n" - "scvtf v20.4s, v20.4s, #0x4\n" - "scvtf v9.4s, v9.4s, #0x4\n" - "fmla v7.4s, v20.4s, v18.4s\n" - "movi v20.4s, #0x0\n" - "movi v18.4s, #0x0\n" - ".inst 0x4e88a674 // smmla v20.4s, v19.16b, v8.16b\n" - ".inst 0x4e9fa672 // smmla v18.4s, v19.16b, v31.16b\n" - "ldr q19, [x21, #0x20]\n" - "fmla v4.4s, v9.4s, v0.4s\n" - "movi v9.4s, #0x0\n" - "movi v0.4s, #0x0\n" - ".inst 0x4e88a629 // smmla v9.4s, v17.16b, v8.16b\n" - "fmul v8.4s, v27.4s, v26.s[0]\n" - ".inst 0x4e9fa620 // smmla v0.4s, v17.16b, v31.16b\n" - "ldr q17, [x21, #0x30]\n" - ".inst 0x4e8fa674 // smmla v20.4s, v19.16b, v15.16b\n" - "fmul v31.4s, v27.4s, v26.s[1]\n" - ".inst 0x4e81a672 // smmla v18.4s, v19.16b, v1.16b\n" - "ldr q19, [x21, #0x40]\n" - ".inst 0x4e8fa629 // smmla v9.4s, v17.16b, v15.16b\n" - "fmul v15.4s, v27.4s, v26.s[2]\n" - "fmul v27.4s, v27.4s, v26.s[3]\n" - ".inst 0x4e81a620 // smmla v0.4s, v17.16b, v1.16b\n" - "ldr q1, [x21, #0x50]\n" - ".inst 0x4e95a674 // smmla v20.4s, v19.16b, v21.16b\n" - ".inst 0x4e90a672 // smmla v18.4s, v19.16b, v16.16b\n" - "ldr q26, [x21, #0x60]\n" - ".inst 0x4e95a429 // smmla v9.4s, v1.16b, v21.16b\n" - ".inst 0x4e90a420 // smmla v0.4s, v1.16b, v16.16b\n" - "ldr q21, [x21, #0x70]\n" - "add x21, x21, #0x88\n" - ".inst 0x4e9da754 // smmla v20.4s, v26.16b, v29.16b\n" - ".inst 0x4e83a752 // smmla v18.4s, v26.16b, v3.16b\n" - ".inst 0x4e9da6a9 // smmla v9.4s, v21.16b, v29.16b\n" - ".inst 0x4e83a6a0 // smmla v0.4s, v21.16b, v3.16b\n" - "uzp1 v29.2d, v20.2d, v18.2d\n" - "uzp2 v21.2d, v20.2d, v18.2d\n" - "scvtf v29.4s, v29.4s, #0x4\n" - "uzp1 v18.2d, v9.2d, v0.2d\n" - "uzp2 v16.2d, v9.2d, v0.2d\n" - "scvtf v21.4s, v21.4s, #0x4\n" - "fmla v6.4s, v29.4s, v8.4s\n" - "scvtf v18.4s, v18.4s, #0x4\n" - "scvtf v16.4s, v16.4s, #0x4\n" - "fmla v30.4s, v21.4s, v31.4s\n" - "fmla v24.4s, v18.4s, v15.4s\n" - "fmla v14.4s, v16.4s, v27.4s\n" - "bgt 3b\n" - "mov x20, %x[res_ptr]\n" - "subs x27, x27, #0x4\n" - "add %x[res_ptr], %x[res_ptr], #0x10\n" - "str q2, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q10, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q12, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q28, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q11, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q13, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q22, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q23, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q25, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q5, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q7, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q4, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q6, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q30, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q24, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "str q14, [x20, #0x0]\n" - "bne 2b\n" - "mov x20, #0x4\n" - "sub x10, x10, #0x10\n" - "cmp x10, #0x10\n" - "mov %x[res_ptr], x26\n" - "madd %x[a_ptr], x20, x9, %x[a_ptr]\n" - "bge 1b\n" - "4:" // Row loop skip - "cbz x10, 9f\n" - "5:" // Row tail: Row loop - "add x24, %x[b_ptr], #0x8\n" - "mov x23, %x[nc]\n" - "add x22, %x[res_ptr], %x[res_stride], LSL #2\n" - "6:" // Row tail: Column loop - "movi v2.16b, #0x0\n" - "movi v10.16b, #0x0\n" - "add x25, %x[a_ptr], #0x8\n" - "mov x21, %x[nb]\n" - "movi v12.16b, #0x0\n" - "movi v28.16b, #0x0\n" - "7:" // Row tail: Block loop - "ldr q6, [x24, #0x0]\n" - "ldr q5, [x24, #0x10]\n" - "movi v17.16b, #0x4\n" - "movi v8.4s, #0x0\n" - "ldr q4, [x25, #0x0]\n" - "ldr q13, [x25, #0x10]\n" - "movi v27.4s, #0x0\n" - "movi v0.4s, #0x0\n" - "ldr q31, [x24, #0x20]\n" - "ldr q14, [x24, #0x30]\n" - "movi v29.4s, #0x0\n" - "movi v22.16b, #0xf0\n" - "ldr q11, [x25, #0x20]\n" - "ldr q23, [x25, #0x30]\n" - "sshl v21.16b, v6.16b, v17.16b\n" - "sshl v16.16b, v5.16b, v17.16b\n" - "ldr q20, [x25, #0x40]\n" - "ldr q26, [x25, #0x50]\n" - "and v6.16b, v6.16b, v22.16b\n" - "and v5.16b, v5.16b, v22.16b\n" - "ldr q25, [x25, #0x60]\n" - "ldr q3, [x25, #0x70]\n" - "sshl v19.16b, v31.16b, v17.16b\n" - "sshl v18.16b, v14.16b, v17.16b\n" - "ldr d17, [x25, #-0x8]\n" - ".inst 0x4e95a488 // smmla v8.4s, v4.16b, v21.16b\n" - ".inst 0x4e90a49b // smmla v27.4s, v4.16b, v16.16b\n" - "and v31.16b, v31.16b, v22.16b\n" - ".inst 0x4e95a5a0 // smmla v0.4s, v13.16b, v21.16b\n" - ".inst 0x4e90a5bd // smmla v29.4s, v13.16b, v16.16b\n" - "and v14.16b, v14.16b, v22.16b\n" - "sub x20, x24, #0x8\n" - "ldr d16, [x20, #0x0]\n" - "subs x21, x21, #0x1\n" - "add x25, x25, #0x88\n" - "fcvtl v17.4s, v17.4h\n" - "add x24, x24, #0x48\n" - ".inst 0x4e93a568 // smmla v8.4s, v11.16b, v19.16b\n" - ".inst 0x4e92a57b // smmla v27.4s, v11.16b, v18.16b\n" - ".inst 0x4e93a6e0 // smmla v0.4s, v23.16b, v19.16b\n" - ".inst 0x4e92a6fd // smmla v29.4s, v23.16b, v18.16b\n" - "fcvtl v16.4s, v16.4h\n" - ".inst 0x4e86a688 // smmla v8.4s, v20.16b, v6.16b\n" - ".inst 0x4e85a69b // smmla v27.4s, v20.16b, v5.16b\n" - "fmul v23.4s, v16.4s, v17.s[0]\n" - "fmul v21.4s, v16.4s, v17.s[1]\n" - "fmul v1.4s, v16.4s, v17.s[2]\n" - "fmul v20.4s, v16.4s, v17.s[3]\n" - ".inst 0x4e86a740 // smmla v0.4s, v26.16b, v6.16b\n" - ".inst 0x4e85a75d // smmla v29.4s, v26.16b, v5.16b\n" - ".inst 0x4e9fa728 // smmla v8.4s, v25.16b, v31.16b\n" - ".inst 0x4e8ea73b // smmla v27.4s, v25.16b, v14.16b\n" - ".inst 0x4e9fa460 // smmla v0.4s, v3.16b, v31.16b\n" - ".inst 0x4e8ea47d // smmla v29.4s, v3.16b, v14.16b\n" - "uzp1 v19.2d, v8.2d, v27.2d\n" - "uzp2 v18.2d, v8.2d, v27.2d\n" - "scvtf v19.4s, v19.4s, #0x4\n" - "uzp1 v17.2d, v0.2d, v29.2d\n" - "uzp2 v16.2d, v0.2d, v29.2d\n" - "scvtf v18.4s, v18.4s, #0x4\n" - "fmla v2.4s, v19.4s, v23.4s\n" - "scvtf v17.4s, v17.4s, #0x4\n" - "scvtf v16.4s, v16.4s, #0x4\n" - "fmla v10.4s, v18.4s, v21.4s\n" - "fmla v12.4s, v17.4s, v1.4s\n" - "fmla v28.4s, v16.4s, v20.4s\n" - "bgt 7b\n" - "mov x20, %x[res_ptr]\n" - "cmp x10, #0x1\n" - "str q2, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "cmp x10, #0x2\n" - "str q10, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "cmp x10, #0x3\n" - "str q12, [x20, #0x0]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "str q28, [x20, #0x0]\n" - "8:" // Row tail: Accumulator store skip - "subs x23, x23, #0x4\n" - "add %x[res_ptr], %x[res_ptr], #0x10\n" - "bne 6b\n" - "subs x10, x10, #0x4\n" - "add %x[a_ptr], %x[a_ptr], x9\n" - "mov %x[res_ptr], x22\n" - "bgt 5b\n" - "9:" // Row tail: Row loop skip - : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr) - : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc) - : "cc", "memory", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x9", "x10", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28" - ); - return; - } -#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) - float sumf[4][4]; - int sumi; - - for (int y = 0; y < nr / 4; y++) { - const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); - for (int x = 0; x < nc / ncols_interleaved; x++) { - const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb); - for (int m = 0; m < 4; m++) { - for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0; - } - for (int l = 0; l < nb; l++) { - for (int k = 0; k < (qk / (2 * blocklen)); k++) { - for (int m = 0; m < 4; m++) { - for (int j = 0; j < ncols_interleaved; j++) { - sumi = 0; - for (int i = 0; i < blocklen; ++i) { - const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); - const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); - sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) + - (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4; - } - sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d[m]); - } - } - } - } - for (int m = 0; m < 4; m++) { - for (int j = 0; j < ncols_interleaved; j++) - s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j]; - } - } - } -} - -void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { - const int qk = QK8_0; - const int nb = n / qk; - const int ncols_interleaved = 8; - const int blocklen = 8; - - assert (n % qk == 0); - assert (nr % 4 == 0); - assert (nc % ncols_interleaved == 0); - - UNUSED(s); - UNUSED(bs); - UNUSED(vx); - UNUSED(vy); - UNUSED(nr); - UNUSED(nc); - UNUSED(nb); - UNUSED(ncols_interleaved); - UNUSED(blocklen); - -#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) -#if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8) - if (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0) { - const void * b_ptr = vx; - const void * a_ptr = vy; - float * res_ptr = s; - size_t res_stride = bs * sizeof(float); - - __asm__ __volatile__( - "mov x20, #0x4\n" - "mov x13, %x[nr]\n" - "mov z28.s, #-0x4\n" - "mov x12, #0x88\n" - "ptrue p1.b\n" - "whilelt p0.s, XZR, x20\n" - "cmp x13, #0x10\n" - "mul x12, %x[nb], x12\n" - "blt 4f\n" - "1:" // Row loop - "add x11, %x[b_ptr], #0x10\n" - "mov x10, %x[nc]\n" - "add x9, %x[res_ptr], %x[res_stride], LSL #4\n" - "2:" // Column loop - "add x28, %x[a_ptr], #0x8\n" - "mov z24.b, #0x0\n" - "mov z15.b, #0x0\n" - "mov x27, %x[nb]\n" - "add x26, x28, x12\n" - "mov z12.b, #0x0\n" - "mov z0.b, #0x0\n" - "add x25, x26, x12\n" - "mov z13.b, #0x0\n" - "mov z1.b, #0x0\n" - "add x24, x25, x12\n" - "mov z20.b, #0x0\n" - "mov z25.b, #0x0\n" - "mov z11.b, #0x0\n" - "mov z16.b, #0x0\n" - "mov z19.b, #0x0\n" - "mov z26.b, #0x0\n" - "mov z8.b, #0x0\n" - "mov z29.b, #0x0\n" - "mov z27.b, #0x0\n" - "mov z10.b, #0x0\n" - "3:" // Block loop - "ld1b { z30.b }, p1/Z, [x11]\n" - "ld1b { z21.b }, p1/Z, [x11, #1, MUL VL]\n" - "mov z18.s, #0x0\n" - "mov z7.s, #0x0\n" - "ld1rqb { z3.b }, p1/Z, [x28]\n" - "ld1rqb { z5.b }, p1/Z, [x28, #16]\n" - "mov z9.s, #0x0\n" - "mov z22.s, #0x0\n" - "ld1b { z4.b }, p1/Z, [x11, #2, MUL VL]\n" - "ld1b { z17.b }, p1/Z, [x11, #3, MUL VL]\n" - "sub x20, x11, #0x10\n" - "sub x23, x28, #0x8\n" - "lsl z31.b, z30.b, #0x4\n" - "lsl z6.b, z21.b, #0x4\n" - "ld1h { z23.s }, p1/Z, [x20]\n" - "sub x22, x26, #0x8\n" - "and z30.b, z30.b, #0xf0\n" - "and z21.b, z21.b, #0xf0\n" - "sub x21, x25, #0x8\n" - "sub x20, x24, #0x8\n" - "lsl z14.b, z4.b, #0x4\n" - "lsl z2.b, z17.b, #0x4\n" - "subs x27, x27, #0x1\n" - "add x11, x11, #0x90\n" - ".inst 0x451f9872 // smmla z18.s, z3.b, z31.b\n" - ".inst 0x45069867 // smmla z7.s, z3.b, z6.b\n" - "ld1rqb { z3.b }, p1/Z, [x28, #32]\n" - "and z4.b, z4.b, #0xf0\n" - ".inst 0x451f98a9 // smmla z9.s, z5.b, z31.b\n" - ".inst 0x450698b6 // smmla z22.s, z5.b, z6.b\n" - "ld1rqb { z5.b }, p1/Z, [x28, #48]\n" - "and z17.b, z17.b, #0xf0\n" - "fcvt z23.s, p1/m, z23.h\n" - ".inst 0x450e9872 // smmla z18.s, z3.b, z14.b\n" - ".inst 0x45029867 // smmla z7.s, z3.b, z2.b\n" - "ld1rqb { z3.b }, p1/Z, [x28, #64]\n" - ".inst 0x450e98a9 // smmla z9.s, z5.b, z14.b\n" - ".inst 0x450298b6 // smmla z22.s, z5.b, z2.b\n" - "ld1rqb { z5.b }, p1/Z, [x28, #80]\n" - "fscale z23.s, p1/m, z23.s, z28.s\n" - ".inst 0x451e9872 // smmla z18.s, z3.b, z30.b\n" - ".inst 0x45159867 // smmla z7.s, z3.b, z21.b\n" - "ld1rqb { z3.b }, p1/Z, [x28, #96]\n" - ".inst 0x451e98a9 // smmla z9.s, z5.b, z30.b\n" - ".inst 0x451598b6 // smmla z22.s, z5.b, z21.b\n" - "ld1rqb { z5.b }, p1/Z, [x28, #112]\n" - "add x28, x28, #0x88\n" - ".inst 0x45049872 // smmla z18.s, z3.b, z4.b\n" - ".inst 0x45119867 // smmla z7.s, z3.b, z17.b\n" - "ld1h { z3.s }, p0/Z, [x23]\n" - ".inst 0x450498a9 // smmla z9.s, z5.b, z4.b\n" - ".inst 0x451198b6 // smmla z22.s, z5.b, z17.b\n" - "fcvt z3.s, p1/m, z3.h\n" - "uzp1 z5.d, z18.d, z7.d\n" - "uzp2 z18.d, z18.d, z7.d\n" - "mov z3.q, z3.q[0]\n" - "uzp1 z7.d, z9.d, z22.d\n" - "uzp2 z22.d, z9.d, z22.d\n" - "fmul z9.s, z23.s, z3.s[0]\n" - "scvtf z5.s, p1/m, z5.s\n" - "scvtf z18.s, p1/m, z18.s\n" - "scvtf z7.s, p1/m, z7.s\n" - "scvtf z22.s, p1/m, z22.s\n" - "fmla z24.s, p1/M, z5.s, z9.s\n" - "ld1rqb { z5.b }, p1/Z, [x26]\n" - "fmul z9.s, z23.s, z3.s[1]\n" - "fmla z15.s, p1/M, z18.s, z9.s\n" - "ld1rqb { z18.b }, p1/Z, [x26, #16]\n" - "fmul z9.s, z23.s, z3.s[2]\n" - "fmul z3.s, z23.s, z3.s[3]\n" - "fmla z12.s, p1/M, z7.s, z9.s\n" - "mov z9.s, #0x0\n" - "ld1h { z7.s }, p0/Z, [x22]\n" - ".inst 0x451f98a9 // smmla z9.s, z5.b, z31.b\n" - "fmla z0.s, p1/M, z22.s, z3.s\n" - "mov z22.s, #0x0\n" - "ld1h { z3.s }, p0/Z, [x21]\n" - ".inst 0x450698b6 // smmla z22.s, z5.b, z6.b\n" - "ld1rqb { z5.b }, p1/Z, [x26, #32]\n" - "fcvt z7.s, p1/m, z7.h\n" - "fcvt z3.s, p1/m, z3.h\n" - ".inst 0x450e98a9 // smmla z9.s, z5.b, z14.b\n" - ".inst 0x450298b6 // smmla z22.s, z5.b, z2.b\n" - "ld1rqb { z5.b }, p1/Z, [x26, #64]\n" - "mov z7.q, z7.q[0]\n" - "mov z3.q, z3.q[0]\n" - ".inst 0x451e98a9 // smmla z9.s, z5.b, z30.b\n" - ".inst 0x451598b6 // smmla z22.s, z5.b, z21.b\n" - "ld1rqb { z5.b }, p1/Z, [x26, #96]\n" - ".inst 0x450498a9 // smmla z9.s, z5.b, z4.b\n" - ".inst 0x451198b6 // smmla z22.s, z5.b, z17.b\n" - "uzp1 z5.d, z9.d, z22.d\n" - "scvtf z5.s, p1/m, z5.s\n" - "uzp2 z22.d, z9.d, z22.d\n" - "fmul z9.s, z23.s, z7.s[0]\n" - "scvtf z22.s, p1/m, z22.s\n" - "fmla z13.s, p1/M, z5.s, z9.s\n" - "ld1rqb { z9.b }, p1/Z, [x25]\n" - "fmul z5.s, z23.s, z7.s[1]\n" - "fmla z1.s, p1/M, z22.s, z5.s\n" - "mov z5.s, #0x0\n" - "mov z22.s, #0x0\n" - ".inst 0x451f9a45 // smmla z5.s, z18.b, z31.b\n" - ".inst 0x45069a56 // smmla z22.s, z18.b, z6.b\n" - "ld1rqb { z18.b }, p1/Z, [x26, #48]\n" - ".inst 0x450e9a45 // smmla z5.s, z18.b, z14.b\n" - ".inst 0x45029a56 // smmla z22.s, z18.b, z2.b\n" - "ld1rqb { z18.b }, p1/Z, [x26, #80]\n" - ".inst 0x451e9a45 // smmla z5.s, z18.b, z30.b\n" - ".inst 0x45159a56 // smmla z22.s, z18.b, z21.b\n" - "ld1rqb { z18.b }, p1/Z, [x26, #112]\n" - "add x26, x26, #0x88\n" - ".inst 0x45049a45 // smmla z5.s, z18.b, z4.b\n" - ".inst 0x45119a56 // smmla z22.s, z18.b, z17.b\n" - "uzp1 z18.d, z5.d, z22.d\n" - "scvtf z18.s, p1/m, z18.s\n" - "uzp2 z22.d, z5.d, z22.d\n" - "fmul z5.s, z23.s, z7.s[2]\n" - "fmul z7.s, z23.s, z7.s[3]\n" - "scvtf z22.s, p1/m, z22.s\n" - "fmla z20.s, p1/M, z18.s, z5.s\n" - "ld1rqb { z18.b }, p1/Z, [x25, #16]\n" - "ld1h { z5.s }, p0/Z, [x20]\n" - "fcvt z5.s, p1/m, z5.h\n" - "fmla z25.s, p1/M, z22.s, z7.s\n" - "mov z22.s, #0x0\n" - "mov z7.s, #0x0\n" - ".inst 0x451f9936 // smmla z22.s, z9.b, z31.b\n" - ".inst 0x45069927 // smmla z7.s, z9.b, z6.b\n" - "ld1rqb { z9.b }, p1/Z, [x25, #32]\n" - "mov z5.q, z5.q[0]\n" - ".inst 0x450e9936 // smmla z22.s, z9.b, z14.b\n" - ".inst 0x45029927 // smmla z7.s, z9.b, z2.b\n" - "ld1rqb { z9.b }, p1/Z, [x25, #64]\n" - ".inst 0x451e9936 // smmla z22.s, z9.b, z30.b\n" - ".inst 0x45159927 // smmla z7.s, z9.b, z21.b\n" - "ld1rqb { z9.b }, p1/Z, [x25, #96]\n" - ".inst 0x45049936 // smmla z22.s, z9.b, z4.b\n" - ".inst 0x45119927 // smmla z7.s, z9.b, z17.b\n" - "uzp1 z9.d, z22.d, z7.d\n" - "scvtf z9.s, p1/m, z9.s\n" - "uzp2 z22.d, z22.d, z7.d\n" - "fmul z7.s, z23.s, z3.s[0]\n" - "scvtf z22.s, p1/m, z22.s\n" - "fmla z11.s, p1/M, z9.s, z7.s\n" - "ld1rqb { z9.b }, p1/Z, [x24]\n" - "fmul z7.s, z23.s, z3.s[1]\n" - "fmla z16.s, p1/M, z22.s, z7.s\n" - "mov z22.s, #0x0\n" - "mov z7.s, #0x0\n" - ".inst 0x451f9a56 // smmla z22.s, z18.b, z31.b\n" - ".inst 0x45069a47 // smmla z7.s, z18.b, z6.b\n" - "ld1rqb { z18.b }, p1/Z, [x25, #48]\n" - ".inst 0x450e9a56 // smmla z22.s, z18.b, z14.b\n" - ".inst 0x45029a47 // smmla z7.s, z18.b, z2.b\n" - "ld1rqb { z18.b }, p1/Z, [x25, #80]\n" - ".inst 0x451e9a56 // smmla z22.s, z18.b, z30.b\n" - ".inst 0x45159a47 // smmla z7.s, z18.b, z21.b\n" - "ld1rqb { z18.b }, p1/Z, [x25, #112]\n" - "add x25, x25, #0x88\n" - ".inst 0x45049a56 // smmla z22.s, z18.b, z4.b\n" - ".inst 0x45119a47 // smmla z7.s, z18.b, z17.b\n" - "uzp1 z18.d, z22.d, z7.d\n" - "scvtf z18.s, p1/m, z18.s\n" - "uzp2 z7.d, z22.d, z7.d\n" - "fmul z22.s, z23.s, z3.s[2]\n" - "fmul z3.s, z23.s, z3.s[3]\n" - "scvtf z7.s, p1/m, z7.s\n" - "fmla z19.s, p1/M, z18.s, z22.s\n" - "ld1rqb { z18.b }, p1/Z, [x24, #16]\n" - "fmul z22.s, z23.s, z5.s[0]\n" - "fmla z26.s, p1/M, z7.s, z3.s\n" - "mov z3.s, #0x0\n" - "mov z7.s, #0x0\n" - ".inst 0x451f9923 // smmla z3.s, z9.b, z31.b\n" - ".inst 0x45069927 // smmla z7.s, z9.b, z6.b\n" - "ld1rqb { z9.b }, p1/Z, [x24, #32]\n" - ".inst 0x450e9923 // smmla z3.s, z9.b, z14.b\n" - ".inst 0x45029927 // smmla z7.s, z9.b, z2.b\n" - "mov z9.s, #0x0\n" - ".inst 0x451f9a49 // smmla z9.s, z18.b, z31.b\n" - "mov z31.s, #0x0\n" - ".inst 0x45069a5f // smmla z31.s, z18.b, z6.b\n" - "ld1rqb { z6.b }, p1/Z, [x24, #48]\n" - "ld1rqb { z18.b }, p1/Z, [x24, #64]\n" - ".inst 0x450e98c9 // smmla z9.s, z6.b, z14.b\n" - "fmul z14.s, z23.s, z5.s[1]\n" - ".inst 0x450298df // smmla z31.s, z6.b, z2.b\n" - "ld1rqb { z6.b }, p1/Z, [x24, #80]\n" - "fmul z2.s, z23.s, z5.s[2]\n" - "fmul z23.s, z23.s, z5.s[3]\n" - ".inst 0x451e9a43 // smmla z3.s, z18.b, z30.b\n" - ".inst 0x45159a47 // smmla z7.s, z18.b, z21.b\n" - "ld1rqb { z5.b }, p1/Z, [x24, #96]\n" - ".inst 0x451e98c9 // smmla z9.s, z6.b, z30.b\n" - ".inst 0x451598df // smmla z31.s, z6.b, z21.b\n" - "ld1rqb { z18.b }, p1/Z, [x24, #112]\n" - "add x24, x24, #0x88\n" - ".inst 0x450498a3 // smmla z3.s, z5.b, z4.b\n" - ".inst 0x451198a7 // smmla z7.s, z5.b, z17.b\n" - ".inst 0x45049a49 // smmla z9.s, z18.b, z4.b\n" - ".inst 0x45119a5f // smmla z31.s, z18.b, z17.b\n" - "uzp1 z18.d, z3.d, z7.d\n" - "uzp2 z5.d, z3.d, z7.d\n" - "scvtf z18.s, p1/m, z18.s\n" - "uzp1 z6.d, z9.d, z31.d\n" - "uzp2 z9.d, z9.d, z31.d\n" - "scvtf z5.s, p1/m, z5.s\n" - "fmla z8.s, p1/M, z18.s, z22.s\n" - "scvtf z6.s, p1/m, z6.s\n" - "scvtf z9.s, p1/m, z9.s\n" - "fmla z29.s, p1/M, z5.s, z14.s\n" - "fmla z27.s, p1/M, z6.s, z2.s\n" - "fmla z10.s, p1/M, z9.s, z23.s\n" - "bgt 3b\n" - "mov x20, %x[res_ptr]\n" - "subs x10, x10, #0x8\n" - "add %x[res_ptr], %x[res_ptr], #0x20\n" - "st1w { z24.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z15.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z12.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z0.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z13.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z1.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z20.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z25.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z11.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z16.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z19.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z26.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z8.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z29.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z27.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "st1w { z10.s }, p1, [x20]\n" - "bne 2b\n" - "mov x20, #0x4\n" - "sub x13, x13, #0x10\n" - "cmp x13, #0x10\n" - "mov %x[res_ptr], x9\n" - "madd %x[a_ptr], x20, x12, %x[a_ptr]\n" - "bge 1b\n" - "4:" // Row loop skip - "cbz x13, 9f\n" - "5:" // Row tail: Row loop - "add x25, %x[b_ptr], #0x10\n" - "mov x24, %x[nc]\n" - "add x23, %x[res_ptr], %x[res_stride], LSL #2\n" - "6:" // Row tail: Column loop - "mov z24.b, #0x0\n" - "mov z15.b, #0x0\n" - "add x28, %x[a_ptr], #0x8\n" - "mov x22, %x[nb]\n" - "mov z12.b, #0x0\n" - "mov z0.b, #0x0\n" - "7:" // Row tail: Block loop - "ld1b { z3.b }, p1/Z, [x25]\n" - "ld1b { z6.b }, p1/Z, [x25, #1, MUL VL]\n" - "mov z2.s, #0x0\n" - "mov z25.s, #0x0\n" - "ld1rqb { z26.b }, p1/Z, [x28]\n" - "ld1rqb { z21.b }, p1/Z, [x28, #16]\n" - "mov z27.s, #0x0\n" - "mov z19.s, #0x0\n" - "ld1b { z29.b }, p1/Z, [x25, #2, MUL VL]\n" - "ld1b { z16.b }, p1/Z, [x25, #3, MUL VL]\n" - "sub x21, x25, #0x10\n" - "sub x20, x28, #0x8\n" - "lsl z20.b, z3.b, #0x4\n" - "lsl z4.b, z6.b, #0x4\n" - "ld1rqb { z10.b }, p1/Z, [x28, #32]\n" - "ld1rqb { z23.b }, p1/Z, [x28, #48]\n" - "and z3.b, z3.b, #0xf0\n" - "and z6.b, z6.b, #0xf0\n" - "ld1rqb { z11.b }, p1/Z, [x28, #64]\n" - "ld1rqb { z7.b }, p1/Z, [x28, #80]\n" - "lsl z8.b, z29.b, #0x4\n" - "lsl z14.b, z16.b, #0x4\n" - "ld1rqb { z18.b }, p1/Z, [x28, #96]\n" - "ld1rqb { z30.b }, p1/Z, [x28, #112]\n" - ".inst 0x45149b42 // smmla z2.s, z26.b, z20.b\n" - ".inst 0x45049b59 // smmla z25.s, z26.b, z4.b\n" - "and z29.b, z29.b, #0xf0\n" - "ld1h { z17.s }, p1/Z, [x21]\n" - ".inst 0x45149abb // smmla z27.s, z21.b, z20.b\n" - ".inst 0x45049ab3 // smmla z19.s, z21.b, z4.b\n" - "and z16.b, z16.b, #0xf0\n" - "ld1h { z4.s }, p0/Z, [x20]\n" - "subs x22, x22, #0x1\n" - "add x28, x28, #0x88\n" - "fcvt z17.s, p1/m, z17.h\n" - "add x25, x25, #0x90\n" - ".inst 0x45089942 // smmla z2.s, z10.b, z8.b\n" - ".inst 0x450e9959 // smmla z25.s, z10.b, z14.b\n" - "fcvt z4.s, p1/m, z4.h\n" - ".inst 0x45089afb // smmla z27.s, z23.b, z8.b\n" - ".inst 0x450e9af3 // smmla z19.s, z23.b, z14.b\n" - "fscale z17.s, p1/m, z17.s, z28.s\n" - "mov z4.q, z4.q[0]\n" - ".inst 0x45039962 // smmla z2.s, z11.b, z3.b\n" - ".inst 0x45069979 // smmla z25.s, z11.b, z6.b\n" - "fmul z23.s, z17.s, z4.s[0]\n" - "fmul z9.s, z17.s, z4.s[1]\n" - "fmul z21.s, z17.s, z4.s[2]\n" - "fmul z4.s, z17.s, z4.s[3]\n" - ".inst 0x450398fb // smmla z27.s, z7.b, z3.b\n" - ".inst 0x450698f3 // smmla z19.s, z7.b, z6.b\n" - ".inst 0x451d9a42 // smmla z2.s, z18.b, z29.b\n" - ".inst 0x45109a59 // smmla z25.s, z18.b, z16.b\n" - ".inst 0x451d9bdb // smmla z27.s, z30.b, z29.b\n" - ".inst 0x45109bd3 // smmla z19.s, z30.b, z16.b\n" - "uzp1 z31.d, z2.d, z25.d\n" - "uzp2 z13.d, z2.d, z25.d\n" - "scvtf z31.s, p1/m, z31.s\n" - "uzp1 z17.d, z27.d, z19.d\n" - "uzp2 z18.d, z27.d, z19.d\n" - "scvtf z13.s, p1/m, z13.s\n" - "fmla z24.s, p1/M, z31.s, z23.s\n" - "scvtf z17.s, p1/m, z17.s\n" - "scvtf z18.s, p1/m, z18.s\n" - "fmla z15.s, p1/M, z13.s, z9.s\n" - "fmla z12.s, p1/M, z17.s, z21.s\n" - "fmla z0.s, p1/M, z18.s, z4.s\n" - "bgt 7b\n" - "mov x20, %x[res_ptr]\n" - "cmp x13, #0x1\n" - "st1w { z24.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "cmp x13, #0x2\n" - "st1w { z15.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "cmp x13, #0x3\n" - "st1w { z12.s }, p1, [x20]\n" - "add x20, x20, %x[res_stride]\n" - "ble 8f\n" - "st1w { z0.s }, p1, [x20]\n" - "8:" // Row tail: Accumulator store skip - "subs x24, x24, #0x8\n" - "add %x[res_ptr], %x[res_ptr], #0x20\n" - "bne 6b\n" - "subs x13, x13, #0x4\n" - "add %x[a_ptr], %x[a_ptr], x12\n" - "mov %x[res_ptr], x23\n" - "bgt 5b\n" - "9:" // Row tail: Row loop skip - : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr) - : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc) - : "cc", "memory", "p0", "p1", "x9", "x10", "x11", "x12", "x13", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28", "z0", "z1", "z2", "z3", "z4", "z5", "z6", "z7", "z8", "z9", "z10", "z11", "z12", "z13", "z14", "z15", "z16", "z17", "z18", "z19", "z20", "z21", "z22", "z23", "z24", "z25", "z26", "z27", "z28", "z29", "z30", "z31" - ); - return; - } -#endif // #if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8) -#elif defined(__AVX2__) || defined(__AVX512F__) - { - const block_q4_0x8 * b_ptr_start = (const block_q4_0x8 *)vx; - const block_q8_0x4 * a_ptr_start = (const block_q8_0x4 *)vy; - int64_t b_nb = n / QK4_0; - int64_t y = 0; - // Mask to mask out nibbles from packed bytes - const __m256i m4b = _mm256_set1_epi8(0x0F); - const __m128i loadMask = _mm_blend_epi32(_mm_setzero_si128(), _mm_set1_epi32(0xFFFFFFFF), 3); - // Lookup table to convert signed nibbles to signed bytes - __m256i signextendlut = _mm256_castsi128_si256(_mm_set_epi8(-1, -2, -3, -4, -5, -6, -7, -8, 7, 6, 5, 4, 3, 2, 1, 0)); - signextendlut = _mm256_permute2f128_si256(signextendlut, signextendlut, 0); - // Permute mask used for easier vector processing at later stages - __m256i requiredOrder = _mm256_set_epi32(3, 2, 1, 0, 7, 6, 5, 4); - int64_t xstart = 0; - int anr = nr - nr%16; // Used to align nr with boundary of 16 - #ifdef __AVX512F__ - int anc = nc - nc%16; // Used to align nc with boundary of 16 - // Mask to mask out nibbles from packed bytes expanded to 512 bit length - const __m512i m4bexpanded = _mm512_set1_epi8(0x0F); - // Lookup table to convert signed nibbles to signed bytes expanded to 512 bit length - __m512i signextendlutexpanded = _mm512_inserti32x8(_mm512_castsi256_si512(signextendlut), signextendlut, 1); - - // Take group of four block_q8_0x4 structures at each pass of the loop and perform dot product operation - for (; y < anr / 4; y += 4) { - - const block_q8_0x4 * a_ptrs[4]; - - a_ptrs[0] = a_ptr_start + (y * nb); - for (int i = 0; i < 3; ++i) { - a_ptrs[i + 1] = a_ptrs[i] + nb; - } - - // Take group of two block_q4_0x8 structures at each pass of the loop and perform dot product operation - for (int64_t x = 0; x < anc / 8; x += 2) { - - const block_q4_0x8 * b_ptr_0 = b_ptr_start + ((x) * b_nb); - const block_q4_0x8 * b_ptr_1 = b_ptr_start + ((x + 1) * b_nb); - - // Master FP accumulators - __m512 acc_rows[16]; - for (int i = 0; i < 16; i++) { - acc_rows[i] = _mm512_setzero_ps(); - } - - for (int64_t b = 0; b < nb; b++) { - // Load the sixteen block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....BE,BF - const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs)); - const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 32)); - const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 64)); - const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 96)); - - const __m256i rhs_raw_mat_89AB_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs)); - const __m256i rhs_raw_mat_CDEF_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 32)); - const __m256i rhs_raw_mat_89AB_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 64)); - const __m256i rhs_raw_mat_CDEF_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 96)); - - // Save the values in the following vectors in the formats B0B1B4B5B8B9BCBD, B2B3B6B7BABBBEBF for further processing and storing of values - const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240); - const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240); - const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240); - const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240); - - const __m256i rhs_raw_mat_89CD_0 = _mm256_blend_epi32(rhs_raw_mat_89AB_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_0, requiredOrder), 240); - const __m256i rhs_raw_mat_ABEF_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_0, requiredOrder), rhs_raw_mat_CDEF_0, 240); - const __m256i rhs_raw_mat_89CD_1 = _mm256_blend_epi32(rhs_raw_mat_89AB_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_1, requiredOrder), 240); - const __m256i rhs_raw_mat_ABEF_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_1, requiredOrder), rhs_raw_mat_CDEF_1, 240); - - const __m512i rhs_raw_mat_014589CD_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_0), rhs_raw_mat_89CD_0, 1); - const __m512i rhs_raw_mat_2367ABEF_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_0), rhs_raw_mat_ABEF_0, 1); - const __m512i rhs_raw_mat_014589CD_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_1), rhs_raw_mat_89CD_1, 1); - const __m512i rhs_raw_mat_2367ABEF_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_1), rhs_raw_mat_ABEF_1, 1); - - // 4-bit -> 8-bit - Sign is maintained - const __m512i rhs_mat_014589CD_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_0, m4bexpanded)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) B8(0-7) B9(0-7) BC(0-7) BD(0-7) - const __m512i rhs_mat_2367ABEF_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_0, m4bexpanded)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) BA(0-7) BB(0-7) BE(0-7) BF(0-7) - - const __m512i rhs_mat_014589CD_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_1, m4bexpanded)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) B8(8-15) B9(8-15) BC(8-15) BD(8-15) - const __m512i rhs_mat_2367ABEF_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_1, m4bexpanded)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) BA(8-15) BB(8-15) BE(8-15) BF(8-15) - - const __m512i rhs_mat_014589CD_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_0, 4), m4bexpanded)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) B8(16-23) B9(16-23) BC(16-23) BD(16-23) - const __m512i rhs_mat_2367ABEF_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_0, 4), m4bexpanded)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) BA(16-23) BB(16-23) BE(16-23) BF(16-23) - - const __m512i rhs_mat_014589CD_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_1, 4), m4bexpanded)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) B8(24-31) B9(24-31) BC(24-31) BD(24-31) - const __m512i rhs_mat_2367ABEF_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_1, 4), m4bexpanded)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) BA(24-31) BB(24-31) BE(24-31) BF(24-31) - - // Shuffle pattern one - right side input - const __m512i rhs_mat_014589CD_0_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) B8(0-3) B9(0-3) B8(0-3) B9(0-3) BC(0-3) BD(0-3) BC(0-3) BD(0-3) - const __m512i rhs_mat_2367ABEF_0_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) BA(0-3) BB(0-3) BA(0-3) BB(0-3) BE(0-3) BF(0-3) BE(0-3) BF(0-3) - - const __m512i rhs_mat_014589CD_1_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) B8(8-11) B9(8-11) B8(8-11) B9(8-11) BC(8-11) BD(8-11) BC(8-11) BD(8-11) - const __m512i rhs_mat_2367ABEF_1_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) BA(8-11) BB(8-11) BA(8-11) BB(8-11) BE(8-11) BF(8-11) BE(8-11) BF(8-11) - - const __m512i rhs_mat_014589CD_2_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) B8(16-19) B9(16-19) B8(16-19) B9(16-19) BC(16-19) BD(16-19) BC(16-19) BD(16-19) - const __m512i rhs_mat_2367ABEF_2_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) BA(16-19) BB(16-19) BA(16-19) BB(16-19) BE(16-19) BF(16-19) BE(16-19) BF(16-19) - - const __m512i rhs_mat_014589CD_3_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) B8(24-27) B9(24-27) B8(24-27) B9(24-27) BC(24-27) BD(24-27) BC(24-27) BD(24-27) - const __m512i rhs_mat_2367ABEF_3_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) BA(24-27) BB(24-27) BA(24-27) BB(24-27) BE(24-27) BF(24-27) BE(24-27) BF(24-27) - - // Shuffle pattern two - right side input - - const __m512i rhs_mat_014589CD_0_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) B8(4-7) B9(4-7) B8(4-7) B9(4-7) BC(4-7) BD(4-7) BC(4-7) BD(4-7) - const __m512i rhs_mat_2367ABEF_0_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) BA(4-7) BB(4-7) BA(4-7) BB(4-7) BE(4-7) BF(4-7) BE(4-7) BF(4-7) - - const __m512i rhs_mat_014589CD_1_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) B8(12-15) B9(12-15) B8(12-15) B9(12-15) BC(12-15) BD(12-15) BC(12-15) BD(12-15) - const __m512i rhs_mat_2367ABEF_1_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) BA(12-15) BB(12-15) BA(12-15) BB(12-15) BE(12-15) BF(12-15) BE(12-15) BF(12-15) - - const __m512i rhs_mat_014589CD_2_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) B8(20-23) B9(20-23) B8(20-23) B9(20-23) BC(20-23) BD(20-23) BC(20-23) BD(20-23) - const __m512i rhs_mat_2367ABEF_2_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) BA(20-23) BB(20-23) BA(20-23) BB(20-23) BE(20-23) BF(20-23) BE(20-23) BF(20-23) - - const __m512i rhs_mat_014589CD_3_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) B8(28-31) B9(28-31) B8(28-31) B9(28-31) BC(28-31) BD(28-31) BC(28-31) BD(28-31) - const __m512i rhs_mat_2367ABEF_3_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) BA(28-31) BB(28-31) BA(28-31) BB(28-31) BE(28-31) BF(28-31) BE(28-31) BF(28-31) - - // Scale values - Load the weight scale values of two block_q4_0x8 - const __m512 col_scale_f32 = GGML_F32Cx8x2_LOAD(b_ptr_0[b].d, b_ptr_1[b].d); - - // Process LHS in pairs of rows - for (int rp = 0; rp < 4; rp++) { - - // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3 - // Loaded as set of 128 bit vectors and repeated and stored into a 256 bit vector before again repeating into 512 bit vector - __m256i lhs_mat_ymm_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs))); - __m256i lhs_mat_ymm_01_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 0); - __m256i lhs_mat_ymm_23_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 17); - __m256i lhs_mat_ymm_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 32))); - __m256i lhs_mat_ymm_01_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 0); - __m256i lhs_mat_ymm_23_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 17); - __m256i lhs_mat_ymm_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 64))); - __m256i lhs_mat_ymm_01_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 0); - __m256i lhs_mat_ymm_23_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 17); - __m256i lhs_mat_ymm_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 96))); - __m256i lhs_mat_ymm_01_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 0); - __m256i lhs_mat_ymm_23_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 17); - - __m512i lhs_mat_01_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_0), lhs_mat_ymm_01_0, 1); - __m512i lhs_mat_23_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_0), lhs_mat_ymm_23_0, 1); - __m512i lhs_mat_01_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_1), lhs_mat_ymm_01_1, 1); - __m512i lhs_mat_23_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_1), lhs_mat_ymm_23_1, 1); - __m512i lhs_mat_01_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_2), lhs_mat_ymm_01_2, 1); - __m512i lhs_mat_23_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_2), lhs_mat_ymm_23_2, 1); - __m512i lhs_mat_01_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_3), lhs_mat_ymm_01_3, 1); - __m512i lhs_mat_23_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_3), lhs_mat_ymm_23_3, 1); - - // Shuffle pattern one - left side input - - const __m512i lhs_mat_01_0_sp1 = _mm512_shuffle_epi32(lhs_mat_01_0, 160); //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) - const __m512i lhs_mat_23_0_sp1 = _mm512_shuffle_epi32(lhs_mat_23_0, 160); //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) - - const __m512i lhs_mat_01_1_sp1 = _mm512_shuffle_epi32(lhs_mat_01_1, 160); //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) - const __m512i lhs_mat_23_1_sp1 = _mm512_shuffle_epi32(lhs_mat_23_1, 160); //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) - - const __m512i lhs_mat_01_2_sp1 = _mm512_shuffle_epi32(lhs_mat_01_2, 160); //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) - const __m512i lhs_mat_23_2_sp1 = _mm512_shuffle_epi32(lhs_mat_23_2, 160); //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) - - const __m512i lhs_mat_01_3_sp1 = _mm512_shuffle_epi32(lhs_mat_01_3, 160); //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) - const __m512i lhs_mat_23_3_sp1 = _mm512_shuffle_epi32(lhs_mat_23_3, 160); //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) - - // Shuffle pattern two - left side input - - const __m512i lhs_mat_01_0_sp2 = _mm512_shuffle_epi32(lhs_mat_01_0, 245); //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) - const __m512i lhs_mat_23_0_sp2 = _mm512_shuffle_epi32(lhs_mat_23_0, 245); //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) - - const __m512i lhs_mat_01_1_sp2 = _mm512_shuffle_epi32(lhs_mat_01_1, 245); //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) - const __m512i lhs_mat_23_1_sp2 = _mm512_shuffle_epi32(lhs_mat_23_1, 245); //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) - - const __m512i lhs_mat_01_2_sp2 = _mm512_shuffle_epi32(lhs_mat_01_2, 245); //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) - const __m512i lhs_mat_23_2_sp2 = _mm512_shuffle_epi32(lhs_mat_23_2, 245); //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) - - const __m512i lhs_mat_01_3_sp2 = _mm512_shuffle_epi32(lhs_mat_01_3, 245); //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) - const __m512i lhs_mat_23_3_sp2 = _mm512_shuffle_epi32(lhs_mat_23_3, 245); //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) - - // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane - // Resembles MMLAs into 2x2 matrices in ARM Version - __m512i iacc_mat_00_sp1 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp1, rhs_mat_014589CD_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp1, rhs_mat_014589CD_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp1, rhs_mat_014589CD_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp1, rhs_mat_014589CD_0_sp1)); - __m512i iacc_mat_01_sp1 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp1, rhs_mat_2367ABEF_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp1, rhs_mat_2367ABEF_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp1, rhs_mat_2367ABEF_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp1, rhs_mat_2367ABEF_0_sp1)); - __m512i iacc_mat_10_sp1 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp1, rhs_mat_014589CD_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp1, rhs_mat_014589CD_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp1, rhs_mat_014589CD_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp1, rhs_mat_014589CD_0_sp1)); - __m512i iacc_mat_11_sp1 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp1, rhs_mat_2367ABEF_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp1, rhs_mat_2367ABEF_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp1, rhs_mat_2367ABEF_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp1, rhs_mat_2367ABEF_0_sp1)); - __m512i iacc_mat_00_sp2 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp2, rhs_mat_014589CD_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp2, rhs_mat_014589CD_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp2, rhs_mat_014589CD_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp2, rhs_mat_014589CD_0_sp2)); - __m512i iacc_mat_01_sp2 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp2, rhs_mat_2367ABEF_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp2, rhs_mat_2367ABEF_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp2, rhs_mat_2367ABEF_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp2, rhs_mat_2367ABEF_0_sp2)); - __m512i iacc_mat_10_sp2 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp2, rhs_mat_014589CD_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp2, rhs_mat_014589CD_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp2, rhs_mat_014589CD_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp2, rhs_mat_014589CD_0_sp2)); - __m512i iacc_mat_11_sp2 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp2, rhs_mat_2367ABEF_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp2, rhs_mat_2367ABEF_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp2, rhs_mat_2367ABEF_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp2, rhs_mat_2367ABEF_0_sp2)); - - // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block - __m512i iacc_mat_00 = _mm512_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2); - __m512i iacc_mat_01 = _mm512_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2); - __m512i iacc_mat_10 = _mm512_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2); - __m512i iacc_mat_11 = _mm512_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2); - - - // Straighten out to make 4 row vectors - __m512i iacc_row_0 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_00, _mm512_shuffle_epi32(iacc_mat_01, 78)); - __m512i iacc_row_1 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01); - __m512i iacc_row_2 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_10, _mm512_shuffle_epi32(iacc_mat_11, 78)); - __m512i iacc_row_3 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11); - - // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes - const __m128i row_scale_f16 = _mm_shuffle_epi32(_mm_maskload_epi32((int const*)(a_ptrs[rp][b].d), loadMask), 68); - const __m512 row_scale_f32 = GGML_F32Cx16_REPEAT_LOAD(row_scale_f16); - - // Multiply with appropiate scales and accumulate - acc_rows[rp * 4] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_0), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[rp * 4]); - acc_rows[rp * 4 + 1] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_1), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[rp * 4 + 1]); - acc_rows[rp * 4 + 2] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_2), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[rp * 4 + 2]); - acc_rows[rp * 4 + 3] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_3), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[rp * 4 + 3]); - } - } - - // Store the accumulated values - for (int i = 0; i < 16; i++) { - _mm512_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]); - } - } - } - // Take a block_q8_0x4 structures at each pass of the loop and perform dot product operation - for (; y < nr / 4; y ++) { - - const block_q8_0x4 * a_ptr = a_ptr_start + (y * nb); - - // Take group of two block_q4_0x8 structures at each pass of the loop and perform dot product operation - for (int64_t x = 0; x < anc / 8; x += 2) { - - const block_q4_0x8 * b_ptr_0 = b_ptr_start + ((x) * b_nb); - const block_q4_0x8 * b_ptr_1 = b_ptr_start + ((x + 1) * b_nb); - - // Master FP accumulators - __m512 acc_rows[4]; - for (int i = 0; i < 4; i++) { - acc_rows[i] = _mm512_setzero_ps(); - } - - for (int64_t b = 0; b < nb; b++) { - // Load the sixteen block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....BE,BF - const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs)); - const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 32)); - const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 64)); - const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 96)); - - const __m256i rhs_raw_mat_89AB_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs)); - const __m256i rhs_raw_mat_CDEF_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 32)); - const __m256i rhs_raw_mat_89AB_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 64)); - const __m256i rhs_raw_mat_CDEF_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 96)); - - // Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of valuess - const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240); - const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240); - const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240); - const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240); - - const __m256i rhs_raw_mat_89CD_0 = _mm256_blend_epi32(rhs_raw_mat_89AB_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_0, requiredOrder), 240); - const __m256i rhs_raw_mat_ABEF_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_0, requiredOrder), rhs_raw_mat_CDEF_0, 240); - const __m256i rhs_raw_mat_89CD_1 = _mm256_blend_epi32(rhs_raw_mat_89AB_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_1, requiredOrder), 240); - const __m256i rhs_raw_mat_ABEF_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_1, requiredOrder), rhs_raw_mat_CDEF_1, 240); - - const __m512i rhs_raw_mat_014589CD_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_0), rhs_raw_mat_89CD_0, 1); - const __m512i rhs_raw_mat_2367ABEF_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_0), rhs_raw_mat_ABEF_0, 1); - const __m512i rhs_raw_mat_014589CD_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_1), rhs_raw_mat_89CD_1, 1); - const __m512i rhs_raw_mat_2367ABEF_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_1), rhs_raw_mat_ABEF_1, 1); - - // 4-bit -> 8-bit - Sign is maintained - const __m512i rhs_mat_014589CD_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_0, m4bexpanded)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) B8(0-7) B9(0-7) BC(0-7) BD(0-7) - const __m512i rhs_mat_2367ABEF_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_0, m4bexpanded)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) BA(0-7) BB(0-7) BE(0-7) BF(0-7) - - const __m512i rhs_mat_014589CD_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_1, m4bexpanded)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) B8(8-15) B9(8-15) BC(8-15) BD(8-15) - const __m512i rhs_mat_2367ABEF_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_1, m4bexpanded)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) BA(8-15) BB(8-15) BE(8-15) BF(8-15) - - const __m512i rhs_mat_014589CD_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_0, 4), m4bexpanded)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) B8(16-23) B9(16-23) BC(16-23) BD(16-23) - const __m512i rhs_mat_2367ABEF_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_0, 4), m4bexpanded)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) BA(16-23) BB(16-23) BE(16-23) BF(16-23) - - const __m512i rhs_mat_014589CD_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_1, 4), m4bexpanded)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) B8(24-31) B9(24-31) BC(24-31) BD(24-31) - const __m512i rhs_mat_2367ABEF_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_1, 4), m4bexpanded)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) BA(24-31) BB(24-31) BE(24-31) BF(24-31) - - // Shuffle pattern one - right side input - const __m512i rhs_mat_014589CD_0_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) B8(0-3) B9(0-3) B8(0-3) B9(0-3) BC(0-3) BD(0-3) BC(0-3) BD(0-3) - const __m512i rhs_mat_2367ABEF_0_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) BA(0-3) BB(0-3) BA(0-3) BB(0-3) BE(0-3) BF(0-3) BE(0-3) BF(0-3) - - const __m512i rhs_mat_014589CD_1_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) B8(8-11) B9(8-11) B8(8-11) B9(8-11) BC(8-11) BD(8-11) BC(8-11) BD(8-11) - const __m512i rhs_mat_2367ABEF_1_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) BA(8-11) BB(8-11) BA(8-11) BB(8-11) BE(8-11) BF(8-11) BE(8-11) BF(8-11) - - const __m512i rhs_mat_014589CD_2_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) B8(16-19) B9(16-19) B8(16-19) B9(16-19) BC(16-19) BD(16-19) BC(16-19) BD(16-19) - const __m512i rhs_mat_2367ABEF_2_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) BA(16-19) BB(16-19) BA(16-19) BB(16-19) BE(16-19) BF(16-19) BE(16-19) BF(16-19) - - const __m512i rhs_mat_014589CD_3_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) B8(24-27) B9(24-27) B8(24-27) B9(24-27) BC(24-27) BD(24-27) BC(24-27) BD(24-27) - const __m512i rhs_mat_2367ABEF_3_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) BA(24-27) BB(24-27) BA(24-27) BB(24-27) BE(24-27) BF(24-27) BE(24-27) BF(24-27) - - // Shuffle pattern two - right side input - - const __m512i rhs_mat_014589CD_0_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) B8(4-7) B9(4-7) B8(4-7) B9(4-7) BC(4-7) BD(4-7) BC(4-7) BD(4-7) - const __m512i rhs_mat_2367ABEF_0_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) BA(4-7) BB(4-7) BA(4-7) BB(4-7) BE(4-7) BF(4-7) BE(4-7) BF(4-7) - - const __m512i rhs_mat_014589CD_1_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) B8(12-15) B9(12-15) B8(12-15) B9(12-15) BC(12-15) BD(12-15) BC(12-15) BD(12-15) - const __m512i rhs_mat_2367ABEF_1_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) BA(12-15) BB(12-15) BA(12-15) BB(12-15) BE(12-15) BF(12-15) BE(12-15) BF(12-15) - - const __m512i rhs_mat_014589CD_2_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) B8(20-23) B9(20-23) B8(20-23) B9(20-23) BC(20-23) BD(20-23) BC(20-23) BD(20-23) - const __m512i rhs_mat_2367ABEF_2_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) BA(20-23) BB(20-23) BA(20-23) BB(20-23) BE(20-23) BF(20-23) BE(20-23) BF(20-23) - - const __m512i rhs_mat_014589CD_3_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) B8(28-31) B9(28-31) B8(28-31) B9(28-31) BC(28-31) BD(28-31) BC(28-31) BD(28-31) - const __m512i rhs_mat_2367ABEF_3_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) BA(28-31) BB(28-31) BA(28-31) BB(28-31) BE(28-31) BF(28-31) BE(28-31) BF(28-31) - - - // Scale values - Load the weight scale values of two block_q4_0x8 - const __m512 col_scale_f32 = GGML_F32Cx8x2_LOAD(b_ptr_0[b].d, b_ptr_1[b].d); - - // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3 - // Loaded as set of 128 bit vectors and repeated and stored into a 256 bit vector before again repeating into 512 bit vector - __m256i lhs_mat_ymm_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs))); - __m256i lhs_mat_ymm_01_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 0); - __m256i lhs_mat_ymm_23_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 17); - __m256i lhs_mat_ymm_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 32))); - __m256i lhs_mat_ymm_01_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 0); - __m256i lhs_mat_ymm_23_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 17); - __m256i lhs_mat_ymm_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 64))); - __m256i lhs_mat_ymm_01_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 0); - __m256i lhs_mat_ymm_23_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 17); - __m256i lhs_mat_ymm_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 96))); - __m256i lhs_mat_ymm_01_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 0); - __m256i lhs_mat_ymm_23_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 17); - - __m512i lhs_mat_01_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_0), lhs_mat_ymm_01_0, 1); - __m512i lhs_mat_23_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_0), lhs_mat_ymm_23_0, 1); - __m512i lhs_mat_01_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_1), lhs_mat_ymm_01_1, 1); - __m512i lhs_mat_23_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_1), lhs_mat_ymm_23_1, 1); - __m512i lhs_mat_01_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_2), lhs_mat_ymm_01_2, 1); - __m512i lhs_mat_23_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_2), lhs_mat_ymm_23_2, 1); - __m512i lhs_mat_01_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_3), lhs_mat_ymm_01_3, 1); - __m512i lhs_mat_23_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_3), lhs_mat_ymm_23_3, 1); - - // Shuffle pattern one - left side input - - const __m512i lhs_mat_01_0_sp1 = _mm512_shuffle_epi32(lhs_mat_01_0, 160); //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) - const __m512i lhs_mat_23_0_sp1 = _mm512_shuffle_epi32(lhs_mat_23_0, 160); //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) - - const __m512i lhs_mat_01_1_sp1 = _mm512_shuffle_epi32(lhs_mat_01_1, 160); //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) - const __m512i lhs_mat_23_1_sp1 = _mm512_shuffle_epi32(lhs_mat_23_1, 160); //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) - - const __m512i lhs_mat_01_2_sp1 = _mm512_shuffle_epi32(lhs_mat_01_2, 160); //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) - const __m512i lhs_mat_23_2_sp1 = _mm512_shuffle_epi32(lhs_mat_23_2, 160); //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) - - const __m512i lhs_mat_01_3_sp1 = _mm512_shuffle_epi32(lhs_mat_01_3, 160); //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) - const __m512i lhs_mat_23_3_sp1 = _mm512_shuffle_epi32(lhs_mat_23_3, 160); //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) - - // Shuffle pattern two - left side input - - const __m512i lhs_mat_01_0_sp2 = _mm512_shuffle_epi32(lhs_mat_01_0, 245); //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) - const __m512i lhs_mat_23_0_sp2 = _mm512_shuffle_epi32(lhs_mat_23_0, 245); //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) - - const __m512i lhs_mat_01_1_sp2 = _mm512_shuffle_epi32(lhs_mat_01_1, 245); //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) - const __m512i lhs_mat_23_1_sp2 = _mm512_shuffle_epi32(lhs_mat_23_1, 245); //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) - - const __m512i lhs_mat_01_2_sp2 = _mm512_shuffle_epi32(lhs_mat_01_2, 245); //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) - const __m512i lhs_mat_23_2_sp2 = _mm512_shuffle_epi32(lhs_mat_23_2, 245); //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) - - const __m512i lhs_mat_01_3_sp2 = _mm512_shuffle_epi32(lhs_mat_01_3, 245); //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) - const __m512i lhs_mat_23_3_sp2 = _mm512_shuffle_epi32(lhs_mat_23_3, 245); //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) - - // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane - // Resembles MMLAs into 2x2 matrices in ARM Version - __m512i iacc_mat_00_sp1 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp1, rhs_mat_014589CD_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp1, rhs_mat_014589CD_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp1, rhs_mat_014589CD_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp1, rhs_mat_014589CD_0_sp1)); - __m512i iacc_mat_01_sp1 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp1, rhs_mat_2367ABEF_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp1, rhs_mat_2367ABEF_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp1, rhs_mat_2367ABEF_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp1, rhs_mat_2367ABEF_0_sp1)); - __m512i iacc_mat_10_sp1 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp1, rhs_mat_014589CD_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp1, rhs_mat_014589CD_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp1, rhs_mat_014589CD_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp1, rhs_mat_014589CD_0_sp1)); - __m512i iacc_mat_11_sp1 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp1, rhs_mat_2367ABEF_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp1, rhs_mat_2367ABEF_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp1, rhs_mat_2367ABEF_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp1, rhs_mat_2367ABEF_0_sp1)); - __m512i iacc_mat_00_sp2 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp2, rhs_mat_014589CD_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp2, rhs_mat_014589CD_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp2, rhs_mat_014589CD_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp2, rhs_mat_014589CD_0_sp2)); - __m512i iacc_mat_01_sp2 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp2, rhs_mat_2367ABEF_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp2, rhs_mat_2367ABEF_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp2, rhs_mat_2367ABEF_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp2, rhs_mat_2367ABEF_0_sp2)); - __m512i iacc_mat_10_sp2 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp2, rhs_mat_014589CD_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp2, rhs_mat_014589CD_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp2, rhs_mat_014589CD_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp2, rhs_mat_014589CD_0_sp2)); - __m512i iacc_mat_11_sp2 = - _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp2, rhs_mat_2367ABEF_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp2, rhs_mat_2367ABEF_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp2, rhs_mat_2367ABEF_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp2, rhs_mat_2367ABEF_0_sp2)); - - // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block - __m512i iacc_mat_00 = _mm512_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2); - __m512i iacc_mat_01 = _mm512_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2); - __m512i iacc_mat_10 = _mm512_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2); - __m512i iacc_mat_11 = _mm512_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2); - - - // Straighten out to make 4 row vectors - __m512i iacc_row_0 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_00, _mm512_shuffle_epi32(iacc_mat_01, 78)); - __m512i iacc_row_1 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01); - __m512i iacc_row_2 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_10, _mm512_shuffle_epi32(iacc_mat_11, 78)); - __m512i iacc_row_3 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11); - - // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes - const __m128i row_scale_f16 = _mm_shuffle_epi32(_mm_maskload_epi32((int const*)(a_ptr[b].d), loadMask), 68); - const __m512 row_scale_f32 = GGML_F32Cx16_REPEAT_LOAD(row_scale_f16); - - // Multiply with appropiate scales and accumulate - acc_rows[0] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_0), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[0]); - acc_rows[1] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_1), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[1]); - acc_rows[2] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_2), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[2]); - acc_rows[3] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_3), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[3]); - } - - // Store the accumulated values - for (int i = 0; i < 4; i++) { - _mm512_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]); - } - } - } - if (anc != nc) { - xstart = anc/8; - y = 0; - } - #endif // __AVX512F__ - - // Take group of four block_q8_0x4 structures at each pass of the loop and perform dot product operation - - for (; y < anr / 4; y += 4) { - const block_q8_0x4 * a_ptrs[4]; - - a_ptrs[0] = a_ptr_start + (y * nb); - for (int i = 0; i < 3; ++i) { - a_ptrs[i + 1] = a_ptrs[i] + nb; - } - - // Take group of eight block_q4_0x8 structures at each pass of the loop and perform dot product operation - for (int64_t x = xstart; x < nc / 8; x++) { - - const block_q4_0x8 * b_ptr = b_ptr_start + (x * b_nb); - - // Master FP accumulators - __m256 acc_rows[16]; - for (int i = 0; i < 16; i++) { - acc_rows[i] = _mm256_setzero_ps(); - } - - for (int64_t b = 0; b < nb; b++) { - // Load the eight block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7 - const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs)); - const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 32)); - const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 64)); - const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 96)); - - // Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of values - const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240); - const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240); - const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240); - const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240); - - // 4-bit -> 8-bit - Sign is maintained - const __m256i rhs_mat_0145_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_0, m4b)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) - const __m256i rhs_mat_2367_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_0, m4b)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) - - const __m256i rhs_mat_0145_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_1, m4b)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) - const __m256i rhs_mat_2367_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_1, m4b)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) - - const __m256i rhs_mat_0145_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_0, 4), m4b)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) - const __m256i rhs_mat_2367_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_0, 4), m4b)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) - - const __m256i rhs_mat_0145_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_1, 4), m4b)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) - const __m256i rhs_mat_2367_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_1, 4), m4b)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) - - // Shuffle pattern one - right side input - const __m256i rhs_mat_0145_0_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) - const __m256i rhs_mat_2367_0_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) - - const __m256i rhs_mat_0145_1_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) - const __m256i rhs_mat_2367_1_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) - - const __m256i rhs_mat_0145_2_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) - const __m256i rhs_mat_2367_2_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) - - const __m256i rhs_mat_0145_3_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) - const __m256i rhs_mat_2367_3_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) - - // Shuffle pattern two - right side input - - const __m256i rhs_mat_0145_0_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) - const __m256i rhs_mat_2367_0_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) - - const __m256i rhs_mat_0145_1_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) - const __m256i rhs_mat_2367_1_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) - - const __m256i rhs_mat_0145_2_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) - const __m256i rhs_mat_2367_2_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) - - const __m256i rhs_mat_0145_3_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) - const __m256i rhs_mat_2367_3_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) - - // Scale values - Load the wight scale values of block_q4_0x8 - const __m256 col_scale_f32 = GGML_F32Cx8_LOAD(b_ptr[b].d); - - // Process LHS in groups of four - for (int rp = 0; rp < 4; rp++) { - // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3 - // Loaded as set of 128 bit vectors and repeated into a 256 bit vector - __m256i lhs_mat_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs))); - __m256i lhs_mat_01_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 0); - __m256i lhs_mat_23_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 17); - __m256i lhs_mat_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 32))); - __m256i lhs_mat_01_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 0); - __m256i lhs_mat_23_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 17); - __m256i lhs_mat_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 64))); - __m256i lhs_mat_01_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 0); - __m256i lhs_mat_23_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 17); - __m256i lhs_mat_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 96))); - __m256i lhs_mat_01_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 0); - __m256i lhs_mat_23_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 17); - - // Shuffle pattern one - left side input - const __m256i lhs_mat_01_0_sp1 = _mm256_shuffle_epi32(lhs_mat_01_0, 160); //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) - const __m256i lhs_mat_23_0_sp1 = _mm256_shuffle_epi32(lhs_mat_23_0, 160); //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) - - const __m256i lhs_mat_01_1_sp1 = _mm256_shuffle_epi32(lhs_mat_01_1, 160); //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) - const __m256i lhs_mat_23_1_sp1 = _mm256_shuffle_epi32(lhs_mat_23_1, 160); //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) - - const __m256i lhs_mat_01_2_sp1 = _mm256_shuffle_epi32(lhs_mat_01_2, 160); //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) - const __m256i lhs_mat_23_2_sp1 = _mm256_shuffle_epi32(lhs_mat_23_2, 160); //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) - - const __m256i lhs_mat_01_3_sp1 = _mm256_shuffle_epi32(lhs_mat_01_3, 160); //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) - const __m256i lhs_mat_23_3_sp1 = _mm256_shuffle_epi32(lhs_mat_23_3, 160); //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) - - // Shuffle pattern two - left side input - const __m256i lhs_mat_01_0_sp2 = _mm256_shuffle_epi32(lhs_mat_01_0, 245); //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) - const __m256i lhs_mat_23_0_sp2 = _mm256_shuffle_epi32(lhs_mat_23_0, 245); //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) - - const __m256i lhs_mat_01_1_sp2 = _mm256_shuffle_epi32(lhs_mat_01_1, 245); //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) - const __m256i lhs_mat_23_1_sp2 = _mm256_shuffle_epi32(lhs_mat_23_1, 245); //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) - - const __m256i lhs_mat_01_2_sp2 = _mm256_shuffle_epi32(lhs_mat_01_2, 245); //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) - const __m256i lhs_mat_23_2_sp2 = _mm256_shuffle_epi32(lhs_mat_23_2, 245); //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) - - const __m256i lhs_mat_01_3_sp2 = _mm256_shuffle_epi32(lhs_mat_01_3, 245); //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) - const __m256i lhs_mat_23_3_sp2 = _mm256_shuffle_epi32(lhs_mat_23_3, 245); //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) - - // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane - // Resembles MMLAs into 2x2 matrices in ARM Version - __m256i iacc_mat_00_sp1 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp1, rhs_mat_0145_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp1, rhs_mat_0145_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp1, rhs_mat_0145_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp1, rhs_mat_0145_0_sp1)); - __m256i iacc_mat_01_sp1 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp1, rhs_mat_2367_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp1, rhs_mat_2367_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp1, rhs_mat_2367_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp1, rhs_mat_2367_0_sp1)); - __m256i iacc_mat_10_sp1 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp1, rhs_mat_0145_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp1, rhs_mat_0145_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp1, rhs_mat_0145_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp1, rhs_mat_0145_0_sp1)); - __m256i iacc_mat_11_sp1 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp1, rhs_mat_2367_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp1, rhs_mat_2367_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp1, rhs_mat_2367_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp1, rhs_mat_2367_0_sp1)); - __m256i iacc_mat_00_sp2 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp2, rhs_mat_0145_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp2, rhs_mat_0145_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp2, rhs_mat_0145_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp2, rhs_mat_0145_0_sp2)); - __m256i iacc_mat_01_sp2 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp2, rhs_mat_2367_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp2, rhs_mat_2367_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp2, rhs_mat_2367_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp2, rhs_mat_2367_0_sp2)); - __m256i iacc_mat_10_sp2 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp2, rhs_mat_0145_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp2, rhs_mat_0145_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp2, rhs_mat_0145_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp2, rhs_mat_0145_0_sp2)); - __m256i iacc_mat_11_sp2 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp2, rhs_mat_2367_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp2, rhs_mat_2367_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp2, rhs_mat_2367_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp2, rhs_mat_2367_0_sp2)); - - // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block - __m256i iacc_mat_00 = _mm256_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2); - __m256i iacc_mat_01 = _mm256_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2); - __m256i iacc_mat_10 = _mm256_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2); - __m256i iacc_mat_11 = _mm256_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2); - - // Straighten out to make 4 row vectors - __m256i iacc_row_0 = _mm256_blend_epi32(iacc_mat_00, _mm256_shuffle_epi32(iacc_mat_01, 78), 204); - __m256i iacc_row_1 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01, 204); - __m256i iacc_row_2 = _mm256_blend_epi32(iacc_mat_10, _mm256_shuffle_epi32(iacc_mat_11, 78), 204); - __m256i iacc_row_3 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11, 204); - - // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes - const __m256 row_scale_f32 = GGML_F32Cx8_REPEAT_LOAD(a_ptrs[rp][b].d, loadMask); - - // Multiply with appropiate scales and accumulate - acc_rows[rp * 4] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_0), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[rp * 4]); - acc_rows[rp * 4 + 1] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_1), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[rp * 4 + 1]); - acc_rows[rp * 4 + 2] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_2), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[rp * 4 + 2]); - acc_rows[rp * 4 + 3] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_3), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[rp * 4 + 3]); - } - } - - // Store the accumulated values - for (int i = 0; i < 16; i++) { - _mm256_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]); - } - } - } - - // Take a block_q8_0x4 structures at each pass of the loop and perform dot product operation - for (; y < nr / 4; y ++) { - - const block_q8_0x4 * a_ptr = a_ptr_start + (y * nb); - - // Load the eight block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7 - for (int64_t x = xstart; x < nc / 8; x++) { - - const block_q4_0x8 * b_ptr = b_ptr_start + (x * b_nb); - - // Master FP accumulators - __m256 acc_rows[4]; - for (int i = 0; i < 4; i++) { - acc_rows[i] = _mm256_setzero_ps(); - } - - for (int64_t b = 0; b < nb; b++) { - // Load the eight block_q8_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7 - const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs)); - const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 32)); - const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 64)); - const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 96)); - - // Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of valuess - const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240); - const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240); - const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240); - const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240); - - // 4-bit -> 8-bit - Sign is maintained - const __m256i rhs_mat_0145_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_0, m4b)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) - const __m256i rhs_mat_2367_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_0, m4b)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) - - const __m256i rhs_mat_0145_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_1, m4b)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) - const __m256i rhs_mat_2367_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_1, m4b)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) - - const __m256i rhs_mat_0145_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_0, 4), m4b)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) - const __m256i rhs_mat_2367_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_0, 4), m4b)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) - - const __m256i rhs_mat_0145_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_1, 4), m4b)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) - const __m256i rhs_mat_2367_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_1, 4), m4b)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) - - // Shuffle pattern one - right side input - const __m256i rhs_mat_0145_0_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) - const __m256i rhs_mat_2367_0_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) - - const __m256i rhs_mat_0145_1_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) - const __m256i rhs_mat_2367_1_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) - - const __m256i rhs_mat_0145_2_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) - const __m256i rhs_mat_2367_2_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) - - const __m256i rhs_mat_0145_3_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) - const __m256i rhs_mat_2367_3_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) - - // Shuffle pattern two - right side input - - const __m256i rhs_mat_0145_0_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) - const __m256i rhs_mat_2367_0_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) - - const __m256i rhs_mat_0145_1_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) - const __m256i rhs_mat_2367_1_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) - - const __m256i rhs_mat_0145_2_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) - const __m256i rhs_mat_2367_2_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) - - const __m256i rhs_mat_0145_3_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) - const __m256i rhs_mat_2367_3_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) - - // Scale values - Load the wight scale values of block_q4_0x8 - const __m256 col_scale_f32 = GGML_F32Cx8_LOAD(b_ptr[b].d); - - // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3 - // Loaded as set of 128 bit vectors and repeated into a 256 bit vector - __m256i lhs_mat_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs))); - __m256i lhs_mat_01_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 0); - __m256i lhs_mat_23_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 17); - __m256i lhs_mat_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 32))); - __m256i lhs_mat_01_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 0); - __m256i lhs_mat_23_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 17); - __m256i lhs_mat_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 64))); - __m256i lhs_mat_01_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 0); - __m256i lhs_mat_23_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 17); - __m256i lhs_mat_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 96))); - __m256i lhs_mat_01_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 0); - __m256i lhs_mat_23_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 17); - - // Shuffle pattern one - left side input - - const __m256i lhs_mat_01_0_sp1 = _mm256_shuffle_epi32(lhs_mat_01_0, 160); //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) - const __m256i lhs_mat_23_0_sp1 = _mm256_shuffle_epi32(lhs_mat_23_0, 160); //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) - - const __m256i lhs_mat_01_1_sp1 = _mm256_shuffle_epi32(lhs_mat_01_1, 160); //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) - const __m256i lhs_mat_23_1_sp1 = _mm256_shuffle_epi32(lhs_mat_23_1, 160); //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) - - const __m256i lhs_mat_01_2_sp1 = _mm256_shuffle_epi32(lhs_mat_01_2, 160); //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) - const __m256i lhs_mat_23_2_sp1 = _mm256_shuffle_epi32(lhs_mat_23_2, 160); //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) - - const __m256i lhs_mat_01_3_sp1 = _mm256_shuffle_epi32(lhs_mat_01_3, 160); //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) - const __m256i lhs_mat_23_3_sp1 = _mm256_shuffle_epi32(lhs_mat_23_3, 160); //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) - - // Shuffle pattern two - left side input - - const __m256i lhs_mat_01_0_sp2 = _mm256_shuffle_epi32(lhs_mat_01_0, 245); //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) - const __m256i lhs_mat_23_0_sp2 = _mm256_shuffle_epi32(lhs_mat_23_0, 245); //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) - - const __m256i lhs_mat_01_1_sp2 = _mm256_shuffle_epi32(lhs_mat_01_1, 245); //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) - const __m256i lhs_mat_23_1_sp2 = _mm256_shuffle_epi32(lhs_mat_23_1, 245); //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) - - const __m256i lhs_mat_01_2_sp2 = _mm256_shuffle_epi32(lhs_mat_01_2, 245); //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) - const __m256i lhs_mat_23_2_sp2 = _mm256_shuffle_epi32(lhs_mat_23_2, 245); //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) - - const __m256i lhs_mat_01_3_sp2 = _mm256_shuffle_epi32(lhs_mat_01_3, 245); //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) - const __m256i lhs_mat_23_3_sp2 = _mm256_shuffle_epi32(lhs_mat_23_3, 245); //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) - - // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane - // Resembles MMLAs into 2x2 matrices in ARM Version - __m256i iacc_mat_00_sp1 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp1, rhs_mat_0145_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp1, rhs_mat_0145_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp1, rhs_mat_0145_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp1, rhs_mat_0145_0_sp1)); - __m256i iacc_mat_01_sp1 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp1, rhs_mat_2367_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp1, rhs_mat_2367_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp1, rhs_mat_2367_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp1, rhs_mat_2367_0_sp1)); - __m256i iacc_mat_10_sp1 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp1, rhs_mat_0145_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp1, rhs_mat_0145_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp1, rhs_mat_0145_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp1, rhs_mat_0145_0_sp1)); - __m256i iacc_mat_11_sp1 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp1, rhs_mat_2367_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp1, rhs_mat_2367_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp1, rhs_mat_2367_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp1, rhs_mat_2367_0_sp1)); - __m256i iacc_mat_00_sp2 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp2, rhs_mat_0145_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp2, rhs_mat_0145_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp2, rhs_mat_0145_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp2, rhs_mat_0145_0_sp2)); - __m256i iacc_mat_01_sp2 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp2, rhs_mat_2367_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp2, rhs_mat_2367_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp2, rhs_mat_2367_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp2, rhs_mat_2367_0_sp2)); - __m256i iacc_mat_10_sp2 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp2, rhs_mat_0145_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp2, rhs_mat_0145_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp2, rhs_mat_0145_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp2, rhs_mat_0145_0_sp2)); - __m256i iacc_mat_11_sp2 = - _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp2, rhs_mat_2367_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp2, rhs_mat_2367_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp2, rhs_mat_2367_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp2, rhs_mat_2367_0_sp2)); - - // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block - __m256i iacc_mat_00 = _mm256_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2); - __m256i iacc_mat_01 = _mm256_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2); - __m256i iacc_mat_10 = _mm256_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2); - __m256i iacc_mat_11 = _mm256_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2); - - - // Straighten out to make 4 row vectors - __m256i iacc_row_0 = _mm256_blend_epi32(iacc_mat_00, _mm256_shuffle_epi32(iacc_mat_01, 78), 204); - __m256i iacc_row_1 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01, 204); - __m256i iacc_row_2 = _mm256_blend_epi32(iacc_mat_10, _mm256_shuffle_epi32(iacc_mat_11, 78), 204); - __m256i iacc_row_3 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11, 204); - - // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes - const __m256 row_scale_f32 = GGML_F32Cx8_REPEAT_LOAD(a_ptr[b].d, loadMask); - - // Multiply with appropiate scales and accumulate - acc_rows[0] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_0), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[0]); - acc_rows[1] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_1), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[1]); - acc_rows[2] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_2), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[2]); - acc_rows[3] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_3), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[3]); - } - - // Store the accumulated values - for (int i = 0; i < 4; i++) { - _mm256_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]); - } - } - } - return; - } -#elif defined(__riscv_v_intrinsic) - if (__riscv_vlenb() >= QK4_0) { - const size_t vl = QK4_0; - - for (int y = 0; y < nr / 4; y++) { - const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); - for (int x = 0; x < nc / ncols_interleaved; x++) { - const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb); - vfloat32m1_t sumf0 = __riscv_vfmv_v_f_f32m1(0.0, vl / 4); - vfloat32m1_t sumf1 = __riscv_vfmv_v_f_f32m1(0.0, vl / 4); - vfloat32m1_t sumf2 = __riscv_vfmv_v_f_f32m1(0.0, vl / 4); - vfloat32m1_t sumf3 = __riscv_vfmv_v_f_f32m1(0.0, vl / 4); - for (int l = 0; l < nb; l++) { - const vint8m4_t rhs_raw_vec = __riscv_vle8_v_i8m4((const int8_t *)b_ptr[l].qs, vl * 4); - const vint8m4_t rhs_vec_lo = __riscv_vsra_vx_i8m4(__riscv_vsll_vx_i8m4(rhs_raw_vec, 4, vl * 4), 4, vl * 4); - const vint8m4_t rhs_vec_hi = __riscv_vsra_vx_i8m4(rhs_raw_vec, 4, vl * 4); - const vint8m2_t rhs_vec_lo_0 = __riscv_vget_v_i8m4_i8m2(rhs_vec_lo, 0); - const vint8m2_t rhs_vec_lo_1 = __riscv_vget_v_i8m4_i8m2(rhs_vec_lo, 1); - const vint8m2_t rhs_vec_hi_0 = __riscv_vget_v_i8m4_i8m2(rhs_vec_hi, 0); - const vint8m2_t rhs_vec_hi_1 = __riscv_vget_v_i8m4_i8m2(rhs_vec_hi, 1); - - // vector version needs Zvfhmin extension - const float a_scales[4] = { - GGML_FP16_TO_FP32(a_ptr[l].d[0]), - GGML_FP16_TO_FP32(a_ptr[l].d[1]), - GGML_FP16_TO_FP32(a_ptr[l].d[2]), - GGML_FP16_TO_FP32(a_ptr[l].d[3]) - }; - const float b_scales[8] = { - GGML_FP16_TO_FP32(b_ptr[l].d[0]), - GGML_FP16_TO_FP32(b_ptr[l].d[1]), - GGML_FP16_TO_FP32(b_ptr[l].d[2]), - GGML_FP16_TO_FP32(b_ptr[l].d[3]), - GGML_FP16_TO_FP32(b_ptr[l].d[4]), - GGML_FP16_TO_FP32(b_ptr[l].d[5]), - GGML_FP16_TO_FP32(b_ptr[l].d[6]), - GGML_FP16_TO_FP32(b_ptr[l].d[7]) - }; - const vfloat32m1_t b_scales_vec = __riscv_vle32_v_f32m1(b_scales, vl / 4); - - const int64_t A0 = *(const int64_t *)&a_ptr[l].qs[0]; - const int64_t A4 = *(const int64_t *)&a_ptr[l].qs[32]; - const int64_t A8 = *(const int64_t *)&a_ptr[l].qs[64]; - const int64_t Ac = *(const int64_t *)&a_ptr[l].qs[96]; - __asm__ __volatile__("" ::: "memory"); // prevent gcc from emitting fused vlse64, violating alignment - vint16m4_t sumi_l0; - { - const vint8m2_t lhs_0_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A0, vl / 4)); - const vint8m2_t lhs_1_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A4, vl / 4)); - const vint8m2_t lhs_2_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A8, vl / 4)); - const vint8m2_t lhs_3_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Ac, vl / 4)); - const vint16m4_t sumi_lo_0 = __riscv_vwmul_vv_i16m4(rhs_vec_lo_0, lhs_0_8, vl * 2); - const vint16m4_t sumi_lo_1 = __riscv_vwmacc_vv_i16m4(sumi_lo_0, rhs_vec_lo_1, lhs_1_8, vl * 2); - const vint16m4_t sumi_hi_0 = __riscv_vwmacc_vv_i16m4(sumi_lo_1, rhs_vec_hi_0, lhs_2_8, vl * 2); - const vint16m4_t sumi_hi_m = __riscv_vwmacc_vv_i16m4(sumi_hi_0, rhs_vec_hi_1, lhs_3_8, vl * 2); - - sumi_l0 = sumi_hi_m; - } - - { - const vuint32m4_t sumi_i32 = __riscv_vreinterpret_v_i32m4_u32m4(__riscv_vreinterpret_v_i16m4_i32m4(sumi_l0)); - const vuint16m2_t sumi_h2_0 = __riscv_vnsrl_wx_u16m2(sumi_i32, 0, vl); - const vuint16m2_t sumi_h2_1 = __riscv_vnsrl_wx_u16m2(sumi_i32, 16, vl); - const vuint16m2_t sumi_h2 = __riscv_vadd_vv_u16m2(sumi_h2_0, sumi_h2_1, vl); - const vuint32m2_t sumi_h2_i32 = __riscv_vreinterpret_v_u16m2_u32m2(sumi_h2); - const vuint16m1_t sumi_h4_0 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 0, vl / 2); - const vuint16m1_t sumi_h4_1 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 16, vl / 2); - const vuint16m1_t sumi_h4 = __riscv_vadd_vv_u16m1(sumi_h4_0, sumi_h4_1, vl / 2); - const vuint32m1_t sumi_h4_i32 = __riscv_vreinterpret_v_u16m1_u32m1(sumi_h4); - const vint16mf2_t sumi_h8_0 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 0, vl / 4)); - const vint16mf2_t sumi_h8_1 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 16, vl / 4)); - const vint32m1_t sumi_h8 = __riscv_vwadd_vv_i32m1(sumi_h8_0, sumi_h8_1, vl / 4); - const vfloat32m1_t facc = __riscv_vfcvt_f_x_v_f32m1(sumi_h8, vl / 4); - - const vfloat32m1_t tmp1 = __riscv_vfmul_vf_f32m1(facc, a_scales[0], vl / 4); - sumf0 = __riscv_vfmacc_vv_f32m1(sumf0, tmp1, b_scales_vec, vl / 4); - } - - const int64_t A1 = *(const int64_t *)&a_ptr[l].qs[8]; - const int64_t A5 = *(const int64_t *)&a_ptr[l].qs[40]; - const int64_t A9 = *(const int64_t *)&a_ptr[l].qs[72]; - const int64_t Ad = *(const int64_t *)&a_ptr[l].qs[104]; - __asm__ __volatile__("" ::: "memory"); // prevent gcc from emitting fused vlse64, violating alignment - vint16m4_t sumi_l1; - { - const vint8m2_t lhs_0_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A1, vl / 4)); - const vint8m2_t lhs_1_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A5, vl / 4)); - const vint8m2_t lhs_2_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A9, vl / 4)); - const vint8m2_t lhs_3_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Ad, vl / 4)); - const vint16m4_t sumi_lo_0 = __riscv_vwmul_vv_i16m4(rhs_vec_lo_0, lhs_0_8, vl * 2); - const vint16m4_t sumi_lo_1 = __riscv_vwmacc_vv_i16m4(sumi_lo_0, rhs_vec_lo_1, lhs_1_8, vl * 2); - const vint16m4_t sumi_hi_0 = __riscv_vwmacc_vv_i16m4(sumi_lo_1, rhs_vec_hi_0, lhs_2_8, vl * 2); - const vint16m4_t sumi_hi_m = __riscv_vwmacc_vv_i16m4(sumi_hi_0, rhs_vec_hi_1, lhs_3_8, vl * 2); - - sumi_l1 = sumi_hi_m; - } - - { - const vuint32m4_t sumi_i32 = __riscv_vreinterpret_v_i32m4_u32m4(__riscv_vreinterpret_v_i16m4_i32m4(sumi_l1)); - const vuint16m2_t sumi_h2_0 = __riscv_vnsrl_wx_u16m2(sumi_i32, 0, vl); - const vuint16m2_t sumi_h2_1 = __riscv_vnsrl_wx_u16m2(sumi_i32, 16, vl); - const vuint16m2_t sumi_h2 = __riscv_vadd_vv_u16m2(sumi_h2_0, sumi_h2_1, vl); - const vuint32m2_t sumi_h2_i32 = __riscv_vreinterpret_v_u16m2_u32m2(sumi_h2); - const vuint16m1_t sumi_h4_0 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 0, vl / 2); - const vuint16m1_t sumi_h4_1 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 16, vl / 2); - const vuint16m1_t sumi_h4 = __riscv_vadd_vv_u16m1(sumi_h4_0, sumi_h4_1, vl / 2); - const vuint32m1_t sumi_h4_i32 = __riscv_vreinterpret_v_u16m1_u32m1(sumi_h4); - const vint16mf2_t sumi_h8_0 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 0, vl / 4)); - const vint16mf2_t sumi_h8_1 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 16, vl / 4)); - const vint32m1_t sumi_h8 = __riscv_vwadd_vv_i32m1(sumi_h8_0, sumi_h8_1, vl / 4); - const vfloat32m1_t facc = __riscv_vfcvt_f_x_v_f32m1(sumi_h8, vl / 4); - - const vfloat32m1_t tmp1 = __riscv_vfmul_vf_f32m1(facc, a_scales[1], vl / 4); - sumf1 = __riscv_vfmacc_vv_f32m1(sumf1, tmp1, b_scales_vec, vl / 4); - } - - const int64_t A2 = *(const int64_t *)&a_ptr[l].qs[16]; - const int64_t A6 = *(const int64_t *)&a_ptr[l].qs[48]; - const int64_t Aa = *(const int64_t *)&a_ptr[l].qs[80]; - const int64_t Ae = *(const int64_t *)&a_ptr[l].qs[112]; - __asm__ __volatile__("" ::: "memory"); // prevent gcc from emitting fused vlse64, violating alignment - vint16m4_t sumi_l2; - { - const vint8m2_t lhs_0_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A2, vl / 4)); - const vint8m2_t lhs_1_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A6, vl / 4)); - const vint8m2_t lhs_2_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Aa, vl / 4)); - const vint8m2_t lhs_3_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Ae, vl / 4)); - const vint16m4_t sumi_lo_0 = __riscv_vwmul_vv_i16m4(rhs_vec_lo_0, lhs_0_8, vl * 2); - const vint16m4_t sumi_lo_1 = __riscv_vwmacc_vv_i16m4(sumi_lo_0, rhs_vec_lo_1, lhs_1_8, vl * 2); - const vint16m4_t sumi_hi_0 = __riscv_vwmacc_vv_i16m4(sumi_lo_1, rhs_vec_hi_0, lhs_2_8, vl * 2); - const vint16m4_t sumi_hi_m = __riscv_vwmacc_vv_i16m4(sumi_hi_0, rhs_vec_hi_1, lhs_3_8, vl * 2); - - sumi_l2 = sumi_hi_m; - } - - { - const vuint32m4_t sumi_i32 = __riscv_vreinterpret_v_i32m4_u32m4(__riscv_vreinterpret_v_i16m4_i32m4(sumi_l2)); - const vuint16m2_t sumi_h2_0 = __riscv_vnsrl_wx_u16m2(sumi_i32, 0, vl); - const vuint16m2_t sumi_h2_1 = __riscv_vnsrl_wx_u16m2(sumi_i32, 16, vl); - const vuint16m2_t sumi_h2 = __riscv_vadd_vv_u16m2(sumi_h2_0, sumi_h2_1, vl); - const vuint32m2_t sumi_h2_i32 = __riscv_vreinterpret_v_u16m2_u32m2(sumi_h2); - const vuint16m1_t sumi_h4_0 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 0, vl / 2); - const vuint16m1_t sumi_h4_1 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 16, vl / 2); - const vuint16m1_t sumi_h4 = __riscv_vadd_vv_u16m1(sumi_h4_0, sumi_h4_1, vl / 2); - const vuint32m1_t sumi_h4_i32 = __riscv_vreinterpret_v_u16m1_u32m1(sumi_h4); - const vint16mf2_t sumi_h8_0 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 0, vl / 4)); - const vint16mf2_t sumi_h8_1 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 16, vl / 4)); - const vint32m1_t sumi_h8 = __riscv_vwadd_vv_i32m1(sumi_h8_0, sumi_h8_1, vl / 4); - const vfloat32m1_t facc = __riscv_vfcvt_f_x_v_f32m1(sumi_h8, vl / 4); - - const vfloat32m1_t tmp1 = __riscv_vfmul_vf_f32m1(facc, a_scales[2], vl / 4); - sumf2 = __riscv_vfmacc_vv_f32m1(sumf2, tmp1, b_scales_vec, vl / 4); - } - - const int64_t A3 = *(const int64_t *)&a_ptr[l].qs[24]; - const int64_t A7 = *(const int64_t *)&a_ptr[l].qs[56]; - const int64_t Ab = *(const int64_t *)&a_ptr[l].qs[88]; - const int64_t Af = *(const int64_t *)&a_ptr[l].qs[120]; - __asm__ __volatile__("" ::: "memory"); // prevent gcc from emitting fused vlse64, violating alignment - vint16m4_t sumi_l3; - { - const vint8m2_t lhs_0_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A3, vl / 4)); - const vint8m2_t lhs_1_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A7, vl / 4)); - const vint8m2_t lhs_2_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Ab, vl / 4)); - const vint8m2_t lhs_3_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Af, vl / 4)); - const vint16m4_t sumi_lo_0 = __riscv_vwmul_vv_i16m4(rhs_vec_lo_0, lhs_0_8, vl * 2); - const vint16m4_t sumi_lo_1 = __riscv_vwmacc_vv_i16m4(sumi_lo_0, rhs_vec_lo_1, lhs_1_8, vl * 2); - const vint16m4_t sumi_hi_0 = __riscv_vwmacc_vv_i16m4(sumi_lo_1, rhs_vec_hi_0, lhs_2_8, vl * 2); - const vint16m4_t sumi_hi_m = __riscv_vwmacc_vv_i16m4(sumi_hi_0, rhs_vec_hi_1, lhs_3_8, vl * 2); - - sumi_l3 = sumi_hi_m; - } - - { - const vuint32m4_t sumi_i32 = __riscv_vreinterpret_v_i32m4_u32m4(__riscv_vreinterpret_v_i16m4_i32m4(sumi_l3)); - const vuint16m2_t sumi_h2_0 = __riscv_vnsrl_wx_u16m2(sumi_i32, 0, vl); - const vuint16m2_t sumi_h2_1 = __riscv_vnsrl_wx_u16m2(sumi_i32, 16, vl); - const vuint16m2_t sumi_h2 = __riscv_vadd_vv_u16m2(sumi_h2_0, sumi_h2_1, vl); - const vuint32m2_t sumi_h2_i32 = __riscv_vreinterpret_v_u16m2_u32m2(sumi_h2); - const vuint16m1_t sumi_h4_0 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 0, vl / 2); - const vuint16m1_t sumi_h4_1 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 16, vl / 2); - const vuint16m1_t sumi_h4 = __riscv_vadd_vv_u16m1(sumi_h4_0, sumi_h4_1, vl / 2); - const vuint32m1_t sumi_h4_i32 = __riscv_vreinterpret_v_u16m1_u32m1(sumi_h4); - const vint16mf2_t sumi_h8_0 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 0, vl / 4)); - const vint16mf2_t sumi_h8_1 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 16, vl / 4)); - const vint32m1_t sumi_h8 = __riscv_vwadd_vv_i32m1(sumi_h8_0, sumi_h8_1, vl / 4); - const vfloat32m1_t facc = __riscv_vfcvt_f_x_v_f32m1(sumi_h8, vl / 4); - - const vfloat32m1_t tmp1 = __riscv_vfmul_vf_f32m1(facc, a_scales[3], vl / 4); - sumf3 = __riscv_vfmacc_vv_f32m1(sumf3, tmp1, b_scales_vec, vl / 4); - } - } - __riscv_vse32_v_f32m1(&s[(y * 4 + 0) * bs + x * ncols_interleaved], sumf0, vl / 4); - __riscv_vse32_v_f32m1(&s[(y * 4 + 1) * bs + x * ncols_interleaved], sumf1, vl / 4); - __riscv_vse32_v_f32m1(&s[(y * 4 + 2) * bs + x * ncols_interleaved], sumf2, vl / 4); - __riscv_vse32_v_f32m1(&s[(y * 4 + 3) * bs + x * ncols_interleaved], sumf3, vl / 4); - } - } - - return; - } -#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) - float sumf[4][8]; - int sumi; - - for (int y = 0; y < nr / 4; y++) { - const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); - for (int x = 0; x < nc / ncols_interleaved; x++) { - const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb); - for (int m = 0; m < 4; m++) { - for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0; - } - for (int l = 0; l < nb; l++) { - for (int k = 0; k < (qk / (2 * blocklen)); k++) { - for (int m = 0; m < 4; m++) { - for (int j = 0; j < ncols_interleaved; j++) { - sumi = 0; - for (int i = 0; i < blocklen; ++i) { - const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); - const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); - sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) + - (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4; - } - sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d[m]); - } - } - } - } - for (int m = 0; m < 4; m++) { - for (int j = 0; j < ncols_interleaved; j++) - s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j]; - } - } - } -} diff --git a/ggml/src/ggml-aarch64.h b/ggml/src/ggml-aarch64.h index 517babaf1..a57868591 100644 --- a/ggml/src/ggml-aarch64.h +++ b/ggml/src/ggml-aarch64.h @@ -1,9 +1,5 @@ -// SPDX-FileCopyrightText: Copyright 2024 Arm Ltd. #pragma once -#define GGML_COMMON_DECL_C -#include "ggml-common.h" - #include "ggml.h" // GGML internal header @@ -12,27 +8,11 @@ extern "C" { #endif -// Quantization -void quantize_q8_0_4x4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_q8_0_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); - -void quantize_mat_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nrows, int64_t n_per_row, int64_t blck_size_interleave); - // Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization") size_t quantize_q4_0_4x4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); size_t quantize_q4_0_4x8(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); size_t quantize_q4_0_8x8(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -// GEMV -void ggml_gemv_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); -void ggml_gemv_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); -void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); - -// GEMM -void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); -void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); -void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); - #ifdef __cplusplus } #endif diff --git a/ggml/src/ggml-alloc.c b/ggml/src/ggml-alloc.c index 041de9e3e..2b2240be8 100644 --- a/ggml/src/ggml-alloc.c +++ b/ggml/src/ggml-alloc.c @@ -466,18 +466,12 @@ static bool ggml_gallocr_is_own(ggml_gallocr_t galloc, struct ggml_tensor * t) { return ggml_gallocr_hash_get(galloc, t)->allocated; } -static void ggml_gallocr_set_node_offset(ggml_gallocr_t galloc, struct ggml_tensor * node, int buffer_id, size_t offset) { - struct hash_node * hn = ggml_gallocr_hash_get(galloc, node); - hn->buffer_id = buffer_id; - hn->offset = offset; - hn->allocated = true; -} - static bool ggml_gallocr_is_allocated(ggml_gallocr_t galloc, struct ggml_tensor * t) { return t->data != NULL || ggml_gallocr_hash_get(galloc, t)->allocated; } static void ggml_gallocr_allocate_node(ggml_gallocr_t galloc, struct ggml_tensor * node, int buffer_id) { + GGML_ASSERT(buffer_id >= 0); struct hash_node * hn = ggml_gallocr_hash_get(galloc, node); if (!ggml_gallocr_is_allocated(galloc, node) && !ggml_is_view(node)) { @@ -816,7 +810,11 @@ static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * } static bool ggml_gallocr_node_needs_realloc(ggml_gallocr_t galloc, struct ggml_tensor * node, struct tensor_alloc * talloc) { - size_t node_size = (node->data || node->view_src) ? 0 : ggml_backend_buft_get_alloc_size(galloc->bufts[talloc->buffer_id], node); + size_t node_size = 0; + if (!node->data && !node->view_src) { + GGML_ASSERT(talloc->buffer_id >= 0); // prevent segfault when misusing the API + node_size = ggml_backend_buft_get_alloc_size(galloc->bufts[talloc->buffer_id], node); + } return talloc->size_max >= node_size; } diff --git a/ggml/src/ggml-amx.cpp b/ggml/src/ggml-amx.cpp deleted file mode 100644 index 144dc9d8a..000000000 --- a/ggml/src/ggml-amx.cpp +++ /dev/null @@ -1,436 +0,0 @@ -#include "ggml-amx.h" -#include "ggml-amx/common.h" -#include "ggml-amx/mmq.h" -#include "ggml-backend-impl.h" -#include "ggml-impl.h" - -#if defined(__gnu_linux__) -#include -#include -#endif - -#include -#include -#include - -#if defined(__AMX_INT8__) - -// AMX buffer interface -static void ggml_backend_amx_buffer_free_buffer(ggml_backend_buffer_t buffer) { - free(buffer->context); -} - -static void * ggml_backend_amx_buffer_get_base(ggml_backend_buffer_t buffer) { - return (void *)(buffer->context); -} - -static void ggml_backend_amx_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) { - memset((char *)tensor->data + offset, value, size); - - GGML_UNUSED(buffer); -} - -static void ggml_backend_amx_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - if (qtype_has_amx_kernels(tensor->type)) { - ggml_backend_amx_convert_weight(tensor, data, offset, size); - } else { - memcpy((char *)tensor->data + offset, data, size); - } - - GGML_UNUSED(buffer); -} - -static void ggml_backend_amx_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { - GGML_ASSERT(!qtype_has_amx_kernels(tensor->type)); - memcpy(data, (const char *)tensor->data + offset, size); - - GGML_UNUSED(buffer); -} - -static bool ggml_backend_amx_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { - if (ggml_backend_buffer_is_host(src->buffer)) { - if (qtype_has_amx_kernels(src->type)) { - ggml_backend_amx_convert_weight(dst, src->data, 0, ggml_backend_amx_get_alloc_size(dst)); - } else { - memcpy(dst->data, src->data, ggml_nbytes(src)); - } - return true; - } - return false; - - GGML_UNUSED(buffer); -} - -static void ggml_backend_amx_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { - memset(buffer->context, value, buffer->size); -} - -static ggml_backend_buffer_i ggml_backend_amx_buffer_interface = { - /* .free_buffer = */ ggml_backend_amx_buffer_free_buffer, - /* .get_base = */ ggml_backend_amx_buffer_get_base, - /* .init_tensor = */ NULL, // no initialization required - /* .memset_tensor = */ ggml_backend_amx_buffer_memset_tensor, - /* .set_tensor = */ ggml_backend_amx_buffer_set_tensor, - /* .get_tensor = */ ggml_backend_amx_buffer_get_tensor, - /* .cpy_tensor = */ ggml_backend_amx_buffer_cpy_tensor, - /* .clear = */ ggml_backend_amx_buffer_clear, - /* .reset = */ NULL, -}; - -static const char * ggml_backend_amx_buffer_type_get_name(ggml_backend_buffer_type_t buft) { - return "AMX"; - - GGML_UNUSED(buft); -} - -static ggml_backend_buffer_t ggml_backend_amx_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - void * data = aligned_alloc(TENSOR_ALIGNMENT, size); - if (data == NULL) { - fprintf(stderr, "%s: failed to allocate buffer of size %zu\n", __func__, size); - return NULL; - } - - return ggml_backend_buffer_init(buft, ggml_backend_amx_buffer_interface, data, size); -} - -static size_t ggml_backend_amx_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return TENSOR_ALIGNMENT; - - GGML_UNUSED(buft); -} - -static size_t ggml_backend_amx_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor* tensor) { - return ggml_backend_amx_get_alloc_size(tensor); - - GGML_UNUSED(buft); -} - -static bool ggml_backend_amx_buffer_type_is_host(ggml_backend_buffer_type_t buft) { - return false; - - GGML_UNUSED(buft); -} - -ggml_backend_buffer_type_t ggml_backend_amx_buffer_type() { - static struct ggml_backend_buffer_type ggml_backend_buffer_type_amx = { - /* .iface = */ { - /* .get_name = */ ggml_backend_amx_buffer_type_get_name, - /* .alloc_buffer = */ ggml_backend_amx_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_amx_buffer_type_get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX - /* .get_alloc_size = */ ggml_backend_amx_buffer_type_get_alloc_size, - /* .is_host = */ ggml_backend_amx_buffer_type_is_host, - }, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_amx_reg(), 0), - /* .context = */ NULL, - }; - - return &ggml_backend_buffer_type_amx; -} - -// backend interface - -static const char * ggml_backend_amx_name(ggml_backend_t backend) { - return "AMX"; - - GGML_UNUSED(backend); -} - -static void ggml_backend_amx_free(ggml_backend_t backend) { - ggml_backend_amx_context * ctx = (ggml_backend_amx_context *)backend->context; - delete ctx; - delete backend; -} - -static enum ggml_status ggml_backend_amx_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { - ggml_backend_amx_context * ctx = (ggml_backend_amx_context *)backend->context; - - for (int i = 0; i < cgraph->n_nodes; i++) { - struct ggml_tensor * node = cgraph->nodes[i]; - - switch (node->op) { - case GGML_OP_MUL_MAT: - ggml_backend_amx_mul_mat(ctx, node); - break; - - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - break; - - default: - fprintf(stderr, "%s: unsupported op %s\n", __func__, ggml_op_desc(node)); - GGML_ASSERT(false); - } - } - - return GGML_STATUS_SUCCESS; - - GGML_UNUSED(backend); -} - -static struct ggml_backend_i ggml_backend_amx_i = { - /* .get_name = */ ggml_backend_amx_name, - /* .free = */ ggml_backend_amx_free, - /* .set_tensor_async = */ NULL, - /* .get_tensor_async = */ NULL, - /* .cpy_tensor_async = */ NULL, - /* .synchronize = */ NULL, - /* .graph_plan_create = */ NULL, - /* .graph_plan_free = */ NULL, - /* .graph_plan_update = */ NULL, - /* .graph_plan_compute = */ NULL, - /* .graph_compute = */ ggml_backend_amx_graph_compute, - /* .event_record = */ NULL, - /* .event_wait = */ NULL, -}; - -static ggml_guid_t ggml_backend_amx_guid() { - static ggml_guid guid = { 0x13, 0xb8, 0xa4, 0xc4, 0xba, 0xfe, 0x51, 0x67, 0x87, 0x44, 0x55, 0x15, 0xb2, 0x35, 0x62, 0x3e }; - return &guid; -} - -#define ARCH_GET_XCOMP_PERM 0x1022 -#define ARCH_REQ_XCOMP_PERM 0x1023 -#define XFEATURE_XTILECFG 17 -#define XFEATURE_XTILEDATA 18 - -static bool ggml_amx_init() { -#if defined(__gnu_linux__) - if (syscall(SYS_arch_prctl, ARCH_REQ_XCOMP_PERM, XFEATURE_XTILEDATA)) { - fprintf(stderr, "AMX is not ready to be used!\n"); - return false; - } - return true; -#elif defined(_WIN32) - return true; -#endif -} - -ggml_backend_t ggml_backend_amx_init() { - - // invoke a Linux system call to request access to AMX features - ggml_amx_init(); - - // backend context - ggml_backend_amx_context * ctx = new ggml_backend_amx_context; - - // ggml amx backend - ggml_backend_t backend = new ggml_backend { - /* .guid = */ ggml_backend_amx_guid(), - /* .interface = */ ggml_backend_amx_i, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_amx_reg(), 0), - /* .context = */ ctx, - }; - - return backend; -} - -bool ggml_backend_is_amx(ggml_backend_t backend) { - return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_amx_guid()); -} - -void ggml_backend_amx_set_n_threads(ggml_backend_t backend_amx, int n_threads) { - GGML_ASSERT(ggml_backend_is_amx(backend_amx)); - - ggml_backend_amx_context * ctx = (ggml_backend_amx_context *)backend_amx->context; - ctx->n_threads = n_threads; -} - -// device interface - -static const char * ggml_backend_amx_device_get_name(ggml_backend_dev_t dev) { - return "AMX"; - - GGML_UNUSED(dev); -} - -static const char * ggml_backend_amx_device_get_description(ggml_backend_dev_t dev) { - return "Intel Advanced Matrix Extensions"; - - GGML_UNUSED(dev); -} - -static void ggml_backend_amx_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { - // TODO - *free = 0; - *total = 0; - - GGML_UNUSED(dev); -} - -static enum ggml_backend_dev_type ggml_backend_amx_device_get_type(ggml_backend_dev_t dev) { - return GGML_BACKEND_DEVICE_TYPE_ACCEL; - - GGML_UNUSED(dev); -} - -static void ggml_backend_amx_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) { - props->name = ggml_backend_amx_device_get_name(dev); - props->description = ggml_backend_amx_device_get_description(dev); - props->type = ggml_backend_amx_device_get_type(dev); - ggml_backend_amx_device_get_memory(dev, &props->memory_free, &props->memory_total); - - // `buffer_from_host_ptr` is intended to be used in mmap, when memory layout unchanged - props->caps = { - /* .async = */ false, - /* .host_buffer = */ false, - /* .buffer_from_host_ptr = */ false, - /* .events = */ false, - }; -} - -static ggml_backend_t ggml_backend_amx_device_init(ggml_backend_dev_t dev, const char * params) { - return ggml_backend_amx_init(); - - GGML_UNUSED(dev); - GGML_UNUSED(params); -} - -static ggml_backend_buffer_type_t ggml_backend_amx_device_get_buffer_type(ggml_backend_dev_t dev) { - return ggml_backend_amx_buffer_type(); - - GGML_UNUSED(dev); -} - -static bool ggml_backend_amx_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { - - // handle only 2d gemm for now - auto is_contiguous_2d = [](const struct ggml_tensor * t) { - return ggml_is_contiguous(t) && t->ne[3] == 1 && t->ne[2] == 1; - }; - - switch (op->op) { - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - return true; - - case GGML_OP_MUL_MAT: { - const struct ggml_tensor * src0 = op->src[0]; - const struct ggml_tensor * src1 = op->src[1]; - - const enum ggml_type type = src0->type; - const int64_t ne0 = op->ne[0]; - - bool is_training = src0->grad || src1->grad; - - // amx kernels enables for Q4_0, Q4_1, Q8_0, F16 - // Q4_K, Q5_K, Q6_K, IQ4_XS enabled for QK_K = 256 - bool has_amx_kernels = qtype_has_amx_kernels(type) || (type == GGML_TYPE_F16); - - bool can_use_amx = - is_contiguous_2d(src0) && // src0 must be contiguous - is_contiguous_2d(src1) && // src1 must be contiguous - !is_training && // inference only - src1->type == GGML_TYPE_F32 && // src1 must be float32 - has_amx_kernels && // with amx kernel impls - ne0 % (TILE_N * 2) == 0; // out_features is 32x - - return can_use_amx; - } - default: - return false; - } - - GGML_UNUSED(dev); -} - -static bool ggml_backend_amx_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { - return buft->iface.get_name == ggml_backend_amx_buffer_type_get_name; - - GGML_UNUSED(dev); -} - -static const struct ggml_backend_device_i ggml_backend_amx_device_i = { - /* .get_name = */ ggml_backend_amx_device_get_name, - /* .get_description = */ ggml_backend_amx_device_get_description, - /* .get_memory = */ ggml_backend_amx_device_get_memory, - /* .get_type = */ ggml_backend_amx_device_get_type, - /* .get_props = */ ggml_backend_amx_device_get_props, - /* .init_backend = */ ggml_backend_amx_device_init, - /* .get_buffer_type = */ ggml_backend_amx_device_get_buffer_type, - /* .get_host_buffer_type = */ NULL, - /* .buffer_from_host_ptr = */ NULL, - /* .supports_op = */ ggml_backend_amx_device_supports_op, - /* .supports_buft = */ ggml_backend_amx_device_supports_buft, - /* .offload_op = */ NULL, - /* .event_new = */ NULL, - /* .event_free = */ NULL, - /* .event_synchronize = */ NULL, -}; - -// backend reg interface - -static const char * ggml_backend_amx_reg_get_name(ggml_backend_reg_t reg) { - return "AMX"; - - GGML_UNUSED(reg); -} - -static size_t ggml_backend_amx_reg_get_device_count(ggml_backend_reg_t reg) { - return 1; - - GGML_UNUSED(reg); -} - -static ggml_backend_dev_t ggml_backend_amx_reg_get_device(ggml_backend_reg_t reg, size_t index) { - GGML_ASSERT(index == 0); - - static ggml_backend_device ggml_backend_amx_device = { - /* .iface = */ ggml_backend_amx_device_i, - /* .reg = */ reg, - /* .context = */ nullptr, - }; - - return &ggml_backend_amx_device; - - GGML_UNUSED(reg); - GGML_UNUSED(index); -} - -static void * ggml_backend_amx_get_proc_address(ggml_backend_reg_t reg, const char * name) { - if (std::strcmp(name, "ggml_backend_set_n_threads") == 0) { - return (void *)ggml_backend_amx_set_n_threads; - } - return NULL; - - GGML_UNUSED(reg); - GGML_UNUSED(name); -} - -static const struct ggml_backend_reg_i ggml_backend_amx_reg_i = { - /* .get_name = */ ggml_backend_amx_reg_get_name, - /* .get_device_count = */ ggml_backend_amx_reg_get_device_count, - /* .get_device = */ ggml_backend_amx_reg_get_device, - /* .get_proc_address = */ ggml_backend_amx_get_proc_address, -}; - -ggml_backend_reg_t ggml_backend_amx_reg(void) { - static struct ggml_backend_reg ggml_backend_amx_reg = { - /* .iface = */ ggml_backend_amx_reg_i, - /* .context = */ NULL, - }; - - return &ggml_backend_amx_reg; -} - -#else // if defined(__AMX_INT8__) - -ggml_backend_t ggml_backend_amx_init(void) { - fprintf(stderr, "GGML is not compiled with AMX support!\n"); - return ggml_backend_t{}; -} - -void ggml_backend_amx_set_n_threads(ggml_backend_t backend_amx, int n_threads) { - fprintf(stderr, "GGML is not compiled with AMX support!\n"); - - GGML_UNUSED(backend_amx); - GGML_UNUSED(n_threads); -} - -#endif diff --git a/ggml/src/ggml-backend-impl.h b/ggml/src/ggml-backend-impl.h index fa8d5b7fb..36d72e95f 100644 --- a/ggml/src/ggml-backend-impl.h +++ b/ggml/src/ggml-backend-impl.h @@ -8,6 +8,8 @@ extern "C" { #endif + #define GGML_BACKEND_API_VERSION 1 + // // Backend buffer type // @@ -63,20 +65,20 @@ extern "C" { enum ggml_backend_buffer_usage usage; }; - ggml_backend_buffer_t ggml_backend_buffer_init( + GGML_API ggml_backend_buffer_t ggml_backend_buffer_init( ggml_backend_buffer_type_t buft, struct ggml_backend_buffer_i iface, void * context, size_t size); // do not use directly, use ggml_backend_tensor_copy instead - bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml_tensor * dst); + GGML_API bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml_tensor * dst); // multi-buffer // buffer that contains a collection of buffers - ggml_backend_buffer_t ggml_backend_multi_buffer_alloc_buffer(ggml_backend_buffer_t * buffers, size_t n_buffers); - bool ggml_backend_buffer_is_multi_buffer(ggml_backend_buffer_t buffer); - void ggml_backend_multi_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage); + GGML_API ggml_backend_buffer_t ggml_backend_multi_buffer_alloc_buffer(ggml_backend_buffer_t * buffers, size_t n_buffers); + GGML_API bool ggml_backend_buffer_is_multi_buffer(ggml_backend_buffer_t buffer); + GGML_API void ggml_backend_multi_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage); // // Backend (stream) @@ -199,17 +201,55 @@ extern "C" { }; struct ggml_backend_reg { - // int api_version; // TODO: for dynamic loading + int api_version; // initialize to GGML_BACKEND_API_VERSION struct ggml_backend_reg_i iface; void * context; }; - // Internal backend registry API - void ggml_backend_register(ggml_backend_reg_t reg); - void ggml_backend_device_register(ggml_backend_dev_t device); - // TODO: backends can be loaded as a dynamic library, in which case it needs to export this function - // typedef ggml_backend_register_t * (*ggml_backend_init)(void); + GGML_API void ggml_backend_register(ggml_backend_reg_t reg); + GGML_API void ggml_backend_device_register(ggml_backend_dev_t device); + + // Add backend dynamic loading support to the backend + + // Initialize the backend + typedef ggml_backend_reg_t (*ggml_backend_init_t)(void); + // Optional: obtain a score for the backend based on the system configuration + // Higher scores are preferred, 0 means the backend is not supported in the current system + typedef int (*ggml_backend_score_t)(void); + +#ifdef GGML_BACKEND_DL +# ifdef __cplusplus +# define GGML_BACKEND_DL_IMPL(reg_fn) \ + extern "C" { \ + GGML_BACKEND_API ggml_backend_reg_t ggml_backend_init(void); \ + } \ + ggml_backend_reg_t ggml_backend_init(void) { \ + return reg_fn(); \ + } +# define GGML_BACKEND_DL_SCORE_IMPL(score_fn) \ + extern "C" { \ + GGML_BACKEND_API int ggml_backend_score(void); \ + } \ + int ggml_backend_score(void) { \ + return score_fn(); \ + } +# else +# define GGML_BACKEND_DL_IMPL(reg_fn) \ + GGML_BACKEND_API ggml_backend_reg_t ggml_backend_init(void); \ + ggml_backend_reg_t ggml_backend_init(void) { \ + return reg_fn(); \ + } +# define GGML_BACKEND_DL_SCORE_IMPL(score_fn) \ + GGML_BACKEND_API int ggml_backend_score(void); \ + int ggml_backend_score(void) { \ + return score_fn(); \ + } +# endif +#else +# define GGML_BACKEND_DL_IMPL(reg_fn) +# define GGML_BACKEND_DL_SCORE_IMPL(score_fn) +#endif #ifdef __cplusplus } diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp new file mode 100644 index 000000000..5cb0fb9d1 --- /dev/null +++ b/ggml/src/ggml-backend-reg.cpp @@ -0,0 +1,529 @@ +#include "ggml-backend-impl.h" +#include "ggml-backend.h" +#include "ggml-impl.h" +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#ifdef _WIN32 +# define WIN32_LEAN_AND_MEAN +# ifndef NOMINMAX +# define NOMINMAX +# endif +# include +#elif defined(__APPLE__) +# include +# include +#else +# include +# include +#endif + +// Backend registry +#ifdef GGML_USE_CPU +#include "ggml-cpu.h" +#endif + +#ifdef GGML_USE_CUDA +#include "ggml-cuda.h" +#endif + +#ifdef GGML_USE_METAL +#include "ggml-metal.h" +#endif + +#ifdef GGML_USE_SYCL +#include "ggml-sycl.h" +#endif + +#ifdef GGML_USE_VULKAN +#include "ggml-vulkan.h" +#endif + +#ifdef GGML_USE_BLAS +#include "ggml-blas.h" +#endif + +#ifdef GGML_USE_RPC +#include "ggml-rpc.h" +#endif + +#ifdef GGML_USE_CANN +#include "ggml-cann.h" +#endif + +#ifdef GGML_USE_KOMPUTE +#include "ggml-kompute.h" +#endif + +#ifdef _WIN32 + +using dl_handle = std::remove_pointer_t; + +struct dl_handle_deleter { + void operator()(HMODULE handle) { + FreeLibrary(handle); + } +}; + +static dl_handle * dl_load_library(const std::wstring & path) { + // suppress error dialogs for missing DLLs + DWORD old_mode = SetErrorMode(SEM_FAILCRITICALERRORS); + SetErrorMode(old_mode | SEM_FAILCRITICALERRORS); + + HMODULE handle = LoadLibraryW(path.c_str()); + + SetErrorMode(old_mode); + + return handle; +} + +static dl_handle * dl_load_library(const std::string & path) { + std::wstring_convert> converter; + return dl_load_library(converter.from_bytes(path)); +} + +static void * dl_get_sym(dl_handle * handle, const char * name) { + DWORD old_mode = SetErrorMode(SEM_FAILCRITICALERRORS); + SetErrorMode(old_mode | SEM_FAILCRITICALERRORS); + + void * p = (void *) GetProcAddress(handle, name); + + SetErrorMode(old_mode); + + return p; +} + +#else + +using dl_handle = void; + +struct dl_handle_deleter { + void operator()(void * handle) { + dlclose(handle); + } +}; + +static void * dl_load_library(const std::string & path) { + dl_handle * handle = dlopen(path.c_str(), RTLD_NOW | RTLD_LOCAL); + + return handle; +} + +static void * dl_get_sym(dl_handle * handle, const char * name) { + return dlsym(handle, name); +} + +#endif + +using dl_handle_ptr = std::unique_ptr; + +struct ggml_backend_reg_entry { + ggml_backend_reg_t reg; + dl_handle_ptr handle; +}; + +struct ggml_backend_registry { + std::vector backends; + std::vector devices; + + ggml_backend_registry() { +#ifdef GGML_USE_CUDA + register_backend(ggml_backend_cuda_reg()); +#endif +#ifdef GGML_USE_METAL + register_backend(ggml_backend_metal_reg()); +#endif +#ifdef GGML_USE_SYCL + register_backend(ggml_backend_sycl_reg()); +#endif +#ifdef GGML_USE_VULKAN + register_backend(ggml_backend_vk_reg()); +#endif +#ifdef GGML_USE_CANN + register_backend(ggml_backend_cann_reg()); +#endif +#ifdef GGML_USE_BLAS + register_backend(ggml_backend_blas_reg()); +#endif +#ifdef GGML_USE_RPC + register_backend(ggml_backend_rpc_reg()); +#endif +#ifdef GGML_USE_KOMPUTE + register_backend(ggml_backend_kompute_reg()); +#endif +#ifdef GGML_USE_CPU + register_backend(ggml_backend_cpu_reg()); +#endif + } + + ~ggml_backend_registry() { + // FIXME: backends cannot be safely unloaded without a function to destroy all the backend resources, + // since backend threads may still be running and accessing resources from the dynamic library + for (auto & entry : backends) { + if (entry.handle) { + entry.handle.release(); // NOLINT + } + } + } + + void register_backend(ggml_backend_reg_t reg, dl_handle_ptr handle = nullptr) { + if (!reg) { + return; + } + +#ifndef NDEBUG + GGML_LOG_DEBUG("%s: registered backend %s (%zu devices)\n", + __func__, ggml_backend_reg_name(reg), ggml_backend_reg_dev_count(reg)); +#endif + backends.push_back({ reg, std::move(handle) }); + for (size_t i = 0; i < ggml_backend_reg_dev_count(reg); i++) { + register_device(ggml_backend_reg_dev_get(reg, i)); + } + } + + void register_device(ggml_backend_dev_t device) { +#ifndef NDEBUG + GGML_LOG_DEBUG("%s: registered device %s (%s)\n", __func__, ggml_backend_dev_name(device), ggml_backend_dev_description(device)); +#endif + devices.push_back(device); + } + + ggml_backend_reg_t load_backend(const char * path, bool silent) { + dl_handle_ptr handle { dl_load_library(path) }; + if (!handle) { + if (!silent) { + GGML_LOG_ERROR("%s: failed to load %s\n", __func__, path); + } + return nullptr; + } + + auto score_fn = (ggml_backend_score_t) dl_get_sym(handle.get(), "ggml_backend_score"); + if (score_fn && score_fn() == 0) { + if (!silent) { + GGML_LOG_INFO("%s: backend %s is not supported on this system\n", __func__, path); + } + return nullptr; + } + + auto backend_init_fn = (ggml_backend_init_t) dl_get_sym(handle.get(), "ggml_backend_init"); + if (!backend_init_fn) { + if (!silent) { + GGML_LOG_ERROR("%s: failed to find ggml_backend_init in %s\n", __func__, path); + } + return nullptr; + } + + ggml_backend_reg_t reg = backend_init_fn(); + if (!reg || reg->api_version != GGML_BACKEND_API_VERSION) { + if (!silent) { + if (!reg) { + GGML_LOG_ERROR("%s: failed to initialize backend from %s: ggml_backend_init returned NULL\n", __func__, path); + } else { + GGML_LOG_ERROR("%s: failed to initialize backend from %s: incompatible API version (backend: %d, current: %d)\n", + __func__, path, reg->api_version, GGML_BACKEND_API_VERSION); + } + } + return nullptr; + } + + GGML_LOG_INFO("%s: loaded %s backend from %s\n", __func__, ggml_backend_reg_name(reg), path); + + register_backend(reg, std::move(handle)); + + return reg; + } + + void unload_backend(ggml_backend_reg_t reg, bool silent) { + auto it = std::find_if(backends.begin(), backends.end(), + [reg](const ggml_backend_reg_entry & entry) { return entry.reg == reg; }); + + if (it == backends.end()) { + if (!silent) { + GGML_LOG_ERROR("%s: backend not found\n", __func__); + } + return; + } + + if (!silent) { + GGML_LOG_DEBUG("%s: unloading %s backend\n", __func__, ggml_backend_reg_name(reg)); + } + + // remove devices + devices.erase( + std::remove_if(devices.begin(), devices.end(), + [reg](ggml_backend_dev_t dev) { return ggml_backend_dev_backend_reg(dev) == reg; }), + devices.end()); + + // remove backend + backends.erase(it); + } +}; + +static ggml_backend_registry & get_reg() { + static ggml_backend_registry reg; + return reg; +} + +// Internal API +void ggml_backend_register(ggml_backend_reg_t reg) { + get_reg().register_backend(reg); +} + +void ggml_backend_device_register(ggml_backend_dev_t device) { + get_reg().register_device(device); +} + +// Backend (reg) enumeration +static bool striequals(const char * a, const char * b) { + for (; *a && *b; a++, b++) { + if (std::tolower(*a) != std::tolower(*b)) { + return false; + } + } + return *a == *b; +} + +size_t ggml_backend_reg_count() { + return get_reg().backends.size(); +} + +ggml_backend_reg_t ggml_backend_reg_get(size_t index) { + GGML_ASSERT(index < ggml_backend_reg_count()); + return get_reg().backends[index].reg; +} + +ggml_backend_reg_t ggml_backend_reg_by_name(const char * name) { + for (size_t i = 0; i < ggml_backend_reg_count(); i++) { + ggml_backend_reg_t reg = ggml_backend_reg_get(i); + if (striequals(ggml_backend_reg_name(reg), name)) { + return reg; + } + } + return nullptr; +} + +// Device enumeration +size_t ggml_backend_dev_count() { + return get_reg().devices.size(); +} + +ggml_backend_dev_t ggml_backend_dev_get(size_t index) { + GGML_ASSERT(index < ggml_backend_dev_count()); + return get_reg().devices[index]; +} + +ggml_backend_dev_t ggml_backend_dev_by_name(const char * name) { + for (size_t i = 0; i < ggml_backend_dev_count(); i++) { + ggml_backend_dev_t dev = ggml_backend_dev_get(i); + if (striequals(ggml_backend_dev_name(dev), name)) { + return dev; + } + } + return nullptr; +} + +ggml_backend_dev_t ggml_backend_dev_by_type(enum ggml_backend_dev_type type) { + for (size_t i = 0; i < ggml_backend_dev_count(); i++) { + ggml_backend_dev_t dev = ggml_backend_dev_get(i); + if (ggml_backend_dev_type(dev) == type) { + return dev; + } + } + return nullptr; +} + +// Convenience functions +ggml_backend_t ggml_backend_init_by_name(const char * name, const char * params) { + ggml_backend_dev_t dev = ggml_backend_dev_by_name(name); + if (!dev) { + return nullptr; + } + return ggml_backend_dev_init(dev, params); +} + +ggml_backend_t ggml_backend_init_by_type(enum ggml_backend_dev_type type, const char * params) { + ggml_backend_dev_t dev = ggml_backend_dev_by_type(type); + if (!dev) { + return nullptr; + } + return ggml_backend_dev_init(dev, params); +} + +ggml_backend_t ggml_backend_init_best(void) { + ggml_backend_dev_t dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU); + if (!dev) { + dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU); + } + if (!dev) { + return nullptr; + } + return ggml_backend_dev_init(dev, nullptr); +} + +// Dynamic loading +ggml_backend_reg_t ggml_backend_load(const char * path) { + return get_reg().load_backend(path, false); +} + +void ggml_backend_unload(ggml_backend_reg_t reg) { + get_reg().unload_backend(reg, true); +} + +static std::string get_executable_path() { +#if defined(__APPLE__) + // get executable path + std::vector path; + uint32_t size; + while (true) { + size = path.size(); + if (_NSGetExecutablePath(path.data(), &size) == 0) { + break; + } + path.resize(size); + } + std::string base_path(path.data(), size); + // remove executable name + auto last_slash = base_path.find_last_of('/'); + if (last_slash != std::string::npos) { + base_path = base_path.substr(0, last_slash); + } + return base_path + "/"; +#elif defined(__linux__) + std::string base_path = "."; + std::vector path(1024); + while (true) { + // get executable path + ssize_t len = readlink("/proc/self/exe", path.data(), path.size()); + if (len == -1) { + break; + } + if (len < (ssize_t) path.size()) { + base_path = std::string(path.data(), len); + // remove executable name + auto last_slash = base_path.find_last_of('/'); + if (last_slash != std::string::npos) { + base_path = base_path.substr(0, last_slash); + } + break; + } + path.resize(path.size() * 2); + } + + return base_path + "/"; +#elif defined(_WIN32) + std::vector path(MAX_PATH); + DWORD len = GetModuleFileNameA(NULL, path.data(), path.size()); + if (len == 0) { + return ""; + } + std::string base_path(path.data(), len); + // remove executable name + auto last_slash = base_path.find_last_of('\\'); + if (last_slash != std::string::npos) { + base_path = base_path.substr(0, last_slash); + } + return base_path + "\\"; +#endif +} + +static std::string backend_filename_prefix() { +#ifdef _WIN32 + return "ggml-"; +#else + return "libggml-"; +#endif +} + +static std::string backend_filename_suffix() { +#ifdef _WIN32 + return ".dll"; +#else + return ".so"; +#endif +} + +static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent) { + // enumerate all the files that match [lib]ggml-name-*.[so|dll] in the search paths + // TODO: search system paths + std::vector search_paths = { "./", get_executable_path() }; + std::string file_prefix = backend_filename_prefix() + name + "-"; + + int best_score = 0; + std::string best_path; + + namespace fs = std::filesystem; + for (const auto & search_path : search_paths) { + if (!fs::exists(search_path)) { + continue; + } + for (const auto & entry : fs::directory_iterator(search_path)) { + if (entry.is_regular_file()) { + std::string filename = entry.path().filename().string(); + std::string ext = entry.path().extension().string(); + if (filename.find(file_prefix) == 0 && ext == backend_filename_suffix()) { + dl_handle_ptr handle { dl_load_library(entry.path().c_str()) }; + if (!handle && !silent) { + GGML_LOG_ERROR("%s: failed to load %s\n", __func__, entry.path().string().c_str()); + } + if (handle) { + auto score_fn = (ggml_backend_score_t) dl_get_sym(handle.get(), "ggml_backend_score"); + if (score_fn) { + int s = score_fn(); +#ifndef NDEBUG + GGML_LOG_DEBUG("%s: %s score: %d\n", __func__, entry.path().string().c_str(), s); +#endif + if (s > best_score) { + best_score = s; + best_path = entry.path().string(); + } + } else { + if (!silent) { + GGML_LOG_INFO("%s: failed to find ggml_backend_score in %s\n", __func__, entry.path().string().c_str()); + } + } + } + } + } + } + } + + if (best_score == 0) { + // try to load the base backend + for (const auto & search_path : search_paths) { + std::string path = search_path + backend_filename_prefix() + name + backend_filename_suffix(); + if (fs::exists(path)) { + return get_reg().load_backend(path.c_str(), silent); + } + } + return nullptr; + } + + return get_reg().load_backend(best_path.c_str(), silent); +} + +void ggml_backend_load_all() { +#ifdef NDEBUG + bool silent = true; +#else + bool silent = false; +#endif + + ggml_backend_load_best("blas", silent); + ggml_backend_load_best("cann", silent); + ggml_backend_load_best("cuda", silent); + ggml_backend_load_best("hip", silent); + ggml_backend_load_best("kompute", silent); + ggml_backend_load_best("metal", silent); + ggml_backend_load_best("rpc", silent); + ggml_backend_load_best("sycl", silent); + ggml_backend_load_best("vulkan", silent); + ggml_backend_load_best("musa", silent); + ggml_backend_load_best("cpu", silent); +} diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp index f397f6252..fdb4b986f 100644 --- a/ggml/src/ggml-backend.cpp +++ b/ggml/src/ggml-backend.cpp @@ -8,6 +8,7 @@ #include #endif +#include "ggml-backend.h" #include "ggml-backend-impl.h" #include "ggml-alloc.h" #include "ggml-impl.h" @@ -251,6 +252,7 @@ void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_ten } void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + GGML_ASSERT(tensor); ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; if (size == 0) { @@ -265,6 +267,7 @@ void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, siz } void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { + GGML_ASSERT(tensor); ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; if (size == 0) { @@ -278,7 +281,7 @@ void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, siz buf->iface.get_tensor(buf, tensor, data, offset, size); } -GGML_API void ggml_backend_tensor_memset(struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) { +void ggml_backend_tensor_memset(struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) { ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; if (size == 0) { @@ -524,805 +527,6 @@ void * ggml_backend_reg_get_proc_address(ggml_backend_reg_t reg, const char * na return reg->iface.get_proc_address(reg, name); } -// Backend registry - -#ifdef GGML_USE_CUDA -#include "ggml-cuda.h" -#endif - -#ifdef GGML_USE_METAL -#include "ggml-metal.h" -#endif - -#ifdef GGML_USE_SYCL -#include "ggml-sycl.h" -#endif - -#ifdef GGML_USE_VULKAN -#include "ggml-vulkan.h" -#endif - -#ifdef GGML_USE_BLAS -#include "ggml-blas.h" -#endif - -#ifdef GGML_USE_RPC -#include "ggml-rpc.h" -#endif - -#ifndef __AMX_INT8__ -#undef GGML_USE_AMX -#endif - -#ifdef GGML_USE_AMX -# include "ggml-amx.h" -#endif - -#ifdef GGML_USE_CANN -#include "ggml-cann.h" -#endif - -#ifdef GGML_USE_KOMPUTE -#include "ggml-kompute.h" -#endif - -struct ggml_backend_registry { - std::vector backends; - std::vector devices; - - ggml_backend_registry() { -#ifdef GGML_USE_CUDA - register_backend(ggml_backend_cuda_reg()); -#endif -#ifdef GGML_USE_METAL - register_backend(ggml_backend_metal_reg()); -#endif -#ifdef GGML_USE_SYCL - register_backend(ggml_backend_sycl_reg()); -#endif -#ifdef GGML_USE_VULKAN - register_backend(ggml_backend_vk_reg()); -#endif -#ifdef GGML_USE_CANN - register_backend(ggml_backend_cann_reg()); -#endif -#ifdef GGML_USE_BLAS - register_backend(ggml_backend_blas_reg()); -#endif -#ifdef GGML_USE_RPC - register_backend(ggml_backend_rpc_reg()); -#endif -#ifdef GGML_USE_AMX - register_backend(ggml_backend_amx_reg()); -#endif -#ifdef GGML_USE_KOMPUTE - register_backend(ggml_backend_kompute_reg()); -#endif - - register_backend(ggml_backend_cpu_reg()); - } - - void register_backend(ggml_backend_reg_t reg) { -#ifndef NDEBUG - GGML_LOG_DEBUG("%s: registered backend %s (%zu devices)\n", - __func__, ggml_backend_reg_name(reg), ggml_backend_reg_dev_count(reg)); -#endif - backends.push_back(reg); - for (size_t i = 0; i < ggml_backend_reg_dev_count(reg); i++) { - register_device(ggml_backend_reg_dev_get(reg, i)); - } - } - - void register_device(ggml_backend_dev_t device) { -#ifndef NDEBUG - GGML_LOG_DEBUG("%s: registered device %s (%s)\n", __func__, ggml_backend_dev_name(device), ggml_backend_dev_description(device)); -#endif - devices.push_back(device); - } -}; - -static ggml_backend_registry & get_reg() { - static ggml_backend_registry reg; - return reg; -} - -// Internal API -void ggml_backend_register(ggml_backend_reg_t reg) { - get_reg().register_backend(reg); -} - -void ggml_backend_device_register(ggml_backend_dev_t device) { - get_reg().register_device(device); -} - -// Backend (reg) enumeration -size_t ggml_backend_reg_count() { - return get_reg().backends.size(); -} - -ggml_backend_reg_t ggml_backend_reg_get(size_t index) { - GGML_ASSERT(index < ggml_backend_reg_count()); - return get_reg().backends[index]; -} - -ggml_backend_reg_t ggml_backend_reg_by_name(const char * name) { - for (size_t i = 0; i < ggml_backend_reg_count(); i++) { - ggml_backend_reg_t reg = ggml_backend_reg_get(i); - if (strcmp(ggml_backend_reg_name(reg), name) == 0) { - return reg; - } - } - return NULL; -} - -// Device enumeration -size_t ggml_backend_dev_count() { - return get_reg().devices.size(); -} - -ggml_backend_dev_t ggml_backend_dev_get(size_t index) { - GGML_ASSERT(index < ggml_backend_dev_count()); - return get_reg().devices[index]; -} - -ggml_backend_dev_t ggml_backend_dev_by_name(const char * name) { - for (size_t i = 0; i < ggml_backend_dev_count(); i++) { - ggml_backend_dev_t dev = ggml_backend_dev_get(i); - if (strcmp(ggml_backend_dev_name(dev), name) == 0) { - return dev; - } - } - return NULL; -} - -ggml_backend_dev_t ggml_backend_dev_by_type(enum ggml_backend_dev_type type) { - for (size_t i = 0; i < ggml_backend_dev_count(); i++) { - ggml_backend_dev_t dev = ggml_backend_dev_get(i); - if (ggml_backend_dev_type(dev) == type) { - return dev; - } - } - return NULL; -} - -// Convenience functions -ggml_backend_t ggml_backend_init_by_name(const char * name, const char * params) { - ggml_backend_dev_t dev = ggml_backend_dev_by_name(name); - if (!dev) { - return NULL; - } - return ggml_backend_dev_init(dev, params); -} - -ggml_backend_t ggml_backend_init_by_type(enum ggml_backend_dev_type type, const char * params) { - ggml_backend_dev_t dev = ggml_backend_dev_by_type(type); - if (!dev) { - return NULL; - } - return ggml_backend_dev_init(dev, params); -} - -ggml_backend_t ggml_backend_init_best(void) { - ggml_backend_dev_t dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU); - if (!dev) { - dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU); - } - if (!dev) { - return NULL; - } - return ggml_backend_dev_init(dev, NULL); -} - -// CPU backend - buffer - -static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) { - uintptr_t data = (uintptr_t)buffer->context; - - // align the buffer - if (data % TENSOR_ALIGNMENT != 0) { - data = GGML_PAD(data, TENSOR_ALIGNMENT); - } - - return (void *)data; -} - -static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) { - ggml_aligned_free(buffer->context, buffer->size); -} - -static void ggml_backend_cpu_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) { - memset((char *)tensor->data + offset, value, size); - - GGML_UNUSED(buffer); -} - -static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - memcpy((char *)tensor->data + offset, data, size); - - GGML_UNUSED(buffer); -} - -static void ggml_backend_cpu_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { - memcpy(data, (const char *)tensor->data + offset, size); - - GGML_UNUSED(buffer); -} - -static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { - if (ggml_backend_buffer_is_host(src->buffer)) { - memcpy(dst->data, src->data, ggml_nbytes(src)); - return true; - } - return false; - - GGML_UNUSED(buffer); -} - -static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { - memset(buffer->context, value, buffer->size); -} - -static const struct ggml_backend_buffer_i ggml_backend_cpu_buffer_i = { - /* .free_buffer = */ ggml_backend_cpu_buffer_free_buffer, - /* .get_base = */ ggml_backend_cpu_buffer_get_base, - /* .init_tensor = */ NULL, // no initialization required - /* .memset_tensor = */ ggml_backend_cpu_buffer_memset_tensor, - /* .set_tensor = */ ggml_backend_cpu_buffer_set_tensor, - /* .get_tensor = */ ggml_backend_cpu_buffer_get_tensor, - /* .cpy_tensor = */ ggml_backend_cpu_buffer_cpy_tensor, - /* .clear = */ ggml_backend_cpu_buffer_clear, - /* .reset = */ NULL, -}; - -static const struct ggml_backend_buffer_i ggml_backend_cpu_buffer_from_ptr_i = { - /* .free_buffer = */ NULL, // ptr is not owned by the buffer, so it does not need to be freed - /* .get_base = */ ggml_backend_cpu_buffer_get_base, - /* .init_tensor = */ NULL, // no initialization required - /* .memset_tensor = */ ggml_backend_cpu_buffer_memset_tensor, - /* .set_tensor = */ ggml_backend_cpu_buffer_set_tensor, - /* .get_tensor = */ ggml_backend_cpu_buffer_get_tensor, - /* .cpy_tensor = */ ggml_backend_cpu_buffer_cpy_tensor, - /* .clear = */ ggml_backend_cpu_buffer_clear, - /* .reset = */ NULL, -}; - -// CPU backend - buffer type - -static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) { - return "CPU"; - - GGML_UNUSED(buft); -} - -static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - void * data = ggml_aligned_malloc(size); - - if (data == NULL) { - GGML_LOG_ERROR("%s: failed to allocate buffer of size %zu\n", __func__, size); - return NULL; - } - - return ggml_backend_buffer_init(buft, ggml_backend_cpu_buffer_i, data, size); -} - -static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return TENSOR_ALIGNMENT; - - GGML_UNUSED(buft); -} - -static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft) { - return true; - - GGML_UNUSED(buft); -} - -ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) { - static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type = { - /* .iface = */ { - /* .get_name = */ ggml_backend_cpu_buffer_type_get_name, - /* .alloc_buffer = */ ggml_backend_cpu_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX - /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes - /* .is_host = */ ggml_backend_cpu_buffer_type_is_host, - }, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0), - /* .context = */ NULL, - }; - - return &ggml_backend_cpu_buffer_type; -} - -static const char * ggml_backend_cpu_buffer_from_ptr_type_get_name(ggml_backend_buffer_type_t buft) { - return "CPU_Mapped"; - - GGML_UNUSED(buft); -} - -static ggml_backend_buffer_type_t ggml_backend_cpu_buffer_from_ptr_type(void) { - static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type = { - /* .iface = */ { - /* .get_name = */ ggml_backend_cpu_buffer_from_ptr_type_get_name, - /* .alloc_buffer = */ ggml_backend_cpu_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX - /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes - /* .is_host = */ ggml_backend_cpu_buffer_type_is_host, - }, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0), - /* .context = */ NULL, - }; - - return &ggml_backend_cpu_buffer_type; -} - -#ifdef GGML_USE_CPU_HBM - -// buffer type HBM - -#include - -static const char * ggml_backend_cpu_hbm_buffer_type_get_name(ggml_backend_buffer_type_t buft) { - return "CPU_HBM"; - - GGML_UNUSED(buft); -} - -static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) { - hbw_free(buffer->context); -} - -static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - void * ptr; - int result = hbw_posix_memalign(&ptr, ggml_backend_cpu_buffer_type_get_alignment(buft), size); - if (result != 0) { - GGML_LOG_ERROR("failed to allocate HBM buffer of size %zu\n", size); - return NULL; - } - - ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); - buffer->buft = buft; - buffer->iface.free_buffer = ggml_backend_cpu_hbm_buffer_free_buffer; - - return buffer; -} - -ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void) { - static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type_hbm = { - /* .iface = */ { - /* .get_name = */ ggml_backend_cpu_hbm_buffer_type_get_name, - /* .alloc_buffer = */ ggml_backend_cpu_hbm_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX - /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes - /* .is_host = */ ggml_backend_cpu_buffer_type_is_host, - }, - /* .context = */ NULL, - }; - - return &ggml_backend_cpu_buffer_type_hbm; -} -#endif - -static ggml_backend_buffer_type_t * ggml_backend_cpu_get_extra_bufts(ggml_backend_dev_t device) { - static ggml_backend_buffer_type_t bufts[] = { -#ifdef GGML_USE_CPU_HBM - ggml_backend_cpu_hbm_buffer_type(), -#endif - NULL - }; - - return bufts; - - GGML_UNUSED(device); -} - -// CPU backend - backend (stream) - -struct ggml_backend_cpu_context { - int n_threads; - ggml_threadpool_t threadpool; - - uint8_t * work_data; - size_t work_size; - - ggml_abort_callback abort_callback; - void * abort_callback_data; -}; - -static const char * ggml_backend_cpu_get_name(ggml_backend_t backend) { - return "CPU"; - - GGML_UNUSED(backend); -} - -static void ggml_backend_cpu_free(ggml_backend_t backend) { - struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; - delete[] cpu_ctx->work_data; - delete cpu_ctx; - delete backend; -} - -struct ggml_backend_plan_cpu { - struct ggml_cplan cplan; - struct ggml_cgraph cgraph; -}; - -static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, const struct ggml_cgraph * cgraph) { - struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; - - struct ggml_backend_plan_cpu * cpu_plan = new ggml_backend_plan_cpu; - - cpu_plan->cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads, cpu_ctx->threadpool); - cpu_plan->cgraph = *cgraph; // FIXME: deep copy - - if (cpu_plan->cplan.work_size > 0) { - cpu_plan->cplan.work_data = new uint8_t[cpu_plan->cplan.work_size]; - if (cpu_plan->cplan.work_data == NULL) { - delete cpu_plan; - return NULL; - } - } - - cpu_plan->cplan.abort_callback = cpu_ctx->abort_callback; - cpu_plan->cplan.abort_callback_data = cpu_ctx->abort_callback_data; - - return cpu_plan; -} - -static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { - struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan; - - delete[] cpu_plan->cplan.work_data; - delete cpu_plan; - - GGML_UNUSED(backend); -} - -static enum ggml_status ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { - struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan; - - return ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan); - - GGML_UNUSED(backend); -} - -static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { - struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; - - struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads, cpu_ctx->threadpool); - - if (cpu_ctx->work_size < cplan.work_size) { - delete[] cpu_ctx->work_data; - cpu_ctx->work_data = new uint8_t[cplan.work_size]; - if (cpu_ctx->work_data == NULL) { - cpu_ctx->work_size = 0; - return GGML_STATUS_ALLOC_FAILED; - } - cpu_ctx->work_size = cplan.work_size; - } - cplan.work_data = (uint8_t *)cpu_ctx->work_data; - - cplan.abort_callback = cpu_ctx->abort_callback; - cplan.abort_callback_data = cpu_ctx->abort_callback_data; - - return ggml_graph_compute(cgraph, &cplan); -} - -static const struct ggml_backend_i ggml_backend_cpu_i = { - /* .get_name = */ ggml_backend_cpu_get_name, - /* .free = */ ggml_backend_cpu_free, - /* .set_tensor_async = */ NULL, - /* .get_tensor_async = */ NULL, - /* .cpy_tensor_async = */ NULL, - /* .synchronize = */ NULL, - /* .graph_plan_create = */ ggml_backend_cpu_graph_plan_create, - /* .graph_plan_free = */ ggml_backend_cpu_graph_plan_free, - /* .graph_plan_update = */ NULL, - /* .graph_plan_compute = */ ggml_backend_cpu_graph_plan_compute, - /* .graph_compute = */ ggml_backend_cpu_graph_compute, - /* .event_record = */ NULL, - /* .event_wait = */ NULL, -}; - -static ggml_guid_t ggml_backend_cpu_guid(void) { - static ggml_guid guid = { 0xaa, 0x67, 0xc7, 0x43, 0x96, 0xe6, 0xa3, 0x8a, 0xe3, 0xaf, 0xea, 0x92, 0x36, 0xbc, 0xfc, 0x89 }; - return &guid; -} - -ggml_backend_t ggml_backend_cpu_init(void) { - struct ggml_backend_cpu_context * ctx = new ggml_backend_cpu_context; - if (ctx == NULL) { - return NULL; - } - - ctx->n_threads = GGML_DEFAULT_N_THREADS; - ctx->threadpool = NULL; - ctx->work_data = NULL; - ctx->work_size = 0; - ctx->abort_callback = NULL; - ctx->abort_callback_data = NULL; - - ggml_backend_t cpu_backend = new ggml_backend { - /* .guid = */ ggml_backend_cpu_guid(), - /* .interface = */ ggml_backend_cpu_i, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0), - /* .context = */ ctx, - }; - - if (cpu_backend == NULL) { - delete ctx; - return NULL; - } - - return cpu_backend; -} - -bool ggml_backend_is_cpu(ggml_backend_t backend) { - return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_cpu_guid()); -} - -void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) { - GGML_ASSERT(ggml_backend_is_cpu(backend_cpu)); - - struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context; - ctx->n_threads = n_threads; -} - -void ggml_backend_cpu_set_threadpool(ggml_backend_t backend_cpu, ggml_threadpool_t threadpool) { - GGML_ASSERT(ggml_backend_is_cpu(backend_cpu)); - - struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context; - - if (ctx->threadpool && ctx->threadpool != threadpool) { - // already had a different threadpool, pause/suspend it before switching - ggml_threadpool_pause(ctx->threadpool); - } - ctx->threadpool = threadpool; -} - -void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data) { - GGML_ASSERT(ggml_backend_is_cpu(backend_cpu)); - - struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context; - ctx->abort_callback = abort_callback; - ctx->abort_callback_data = abort_callback_data; -} - -ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size) { - GGML_ASSERT((uintptr_t)ptr % TENSOR_ALIGNMENT == 0 && "buffer pointer must be aligned"); - return ggml_backend_buffer_init(ggml_backend_cpu_buffer_from_ptr_type(), ggml_backend_cpu_buffer_from_ptr_i, ptr, size); -} - -// CPU backend - device - -struct ggml_backend_cpu_device_context { - std::string description = "CPU"; - - ggml_backend_cpu_device_context() { -#ifdef __APPLE__ - size_t len = 0; - if (!sysctlbyname("machdep.cpu.brand_string", NULL, &len, NULL, 0)) { - description.resize(len); - sysctlbyname("machdep.cpu.brand_string", &description[0], &len, NULL, 0); // NOLINT - } -#elif defined(__linux__) - FILE * f = fopen("/proc/cpuinfo", "r"); - if (f) { - char buf[1024]; - while (fgets(buf, sizeof(buf), f)) { - if (strncmp(buf, "model name", 10) == 0) { - char * p = strchr(buf, ':'); - if (p) { - p++; - while (std::isspace(*p)) { - p++; - } - while (std::isspace(p[strlen(p) - 1])) { - p[strlen(p) - 1] = '\0'; - } - description = p; - break; - } - } - } - fclose(f); - } -#elif defined(_WIN32) - HKEY hKey; - if (RegOpenKeyEx(HKEY_LOCAL_MACHINE, - TEXT("HARDWARE\\DESCRIPTION\\System\\CentralProcessor\\0"), - 0, - KEY_READ, - &hKey) == ERROR_SUCCESS) { - DWORD cpu_brand_size = 0; - if (RegQueryValueExA(hKey, - TEXT("ProcessorNameString"), - NULL, - NULL, - NULL, - &cpu_brand_size) == ERROR_SUCCESS) { - description.resize(cpu_brand_size); - if (RegQueryValueExA(hKey, - TEXT("ProcessorNameString"), - NULL, - NULL, - (LPBYTE)&description[0], // NOLINT - &cpu_brand_size) == ERROR_SUCCESS) { - if (description.find('\0') != std::string::npos) { - description.resize(description.find('\0')); - } - } - } - RegCloseKey(hKey); - } -#endif - } -}; - -static const char * ggml_backend_cpu_device_get_name(ggml_backend_dev_t dev) { - return "CPU"; - - GGML_UNUSED(dev); -} - -static const char * ggml_backend_cpu_device_get_description(ggml_backend_dev_t dev) { - struct ggml_backend_cpu_device_context * ctx = (struct ggml_backend_cpu_device_context *)dev->context; - - return ctx->description.c_str(); -} - -static void ggml_backend_cpu_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { - // TODO - *free = 0; - *total = 0; - - GGML_UNUSED(dev); -} - -static enum ggml_backend_dev_type ggml_backend_cpu_device_get_type(ggml_backend_dev_t dev) { - return GGML_BACKEND_DEVICE_TYPE_CPU; - - GGML_UNUSED(dev); -} - -static void ggml_backend_cpu_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) { - props->name = ggml_backend_cpu_device_get_name(dev); - props->description = ggml_backend_cpu_device_get_description(dev); - props->type = ggml_backend_cpu_device_get_type(dev); - ggml_backend_cpu_device_get_memory(dev, &props->memory_free, &props->memory_total); - props->caps = { - /* .async = */ false, - /* .host_buffer = */ false, - /* .buffer_from_host_ptr = */ true, - /* .events = */ false, - }; -} - -static ggml_backend_t ggml_backend_cpu_device_init_backend(ggml_backend_dev_t dev, const char * params) { - return ggml_backend_cpu_init(); - - GGML_UNUSED(dev); - GGML_UNUSED(params); -} - -static ggml_backend_buffer_type_t ggml_backend_cpu_device_get_buffer_type(ggml_backend_dev_t dev) { - return ggml_backend_cpu_buffer_type(); - - GGML_UNUSED(dev); -} - -static ggml_backend_buffer_t ggml_backend_cpu_device_buffer_from_host_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) { - return ggml_backend_cpu_buffer_from_ptr(ptr, size); - - GGML_UNUSED(dev); - GGML_UNUSED(max_tensor_size); -} - -static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { - switch (op->op) { - case GGML_OP_CPY: - return - op->type != GGML_TYPE_IQ2_XXS && - op->type != GGML_TYPE_IQ2_XS && - op->type != GGML_TYPE_IQ1_S && - op->type != GGML_TYPE_IQ1_M; // missing type_traits.from_float - case GGML_OP_MUL_MAT: - return op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == ggml_get_type_traits(op->src[0]->type)->vec_dot_type; - case GGML_OP_ROPE_BACK: - return op->src[2] == NULL && (op->op_params[2] & 4) == 0; - case GGML_OP_IM2COL_BACK: - return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32; - case GGML_OP_OUT_PROD: - return (op->src[0]->type == GGML_TYPE_F32 || ggml_is_quantized(op->src[0]->type)) && op->src[1]->type == GGML_TYPE_F32; - default: - return true; - } - - GGML_UNUSED(dev); -} - -static bool ggml_backend_cpu_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { - return ggml_backend_buft_is_host(buft); - - GGML_UNUSED(dev); -} - -static const struct ggml_backend_device_i ggml_backend_cpu_device_i = { - /* .get_name = */ ggml_backend_cpu_device_get_name, - /* .get_description = */ ggml_backend_cpu_device_get_description, - /* .get_memory = */ ggml_backend_cpu_device_get_memory, - /* .get_type = */ ggml_backend_cpu_device_get_type, - /* .get_props = */ ggml_backend_cpu_device_get_props, - /* .init_backend = */ ggml_backend_cpu_device_init_backend, - /* .get_buffer_type = */ ggml_backend_cpu_device_get_buffer_type, - /* .get_host_buffer_type = */ NULL, - /* .buffer_from_host_ptr = */ ggml_backend_cpu_device_buffer_from_host_ptr, - /* .supports_op = */ ggml_backend_cpu_device_supports_op, - /* .supports_buft = */ ggml_backend_cpu_device_supports_buft, - /* .offload_op = */ NULL, - /* .event_new = */ NULL, - /* .event_free = */ NULL, - /* .event_synchronize = */ NULL, -}; - -// CPU backend - backend (reg) - -static const char * ggml_backend_cpu_reg_get_name(ggml_backend_reg_t reg) { - return "CPU"; - - GGML_UNUSED(reg); -} - -static size_t ggml_backend_cpu_reg_get_device_count(ggml_backend_reg_t reg) { - return 1; - - GGML_UNUSED(reg); -} - -static ggml_backend_dev_t ggml_backend_cpu_reg_get_device(ggml_backend_reg_t reg, size_t index) { - GGML_ASSERT(index == 0); - - static ggml_backend_cpu_device_context ctx; - static ggml_backend_device ggml_backend_cpu_device = { - /* .iface = */ ggml_backend_cpu_device_i, - /* .reg = */ reg, - /* .context = */ &ctx, - }; - - return &ggml_backend_cpu_device; -} - -static void * ggml_backend_cpu_get_proc_address(ggml_backend_reg_t reg, const char * name) { - if (strcmp(name, "ggml_backend_set_n_threads") == 0) { - return (void *)ggml_backend_cpu_set_n_threads; - } - if (strcmp(name, "ggml_backend_dev_get_extra_bufts") == 0) { - return (void *)ggml_backend_cpu_get_extra_bufts; - } - - return NULL; - - GGML_UNUSED(reg); -} - -static const struct ggml_backend_reg_i ggml_backend_cpu_reg_i = { - /* .get_name = */ ggml_backend_cpu_reg_get_name, - /* .get_device_count = */ ggml_backend_cpu_reg_get_device_count, - /* .get_device = */ ggml_backend_cpu_reg_get_device, - /* .get_proc_address = */ ggml_backend_cpu_get_proc_address, -}; - -ggml_backend_reg_t ggml_backend_cpu_reg(void) { - static struct ggml_backend_reg ggml_backend_cpu_reg = { - /* .iface = */ ggml_backend_cpu_reg_i, - /* .context = */ NULL, - }; - - return &ggml_backend_cpu_reg; -} - // multi-buffer buffer struct ggml_backend_multi_buffer_context { @@ -1487,7 +691,7 @@ static int ggml_backend_sched_backend_id(ggml_backend_sched_t sched, ggml_backen } static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, const struct ggml_tensor * tensor, const struct ggml_tensor * op) { - ggml_backend_buffer_t buffer = tensor->buffer; + ggml_backend_buffer_t buffer = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; if (buffer == NULL) { return -1; } @@ -1508,7 +712,7 @@ static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, co return -1; } -#if 1 +#if 0 #define GGML_SCHED_MAX_SPLITS_DEBUG 4096 static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_SCHED_MAX_SPLITS_DEBUG*GGML_SCHED_MAX_SPLIT_INPUTS][128]; // debug only #define SET_CAUSE(node, ...) sprintf(causes[hash_id(node)], __VA_ARGS__) @@ -1520,8 +724,6 @@ static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_SCHED_MAX_SPLITS_DEBUG*GGML // returns the backend that should be used for the node based on the current locations static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, struct ggml_tensor * tensor) { - // TODO: use supports_op to check if the backend supports the op - // assign pre-allocated nodes to their backend int cur_backend_id = ggml_backend_sched_backend_from_buffer(sched, tensor, tensor); if (cur_backend_id != -1) { @@ -1540,7 +742,8 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st if (tensor->buffer || (tensor->view_src && tensor->view_src->buffer)) { // since the tensor is pre-allocated, it cannot be moved to another backend - GGML_ABORT("pre-allocated tensor in a backend that cannot run the operation"); + ggml_backend_buffer_t buffer = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; + GGML_ABORT("pre-allocated tensor (%s) in a buffer (%s) that cannot run the operation (%s)", tensor->name, ggml_backend_buffer_name(buffer), ggml_op_name(tensor->op)); } // graph input @@ -2247,7 +1450,7 @@ ggml_backend_sched_t ggml_backend_sched_new( bool parallel) { GGML_ASSERT(n_backends > 0); GGML_ASSERT(n_backends <= GGML_SCHED_MAX_BACKENDS); - GGML_ASSERT(ggml_backend_is_cpu(backends[n_backends - 1])); // last backend must be CPU + GGML_ASSERT(ggml_backend_dev_type(ggml_backend_get_device(backends[n_backends - 1])) == GGML_BACKEND_DEVICE_TYPE_CPU); struct ggml_backend_sched * sched = (ggml_backend_sched *) calloc(1, sizeof(struct ggml_backend_sched)); @@ -2336,12 +1539,13 @@ bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * ggml_backend_sched_split_graph(sched, measure_graph); + ggml_backend_sched_synchronize(sched); + if (!ggml_gallocr_reserve_n(sched->galloc, &sched->graph, sched->node_backend_ids, sched->leaf_backend_ids)) { return false; } ggml_backend_sched_reset(sched); - ggml_backend_sched_synchronize(sched); return true; } @@ -2642,3 +1846,154 @@ bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t return true; } + +// CPU backend - buffer + +static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) { + uintptr_t data = (uintptr_t)buffer->context; + + // align the buffer + if (data % TENSOR_ALIGNMENT != 0) { + data = GGML_PAD(data, TENSOR_ALIGNMENT); + } + + return (void *)data; +} + +static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_aligned_free(buffer->context, buffer->size); +} + +static void ggml_backend_cpu_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) { + memset((char *)tensor->data + offset, value, size); + + GGML_UNUSED(buffer); +} + +static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + memcpy((char *)tensor->data + offset, data, size); + + GGML_UNUSED(buffer); +} + +static void ggml_backend_cpu_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { + memcpy(data, (const char *)tensor->data + offset, size); + + GGML_UNUSED(buffer); +} + +static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { + if (ggml_backend_buffer_is_host(src->buffer)) { + memcpy(dst->data, src->data, ggml_nbytes(src)); + return true; + } + return false; + + GGML_UNUSED(buffer); +} + +static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + memset(buffer->context, value, buffer->size); +} + +static const struct ggml_backend_buffer_i ggml_backend_cpu_buffer_i = { + /* .free_buffer = */ ggml_backend_cpu_buffer_free_buffer, + /* .get_base = */ ggml_backend_cpu_buffer_get_base, + /* .init_tensor = */ NULL, // no initialization required + /* .memset_tensor = */ ggml_backend_cpu_buffer_memset_tensor, + /* .set_tensor = */ ggml_backend_cpu_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_cpu_buffer_get_tensor, + /* .cpy_tensor = */ ggml_backend_cpu_buffer_cpy_tensor, + /* .clear = */ ggml_backend_cpu_buffer_clear, + /* .reset = */ NULL, +}; + +static const struct ggml_backend_buffer_i ggml_backend_cpu_buffer_from_ptr_i = { + /* .free_buffer = */ NULL, // ptr is not owned by the buffer, so it does not need to be freed + /* .get_base = */ ggml_backend_cpu_buffer_get_base, + /* .init_tensor = */ NULL, // no initialization required + /* .memset_tensor = */ ggml_backend_cpu_buffer_memset_tensor, + /* .set_tensor = */ ggml_backend_cpu_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_cpu_buffer_get_tensor, + /* .cpy_tensor = */ ggml_backend_cpu_buffer_cpy_tensor, + /* .clear = */ ggml_backend_cpu_buffer_clear, + /* .reset = */ NULL, +}; + +// CPU backend buffer type + +// this buffer type is defined here to make it available to all backends + +static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + return "CPU"; + + GGML_UNUSED(buft); +} + +static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + void * data = ggml_aligned_malloc(size); + + if (data == NULL) { + GGML_LOG_ERROR("%s: failed to allocate buffer of size %zu\n", __func__, size); + return NULL; + } + + return ggml_backend_buffer_init(buft, ggml_backend_cpu_buffer_i, data, size); +} + +static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + return TENSOR_ALIGNMENT; + + GGML_UNUSED(buft); +} + +static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft) { + return true; + + GGML_UNUSED(buft); +} + +ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) { + static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type = { + /* .iface = */ { + /* .get_name = */ ggml_backend_cpu_buffer_type_get_name, + /* .alloc_buffer = */ ggml_backend_cpu_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes + /* .is_host = */ ggml_backend_cpu_buffer_type_is_host, + }, + /* .device = */ NULL, // FIXME ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0), + /* .context = */ NULL, + }; + + return &ggml_backend_cpu_buffer_type; +} + +static const char * ggml_backend_cpu_buffer_from_ptr_type_get_name(ggml_backend_buffer_type_t buft) { + return "CPU_Mapped"; + + GGML_UNUSED(buft); +} + +static ggml_backend_buffer_type_t ggml_backend_cpu_buffer_from_ptr_type(void) { + static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type = { + /* .iface = */ { + /* .get_name = */ ggml_backend_cpu_buffer_from_ptr_type_get_name, + /* .alloc_buffer = */ ggml_backend_cpu_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes + /* .is_host = */ ggml_backend_cpu_buffer_type_is_host, + }, + /* .device = */ NULL, // FIXME ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0), + /* .context = */ NULL, + }; + + return &ggml_backend_cpu_buffer_type; +} + +ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size) { + GGML_ASSERT((uintptr_t)ptr % TENSOR_ALIGNMENT == 0 && "buffer pointer must be aligned"); + return ggml_backend_buffer_init(ggml_backend_cpu_buffer_from_ptr_type(), ggml_backend_cpu_buffer_from_ptr_i, ptr, size); +} diff --git a/ggml/src/ggml-blas/CMakeLists.txt b/ggml/src/ggml-blas/CMakeLists.txt new file mode 100644 index 000000000..0bf3c05d9 --- /dev/null +++ b/ggml/src/ggml-blas/CMakeLists.txt @@ -0,0 +1,87 @@ +if (GGML_STATIC) + set(BLA_STATIC ON) +endif() +#if (CMAKE_VERSION VERSION_GREATER_EQUAL 3.22) +# set(BLA_SIZEOF_INTEGER 8) +#endif() + +set(BLA_VENDOR ${GGML_BLAS_VENDOR}) +find_package(BLAS) + +if (BLAS_FOUND) + message(STATUS "BLAS found, Libraries: ${BLAS_LIBRARIES}") + + ggml_add_backend_library(ggml-blas + ggml-blas.cpp + ) + + if (${GGML_BLAS_VENDOR} MATCHES "Apple") + add_compile_definitions(ACCELERATE_NEW_LAPACK) + add_compile_definitions(ACCELERATE_LAPACK_ILP64) + add_compile_definitions(GGML_BLAS_USE_ACCELERATE) + elseif ("${BLAS_INCLUDE_DIRS}" STREQUAL "") + # BLAS_INCLUDE_DIRS is missing in FindBLAS.cmake. + # see https://gitlab.kitware.com/cmake/cmake/-/issues/20268 + find_package(PkgConfig REQUIRED) + if (${GGML_BLAS_VENDOR} MATCHES "Generic") + pkg_check_modules(DepBLAS blas) + elseif (${GGML_BLAS_VENDOR} MATCHES "OpenBLAS") + # As of openblas v0.3.22, the 64-bit is named openblas64.pc + pkg_check_modules(DepBLAS openblas64) + if (NOT DepBLAS_FOUND) + pkg_check_modules(DepBLAS openblas) + endif() + elseif (${GGML_BLAS_VENDOR} MATCHES "FLAME") + add_compile_definitions(GGML_BLAS_USE_BLIS) + pkg_check_modules(DepBLAS blis) + elseif (${GGML_BLAS_VENDOR} MATCHES "ATLAS") + pkg_check_modules(DepBLAS blas-atlas) + elseif (${GGML_BLAS_VENDOR} MATCHES "FlexiBLAS") + pkg_check_modules(DepBLAS flexiblas_api) + elseif (${GGML_BLAS_VENDOR} MATCHES "Intel") + add_compile_definitions(GGML_BLAS_USE_MKL) + # all Intel* libraries share the same include path + pkg_check_modules(DepBLAS mkl-sdl) + elseif (${GGML_BLAS_VENDOR} MATCHES "NVHPC") + # this doesn't provide pkg-config + # suggest to assign BLAS_INCLUDE_DIRS on your own + if ("${NVHPC_VERSION}" STREQUAL "") + message(WARNING "Better to set NVHPC_VERSION") + else() + set(DepBLAS_FOUND ON) + set(DepBLAS_INCLUDE_DIRS "/opt/nvidia/hpc_sdk/${CMAKE_SYSTEM_NAME}_${CMAKE_SYSTEM_PROCESSOR}/${NVHPC_VERSION}/math_libs/include") + endif() + endif() + if (DepBLAS_FOUND) + set(BLAS_INCLUDE_DIRS ${DepBLAS_INCLUDE_DIRS}) + else() + message(WARNING "BLAS_INCLUDE_DIRS neither been provided nor been automatically" + " detected by pkgconfig, trying to find cblas.h from possible paths...") + find_path(BLAS_INCLUDE_DIRS + NAMES cblas.h + HINTS + /usr/include + /usr/local/include + /usr/include/openblas + /opt/homebrew/opt/openblas/include + /usr/local/opt/openblas/include + /usr/include/x86_64-linux-gnu/openblas/include + ) + endif() + endif() + + message(STATUS "BLAS found, Includes: ${BLAS_INCLUDE_DIRS}") + + target_compile_options(ggml-blas PRIVATE ${BLAS_LINKER_FLAGS}) + + if (${BLAS_INCLUDE_DIRS} MATCHES "mkl" AND (${GGML_BLAS_VENDOR} MATCHES "Generic" OR ${GGML_BLAS_VENDOR} MATCHES "Intel")) + add_compile_definitions(GGML_BLAS_USE_MKL) + endif() + + target_link_libraries (ggml-blas PRIVATE ${BLAS_LIBRARIES}) + target_include_directories(ggml-blas PRIVATE ${BLAS_INCLUDE_DIRS}) +else() + message(ERROR "BLAS not found, please refer to " + "https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors" + " to set correct GGML_BLAS_VENDOR") +endif() diff --git a/ggml/src/ggml-blas.cpp b/ggml/src/ggml-blas/ggml-blas.cpp similarity index 98% rename from ggml/src/ggml-blas.cpp rename to ggml/src/ggml-blas/ggml-blas.cpp index 8d96220b9..ec158dfac 100644 --- a/ggml/src/ggml-blas.cpp +++ b/ggml/src/ggml-blas/ggml-blas.cpp @@ -6,7 +6,7 @@ #include #include -#if defined(GGML_USE_ACCELERATE) +#if defined(GGML_BLAS_USE_ACCELERATE) # include #elif defined(GGML_BLAS_USE_MKL) # include @@ -320,7 +320,7 @@ static const char * ggml_backend_blas_device_get_name(ggml_backend_dev_t dev) { } static const char * ggml_backend_blas_device_get_description(ggml_backend_dev_t dev) { - #if defined(GGML_USE_ACCELERATE) + #if defined(GGML_BLAS_USE_ACCELERATE) return "Accelerate"; #elif defined(GGML_BLAS_USE_MKL) return "MKL"; @@ -506,9 +506,12 @@ static const struct ggml_backend_reg_i ggml_backend_blas_reg_i = { ggml_backend_reg_t ggml_backend_blas_reg(void) { static struct ggml_backend_reg ggml_backend_blas_reg = { - /* .iface = */ ggml_backend_blas_reg_i, - /* .context = */ NULL, + /* .api_version = */ GGML_BACKEND_API_VERSION, + /* .iface = */ ggml_backend_blas_reg_i, + /* .context = */ NULL, }; return &ggml_backend_blas_reg; } + +GGML_BACKEND_DL_IMPL(ggml_backend_blas_reg) diff --git a/ggml/src/ggml-cann/CMakeLists.txt b/ggml/src/ggml-cann/CMakeLists.txt new file mode 100644 index 000000000..05cf06bfa --- /dev/null +++ b/ggml/src/ggml-cann/CMakeLists.txt @@ -0,0 +1,76 @@ +if ("cann${CANN_INSTALL_DIR}" STREQUAL "cann" AND DEFINED ENV{ASCEND_TOOLKIT_HOME}) + set(CANN_INSTALL_DIR $ENV{ASCEND_TOOLKIT_HOME}) + message(STATUS "CANN: updated CANN_INSTALL_DIR from ASCEND_TOOLKIT_HOME=$ENV{ASCEND_TOOLKIT_HOME}") +endif() + +# Auto-detech Soc type and Soc version, if detect failed, will abort build +set(SOC_VERSION "") +function(detect_ascend_soc_type SOC_VERSION) + execute_process( + COMMAND bash -c "npu-smi info|awk -F' ' 'NF > 0 && NR==7 {print $3}'" + OUTPUT_VARIABLE npu_info + RESULT_VARIABLE npu_result + OUTPUT_STRIP_TRAILING_WHITESPACE + ) + if("${npu_info}" STREQUAL "" OR ${npu_result}) + message(FATAL_ERROR "Auto-detech ascend soc type failed, please specify manually or check ascend device working normally.") + endif() + set(${SOC_VERSION} "Ascend${npu_info}" PARENT_SCOPE) +endfunction() + +if(NOT SOC_TYPE) + detect_ascend_soc_type(SOC_VERSION) + set(SOC_TYPE "${SOC_VERSION}") + message(STATUS "CANN: SOC_VERSION auto-detected is:${SOC_VERSION}") +endif() + +string(TOLOWER ${SOC_TYPE} SOC_VERSION) # SOC_VERSION need lower + +# Construct Soc specify compile option: ASCEND_#Soc_Major_SN. Such as ASCEND_910B, ASCEND_310P. +string(REGEX MATCH "[0-9]+[a-zA-Z]" SOC_TYPE_MAJOR_SN "${SOC_VERSION}") +set(SOC_TYPE_COMPILE_OPTION "ASCEND_${SOC_TYPE_MAJOR_SN}") +string(TOUPPER ${SOC_TYPE_COMPILE_OPTION} SOC_TYPE_COMPILE_OPTION) + +if (CANN_INSTALL_DIR) + # Only Support Linux. + if (NOT UNIX) + message(FATAL_ERROR "CANN: CANN toolkit supports unix but not ${CMAKE_SYSTEM_NAME}") + endif() + + # Supported platforms: x86-64, arm64 + if (CMAKE_SYSTEM_PROCESSOR STREQUAL "aarch64") + elseif (CMAKE_SYSTEM_PROCESSOR STREQUAL "x86_64" OR CMAKE_SYSTEM_PROCESSOR STREQUAL "amd64") + else() + message(FATAL_ERROR "CANN: CANN toolkit supports x86-64 and arm64 but not ${CMAKE_SYSTEM_PROCESSOR}") + endif() + + # Set header and libs + set(CANN_INCLUDE_DIRS + ${CANN_INSTALL_DIR}/include + ${CANN_INSTALL_DIR}/include/aclnn + ${CANN_INSTALL_DIR}/acllib/include + ) + + add_subdirectory(kernels) + list(APPEND CANN_LIBRARIES + ascendcl + nnopbase + opapi + acl_op_compiler + ascendc_kernels + ) + + file(GLOB GGML_SOURCES_CANN "*.cpp") + + ggml_add_backend_library(ggml-cann ${GGML_SOURCES_CANN}) + target_link_libraries(ggml-cann PRIVATE ${CANN_LIBRARIES}) + target_include_directories(ggml-cann PRIVATE ${CANN_INCLUDE_DIRS}) + target_link_directories(ggml-cann PRIVATE ${CANN_INSTALL_DIR}/lib64) + + target_compile_definitions(ggml-cann PRIVATE "-D${SOC_TYPE_COMPILE_OPTION}") + + message(STATUS "CANN: CANN_INCLUDE_DIRS = ${CANN_INCLUDE_DIRS}") + message(STATUS "CANN: CANN_LIBRARIES = ${CANN_LIBRARIES}") +else() + message(FATAL_ERROR "CANN: Can't find CANN_INSTALL_DIR, did you forget to source set_var.sh?") +endif() diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp index a4ec8418e..b2d857e1e 100644 --- a/ggml/src/ggml-cann/aclnn_ops.cpp +++ b/ggml/src/ggml-cann/aclnn_ops.cpp @@ -22,11 +22,14 @@ #include "aclnn_ops.h" +#include #include +#include #include #include #include #include +#include #include #include #include @@ -34,6 +37,7 @@ #include #include #include +#include #include #include #include @@ -53,6 +57,7 @@ #include #include +#include "ggml-impl.h" #include "kernels/ascendc_kernels.h" #define GGML_COMMON_DECL_C @@ -241,10 +246,14 @@ void ggml_cann_concat(ggml_backend_cann_context& ctx, ggml_tensor* dst) { aclTensor* acl_src1 = ggml_cann_create_tensor(src1); aclTensor* acl_dst = ggml_cann_create_tensor(dst); - int64_t concat_dim = 1; + const int32_t dim = ggml_get_op_params_i32(dst, 0); + + GGML_ASSERT(dim >= 0 && dim < 4); + int32_t acl_dim = 3 - dim; + aclTensor* tensors[] = {acl_src0, acl_src1}; aclTensorList* tensorList = aclCreateTensorList(tensors, 2); - aclnn_concat(ctx, tensorList, acl_dst, concat_dim); + aclnn_concat(ctx, tensorList, acl_dst, acl_dim); ACL_CHECK(aclDestroyTensorList(tensorList)); ACL_CHECK(aclDestroyTensor(acl_dst)); @@ -1096,9 +1105,9 @@ static aclTensor* aclnn_zero(ggml_backend_cann_context& ctx, void* buffer, } /** - * @brief Creates an ACL tensor initialized with ones using a provided buffer. + * @brief Creates an ACL tensor initialized with value using a provided buffer. * - * This function initializes a tensor with ones using the specified buffer and + * This function initializes a tensor with value using the specified buffer and * tensor parameters. * * @param ctx The context for the CANN backend operations. @@ -1111,12 +1120,12 @@ static aclTensor* aclnn_zero(ggml_backend_cann_context& ctx, void* buffer, * @param type_size The size of each element in the tensor data type. * @param value The value to be used for initializing the tensor (default * is 1.0). - * @return An ACL tensor initialized with ones. + * @return An ACL tensor initialized with value. */ -static aclTensor* aclnn_ones(ggml_backend_cann_context& ctx, void* buffer, - size_t n_bytes, int64_t* ne, int64_t dims, - aclDataType type, size_t type_size, - float value = 1.0f) { +static aclTensor* aclnn_values(ggml_backend_cann_context& ctx, void* buffer, + size_t n_bytes, int64_t* ne, int64_t dims, + aclDataType type, size_t type_size, + float value = 1.0f) { aclTensor* acl_tensor = aclnn_zero(ctx, buffer, n_bytes, ne, dims, type, type_size); float alpha_host = 1.0f; @@ -1158,7 +1167,7 @@ void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) { size_t one_tensor_n_bytes = src->ne[0] * ggml_element_size(src); ggml_cann_pool_alloc one_tensor_allocator(ctx.pool(), one_tensor_n_bytes); - aclTensor* acl_gamma = aclnn_ones( + aclTensor* acl_gamma = aclnn_values( ctx, one_tensor_allocator.get(), one_tensor_n_bytes, src->ne, 1, ggml_cann_type_mapping(src->type), ggml_element_size(src)); @@ -1202,9 +1211,9 @@ void ggml_cann_diag_mask(ggml_backend_cann_context& ctx, ggml_tensor* dst, ggml_cann_pool_alloc one_tensor_allocator(ctx.pool(), one_tensor_n_bytes); aclTensor* mask_tensor = - aclnn_ones(ctx, one_tensor_allocator.get(), one_tensor_n_bytes, src->ne, - GGML_MAX_DIMS, ggml_cann_type_mapping(src->type), - ggml_element_size(src), value); + aclnn_values(ctx, one_tensor_allocator.get(), one_tensor_n_bytes, + src->ne, GGML_MAX_DIMS, ggml_cann_type_mapping(src->type), + ggml_element_size(src), value); uint64_t workspaceSize = 0; aclOpExecutor* executor; @@ -1437,10 +1446,6 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ggml_tensor* src0 = dst->src[0]; // kernel ggml_tensor* src1 = dst->src[1]; // input - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32); - GGML_TENSOR_BINARY_OP_LOCALS; // aclnnIm2col only works on 2D. set s1, p1, d1 to 1 to perform 2D @@ -1462,9 +1467,6 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) { const int64_t OH = is_2D ? ne2 : 1; const int64_t OW = ne1; - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb10 == sizeof(float)); - // memory allocated increased to 3x when is_2D == false const int64_t n_bytes_factor = is_2D ? 1 : 3; @@ -1768,6 +1770,92 @@ static void aclnn_sin(ggml_backend_cann_context& ctx, aclTensor* acl_src, ACL_CHECK(aclnnSin(workspaceAddr, workspaceSize, executor, ctx.stream())); } +/** + * @brief Performs element-wise division of tensor1 by tensor2 , multiplies the + result by the scalar value and adds it to self . + * + * Performs element-wise division of tensor1 by tensor2, + * multiplies the result by the scalar value and adds it to self . + * The operation is defined as: + * \f[ + * \text{out}_i = \text{selft}_i + \text{value} \times + \frac{\text{tensor1}_i}{\text{tensor2}_i} + * \f] + + * @param ctx The context for the CANN backend operations. + * @param acl_self The source tensor on which the addcdiv function will be + applied. + * @param tensor1 Numerator tensor. + * @param tensor2 Denominator tensor. + * @param value The value to be used for coefficient. + */ +static void aclnn_inplace_addcdiv(ggml_backend_cann_context& ctx, + aclTensor* acl_self, aclTensor* tensor1, + aclTensor* tensor2, float value) { + uint64_t workspaceSize = 0; + aclOpExecutor* executor; + void* workspaceAddr = nullptr; + aclScalar* acl_value = aclCreateScalar(&value, aclDataType::ACL_FLOAT); + + ACL_CHECK(aclnnInplaceAddcdivGetWorkspaceSize( + acl_self, tensor1, tensor2, acl_value, &workspaceSize, &executor)); + if (workspaceSize > 0) { + ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); + workspaceAddr = workspace_allocator.get(); + } + + ACL_CHECK(aclnnInplaceAddcdiv(workspaceAddr, workspaceSize, executor, + ctx.stream())); +} + +/** + * @brief Matrix division, optionally in-place. + * + * This function division each element of the source tensor `acl_src` by the + * tensor `acl_other` and stores the result in the destination tensor `acl_dst`. + * If `inplace` is true, `acl_dst` will not be used and the operation is + * performed in-place on `acl_src`. The operation is defined as: \f[ + * \text{dst}_i = \frac{\text{acl_src}_i}{\text{acl_other}_i} + * \f] + * + * @param ctx The context for the CANN backend operations. + * @param acl_src Numerator tensor.. + * @param acl_other Denominator tensor. + * @param acl_dst The destination tensor where the result will be stored if + * `inplace` is false. + * @param inplace Flag indicating whether to perform the operation in-place on + * `acl_src`. + */ +static void aclnn_div_tensor(ggml_backend_cann_context& ctx, aclTensor* acl_src, + aclTensor* acl_other, aclTensor* acl_dst, + bool inplace) { + uint64_t workspaceSize = 0; + aclOpExecutor* executor; + void* workspaceAddr = nullptr; + + if (inplace) { + ACL_CHECK(aclnnInplaceDivGetWorkspaceSize(acl_src, acl_other, + &workspaceSize, &executor)); + if (workspaceSize > 0) { + ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); + workspaceAddr = workspace_allocator.get(); + } + + ACL_CHECK(aclnnInplaceDiv(workspaceAddr, workspaceSize, executor, + ctx.stream())); + } else { + ACL_CHECK(aclnnDivGetWorkspaceSize(acl_src, acl_other, acl_dst, + &workspaceSize, &executor)); + if (workspaceSize > 0) { + ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); + workspaceAddr = workspace_allocator.get(); + } + + ACL_CHECK( + aclnnDiv(workspaceAddr, workspaceSize, executor, ctx.stream())); + } +} + void ggml_cann_timestep_embedding(ggml_backend_cann_context& ctx, ggml_tensor* dst) { const ggml_tensor* src = dst->src[0]; @@ -2311,7 +2399,16 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ctx.stream())); switch (src0->type) { - case GGML_TYPE_F32: + case GGML_TYPE_F32: { +#ifdef ASCEND_310P + // Special operation for get_row_f32 kernel of 310P: clear the + // content of dest data buffer when row is not aligned to 32 bytes + if ((src0->ne[0] % 8) != 0) { + size_t dst_len = src1->ne[0] * src1->ne[1] * src1->ne[2] * + src0->ne[0] * ggml_type_size(GGML_TYPE_F32); + ACL_CHECK(aclrtMemset((char*)dst->data, dst_len, 0, dst_len)); + } +#endif aclrtlaunch_ascendc_get_row_f32( 24, ctx.stream(), src0->data, src1->data, dst->data, ((ggml_tensor*)src0->extra)->ne, @@ -2320,7 +2417,19 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ((ggml_tensor*)src1->extra)->nb, ((ggml_tensor*)dst->extra)->ne, ((ggml_tensor*)dst->extra)->nb); break; - case GGML_TYPE_F16: + } + case GGML_TYPE_F16: { +#ifdef ASCEND_310P + // Special operation for get_row_f16 kernel of 310P: clear the + // content of dest data buffer when row is not aligned to 32 bytes + if ((src0->ne[0] % 16) != 0) { + size_t dst_len = + src1->ne[0] * src1->ne[1] * src1->ne[2] * src0->ne[0] * + ggml_type_size( + GGML_TYPE_F32); // out is also f32, even input is f16 + ACL_CHECK(aclrtMemset((char*)dst->data, dst_len, 0, dst_len)); + } +#endif aclrtlaunch_ascendc_get_row_f16( 24, ctx.stream(), src0->data, src1->data, dst->data, ((ggml_tensor*)src0->extra)->ne, @@ -2329,6 +2438,7 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ((ggml_tensor*)src1->extra)->nb, ((ggml_tensor*)dst->extra)->ne, ((ggml_tensor*)dst->extra)->nb); break; + } case GGML_TYPE_Q4_0: aclrtlaunch_ascendc_get_row_q4_0( 24, ctx.stream(), src0->data, src1->data, dst->data, @@ -2407,7 +2517,6 @@ static void aclnn_mat_mul(ggml_backend_cann_context& ctx, aclTensor* acl_input, aclTensor* acl_weight, aclTensor* acl_dst) { int8_t cube_math_type = 1; // ALLOW_FP32_DOWN_PRECISION, when input is // fp32, atlas a2 will transpose it to HFLOAT32. - uint64_t workspaceSize = 0; aclOpExecutor* executor; void* workspaceAddr = nullptr; @@ -2425,6 +2534,81 @@ static void aclnn_mat_mul(ggml_backend_cann_context& ctx, aclTensor* acl_input, aclnnMatmul(workspaceAddr, workspaceSize, executor, ctx.stream())); } +/** + * @brief Performs matrix multiplication of two 2D tensors. + * + * This function computes the matrix multiplication of the input tensor + * `acl_input` and the weight tensor `acl_weight`, and stores the result in the + * destination tensor `acl_dst`. + * The operation is defined as: + * \f[ + * \text {acl_dst}=\text {acl_input@acl_weight} + * \f] + * + * @param ctx The context for the CANN backend operations. + * @param acl_input The input tensor for the matrix multiplication. + * @param acl_weight The weight tensor for the matrix multiplication. + * @param acl_dst The destination tensor where the result of the matrix + * multiplication will be stored. + */ +static void aclnn_mat_mul_2d(ggml_backend_cann_context& ctx, + aclTensor* acl_input, aclTensor* acl_weight, + aclTensor* acl_dst) { + int8_t cube_math_type = 2; + uint64_t workspaceSize = 0; + aclOpExecutor* executor; + void* workspaceAddr = nullptr; + + ACL_CHECK(aclnnMmGetWorkspaceSize(acl_input, acl_weight, acl_dst, + cube_math_type, &workspaceSize, + &executor)); + + if (workspaceSize > 0) { + ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); + workspaceAddr = workspace_allocator.get(); + } + + ACL_CHECK(aclnnMm(workspaceAddr, workspaceSize, executor, ctx.stream())); +} + +/** + * @brief Performs matrix multiplication of two 3D tensors. + * + * This function computes the matrix multiplication of the input tensor + * `acl_input` and the weight tensor `acl_weight`, and stores the result in the + * destination tensor `acl_dst`. + * The operation is defined as: + * \f[ + * \text {acl_dst}=\text {acl_input@acl_weight} + * \f] + * + * @param ctx The context for the CANN backend operations. + * @param acl_input The input tensor for the matrix multiplication. + * @param acl_weight The weight tensor for the matrix multiplication. + * @param acl_dst The destination tensor where the result of the matrix + * multiplication will be stored. + */ +static void aclnn_mat_mul_3d(ggml_backend_cann_context& ctx, + aclTensor* acl_input, aclTensor* acl_weight, + aclTensor* acl_dst) { + int8_t cube_math_type = 2; + uint64_t workspaceSize = 0; + aclOpExecutor* executor; + void* workspaceAddr = nullptr; + + ACL_CHECK(aclnnBatchMatMulGetWorkspaceSize(acl_input, acl_weight, acl_dst, + cube_math_type, &workspaceSize, + &executor)); + + if (workspaceSize > 0) { + ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); + workspaceAddr = workspace_allocator.get(); + } + + ACL_CHECK( + aclnnBatchMatMul(workspaceAddr, workspaceSize, executor, ctx.stream())); +} + /** * @brief Performs matrix multiplication with floating-point precision on * tensors using the CANN backend. @@ -2446,20 +2630,39 @@ static void ggml_cann_mat_mul_fp(ggml_backend_cann_context& ctx, // broadcast, when weight ne2 or ne3 is not 1, weight need repeat. BCAST_MUL_MAT_SHAPE(input, weight, dst); - // transpose weight: [1,2,3,4] -> [1,2,4,3] + int64_t n_dims = bcast_dims; + if (bcast_input_ne[3] == bcast_weight_ne[3] && bcast_input_ne[3] == 1) { + if (bcast_input_ne[2] == 1 && bcast_weight_ne[2] == 1) { + n_dims = 2; + } else if (bcast_input_ne[2] == 1) { + n_dims = 3; + } + } + + aclTensor* acl_input_tensor = + ggml_cann_create_tensor(input, bcast_input_ne, bcast_input_nb, n_dims); int64_t transpose_ne[] = {bcast_weight_ne[1], bcast_weight_ne[0], bcast_weight_ne[2], bcast_weight_ne[3], bcast_weight_ne[4], bcast_weight_ne[5]}; size_t transpose_nb[] = {bcast_weight_nb[1], bcast_weight_nb[0], bcast_weight_nb[2], bcast_weight_nb[3], bcast_weight_nb[4], bcast_weight_nb[5]}; - aclTensor* acl_weight_tensor = - ggml_cann_create_tensor(weight, transpose_ne, transpose_nb, bcast_dims); - aclTensor* acl_input_tensor = - ggml_cann_create_tensor(input, BCAST_MUL_MAT_PARAM(input)); - aclTensor* acl_dst = ggml_cann_create_tensor(dst, BCAST_MUL_MAT_PARAM(dst)); - aclnn_mat_mul(ctx, acl_input_tensor, acl_weight_tensor, acl_dst); + ggml_cann_create_tensor(weight, transpose_ne, transpose_nb, n_dims); + aclTensor* acl_dst = + ggml_cann_create_tensor(dst, bcast_dst_ne, bcast_dst_nb, n_dims); + + switch (n_dims) { + case 2: + aclnn_mat_mul_2d(ctx, acl_input_tensor, acl_weight_tensor, acl_dst); + break; + case 3: + aclnn_mat_mul_3d(ctx, acl_input_tensor, acl_weight_tensor, acl_dst); + break; + default: + aclnn_mat_mul(ctx, acl_input_tensor, acl_weight_tensor, acl_dst); + break; + } ACL_CHECK(aclDestroyTensor(acl_weight_tensor)); ACL_CHECK(aclDestroyTensor(acl_input_tensor)); @@ -2480,51 +2683,47 @@ static void ggml_cann_mat_mul_fp(ggml_backend_cann_context& ctx, * multiplication will be stored. */ static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx, - ggml_tensor* dst, - const enum ggml_type type) { + ggml_tensor* dst, + const enum ggml_type type) { ggml_tensor* src0 = dst->src[0]; // weight ggml_tensor* src1 = dst->src[1]; // input - // The shape of the weight is NCHW. Matrix multiplication uses HW dims. HC - // is regarded as batch. weight need transpose. - int64_t weight_ne[] = {src0->ne[1], src0->ne[0]}; + // The shape of the weight is NCHW. + // Matrix multiplication uses HW dims. + // HC is regarded as batch. + // weight need transpose. float weight_elem_size; if (type == GGML_TYPE_Q4_0) { weight_elem_size = float(sizeof(uint8_t)) / 2; - } - else if (type == GGML_TYPE_Q8_0) { + } else if (type == GGML_TYPE_Q8_0) { weight_elem_size = float(sizeof(uint8_t)); - } - else { + } else { GGML_ABORT("Only support Q4_0 and Q8_0 MUL_MAT"); } - float weight_nb[] = {weight_elem_size * src0->ne[0], weight_elem_size}; - - // size of one matrix is element_size * height * width. - size_t weight_stride = weight_elem_size * src0->ne[0] * src0->ne[1]; + float weight_nb[] = {src0->ne[0] * weight_elem_size, weight_elem_size}; + size_t weight_stride = src0->ne[1] * src0->ne[0] * weight_elem_size; size_t weight_size = weight_stride * src0->ne[2] * src0->ne[3]; // scale stored at the end of weight. Also need transpose. - GGML_ASSERT(QK4_0 == QK8_0); - int64_t scale_ne[] = {src0->ne[1], src0->ne[0] / QK8_0}; size_t scale_elem_size = sizeof(uint16_t); size_t scale_nb[] = {src0->ne[0] / QK8_0 * scale_elem_size, scale_elem_size}; - size_t scale_stride = scale_elem_size * src0->ne[0] * src0->ne[1] / QK8_0; + size_t scale_stride = src0->ne[1] * src0->ne[0] / QK8_0 * scale_elem_size; char* scale_offset = (char*)src0->data + weight_size; // input - void* input_buffer; size_t input_elem_size = sizeof(uint16_t); int64_t input_ne[] = {src1->ne[0], src1->ne[1]}; - size_t input_nb[] = {input_elem_size, input_elem_size * src1->ne[0]}; - size_t input_stride = input_elem_size * src1->ne[0] * src1->ne[1]; - + size_t input_nb[] = {input_elem_size, input_ne[0] * input_elem_size}; + size_t input_stride = input_ne[0] * input_ne[1] * input_elem_size; ggml_cann_pool_alloc input_alloctor(ctx.pool()); + void* input_buffer = src1->data; + + // case in if (src1->type != GGML_TYPE_F16) { aclTensor* acl_src1_tensor = ggml_cann_create_tensor(src1); - input_alloctor.alloc(ggml_nelements(src1) * input_elem_size); - input_buffer = input_alloctor.get(); + input_buffer = + input_alloctor.alloc(ggml_nelements(src1) * input_elem_size); int64_t* input_cast_ne = src1->ne; size_t input_cast_nb[GGML_MAX_DIMS]; @@ -2537,85 +2736,136 @@ static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx, input_buffer, ACL_FLOAT16, input_elem_size, input_cast_ne, input_cast_nb, GGML_MAX_DIMS); aclnn_cast(ctx, acl_src1_tensor, acl_input_tensor, ACL_FLOAT16); + ACL_CHECK(aclDestroyTensor(acl_input_tensor)); ACL_CHECK(aclDestroyTensor(acl_src1_tensor)); - } else { - input_buffer = src1->data; } // output size_t output_elem_size = sizeof(uint16_t); - int64_t output_ne[] = {dst->ne[0], dst->ne[1]}; - size_t output_nb[] = {output_elem_size, output_elem_size * dst->ne[0]}; - ggml_cann_pool_alloc output_alloctor( - ctx.pool(), ggml_nelements(dst) * output_elem_size); - void* output_buffer = output_alloctor.get(); - size_t output_stride = output_elem_size * dst->ne[0] * dst->ne[1]; + size_t output_nb[] = {output_elem_size, dst->ne[0] * output_elem_size}; + ggml_cann_pool_alloc output_allocator(ctx.pool()); + void* output_buffer = + output_allocator.alloc(ggml_nelements(dst) * output_elem_size); + size_t output_stride = dst->ne[0] * dst->ne[1] * output_elem_size; // aclnn + int64_t max_elem_size = 65535; + int64_t split_size = (src0->ne[1] / max_elem_size) + 1; + ggml_cann_pool_alloc workspace_allocator(ctx.pool()); + aclOpExecutor* executor = nullptr; uint64_t workspaceSize = 0; - aclOpExecutor* executor; void* workspaceAddr = nullptr; - for (int64_t n1 = 0; n1 < src1->ne[3]; n1++) { for (int64_t c1 = 0; c1 < src1->ne[2]; c1++) { int64_t n0 = n1 / (src1->ne[3] / src0->ne[3]); int64_t c0 = c1 / (src1->ne[2] / src0->ne[2]); - int64_t batch1 = n1 * src1->ne[2] + c1; - int64_t batch0 = n0 * src0->ne[2] + c0; + int64_t batch1 = (n1 * src1->ne[2]) + c1; + int64_t batch0 = (n0 * src0->ne[2]) + c0; aclTensor* acl_input_tensor = ggml_cann_create_tensor( (char*)input_buffer + batch1 * input_stride, ACL_FLOAT16, input_elem_size, input_ne, input_nb, 2); + + // first split + int64_t weight_ne_offset = 0; + int64_t weight_ne[2] = { + max_elem_size > src0->ne[1] ? src0->ne[1] : max_elem_size, + src0->ne[0]}; + int64_t scale_ne_offset = 0; + int64_t scale_ne[2] = {weight_ne[0], weight_ne[1] / QK8_0}; + int64_t output_ne_offset = 0; + int64_t output_ne[2] = {weight_ne[0], dst->ne[1]}; + aclTensor* acl_weight_tensor = ggml_cann_create_tensor( (char*)src0->data + batch0 * weight_stride, ggml_cann_type_mapping(type), weight_elem_size, weight_ne, - weight_nb, 2); + weight_nb, 2, ACL_FORMAT_ND, weight_ne_offset); aclTensor* acl_scale_tensor = ggml_cann_create_tensor( scale_offset + batch0 * scale_stride, ACL_FLOAT16, - scale_elem_size, scale_ne, scale_nb, 2); + scale_elem_size, scale_ne, scale_nb, 2, ACL_FORMAT_ND, + scale_ne_offset); aclTensor* acl_output_tensor = ggml_cann_create_tensor( (char*)output_buffer + batch1 * output_stride, ACL_FLOAT16, - output_elem_size, output_ne, output_nb, 2); + output_elem_size, output_ne, output_nb, 2, ACL_FORMAT_ND, + output_ne_offset); ACL_CHECK(aclnnWeightQuantBatchMatmulV2GetWorkspaceSize( acl_input_tensor, acl_weight_tensor, acl_scale_tensor, nullptr, nullptr, nullptr, nullptr, QK8_0, acl_output_tensor, &workspaceSize, &executor)); - - if (workspaceSize > 0 && workspaceAddr == nullptr) { - ggml_cann_pool_alloc workspace_allocator(ctx.pool(), - workspaceSize); - workspaceAddr = workspace_allocator.get(); + if (workspaceAddr == nullptr) { + workspaceAddr = workspace_allocator.alloc(workspaceSize); } - ACL_CHECK(aclnnWeightQuantBatchMatmulV2( workspaceAddr, workspaceSize, executor, ctx.stream())); - ACL_CHECK(aclDestroyTensor(acl_input_tensor)); ACL_CHECK(aclDestroyTensor(acl_weight_tensor)); ACL_CHECK(aclDestroyTensor(acl_scale_tensor)); ACL_CHECK(aclDestroyTensor(acl_output_tensor)); + + // other splits + for (int64_t split = 1; split < split_size; split++) { + weight_ne_offset += + weight_elem_size * weight_ne[0] * weight_ne[1]; + weight_ne[0] = max_elem_size * (split + 1) > src0->ne[1] + ? src0->ne[1] - (max_elem_size * split) + : max_elem_size; + scale_ne_offset += scale_elem_size * scale_ne[0] * scale_ne[1]; + scale_ne[0] = weight_ne[0]; + output_ne_offset += + output_elem_size * output_ne[0] * output_ne[1]; + output_ne[0] = weight_ne[0]; + + acl_weight_tensor = ggml_cann_create_tensor( + (char*)src0->data + batch0 * weight_stride, + ggml_cann_type_mapping(type), weight_elem_size, weight_ne, + weight_nb, 2, ACL_FORMAT_ND, weight_ne_offset); + acl_scale_tensor = ggml_cann_create_tensor( + scale_offset + batch0 * scale_stride, ACL_FLOAT16, + scale_elem_size, scale_ne, scale_nb, 2, ACL_FORMAT_ND, + scale_ne_offset); + acl_output_tensor = ggml_cann_create_tensor( + (char*)output_buffer + batch1 * output_stride, ACL_FLOAT16, + output_elem_size, output_ne, output_nb, 2, ACL_FORMAT_ND, + output_ne_offset); + + ACL_CHECK(aclnnWeightQuantBatchMatmulV2GetWorkspaceSize( + acl_input_tensor, acl_weight_tensor, acl_scale_tensor, + nullptr, nullptr, nullptr, nullptr, QK8_0, + acl_output_tensor, &workspaceSize, &executor)); + ACL_CHECK(aclnnWeightQuantBatchMatmulV2( + workspaceAddr, workspaceSize, executor, ctx.stream())); + + ACL_CHECK(aclDestroyTensor(acl_weight_tensor)); + ACL_CHECK(aclDestroyTensor(acl_scale_tensor)); + ACL_CHECK(aclDestroyTensor(acl_output_tensor)); + } + + ACL_CHECK(aclDestroyTensor(acl_input_tensor)); } } // cast out - int64_t* output_cast_ne = dst->ne; - size_t output_cast_nb[GGML_MAX_DIMS]; - output_cast_nb[0] = sizeof(uint16_t); - for (int i = 1; i < GGML_MAX_DIMS; i++) { - output_cast_nb[i] = output_cast_nb[i - 1] * output_cast_ne[i - 1]; + if (dst->type != GGML_TYPE_F16) { + int64_t* output_cast_ne = dst->ne; + size_t output_cast_nb[GGML_MAX_DIMS]; + output_cast_nb[0] = sizeof(uint16_t); + for (int i = 1; i < GGML_MAX_DIMS; i++) { + output_cast_nb[i] = output_cast_nb[i - 1] * output_cast_ne[i - 1]; + } + + aclTensor* acl_output_tensor = ggml_cann_create_tensor( + output_buffer, ACL_FLOAT16, output_elem_size, output_cast_ne, + output_cast_nb, GGML_MAX_DIMS); + aclTensor* acl_dst_tensor = ggml_cann_create_tensor(dst); + aclnn_cast(ctx, acl_output_tensor, acl_dst_tensor, + ggml_cann_type_mapping(dst->type)); + + ACL_CHECK(aclDestroyTensor(acl_output_tensor)); + ACL_CHECK(aclDestroyTensor(acl_dst_tensor)); } - - aclTensor* acl_output_tensor = - ggml_cann_create_tensor(output_buffer, ACL_FLOAT16, output_elem_size, - output_cast_ne, output_cast_nb, GGML_MAX_DIMS); - aclTensor* acl_dst_tensor = ggml_cann_create_tensor(dst); - aclnn_cast(ctx, acl_output_tensor, acl_dst_tensor, ACL_FLOAT); - - ACL_CHECK(aclDestroyTensor(acl_output_tensor)); - ACL_CHECK(aclDestroyTensor(acl_dst_tensor)); } void ggml_cann_mul_mat(ggml_backend_cann_context& ctx, ggml_tensor* dst) { @@ -2714,12 +2964,14 @@ static void aclnn_index_fill_tensor(ggml_backend_cann_context& ctx, static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, aclTensor* acl_cos_repeat_tensor, aclTensor* acl_sin_repeat_tensor, - float theta_scale, bool is_neox) { + float theta_scale, float freq_scale, + float attn_factor, bool is_neox) { // int sin/cos cache, cache has different repeat method depond on // @param.is_neox ggml_tensor* src0 = dst->src[0]; // input ggml_tensor* src1 = dst->src[1]; // position + ggml_tensor* src2 = dst->src[2]; // freq_factors // arange, [0,1,...,ne0/2] int64_t arange_length = src0->ne[0] / 2; @@ -2748,11 +3000,26 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, ggml_cann_pool_alloc theta_scale_allocator(ctx.pool(), arange_length * sizeof(float_t)); void* theta_scale_buffer = theta_scale_allocator.get(); - aclTensor* acl_theta_scale_tensor = aclnn_ones( + aclTensor* acl_theta_scale_tensor = aclnn_values( ctx, theta_scale_buffer, arange_length * sizeof(float_t), arange_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float_t), theta_scale); aclnn_pow_tensor_tensor(ctx, acl_theta_scale_tensor, acl_arange_tensor); + // freq_scale + if (freq_scale != 1) { + aclnn_muls(ctx, acl_theta_scale_tensor, freq_scale, nullptr, true); + } + + // freq_factors + if (src2) { + aclTensor* acl_freq_factors_tensor = ggml_cann_create_tensor( + src2->data, ggml_cann_type_mapping(src2->type), + ggml_type_size(src2->type), arange_ne, arange_nb, GGML_MAX_DIMS); + aclnn_div_tensor(ctx, acl_theta_scale_tensor, acl_freq_factors_tensor, + nullptr, true); + ACL_CHECK(aclDestroyTensor(acl_freq_factors_tensor)); + } + // position GGML_ASSERT(src1->type == GGML_TYPE_I32); int64_t position_length = src1->ne[0]; @@ -2816,6 +3083,12 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, GGML_MAX_DIMS, ACL_FORMAT_ND); aclnn_cos(ctx, acl_permute_tensor, acl_cos_tensor); + // attn_factor + if (attn_factor != 1) { + aclnn_muls(ctx, acl_sin_tensor, attn_factor, nullptr, true); + aclnn_muls(ctx, acl_cos_tensor, attn_factor, nullptr, true); + } + // repeat if (is_neox) { int64_t repeatsArray[] = {1, 1, 1, 2}; @@ -2841,15 +3114,27 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, ACL_CHECK(aclDestroyTensor(acl_cos_tensor)); } +#ifdef __cplusplus +extern "C" { +#endif +aclnnStatus aclnnRotaryPositionEmbeddingGetWorkspaceSize( + const aclTensor* x, const aclTensor* cos, const aclTensor* sin, + int64_t mode, const aclTensor* yOut, uint64_t* workspaceSize, + aclOpExecutor** executor); +aclnnStatus aclnnRotaryPositionEmbedding(void* workspace, + uint64_t workspaceSize, + aclOpExecutor* executor, + aclrtStream stream); +#ifdef __cplusplus +} +#endif + void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { // TODO: use ascendc // Only test with LLAMA model. ggml_tensor* src0 = dst->src[0]; // input ggml_tensor* src2 = dst->src[2]; // freq_factors - // TODO: with freq_factors - GGML_ASSERT(src2 == NULL); - // param float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; // const int n_past = ((int32_t *) dst->op_params)[0]; @@ -2867,13 +3152,11 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { memcpy(&beta_fast, (int32_t*)dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t*)dst->op_params + 10, sizeof(float)); - GGML_ASSERT(n_dims <= ne0); + // TODO: n_dims <= ne0 + GGML_ASSERT(n_dims == ne0); GGML_ASSERT(n_dims % 2 == 0); - // TODO: ext_factor != 0 GGML_ASSERT(ext_factor == 0); - // TODO: freq_scale != 1 - GGML_ASSERT(freq_scale == 1); const float theta_scale = powf(freq_base, -2.0f / n_dims); @@ -2904,7 +3187,13 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ggml_cann_create_tensor(cos_buffer, ACL_FLOAT, sizeof(float_t), sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS); aclnn_cache_init(ctx, dst, acl_cos_reshape_tensor, acl_sin_reshape_tensor, - theta_scale, is_neox); + theta_scale, freq_scale, attn_factor, is_neox); + + aclTensor* acl_src = ggml_cann_create_tensor(src0); + aclTensor* acl_dst = ggml_cann_create_tensor(dst); + +#ifdef ASCEND_310P + // Special ROPE operation for 310P // roll input void* input_roll_buffer; @@ -2947,7 +3236,7 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { for (int i = 1; i < GGML_MAX_DIMS; i++) { minus_one_nb[i] = minus_one_nb[i - 1] * minus_one_ne[i - 1]; } - acl_minus_one_tensor = aclnn_ones( + acl_minus_one_tensor = aclnn_values( ctx, minus_one_scale_buffer, sizeof(float_t) * src0->ne[0], minus_one_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float_t), 1); int64_t dim = 3; @@ -2974,17 +3263,15 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ACL_CHECK(aclDestroyTensor(acl_input_roll_tensor)); ACL_CHECK(aclDestroyTensor(acl_input_tensor)); - // init [-1, -1, -1, 1, 1，1，...] minus_one_scale_buffer = minus_one_scale_allocator.get(); - int64_t minus_one_ne[4] = {src0->ne[0], 1, 1, 1}; size_t minus_one_nb[GGML_MAX_DIMS]; minus_one_nb[0] = sizeof(float_t); for (int i = 1; i < GGML_MAX_DIMS; i++) { minus_one_nb[i] = minus_one_nb[i - 1] * minus_one_ne[i - 1]; } - acl_minus_one_tensor = aclnn_ones( + acl_minus_one_tensor = aclnn_values( ctx, minus_one_scale_buffer, sizeof(float_t) * src0->ne[0], minus_one_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float_t), 1); // -1 * first half @@ -3026,14 +3313,12 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { acl_input_roll_mul_scale_tensor); // output - aclTensor* acl_src0 = ggml_cann_create_tensor(src0); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); void* output_fp32_buffer; if (src0->type == GGML_TYPE_F32) { - aclnn_inplace_mul(ctx, acl_src0, acl_cos_reshape_tensor); + aclnn_inplace_mul(ctx, acl_src, acl_cos_reshape_tensor); aclnn_inplace_mul(ctx, acl_input_roll_mul_scale_tensor, acl_sin_reshape_tensor); - aclnn_add(ctx, acl_src0, acl_input_roll_mul_scale_tensor, acl_dst); + aclnn_add(ctx, acl_src, acl_input_roll_mul_scale_tensor, acl_dst); // TODO: ne0 != n_dims in mode2 } else if (src0->type == GGML_TYPE_F16) { size_t input_fp32_nb[GGML_MAX_DIMS]; @@ -3060,7 +3345,7 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { aclTensor* output_fp32_tensor = ggml_cann_create_tensor( output_fp32_buffer, ACL_FLOAT, sizeof(float_t), dst->ne, input_fp32_nb, GGML_MAX_DIMS); - aclnn_mul(ctx, acl_src0, acl_cos_reshape_tensor, input_fp32_tensor1); + aclnn_mul(ctx, acl_src, acl_cos_reshape_tensor, input_fp32_tensor1); aclnn_mul(ctx, acl_input_roll_mul_scale_tensor, acl_sin_reshape_tensor, input_fp32_tensor2); aclnn_add(ctx, input_fp32_tensor1, input_fp32_tensor2, @@ -3070,13 +3355,73 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ACL_CHECK(aclDestroyTensor(input_fp32_tensor1)); ACL_CHECK(aclDestroyTensor(input_fp32_tensor2)); ACL_CHECK(aclDestroyTensor(output_fp32_tensor)); + ACL_CHECK(aclDestroyTensor(acl_sin_reshape_tensor)); + ACL_CHECK(aclDestroyTensor(acl_minus_one_tensor)); + ACL_CHECK(aclDestroyTensor(acl_input_roll_mul_scale_tensor)); + ACL_CHECK(aclDestroyTensor(acl_input_roll_reshape_tensor)); + ACL_CHECK(aclDestroyTensor(acl_src)); + } + return; +#endif + + // src0 == GGML_TYPE_F16 + // TODO: optimization this `if` code + if (src0->type == GGML_TYPE_F16) { + ggml_cann_pool_alloc sin_final_allocator( + ctx.pool(), src0->ne[0] * src0->ne[2] * ggml_type_size(src0->type)); + ggml_cann_pool_alloc cos_final_allocator( + ctx.pool(), src0->ne[0] * src0->ne[2] * ggml_type_size(src0->type)); + void* sin_final_buffer = sin_final_allocator.get(); + void* cos_final_buffer = cos_final_allocator.get(); + + int64_t sin_final_ne[4] = {src0->ne[0], 1, src0->ne[2], 1}; + size_t sin_final_nb[GGML_MAX_DIMS]; + sin_final_nb[0] = ggml_type_size(src0->type); + for (int i = 1; i < GGML_MAX_DIMS; i++) { + sin_final_nb[i] = sin_final_nb[i - 1] * sin_final_ne[i - 1]; + } + aclTensor* acl_sin_final_tensor = ggml_cann_create_tensor( + sin_final_buffer, ggml_cann_type_mapping(src0->type), + ggml_type_size(src0->type), sin_final_ne, sin_final_nb, + GGML_MAX_DIMS); + aclTensor* acl_cos_final_tensor = ggml_cann_create_tensor( + cos_final_buffer, ggml_cann_type_mapping(src0->type), + ggml_type_size(src0->type), sin_final_ne, sin_final_nb, + GGML_MAX_DIMS); + + aclnn_cast(ctx, acl_sin_reshape_tensor, acl_sin_final_tensor, + ggml_cann_type_mapping(src0->type)); + aclnn_cast(ctx, acl_cos_reshape_tensor, acl_cos_final_tensor, + ggml_cann_type_mapping(src0->type)); + ACL_CHECK(aclDestroyTensor(acl_cos_reshape_tensor)); + ACL_CHECK(aclDestroyTensor(acl_sin_reshape_tensor)); + acl_sin_reshape_tensor = acl_sin_final_tensor; + acl_cos_reshape_tensor = acl_cos_final_tensor; } - ACL_CHECK(aclDestroyTensor(acl_sin_reshape_tensor)); + uint64_t workspaceSize = 0; + aclOpExecutor* executor; + + void* workspaceAddr = nullptr; + + int acl_mode = mode; + if (mode == 0) { + acl_mode = 1; + } + + ACL_CHECK(aclnnRotaryPositionEmbeddingGetWorkspaceSize( + acl_src, acl_cos_reshape_tensor, acl_sin_reshape_tensor, acl_mode, + acl_dst, &workspaceSize, &executor)); + if (workspaceSize > 0) { + ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); + workspaceAddr = workspace_allocator.get(); + } + + ACL_CHECK(aclnnRotaryPositionEmbedding(workspaceAddr, workspaceSize, + executor, ctx.stream())); + + ACL_CHECK(aclDestroyTensor(acl_src)); ACL_CHECK(aclDestroyTensor(acl_cos_reshape_tensor)); - ACL_CHECK(aclDestroyTensor(acl_minus_one_tensor)); - ACL_CHECK(aclDestroyTensor(acl_input_roll_mul_scale_tensor)); - ACL_CHECK(aclDestroyTensor(acl_input_roll_reshape_tensor)); - ACL_CHECK(aclDestroyTensor(acl_src0)); + ACL_CHECK(aclDestroyTensor(acl_sin_reshape_tensor)); ACL_CHECK(aclDestroyTensor(acl_dst)); } diff --git a/ggml/src/ggml-cann/common.h b/ggml/src/ggml-cann/common.h index edfa49614..5164cb74e 100644 --- a/ggml/src/ggml-cann/common.h +++ b/ggml/src/ggml-cann/common.h @@ -211,17 +211,20 @@ struct ggml_cann_pool_alloc { struct ggml_backend_cann_context { int32_t device; /**< Device ID. */ std::string name; /**< Name of the device. */ + std::string description; /**< Description of the device. */ aclrtEvent copy_event = nullptr; /**< Event for managing copy operations. */ - aclrtStream streams[GGML_CANN_MAX_STREAMS] = { - {nullptr}}; /**< Array of streams for the device. */ + aclrtStream streams[GGML_CANN_MAX_STREAMS] = {nullptr}; /**< Array of streams for the device. */ /** * @brief Constructor for initializing the context with a given device. * @param device Device ID. */ explicit ggml_backend_cann_context(int device) - : device(device), name("CANN" + std::to_string(device)) {} + : device(device), name("CANN" + std::to_string(device)) { + ggml_cann_set_device(device); + description = aclrtGetSocName(); + } /** * @brief Destructor for cleaning up resources. diff --git a/ggml/src/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp similarity index 96% rename from ggml/src/ggml-cann.cpp rename to ggml/src/ggml-cann/ggml-cann.cpp index f8ac11e41..04e25b8ab 100644 --- a/ggml/src/ggml-cann.cpp +++ b/ggml/src/ggml-cann/ggml-cann.cpp @@ -122,6 +122,10 @@ static ggml_cann_device_info ggml_cann_init() { ACL_CHECK(aclrtMemGetAllocationGranularity( &prop, ACL_RT_MEM_ALLOC_GRANULARITY_RECOMMENDED, &info.devices[id].vmm_granularity)); + + size_t free, total; + ggml_backend_cann_get_device_memory(id, &free, &total); + info.devices[id].total_vram = free; } // TODO: add more device info later. @@ -208,6 +212,11 @@ struct ggml_cann_pool_leg : public ggml_cann_pool { * @return A pointer to the allocated buffer. */ void* alloc(size_t size, size_t* actual_size) override { + const size_t alignment = 128; + size = GGML_PAD(size, alignment); + if (size == 0) { + size = alignment; + } #ifdef DEBUG_CANN_MALLOC int nnz = 0; size_t max_size = 0; @@ -246,13 +255,11 @@ struct ggml_cann_pool_leg : public ggml_cann_pool { return ptr; } void* ptr; - size_t look_ahead_size = (size_t)(1.05 * size); - look_ahead_size = 256 * ((look_ahead_size + 255) / 256); ggml_cann_set_device(device); ACL_CHECK( - aclrtMalloc(&ptr, look_ahead_size, ACL_MEM_MALLOC_HUGE_FIRST)); - *actual_size = look_ahead_size; - pool_size += look_ahead_size; + aclrtMalloc(&ptr, size, ACL_MEM_MALLOC_HUGE_FIRST)); + *actual_size = size; + pool_size += size; #ifdef DEBUG_CANN_MALLOC GGML_LOG_INFO( "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, " @@ -296,7 +303,7 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool { /** * @brief The maximum size of the virtual memory pool (32 GB). */ - static const size_t CANN_POOL_VMM_MAX_SIZE = 1ull << 35; // 32 GB + size_t max_size; /** * @brief The device ID associated with this buffer pool. @@ -341,7 +348,11 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool { */ explicit ggml_cann_pool_vmm(int device) : device(device), - granularity(ggml_cann_info().devices[device].vmm_granularity) {} + granularity(ggml_cann_info().devices[device].vmm_granularity) { + auto dev = ggml_cann_info().devices[device]; + granularity = dev.vmm_granularity; + max_size = dev.total_vram; + } /** * @brief Destructor to free all buffers in the virtual memory pool. @@ -370,17 +381,19 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool { // round up the allocation size to the alignment to ensure that all // allocations are aligned for all data types const size_t alignment = 128; - size = alignment * ((size + alignment - 1) / alignment); + size = GGML_PAD(size, alignment); + if (size == 0) { + size = alignment; + } size_t avail = pool_size - pool_used; if (size > avail) { // round up to the next multiple of the granularity size_t reserve_size = size - avail; - reserve_size = - granularity * ((reserve_size + granularity - 1) / granularity); + reserve_size = GGML_PAD(reserve_size, granularity); - GGML_ASSERT(pool_size + reserve_size <= CANN_POOL_VMM_MAX_SIZE); + GGML_ASSERT(pool_size + reserve_size <= max_size); // allocate more physical memory aclrtPhysicalMemProp prop = {}; @@ -396,7 +409,7 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool { // reserve virtual address space (if not already reserved) if (pool_addr == 0) { ACL_CHECK(aclrtReserveMemAddress( - &pool_addr, CANN_POOL_VMM_MAX_SIZE, 0, NULL, 1)); + &pool_addr, max_size, 0, NULL, 1)); } // map at the end of the pool @@ -409,10 +422,11 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool { // add to the pool pool_size += reserve_size; - // GGML_LOG_INFO("cann pool[%d]: size increased to %llu MB ( - // reserved %llu MB)\n", - // device, (unsigned long long) (pool_size/1024/1024), - // (unsigned long long) (reserve_size/1024/1024)); +#ifdef DEBUG_CANN_MALLOC + GGML_LOG_INFO("cann pool[%d]: size increased to %llu MB (reserved %llu MB)\n", + device, (unsigned long long) (pool_size/1024/1024), + (unsigned long long) (reserve_size/1024/1024)); +#endif } GGML_ASSERT(pool_addr != 0); @@ -457,7 +471,6 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool { */ std::unique_ptr ggml_backend_cann_context::new_pool_for_device( int device) { - // return std::unique_ptr(new ggml_cann_pool_leg(device)); return std::unique_ptr(new ggml_cann_pool_vmm(device)); } @@ -1130,10 +1143,10 @@ ggml_backend_cann_buffer_type(int32_t device) { static bool ggml_backend_cann_buffer_type_initialized = false; if (!ggml_backend_cann_buffer_type_initialized) { - for (int32_t i = 0; i < GGML_CANN_MAX_DEVICES; i++) { + for (int32_t i = 0; i < ggml_cann_info().device_count; i++) { ggml_backend_cann_buffer_types[i] = { /* .iface = */ ggml_backend_cann_buffer_type_interface, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), device), + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), i), /* .context = */ new ggml_backend_cann_buffer_type_context{ i, "CANN" + std::to_string(i)}, @@ -1199,10 +1212,15 @@ static void * ggml_cann_host_malloc(size_t size) { return nullptr; } + const size_t alignment = 128; + size = GGML_PAD(size, alignment); + if (size == 0) { + size = alignment; + } + void * hostPtr = nullptr; aclError err = aclrtMallocHost((void **) &hostPtr, size); if (err != ACL_SUCCESS) { - GGML_LOG_WARN("%s: failed to allocate %.2f MiB of pinned memory: %s\n", __func__, size / 1024.0 / 1024.0, aclGetRecentErrMsg()); return nullptr; @@ -1227,7 +1245,6 @@ static ggml_backend_buffer_t ggml_backend_cann_host_buffer_type_alloc_buffer(ggm ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(hostPtr, size); buffer->buft = buft; - buffer->iface.get_name = ggml_backend_cann_host_buffer_name; buffer->iface.free_buffer = ggml_backend_cann_host_buffer_free; return buffer; @@ -1670,12 +1687,14 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, } case GGML_OP_MUL_MAT: { switch (op->src[0]->type) { + case GGML_TYPE_Q8_0: + // Current groupsize should not be greater than k-1 in + // aclnnWeightQuantBatchMatmulV2GetWorkspaceSize + if (op->src[0]->ne[0] <= QK8_0) { + return false; + } case GGML_TYPE_F16: case GGML_TYPE_F32: - case GGML_TYPE_Q8_0: - // TODO: fix me - // Current groupsize should not be greater than k-1 in - // aclnnWeightQuantBatchMatmulV2GetWorkspaceSize(). case GGML_TYPE_Q4_0: return true; default: @@ -1707,9 +1726,41 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, return false; } } + case GGML_OP_CONT: { + // TODO: support GGML_TYPE_BF16 + switch (op->src[0]->type) { + case GGML_TYPE_F32: + case GGML_TYPE_F16: + return true; + default: + return false; + } + } + case GGML_OP_ROPE: { + // TODO: with ops-test v == 1 + float * ext_factor = (float*)((int32_t*)op->op_params + 7); + // TODO: n_dims <= ne0 + if (op->src[0]->ne[0] != op->op_params[1]) { + return false; + } + // TODO: ext_factor != 0 + if (*ext_factor != 0) { + return false; + } + return true; + } + case GGML_OP_UPSCALE: { + // aclnnUpsampleNearest2dGetWorkspaceSize not support + // selfDimN[2]/outDimN[2] or selfDimC[3]/outDimC[3] not equal + if (op->src[0]->ne[2] * op->ne[3] != op->src[0]->ne[3] * op->ne[2]) { + return false; + } + return true; + } + case GGML_OP_IM2COL: + case GGML_OP_CONCAT: case GGML_OP_DUP: case GGML_OP_REPEAT: - case GGML_OP_CONCAT: case GGML_OP_NONE: case GGML_OP_RESHAPE: case GGML_OP_VIEW: @@ -1723,17 +1774,13 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, case GGML_OP_SCALE: case GGML_OP_SQR: case GGML_OP_CLAMP: - case GGML_OP_CONT: case GGML_OP_DIAG_MASK_INF: case GGML_OP_SOFT_MAX: - case GGML_OP_ROPE: - case GGML_OP_IM2COL: case GGML_OP_POOL_2D: case GGML_OP_SUM_ROWS: case GGML_OP_ARGSORT: case GGML_OP_ACC: case GGML_OP_GROUP_NORM: - case GGML_OP_UPSCALE: case GGML_OP_PAD: case GGML_OP_ARANGE: case GGML_OP_TIMESTEP_EMBEDDING: @@ -2065,16 +2112,17 @@ ggml_backend_reg_t ggml_backend_cann_reg() { dev_ctx->name = GGML_CANN_NAME + std::to_string(i); ggml_cann_set_device(i); ggml_backend_dev_t dev = new ggml_backend_device { - /* .interface = */ ggml_backend_cann_device_interface, - /* .reg = */ ®, - /* .context = */ dev_ctx + /* .iface = */ ggml_backend_cann_device_interface, + /* .reg = */ ®, + /* .context = */ dev_ctx }; ctx->devices.push_back(dev); } reg = ggml_backend_reg { - /* .interface = */ ggml_backend_cann_reg_interface, - /* .context = */ ctx + /* .api_version = */ GGML_BACKEND_API_VERSION, + /* .iface = */ ggml_backend_cann_reg_interface, + /* .context = */ ctx }; } @@ -2127,3 +2175,5 @@ void ggml_backend_cann_get_device_memory(int32_t device, size_t* free, ggml_cann_set_device(device); ACL_CHECK(aclrtGetMemInfo(ACL_HBM_MEM, free, total)); } + +GGML_BACKEND_DL_IMPL(ggml_backend_cann_reg) diff --git a/ggml/src/ggml-cann/kernels/CMakeLists.txt b/ggml/src/ggml-cann/kernels/CMakeLists.txt index 5b4fef91b..d687220c3 100644 --- a/ggml/src/ggml-cann/kernels/CMakeLists.txt +++ b/ggml/src/ggml-cann/kernels/CMakeLists.txt @@ -1,7 +1,3 @@ -if (NOT SOC_TYPE) - set (SOC_TYPE "Ascend910B3") -endif() - file(GLOB SRC_FILES get_row_f32.cpp get_row_f16.cpp @@ -13,7 +9,6 @@ file(GLOB SRC_FILES dup.cpp ) -string(TOLOWER ${SOC_TYPE} SOC_VERSION) set(ASCEND_CANN_PACKAGE_PATH ${CANN_INSTALL_DIR}) set(RUN_MODE "npu" CACHE STRING "run mode: npu/sim") @@ -30,4 +25,6 @@ ascendc_library(ascendc_kernels STATIC ${SRC_FILES} ) +message(STATUS "CANN: compile ascend kernels witch SOC_TYPE:${SOC_TYPE}, SOC_VERSION:${SOC_VERSION}, compile macro:-D${SOC_TYPE_COMPILE_OPTION}.") +ascendc_compile_definitions(ascendc_kernels PRIVATE "-D${SOC_TYPE_COMPILE_OPTION}") # ascendc_compile_definitions(ascendc_kernels PRIVATE -DASCENDC_DUMP) diff --git a/ggml/src/ggml-cann/kernels/dup.cpp b/ggml/src/ggml-cann/kernels/dup.cpp index e2c651152..c7ba38d10 100644 --- a/ggml/src/ggml-cann/kernels/dup.cpp +++ b/ggml/src/ggml-cann/kernels/dup.cpp @@ -5,6 +5,7 @@ using namespace AscendC; #define BUFFER_NUM 2 +const int64_t SUPPORTED_MAX_DIM = 65535; // currently the limit of max block dim supportted by dup kernel is 65535template template class DupByRows { @@ -51,24 +52,36 @@ class DupByRows { __aicore__ inline void copy_in() { LocalTensor src_local = src_queue.AllocTensor(); - - DataCopyExtParams dataCopyParams; - dataCopyParams.blockCount = 1; - dataCopyParams.blockLen = num_elem * sizeof(SRC_T); - DataCopyPadExtParams padParams; - DataCopyPad(src_local, src_gm, dataCopyParams, padParams); - + const size_t elem_per_block = 32 / sizeof(SRC_T); + size_t tail = num_elem % elem_per_block; + size_t cpy_elements_len = tail > 0 ? num_elem + 1 : num_elem; + DataCopy(src_local, src_gm, cpy_elements_len); src_queue.EnQue(src_local); } __aicore__ inline void copy_out() { LocalTensor dst_local = dst_queue.DeQue(); - +#ifdef ASCEND_310P + const size_t elem_per_block = 32 / sizeof(DST_T); + size_t tail = num_elem % elem_per_block; + size_t len = num_elem & ~(elem_per_block - 1); + if (len > 0) { + DataCopy(dst_gm, dst_local, len); + } + if(tail != 0) { + for (size_t i = tail; i < elem_per_block; i++) { + dst_local[len + i].SetValue(0, 0); + } + SetAtomicAdd(); + DataCopy(dst_gm[len], dst_local[len], elem_per_block); + SetAtomicNone(); + } +#else DataCopyExtParams dataCopyParams; dataCopyParams.blockCount = 1; dataCopyParams.blockLen = num_elem * sizeof(DST_T); DataCopyPad(dst_gm, dst_local, dataCopyParams); - +#endif dst_queue.FreeTensor(dst_local); } diff --git a/ggml/src/ggml-cann/kernels/get_row_f16.cpp b/ggml/src/ggml-cann/kernels/get_row_f16.cpp index c704b5b2e..416b45104 100644 --- a/ggml/src/ggml-cann/kernels/get_row_f16.cpp +++ b/ggml/src/ggml-cann/kernels/get_row_f16.cpp @@ -14,7 +14,7 @@ class GET_ROW_F16 { int64_t *output_ne_ub, size_t *output_nb_ub) { // TODO, use template for F16/f32 int64_t op_block_num = GetBlockNum(); - int64_t op_block_idx = GetBlockIdx(); + op_block_idx = GetBlockIdx(); for (int i = 0; i < 4; i++) { input_ne[i] = input_ne_ub[i]; @@ -59,32 +59,42 @@ class GET_ROW_F16 { } __aicore__ inline void copy_in(uint32_t offset, size_t len) { + size_t origin_len = len; LocalTensor input_local = input_queue.AllocTensor(); - size_t tail = len % 32; - len = len & ~31; - DataCopy(input_local, input_gm[offset], len); + const size_t elem_per_block = 32 / sizeof(half); + size_t tail = len % elem_per_block; + len = len & ~(elem_per_block - 1); if(tail != 0) { - DataCopyExtParams dataCopyParams; - dataCopyParams.blockCount = 1; - dataCopyParams.blockLen = tail * sizeof(half); - DataCopyPadExtParams padParams; - DataCopyPad(input_local[len], input_gm[offset + len], - dataCopyParams, padParams); + len += elem_per_block; } + DataCopy(input_local, input_gm[offset], len); input_queue.EnQue(input_local); } __aicore__ inline void copy_out(uint32_t offset, size_t len) { LocalTensor output_local = output_queue.DeQue(); - size_t tail = len % 32; - len = len & ~31; - DataCopy(output_gm[offset], output_local, len); + const size_t elem_per_block = 32 / sizeof(float); + size_t tail = len % elem_per_block; + len = len & ~(elem_per_block - 1); + if (len > 0) { + DataCopy(output_gm[offset], output_local, len); + } + if(tail != 0) { +#ifdef ASCEND_310P + for (size_t i = tail; i < elem_per_block; i++) { + output_local[len + i].SetValue(0, 0); + } + SetAtomicAdd(); + DataCopy(output_gm[offset + len], output_local[len], elem_per_block); + SetAtomicNone(); +#else DataCopyExtParams dataCopyParams; dataCopyParams.blockCount = 1; dataCopyParams.blockLen = tail * sizeof(float); DataCopyPad(output_gm[offset + len], output_local[len], dataCopyParams); +#endif } output_queue.FreeTensor(output_local); } @@ -150,6 +160,7 @@ class GET_ROW_F16 { GlobalTensor output_gm; TQue input_queue; TQue output_queue; + int64_t op_block_idx; }; template diff --git a/ggml/src/ggml-cann/kernels/get_row_f32.cpp b/ggml/src/ggml-cann/kernels/get_row_f32.cpp index 9db080af3..02116905b 100644 --- a/ggml/src/ggml-cann/kernels/get_row_f32.cpp +++ b/ggml/src/ggml-cann/kernels/get_row_f32.cpp @@ -13,7 +13,7 @@ class GET_ROW_F32 { int64_t *indices_ne_ub, size_t *indices_nb_ub, int64_t *output_ne_ub, size_t *output_nb_ub) { int64_t op_block_num = GetBlockNum(); - int64_t op_block_idx = GetBlockIdx(); + op_block_idx = GetBlockIdx(); for (int i = 0; i < 4; i++) { input_ne[i] = input_ne_ub[i]; @@ -55,31 +55,40 @@ class GET_ROW_F32 { __aicore__ inline void copy_in(uint32_t offset, size_t len) { LocalTensor input_local = input_queue.AllocTensor(); - size_t tail = len % 32; - len = len & ~31; - DataCopy(input_local, input_gm[offset], len); + const size_t elem_per_block = 32 / sizeof(float); + size_t tail = len % elem_per_block; + len = len & ~(elem_per_block - 1); if(tail != 0) { - DataCopyExtParams dataCopyParams; - dataCopyParams.blockCount = 1; - dataCopyParams.blockLen = tail * sizeof(float); - DataCopyPadExtParams padParams; - DataCopyPad(input_local[len], input_gm[offset + len], - dataCopyParams, padParams); + len += elem_per_block; } + DataCopy(input_local, input_gm[offset], len); input_queue.EnQue(input_local); } __aicore__ inline void copy_out(uint32_t offset, size_t len) { LocalTensor output_local = output_queue.DeQue(); - size_t tail = len % 32; - len = len & ~31; - DataCopy(output_gm[offset], output_local, len); + const size_t elem_per_block = 32 / sizeof(float); + size_t tail = len % elem_per_block; + len = len & ~(elem_per_block - 1); + if (len > 0) { + DataCopy(output_gm[offset], output_local, len); + } + if(tail != 0) { +#ifdef ASCEND_310P + for (size_t i = tail; i < elem_per_block; i++) { + output_local[len + i].SetValue(0, 0); + } + SetAtomicAdd(); + DataCopy(output_gm[offset + len], output_local[len], elem_per_block); + SetAtomicNone(); +#else DataCopyExtParams dataCopyParams; dataCopyParams.blockCount = 1; dataCopyParams.blockLen = tail * sizeof(float); DataCopyPad(output_gm[offset + len], output_local[len], dataCopyParams); +#endif } output_queue.FreeTensor(output_local); } @@ -144,6 +153,7 @@ class GET_ROW_F32 { GlobalTensor output_gm; TQue input_queue; TQue output_queue; + int64_t op_block_idx; }; template diff --git a/ggml/src/ggml-cann/kernels/get_row_q4_0.cpp b/ggml/src/ggml-cann/kernels/get_row_q4_0.cpp index a80bfeec2..4fbe72208 100644 --- a/ggml/src/ggml-cann/kernels/get_row_q4_0.cpp +++ b/ggml/src/ggml-cann/kernels/get_row_q4_0.cpp @@ -2,6 +2,15 @@ // optimize me. Use template to avoid copy code. using namespace AscendC; +#ifdef ASCEND_310P // 310P not support 4bit get row + extern "C" __global__ __aicore__ void ascendc_get_row_q4_0( + GM_ADDR input_gm, GM_ADDR indices_gm, GM_ADDR output_gm, + GM_ADDR input_ne_gm, GM_ADDR indices_ne_gm, GM_ADDR indices_nb_gm, + GM_ADDR output_ne_gm, GM_ADDR output_nb_gm) { + // let following test cases can continue run, here just print error information. Of Cource the test case that call this operator is failed. + printf("Ascend310P not support 4bit get row.\n"); + } +#else #define BUFFER_NUM 2 @@ -191,3 +200,5 @@ extern "C" __global__ __aicore__ void ascendc_get_row_q4_0( indices_nb_ub, output_ne_ub, output_nb_ub); op.calculate(); } + +#endif // #ifdef ASCEND_310P diff --git a/ggml/src/ggml-cann/kernels/quantize_f16_q8_0.cpp b/ggml/src/ggml-cann/kernels/quantize_f16_q8_0.cpp index 8423b3f02..504b43afa 100644 --- a/ggml/src/ggml-cann/kernels/quantize_f16_q8_0.cpp +++ b/ggml/src/ggml-cann/kernels/quantize_f16_q8_0.cpp @@ -1,6 +1,14 @@ #include "kernel_operator.h" using namespace AscendC; +#ifdef ASCEND_310P + extern "C" __global__ __aicore__ void ascendc_quantize_f16_q8_0( + GM_ADDR input_gm, GM_ADDR output_gm, GM_ADDR input_ne_gm, + GM_ADDR input_nb_gm, GM_ADDR output_ne_gm) { + // let following test cases can continue run, here just print error information. Of Cource the test case that call this operator is failed. + printf("Ascend310P not support f16->8bit quantization.\n"); + } +#else #define BUFFER_NUM 2 #define QK8_0 32 @@ -206,3 +214,5 @@ extern "C" __global__ __aicore__ void ascendc_quantize_f16_q8_0( op.init(input_gm, output_gm, input_ne_ub, input_nb_ub, output_ne_ub); op.calculate(); } + +#endif // #ifdef ASCEND_310P diff --git a/ggml/src/ggml-cann/kernels/quantize_f32_q8_0.cpp b/ggml/src/ggml-cann/kernels/quantize_f32_q8_0.cpp index b7c575093..05b0bc1df 100644 --- a/ggml/src/ggml-cann/kernels/quantize_f32_q8_0.cpp +++ b/ggml/src/ggml-cann/kernels/quantize_f32_q8_0.cpp @@ -1,6 +1,14 @@ #include "kernel_operator.h" using namespace AscendC; +#ifdef ASCEND_310P // 310P not support f32->8bit quantization + extern "C" __global__ __aicore__ void ascendc_quantize_f32_q8_0( + GM_ADDR input_gm, GM_ADDR output_gm, GM_ADDR input_ne_gm, + GM_ADDR input_nb_gm, GM_ADDR output_ne_gm) { + // let following test cases can continue run, here just print error information. Of Cource the test case that call this operator is failed. + printf("Ascend310P not support f32->8bit quantization.\n"); + } +#else #define BUFFER_NUM 2 #define QK8_0 32 @@ -204,3 +212,5 @@ extern "C" __global__ __aicore__ void ascendc_quantize_f32_q8_0( op.init(input_gm, output_gm, input_ne_ub, input_nb_ub, output_ne_ub); op.calculate(); } + +#endif // #ifdef ASCEND_310P diff --git a/ggml/src/ggml-cann/kernels/quantize_float_to_q4_0.cpp b/ggml/src/ggml-cann/kernels/quantize_float_to_q4_0.cpp index 9c8c86b66..1188937b7 100644 --- a/ggml/src/ggml-cann/kernels/quantize_float_to_q4_0.cpp +++ b/ggml/src/ggml-cann/kernels/quantize_float_to_q4_0.cpp @@ -1,6 +1,21 @@ #include "kernel_operator.h" using namespace AscendC; +#ifdef ASCEND_310P // 310P not support float->4bit quantization + extern "C" __global__ __aicore__ void ascendc_quantize_f32_to_q4_0( + GM_ADDR input_gm, GM_ADDR output_gm, GM_ADDR input_ne_gm, + GM_ADDR input_nb_gm, GM_ADDR output_ne_gm) { + // let following test cases can continue run, here just print error information. Of Cource the test case that call this operator is failed. + printf("Ascend310P not support f32->4bit quantization.\n"); + } + + extern "C" __global__ __aicore__ void ascendc_quantize_f16_to_q4_0( + GM_ADDR input_gm, GM_ADDR output_gm, GM_ADDR input_ne_gm, + GM_ADDR input_nb_gm, GM_ADDR output_ne_gm) { + // let following test cases can continue run, here just print error information. Of Cource the test case that call this operator is failed. + printf("Ascend310P not support f16->4bit quantization.\n"); + } +#else #define BUFFER_NUM 2 #define Group_Size 32 @@ -276,3 +291,5 @@ extern "C" __global__ __aicore__ void ascendc_quantize_f32_to_q4_0( op.init(input_gm, output_gm, input_ne_ub, input_nb_ub, output_ne_ub); op.calculate(); } + +#endif // #ifdef ASCEND_310P diff --git a/ggml/src/ggml-common.h b/ggml/src/ggml-common.h index 050161393..27253a6c2 100644 --- a/ggml/src/ggml-common.h +++ b/ggml/src/ggml-common.h @@ -418,6 +418,12 @@ typedef struct { } block_iq4_xs; static_assert(sizeof(block_iq4_xs) == sizeof(ggml_half) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding"); +typedef struct { + ggml_half d[4]; // deltas for 4 iq4_nl blocks + uint8_t qs[QK4_NL * 2];// nibbles / quants for 4 iq4_nl blocks +} block_iq4_nlx4; +static_assert(sizeof(block_iq4_nlx4) == 4 * sizeof(ggml_half) + QK4_NL * 2, "wrong iq4_nlx4 block size/padding"); + #endif // GGML_COMMON_DECL #endif // GGML_COMMON_DECL diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt new file mode 100644 index 000000000..bc326c059 --- /dev/null +++ b/ggml/src/ggml-cpu/CMakeLists.txt @@ -0,0 +1,354 @@ +function(ggml_add_cpu_backend_variant_impl tag_name) + if (tag_name) + set(GGML_CPU_NAME ggml-cpu-${tag_name}) + else() + set(GGML_CPU_NAME ggml-cpu) + endif() + + ggml_add_backend_library(${GGML_CPU_NAME}) + + list (APPEND GGML_CPU_SOURCES + ggml-cpu/ggml-cpu.c + ggml-cpu/ggml-cpu.cpp + ggml-cpu/ggml-cpu-aarch64.c + ggml-cpu/ggml-cpu-aarch64.h + ggml-cpu/ggml-cpu-quants.c + ggml-cpu/ggml-cpu-quants.h + ggml-cpu/amx/amx.cpp + ggml-cpu/amx/amx.h + ggml-cpu/amx/mmq.cpp + ggml-cpu/amx/mmq.h + ggml-cpu/ggml-cpu-impl.h + ) + + target_compile_features(${GGML_CPU_NAME} PRIVATE c_std_11 cxx_std_17) + target_include_directories(${GGML_CPU_NAME} PRIVATE . ggml-cpu) + + if (APPLE AND GGML_ACCELERATE) + find_library(ACCELERATE_FRAMEWORK Accelerate) + if (ACCELERATE_FRAMEWORK) + message(STATUS "Accelerate framework found") + + target_compile_definitions(${GGML_CPU_NAME} PRIVATE GGML_USE_ACCELERATE) + target_compile_definitions(${GGML_CPU_NAME} PRIVATE ACCELERATE_NEW_LAPACK) + target_compile_definitions(${GGML_CPU_NAME} PRIVATE ACCELERATE_LAPACK_ILP64) + + target_link_libraries(${GGML_CPU_NAME} PRIVATE ${ACCELERATE_FRAMEWORK}) + else() + message(WARNING "Accelerate framework not found") + endif() + endif() + + if (GGML_OPENMP) + find_package(OpenMP) + if (OpenMP_FOUND) + target_compile_definitions(${GGML_CPU_NAME} PRIVATE GGML_USE_OPENMP) + + target_link_libraries(${GGML_CPU_NAME} PRIVATE OpenMP::OpenMP_C OpenMP::OpenMP_CXX) + else() + message(WARNING "OpenMP not found") + endif() + endif() + + if (GGML_LLAMAFILE) + target_compile_definitions(${GGML_CPU_NAME} PRIVATE GGML_USE_LLAMAFILE) + + list(APPEND GGML_CPU_SOURCES + ggml-cpu/llamafile/sgemm.cpp + ggml-cpu/llamafile/sgemm.h) + endif() + + if (GGML_CPU_HBM) + find_library(memkind memkind REQUIRED) + + message(STATUS "Using memkind for CPU HBM") + + target_compile_definitions(${GGML_CPU_NAME} PRIVATE GGML_USE_CPU_HBM) + + target_link_libraries(${GGML_CPU_NAME} PUBLIC memkind) + endif() + + if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR + CMAKE_GENERATOR_PLATFORM_LWR STREQUAL "arm64" OR + (NOT CMAKE_OSX_ARCHITECTURES AND + NOT CMAKE_GENERATOR_PLATFORM_LWR AND + CMAKE_SYSTEM_PROCESSOR MATCHES "^(aarch64|arm.*|ARM64)$")) + + message(STATUS "ARM detected") + + if (MSVC) + list(APPEND ARCH_DEFINITIONS __aarch64__) # MSVC defines _M_ARM64 instead + list(APPEND ARCH_DEFINITIONS __ARM_NEON) + list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_FMA) + + set(CMAKE_REQUIRED_FLAGS_PREV ${CMAKE_REQUIRED_FLAGS}) + string(JOIN " " CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS} "/arch:armv8.2") + + check_cxx_source_compiles("#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_DOTPROD) + if (GGML_COMPILER_SUPPORT_DOTPROD) + list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_DOTPROD) + + message(STATUS "ARM feature DOTPROD enabled") + endif () + + check_cxx_source_compiles("#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vmmlaq_f32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_MATMUL_INT8) + + if (GGML_COMPILER_SUPPORT_MATMUL_INT8) + list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_MATMUL_INT8) + + message(STATUS "ARM feature MATMUL_INT8 enabled") + endif () + + check_cxx_source_compiles("#include \nint main() { float16_t _a; float16x8_t _s = vdupq_n_f16(_a); return 0; }" GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC) + if (GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC) + list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_FP16_VECTOR_ARITHMETIC) + + message(STATUS "ARM feature FP16_VECTOR_ARITHMETIC enabled") + endif () + + set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_PREV}) + elseif (APPLE) + if (GGML_NATIVE) + set(USER_PROVIDED_MARCH FALSE) + foreach(flag_var IN ITEMS CMAKE_C_FLAGS CMAKE_CXX_FLAGS CMAKE_REQUIRED_FLAGS) + if ("${${flag_var}}" MATCHES "-march=[a-zA-Z0-9+._-]+") + set(USER_PROVIDED_MARCH TRUE) + break() + endif() + endforeach() + + if (NOT USER_PROVIDED_MARCH) + set(MARCH_FLAGS "-march=armv8.2a") + + check_cxx_source_compiles("#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_DOTPROD) + if (GGML_COMPILER_SUPPORT_DOTPROD) + set(MARCH_FLAGS "${MARCH_FLAGS}+dotprod") + list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_DOTPROD) + + message(STATUS "ARM feature DOTPROD enabled") + endif () + + set(TEST_I8MM_FLAGS "-march=armv8.2a+i8mm") + + set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS}) + set(CMAKE_REQUIRED_FLAGS "${CMAKE_REQUIRED_FLAGS} ${TEST_I8MM_FLAGS}") + + check_cxx_source_compiles("#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vmmlaq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_MATMUL_INT8) + if (GGML_COMPILER_SUPPORT_MATMUL_INT8) + set(MARCH_FLAGS "${MARCH_FLAGS}+i8mm") + list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_MATMUL_INT8) + + message(STATUS "ARM feature MATMUL_INT8 enabled") + endif () + + set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE}) + + list(APPEND ARCH_FLAGS "${MARCH_FLAGS}") + endif () + endif () + else() + check_cxx_compiler_flag(-mfp16-format=ieee COMPILER_SUPPORTS_FP16_FORMAT_I3E) + if (NOT "${COMPILER_SUPPORTS_FP16_FORMAT_I3E}" STREQUAL "") + list(APPEND ARCH_FLAGS -mfp16-format=ieee) + endif() + if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv6") + # Raspberry Pi 1, Zero + list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access) + endif() + if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv7") + if ("${CMAKE_SYSTEM_NAME}" STREQUAL "Android") + # Android armeabi-v7a + list(APPEND ARCH_FLAGS -mfpu=neon-vfpv4 -mno-unaligned-access -funsafe-math-optimizations) + else() + # Raspberry Pi 2 + list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access -funsafe-math-optimizations) + endif() + endif() + if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv8") + # Android arm64-v8a + # Raspberry Pi 3, 4, Zero 2 (32-bit) + list(APPEND ARCH_FLAGS -mno-unaligned-access) + endif() + if (GGML_SVE) + list(APPEND ARCH_FLAGS -march=armv8.6-a+sve) + endif() + endif() + elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR + (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND + CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64)$")) + if (MSVC) + # instruction set detection for MSVC only + if (GGML_NATIVE) + include(ggml-cpu/cmake/FindSIMD.cmake) + endif () + if (GGML_AVX512) + list(APPEND ARCH_FLAGS /arch:AVX512) + # /arch:AVX512 includes: __AVX512F__, __AVX512CD__, __AVX512BW__, __AVX512DQ__, and __AVX512VL__ + # MSVC has no compile-time flags enabling specific + # AVX512 extensions, neither it defines the + # macros corresponding to the extensions. + # Do it manually. + list(APPEND ARCH_DEFINITIONS GGML_AVX512) + if (GGML_AVX512_VBMI) + list(APPEND ARCH_DEFINITIONS __AVX512VBMI__) + if (CMAKE_C_COMPILER_ID STREQUAL "Clang") + list(APPEND ARCH_FLAGS -mavx512vbmi) + endif() + endif() + if (GGML_AVX512_VNNI) + list(APPEND ARCH_DEFINITIONS __AVX512VNNI__ GGML_AVX512_VNNI) + if (CMAKE_C_COMPILER_ID STREQUAL "Clang") + list(APPEND ARCH_FLAGS -mavx512vnni) + endif() + endif() + if (GGML_AVX512_BF16) + list(APPEND ARCH_DEFINITIONS __AVX512BF16__ GGML_AVX512_BF16) + if (CMAKE_C_COMPILER_ID STREQUAL "Clang") + list(APPEND ARCH_FLAGS -mavx512bf16) + endif() + endif() + if (GGML_AMX_TILE) + list(APPEND ARCH_DEFINITIONS __AMX_TILE__ GGML_AMX_TILE) + endif() + if (GGML_AMX_INT8) + list(APPEND ARCH_DEFINITIONS __AMX_INT8__ GGML_AMX_INT8) + endif() + if (GGML_AMX_BF16) + list(APPEND ARCH_DEFINITIONS __AMX_BF16__ GGML_AMX_BF16) + endif() + elseif (GGML_AVX2) + list(APPEND ARCH_FLAGS /arch:AVX2) + list(APPEND ARCH_DEFINITIONS GGML_AVX2 GGML_FMA GGML_F16C) + elseif (GGML_AVX) + list(APPEND ARCH_FLAGS /arch:AVX) + list(APPEND ARCH_DEFINITIONS GGML_AVX) + else () + list(APPEND ARCH_FLAGS /arch:SSE4.2) + list(APPEND ARCH_DEFINITIONS GGML_SSE42) + endif() + if (GGML_AVX_VNNI) + # MSVC generates AVX512 with AVX-VNNI intrinsics even with /arch:AVX2 + #list(APPEND ARCH_DEFINITIONS __AVXVNNI__ GGML_AVX_VNNI) + endif() + else () + if (GGML_NATIVE) + list(APPEND ARCH_FLAGS -march=native) + else () + list(APPEND ARCH_FLAGS -msse4.2) + list(APPEND ARCH_DEFINITIONS GGML_SSE42) + if (GGML_F16C) + list(APPEND ARCH_FLAGS -mf16c) + list(APPEND ARCH_DEFINITIONS GGML_F16C) + endif() + if (GGML_FMA) + list(APPEND ARCH_FLAGS -mfma) + list(APPEND ARCH_DEFINITIONS GGML_FMA) + endif() + if (GGML_AVX) + list(APPEND ARCH_FLAGS -mavx) + list(APPEND ARCH_DEFINITIONS GGML_AVX) + endif() + if (GGML_AVX2) + list(APPEND ARCH_FLAGS -mavx2) + list(APPEND ARCH_DEFINITIONS GGML_AVX2) + endif() + if (GGML_AVX_VNNI) + list(APPEND ARCH_FLAGS -mavxvnni) + list(APPEND ARCH_DEFINITIONS GGML_AVX_VNNI) + endif() + if (GGML_AVX512) + list(APPEND ARCH_FLAGS -mavx512f) + list(APPEND ARCH_FLAGS -mavx512cd) + list(APPEND ARCH_FLAGS -mavx512vl) + list(APPEND ARCH_FLAGS -mavx512dq) + list(APPEND ARCH_FLAGS -mavx512bw) + list(APPEND ARCH_DEFINITIONS GGML_AVX512) + endif() + if (GGML_AVX512_VBMI) + list(APPEND ARCH_FLAGS -mavx512vbmi) + list(APPEND ARCH_DEFINITIONS GGML_AVX512_VBMI) + endif() + if (GGML_AVX512_VNNI) + list(APPEND ARCH_FLAGS -mavx512vnni) + list(APPEND ARCH_DEFINITIONS GGML_AVX512_VNNI) + endif() + if (GGML_AVX512_BF16) + list(APPEND ARCH_FLAGS -mavx512bf16) + list(APPEND ARCH_DEFINITIONS GGML_AVX512_BF16) + endif() + if (GGML_AMX_TILE) + list(APPEND ARCH_FLAGS -mamx-tile) + list(APPEND ARCH_DEFINITIONS GGML_AMX_TILE) + endif() + if (GGML_AMX_INT8) + list(APPEND ARCH_FLAGS -mamx-int8) + list(APPEND ARCH_DEFINITIONS GGML_AMX_INT8) + endif() + if (GGML_AMX_BF16) + list(APPEND ARCH_FLAGS -mamx-bf16) + list(APPEND ARCH_DEFINITIONS GGML_AMX_BF16) + endif() + endif() + endif() + elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64") + message(STATUS "PowerPC detected") + execute_process(COMMAND bash -c "grep POWER10 /proc/cpuinfo | head -n 1" OUTPUT_VARIABLE POWER10_M) + string(FIND "${POWER10_M}" "POWER10" substring_index) + if (NOT DEFINED substring_index OR "${substring_index}" STREQUAL "") + set(substring_index -1) + endif() + + if (${substring_index} GREATER_EQUAL 0) + list(APPEND ARCH_FLAGS -mcpu=power10) + elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64le") + list(APPEND ARCH_FLAGS -mcpu=powerpc64le) + else() + list(APPEND ARCH_FLAGS -mcpu=native -mtune=native) + # TODO: Add targets for Power8/Power9 (Altivec/VSX) and Power10(MMA) and query for big endian systems (ppc64/le/be) + endif() + elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64") + message(STATUS "loongarch64 detected") + + list(APPEND ARCH_FLAGS -march=loongarch64) + if (GGML_LASX) + list(APPEND ARCH_FLAGS -mlasx) + endif() + if (GGML_LSX) + list(APPEND ARCH_FLAGS -mlsx) + endif() + elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "riscv64") + message(STATUS "RISC-V detected") + if (GGML_RVV) + list(APPEND ARCH_FLAGS -march=rv64gcv -mabi=lp64d) + endif() + else() + message(STATUS "Unknown architecture") + endif() + + if (GGML_CPU_AARCH64) + target_compile_definitions(${GGML_CPU_NAME} PRIVATE GGML_USE_CPU_AARCH64) + endif() + + message(STATUS "Adding CPU backend variant ${GGML_CPU_NAME}: ${ARCH_FLAGS} ${ARCH_DEFINITIONS}") + target_sources(${GGML_CPU_NAME} PRIVATE ${GGML_CPU_SOURCES}) + target_compile_options(${GGML_CPU_NAME} PRIVATE ${ARCH_FLAGS}) + target_compile_definitions(${GGML_CPU_NAME} PRIVATE ${ARCH_DEFINITIONS}) + + if (GGML_BACKEND_DL) + # The feature detection code is compiled as a separate target so that + # it can be built without the architecture flags + # Since multiple variants of the CPU backend may be included in the same + # build, using set_source_files_properties() to set the arch flags is not possible + set(GGML_CPU_FEATS_NAME ${GGML_CPU_NAME}-feats) + add_library(${GGML_CPU_FEATS_NAME} OBJECT ggml-cpu/cpu-feats-x86.cpp) + target_include_directories(${GGML_CPU_FEATS_NAME} PRIVATE . .. ../include) + target_compile_definitions(${GGML_CPU_FEATS_NAME} PRIVATE ${ARCH_DEFINITIONS}) + target_compile_definitions(${GGML_CPU_FEATS_NAME} PRIVATE GGML_BACKEND_DL GGML_BACKEND_BUILD GGML_BACKEND_SHARED) + set_target_properties(${GGML_CPU_FEATS_NAME} PROPERTIES POSITION_INDEPENDENT_CODE ON) + target_link_libraries(${GGML_CPU_NAME} PRIVATE ${GGML_CPU_FEATS_NAME}) + endif() + + if (EMSCRIPTEN) + set_target_properties(${GGML_CPU_NAME} PROPERTIES COMPILE_FLAGS "-msimd128") + endif() +endfunction() diff --git a/ggml/src/ggml-cpu/amx/amx.cpp b/ggml/src/ggml-cpu/amx/amx.cpp new file mode 100644 index 000000000..09c0df0f5 --- /dev/null +++ b/ggml/src/ggml-cpu/amx/amx.cpp @@ -0,0 +1,196 @@ +#include "amx.h" +#include "common.h" +#include "mmq.h" +#include "ggml-backend-impl.h" +#include "ggml-backend.h" +#include "ggml-impl.h" +#include "ggml-cpu.h" + +#if defined(__gnu_linux__) +#include +#include +#endif + +#include +#include +#include + +#if defined(__AMX_INT8__) && defined(__AVX512VNNI__) + +// AMX buffer interface +static void ggml_backend_amx_buffer_free_buffer(ggml_backend_buffer_t buffer) { + free(buffer->context); +} + +static void * ggml_backend_amx_buffer_get_base(ggml_backend_buffer_t buffer) { + return (void *)(buffer->context); +} + +static void ggml_backend_amx_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) { + memset((char *)tensor->data + offset, value, size); + + GGML_UNUSED(buffer); +} + +static void ggml_backend_amx_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + if (qtype_has_amx_kernels(tensor->type)) { + ggml_backend_amx_convert_weight(tensor, data, offset, size); + } else { + memcpy((char *)tensor->data + offset, data, size); + } + + GGML_UNUSED(buffer); +} + +static void ggml_backend_amx_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { + GGML_ASSERT(!qtype_has_amx_kernels(tensor->type)); + memcpy(data, (const char *)tensor->data + offset, size); + + GGML_UNUSED(buffer); +} + +static bool ggml_backend_amx_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { + if (ggml_backend_buffer_is_host(src->buffer)) { + if (qtype_has_amx_kernels(src->type)) { + ggml_backend_amx_convert_weight(dst, src->data, 0, ggml_nbytes(dst)); + } else { + memcpy(dst->data, src->data, ggml_nbytes(src)); + } + return true; + } + return false; + + GGML_UNUSED(buffer); +} + +static void ggml_backend_amx_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + memset(buffer->context, value, buffer->size); +} + +static ggml_backend_buffer_i ggml_backend_amx_buffer_interface = { + /* .free_buffer = */ ggml_backend_amx_buffer_free_buffer, + /* .get_base = */ ggml_backend_amx_buffer_get_base, + /* .init_tensor = */ NULL, // no initialization required + /* .memset_tensor = */ ggml_backend_amx_buffer_memset_tensor, + /* .set_tensor = */ ggml_backend_amx_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_amx_buffer_get_tensor, + /* .cpy_tensor = */ ggml_backend_amx_buffer_cpy_tensor, + /* .clear = */ ggml_backend_amx_buffer_clear, + /* .reset = */ NULL, +}; + +static const char * ggml_backend_amx_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + return "AMX"; + + GGML_UNUSED(buft); +} + +static ggml_backend_buffer_t ggml_backend_amx_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + void * data = aligned_alloc(TENSOR_ALIGNMENT, size); + if (data == NULL) { + fprintf(stderr, "%s: failed to allocate buffer of size %zu\n", __func__, size); + return NULL; + } + + return ggml_backend_buffer_init(buft, ggml_backend_amx_buffer_interface, data, size); +} + +static size_t ggml_backend_amx_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + return TENSOR_ALIGNMENT; + + GGML_UNUSED(buft); +} + +static size_t ggml_backend_amx_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor* tensor) { + return ggml_backend_amx_get_alloc_size(tensor); + + GGML_UNUSED(buft); +} + +static bool ggml_backend_amx_buffer_type_is_host(ggml_backend_buffer_type_t buft) { + return false; + + GGML_UNUSED(buft); +} + +#define ARCH_GET_XCOMP_PERM 0x1022 +#define ARCH_REQ_XCOMP_PERM 0x1023 +#define XFEATURE_XTILECFG 17 +#define XFEATURE_XTILEDATA 18 + +static bool ggml_amx_init() { +#if defined(__gnu_linux__) + if (syscall(SYS_arch_prctl, ARCH_REQ_XCOMP_PERM, XFEATURE_XTILEDATA)) { + fprintf(stderr, "AMX is not ready to be used!\n"); + return false; + } + return true; +#elif defined(_WIN32) + return true; +#endif +} +ggml_backend_buffer_type_t ggml_backend_amx_buffer_type() { + static struct ggml_backend_buffer_type ggml_backend_buffer_type_amx = { + /* .iface = */ { + /* .get_name = */ ggml_backend_amx_buffer_type_get_name, + /* .alloc_buffer = */ ggml_backend_amx_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_amx_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_alloc_size = */ ggml_backend_amx_buffer_type_get_alloc_size, + /* .is_host = */ ggml_backend_amx_buffer_type_is_host, + }, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0), + /* .context = */ NULL, + }; + + if (!ggml_amx_init()) { + return NULL; + } + + return &ggml_backend_buffer_type_amx; +} + +bool ggml_backend_amx_buft_is_amx(ggml_backend_buffer_type_t buft) { + return buft->iface.get_name == ggml_backend_amx_buffer_type_get_name; +} + +bool ggml_backend_amx_device_supports_op(const struct ggml_tensor * op) { + // handle only 2d gemm for now + auto is_contiguous_2d = [](const struct ggml_tensor * t) { + return ggml_is_contiguous(t) && t->ne[3] == 1 && t->ne[2] == 1; + }; + + switch (op->op) { + case GGML_OP_NONE: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + return true; + + case GGML_OP_MUL_MAT: { + const struct ggml_tensor * src0 = op->src[0]; + const struct ggml_tensor * src1 = op->src[1]; + + const enum ggml_type type = src0->type; + const int64_t ne0 = op->ne[0]; + + // amx kernels enables for Q4_0, Q4_1, Q8_0, F16 + // Q4_K, Q5_K, Q6_K, IQ4_XS enabled for QK_K = 256 + bool has_amx_kernels = qtype_has_amx_kernels(type) || (type == GGML_TYPE_F16); + + bool can_use_amx = + is_contiguous_2d(src0) && // src0 must be contiguous + is_contiguous_2d(src1) && // src1 must be contiguous + src1->type == GGML_TYPE_F32 && // src1 must be float32 + has_amx_kernels && // with amx kernel impls + ne0 % (TILE_N * 2) == 0; // out_features is 32x + + return can_use_amx; + } + default: + return false; + } +} + +#endif // defined(__AMX_INT8__) && defined(__AVX512VNNI__) diff --git a/ggml/src/ggml-cpu/amx/amx.h b/ggml/src/ggml-cpu/amx/amx.h new file mode 100644 index 000000000..c43546273 --- /dev/null +++ b/ggml/src/ggml-cpu/amx/amx.h @@ -0,0 +1,20 @@ +#include "ggml-backend.h" +#include "ggml-cpu-impl.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#if defined(__AMX_INT8__) && defined(__AVX512VNNI__) + +ggml_backend_buffer_type_t ggml_backend_amx_buffer_type(void); +bool ggml_backend_amx_buft_is_amx(ggml_backend_buffer_type_t buft); +bool ggml_backend_amx_device_supports_op(const struct ggml_tensor * op); +void ggml_backend_amx_mul_mat(const struct ggml_compute_params * params, struct ggml_tensor * dst); +size_t ggml_backend_amx_desired_wsize(const struct ggml_tensor * dst); + +#endif + +#ifdef __cplusplus +} +#endif diff --git a/ggml/src/ggml-amx/common.h b/ggml/src/ggml-cpu/amx/common.h similarity index 79% rename from ggml/src/ggml-amx/common.h rename to ggml/src/ggml-cpu/amx/common.h index 2b6c63527..40074c3fc 100644 --- a/ggml/src/ggml-amx/common.h +++ b/ggml/src/ggml-cpu/amx/common.h @@ -1,7 +1,7 @@ #pragma once #include "ggml.h" -#include "ggml-cpu-impl.h" // +#include "ggml-cpu-impl.h" #include #include @@ -73,16 +73,23 @@ inline void parallel_for(int nth, int n, const func_t& f) { #endif } +template +inline void parallel_for_ggml(const ggml_compute_params * params, int n, const func_t & f) { + int tbegin, tend; + balance211(n, params->nth, params->ith, tbegin, tend); + f(tbegin, tend); +} + // quantized types that have AMX support inline bool qtype_has_amx_kernels(const enum ggml_type type) { // TODO: fix padding for vnni format return (type == GGML_TYPE_Q4_0) || - (type == GGML_TYPE_Q4_1); - //(type == GGML_TYPE_Q8_0) || - //(type == GGML_TYPE_Q4_K) || - //(type == GGML_TYPE_Q5_K) || - //(type == GGML_TYPE_Q6_K) || - //(type == GGML_TYPE_IQ4_XS); + (type == GGML_TYPE_Q4_1) || + (type == GGML_TYPE_Q8_0) || + (type == GGML_TYPE_Q4_K) || + (type == GGML_TYPE_Q5_K) || + (type == GGML_TYPE_Q6_K) || + (type == GGML_TYPE_IQ4_XS); } // ggml backend context diff --git a/ggml/src/ggml-amx/mmq.cpp b/ggml/src/ggml-cpu/amx/mmq.cpp similarity index 96% rename from ggml/src/ggml-amx/mmq.cpp rename to ggml/src/ggml-cpu/amx/mmq.cpp index 239d15121..0ec3aa86d 100644 --- a/ggml/src/ggml-amx/mmq.cpp +++ b/ggml/src/ggml-cpu/amx/mmq.cpp @@ -4,8 +4,11 @@ #pragma GCC diagnostic ignored "-Wunused-local-typedefs" #endif +#include "amx.h" #include "mmq.h" #include "ggml-impl.h" +#include "ggml-cpu-impl.h" +#include "ggml-cpu-quants.h" #include "ggml-quants.h" #include #include @@ -33,7 +36,7 @@ #define ALWAYS_INLINE inline #endif -#if defined(__AMX_INT8__) +#if defined(__AMX_INT8__) && defined(__AVX512VNNI__) namespace { @@ -496,19 +499,19 @@ inline void from_float(const float * x, char * vy, int64_t k); template <> inline void from_float(const float * x, char * vy, int64_t k) { - quantize_row_q8_0(x, vy, k); + quantize_row_q8_0(x, (block_q8_0 *)vy, k); } template <> inline void from_float(const float * x, char * vy, int64_t k) { - quantize_row_q8_1(x, vy, k); + quantize_row_q8_1(x, (block_q8_1 *)vy, k); } template <> inline void from_float(const float * x, char * vy, int64_t k) { #if 1 // TODO: this is reference impl! - quantize_row_q8_K(x, vy, k); + quantize_row_q8_K_ref(x, (block_q8_K *)vy, k); #else quantize_row_q8_K_vnni(x, vy, k); #endif @@ -949,7 +952,7 @@ template> void unpack_B(packed_B_t * RESTRICT tile, const void * RESTRICT packed_B) { GGML_UNUSED(tile); GGML_UNUSED(packed_B); -}; +} template <> void unpack_B(int8_t * RESTRICT tile, const void * RESTRICT packed_B) { @@ -1337,21 +1340,19 @@ struct tinygemm_kernel_avx __m512 vb[COLS]; __m512 vc[ROWS * COLS]; - auto loadc = [&](int idx) { + auto loadc = [&](auto idx) { vc[idx] = _mm512_setzero_ps(); }; Unroll{}(loadc); - auto compute = [&](int idx, int k) { - // TODO: use `constexpr` here to get rid of interger div - // when upgraded to C++17 - const int row = idx / COLS; - const int col = idx % COLS; + auto compute = [&](auto idx, auto k) { + constexpr int row = idx / COLS; + constexpr int col = idx % COLS; - if (col == 0) { + if constexpr (col == 0) { va = _mm512_loadu_ps(A + row * K + k); } - if (row == 0) { + if constexpr (row == 0) { vb[col] = _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(B + col * K + k))); } vc[idx] = _mm512_fmadd_ps(va, vb[col], vc[idx]); @@ -1361,9 +1362,9 @@ struct tinygemm_kernel_avx Unroll{}(compute, k); } - auto storec = [&](int idx) { - const int row = idx / COLS; - const int col = idx % COLS; + auto storec = [&](auto idx) { + constexpr int row = idx / COLS; + constexpr int col = idx % COLS; C[row * ldc + col] = _mm512_reduce_add_ps(vc[idx]); }; Unroll{}(storec); @@ -1426,14 +1427,14 @@ struct tinygemm_kernel_vnni{}(loadc); - auto compute = [&](int col, int i) { + auto compute = [&](auto col, auto i) { // load a and compute compensation - if (col == 0) { + if constexpr (col == 0) { const int32_t * a_ptr = reinterpret_cast(A[0 * KB + i].qs); vcomp = _mm512_setzero_si512(); for (int k = 0; k < 8; ++k) { @@ -1465,7 +1466,7 @@ struct tinygemm_kernel_vnni{}(storec); @@ -1489,14 +1490,14 @@ struct tinygemm_kernel_vnni const __m512i lowMask = _mm512_set1_epi8(0xF); - auto loadc = [&](int col) { + auto loadc = [&](auto col) { vc[col] = _mm512_setzero_ps(); }; Unroll{}(loadc); - auto compute = [&](int col, int i) { + auto compute = [&](auto col, auto i) { // load a - if (col == 0) { + if constexpr (col == 0) { const int32_t * a_ptr = reinterpret_cast(A[0 * KB + i].qs); for (int k = 0; k < 8; ++k) { va[k] = _mm512_set1_epi32(a_ptr[k]); @@ -1530,7 +1531,7 @@ struct tinygemm_kernel_vnni } //store to C - auto storec = [&](int col) { + auto storec = [&](auto col) { _mm512_storeu_ps((__m512i*)(C + 0 * ldc + col * 16), vc[col]); }; Unroll{}(storec); @@ -1561,14 +1562,14 @@ struct tinygemm_kernel_vnni(0x80)); - auto loadc = [&](int col) { + auto loadc = [&](auto col) { vc[col] = _mm512_setzero_ps(); }; Unroll{}(loadc); - auto compute = [&](int col, int i) { + auto compute = [&](auto col, auto i) { // load a and add offset 128 - if (col == 0) { + if constexpr (col == 0) { const int32_t * a_ptr = reinterpret_cast(A[0 * KB + i].qs); for (int k = 0; k < 8; ++k) { va[k] = _mm512_set1_epi32(a_ptr[k]); @@ -1601,7 +1602,7 @@ struct tinygemm_kernel_vnni{}(storec); @@ -1633,7 +1634,7 @@ struct tinygemm_kernel_vnni{}(loadc); @@ -1647,9 +1648,9 @@ struct tinygemm_kernel_vnni{}(storec); @@ -1734,15 +1735,15 @@ struct tinygemm_kernel_vnni{}(loadc); // Q5_K and Q4_K shares the same vnni formats, refer to notes above. - auto compute = [&](int col, int i) { + auto compute = [&](auto col, auto i) { // load a - if (col == 0) { + if constexpr (col == 0) { for (int k_group = 0; k_group < QK_K / 32; ++k_group) { va[k_group] = _mm512_castsi256_si512(_mm256_loadu_si256((const __m256i *)(A[0 * KB + i].qs + k_group * 32))); } @@ -1807,7 +1808,7 @@ struct tinygemm_kernel_vnni{}(storec); @@ -1840,13 +1841,13 @@ struct tinygemm_kernel_vnni{}(loadc); - auto compute = [&](int col, int i) { - if (col == 0) { + auto compute = [&](auto col, auto i) { + if constexpr (col == 0) { // load a va[0] = _mm512_loadu_si512((const __m512i *)(A[0 * KB + i].qs + 0)); va[1] = _mm512_loadu_si512((const __m512i *)(A[0 * KB + i].qs + 64)); @@ -1958,13 +1959,13 @@ struct tinygemm_kernel_vnni(0x80)); const __m512i values256 = _mm512_add_epi8(values128, off); - auto loadc = [&](int col) { + auto loadc = [&](auto col) { vc[col] = _mm512_setzero_ps(); }; Unroll{}(loadc); - auto compute = [&](int col, int i) { - if (col == 0) { + auto compute = [&](auto col, auto i) { + if constexpr (col == 0) { // load a va[0] = _mm512_loadu_si512((const __m512i *)(A[0 * KB + i].qs + 0)); va[1] = _mm512_loadu_si512((const __m512i *)(A[0 * KB + i].qs + 64)); @@ -2014,7 +2015,7 @@ struct tinygemm_kernel_vnni{}(storec); @@ -2326,9 +2327,7 @@ size_t ggml_backend_amx_get_alloc_size(const struct ggml_tensor * tensor) { // pack weight to vnni format void ggml_backend_amx_convert_weight(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - - size_t alloc_size = ggml_backend_amx_get_alloc_size(tensor); - GGML_ASSERT(alloc_size == size); + GGML_ASSERT(offset == 0 && size == ggml_nbytes(tensor)); // only full tensor conversion is supported for now const enum ggml_type TYPE = tensor->type; @@ -2347,6 +2346,29 @@ void ggml_backend_amx_convert_weight(struct ggml_tensor * tensor, const void * d }); } +size_t ggml_backend_amx_desired_wsize(const struct ggml_tensor * dst) { + struct ggml_tensor * src0 = dst->src[0]; + + const enum ggml_type TYPE = src0->type; + + const bool is_floating_type = TYPE == GGML_TYPE_F16; + if (is_floating_type) { + return 0; + } + + const int M = dst->ne[1]; + const int K = src0->ne[0]; + + size_t desired_wsize = 0; + + GGML_DISPATCH_QTYPES(TYPE, [&] { + const size_t row_size_A = K / blck_size * sizeof(vec_dot_type); + desired_wsize = M * row_size_A; + }); + + return desired_wsize; +} + // NB: mixed dtype gemm with Advanced Matrix Extensions (Intel AMX) // // src0: weight in shape of {N, K}, quantized @@ -2355,14 +2377,12 @@ void ggml_backend_amx_convert_weight(struct ggml_tensor * tensor, const void * d // // the function performs: dst = src1 @ src0.T // -void ggml_backend_amx_mul_mat(ggml_backend_amx_context * ctx, struct ggml_tensor * dst) { +void ggml_backend_amx_mul_mat(const ggml_compute_params * params, struct ggml_tensor * dst) { struct ggml_tensor * src0 = dst->src[0]; struct ggml_tensor * src1 = dst->src[1]; const enum ggml_type TYPE = src0->type; - const int n_threads = ctx->n_threads; - // f16 only has avx512 kernels for now, // amx kernels will be added once 6th gen xeon is released. const bool is_floating_type = TYPE == GGML_TYPE_F16; @@ -2378,7 +2398,7 @@ void ggml_backend_amx_mul_mat(ggml_backend_amx_context * ctx, struct ggml_tensor const int MB = div_up(M, BLOCK_M); const int NB = div_up(N, BLOCK_N); - parallel_for(n_threads, MB * NB, [&](int begin, int end) { + parallel_for_ggml(params, MB * NB, [&](int begin, int end) { GGML_DISPATCH_FLOATING_TYPES(TYPE, [&] { for (int i = begin; i < end; ++i) { int mb = i / NB; @@ -2411,27 +2431,29 @@ void ggml_backend_amx_mul_mat(ggml_backend_amx_context * ctx, struct ggml_tensor } // pointer to work space, used convert A from float to quantized type - void * wdata = nullptr; + void * wdata = params->wdata; //TODO: performance improvement: merge quant A - GGML_DISPATCH_QTYPES(TYPE, [&] { - const size_t row_size_A = K / blck_size * sizeof(vec_dot_type); - const size_t desired_wsize = M * row_size_A; - if (ctx->work_size < desired_wsize) { - ctx->work_data.reset(new char[desired_wsize]); - ctx->work_size = desired_wsize; - } - wdata = ctx->work_data.get(); + if (params->ith == 0) { + GGML_DISPATCH_QTYPES(TYPE, [&] { + const size_t row_size_A = K / blck_size * sizeof(vec_dot_type); + const size_t desired_wsize = M * row_size_A; + if (params->wsize < desired_wsize) { + GGML_ABORT("insufficient work space size"); + } - // Q4_0, Q4_1, Q8_0 handles 1 TILE_K per blck_size - // Q4_K, Q5_K, Q6_K, IQ4_XS handles 8 TILE_K per blck_size - GGML_ASSERT(TILE_K == blck_size || TILE_K * 8 == blck_size); + // Q4_0, Q4_1, Q8_0 handles 1 TILE_K per blck_size + // Q4_K, Q5_K, Q6_K, IQ4_XS handles 8 TILE_K per blck_size + GGML_ASSERT(TILE_K == blck_size || TILE_K * 8 == blck_size); - const float * A_data = static_cast(src1->data); - for (int m = 0; m < M; ++m) { - from_float(A_data + m * K, (char *)wdata + m * row_size_A, K); - } - }); + const float * A_data = static_cast(src1->data); + for (int m = 0; m < M; ++m) { + from_float(A_data + m * K, (char *)wdata + m * row_size_A, K); + } + }); + } + + ggml_barrier(params->threadpool); if (M == 1) { // MB = 1 and handle 8 tiles in each block @@ -2439,7 +2461,7 @@ void ggml_backend_amx_mul_mat(ggml_backend_amx_context * ctx, struct ggml_tensor constexpr int BLOCK_N = TILE_N * kTilesN; const int NB = div_up(N, BLOCK_N); - parallel_for(n_threads, NB, [&](int begin, int end) { + parallel_for_ggml(params, NB, [&](int begin, int end) { GGML_DISPATCH_QTYPES(TYPE, [&] { const int KB = K / blck_size; const int TILE_SIZE = get_tile_size(); @@ -2469,7 +2491,7 @@ void ggml_backend_amx_mul_mat(ggml_backend_amx_context * ctx, struct ggml_tensor const int MB = div_up(M, BLOCK_M); const int NB = div_up(N, BLOCK_N); - parallel_for(n_threads, MB * NB, [&](int begin, int end) { + parallel_for_ggml(params, MB * NB, [&](int begin, int end) { // init tile config for each thread ggml_tile_config_init(); @@ -2497,13 +2519,4 @@ void ggml_backend_amx_mul_mat(ggml_backend_amx_context * ctx, struct ggml_tensor }); } -#else // if defined(__AMX_INT8__) - -void ggml_backend_amx_mul_mat(ggml_backend_amx_context * ctx, struct ggml_tensor * dst) { - fprintf(stderr, "GGML is not compiled with AMX support!\n"); - - GGML_UNUSED(ctx); - GGML_UNUSED(dst); -} - -#endif // if defined(__AMX_INT8__) +#endif // if defined(__AMX_INT8__) && defined(__AVX512VNNI__) diff --git a/ggml/src/ggml-amx/mmq.h b/ggml/src/ggml-cpu/amx/mmq.h similarity index 72% rename from ggml/src/ggml-amx/mmq.h rename to ggml/src/ggml-cpu/amx/mmq.h index cf0920620..f37366093 100644 --- a/ggml/src/ggml-amx/mmq.h +++ b/ggml/src/ggml-cpu/amx/mmq.h @@ -1,6 +1,5 @@ #pragma once #include "common.h" -#include #ifdef __cplusplus extern "C" { @@ -10,7 +9,7 @@ size_t ggml_backend_amx_get_alloc_size(const struct ggml_tensor * tensor); void ggml_backend_amx_convert_weight(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); -void ggml_backend_amx_mul_mat(ggml_backend_amx_context * ctx, struct ggml_tensor * dst); +void ggml_backend_amx_mul_mat(const struct ggml_compute_params * params, struct ggml_tensor * dst); #ifdef __cplusplus } diff --git a/ggml/cmake/FindSIMD.cmake b/ggml/src/ggml-cpu/cmake/FindSIMD.cmake similarity index 100% rename from ggml/cmake/FindSIMD.cmake rename to ggml/src/ggml-cpu/cmake/FindSIMD.cmake diff --git a/ggml/src/ggml-cpu/cpu-feats-x86.cpp b/ggml/src/ggml-cpu/cpu-feats-x86.cpp new file mode 100644 index 000000000..e8133d411 --- /dev/null +++ b/ggml/src/ggml-cpu/cpu-feats-x86.cpp @@ -0,0 +1,323 @@ +#include "ggml-backend-impl.h" + +#if defined(__x86_64__) || (defined(_MSC_VER) && defined(_M_AMD64)) + +#ifdef _MSC_VER +#include +#endif + +#include +#include +#include +#include +#include + +// ref: https://cdrdv2-public.intel.com/782156/325383-sdm-vol-2abcd.pdf +struct cpuid_x86 { + bool SSE3(void) { return f_1_ecx[0]; } + bool PCLMULQDQ(void) { return f_1_ecx[1]; } + bool MONITOR(void) { return f_1_ecx[3]; } + bool SSSE3(void) { return f_1_ecx[9]; } + bool FMA(void) { return f_1_ecx[12]; } + bool CMPXCHG16B(void) { return f_1_ecx[13]; } + bool SSE41(void) { return f_1_ecx[19]; } + bool SSE42(void) { return f_1_ecx[20]; } + bool MOVBE(void) { return f_1_ecx[22]; } + bool POPCNT(void) { return f_1_ecx[23]; } + bool AES(void) { return f_1_ecx[25]; } + bool XSAVE(void) { return f_1_ecx[26]; } + bool OSXSAVE(void) { return f_1_ecx[27]; } + bool AVX(void) { return f_1_ecx[28]; } + bool F16C(void) { return f_1_ecx[29]; } + bool RDRAND(void) { return f_1_ecx[30]; } + + bool MSR(void) { return f_1_edx[5]; } + bool CX8(void) { return f_1_edx[8]; } + bool SEP(void) { return f_1_edx[11]; } + bool CMOV(void) { return f_1_edx[15]; } + bool CLFSH(void) { return f_1_edx[19]; } + bool MMX(void) { return f_1_edx[23]; } + bool FXSR(void) { return f_1_edx[24]; } + bool SSE(void) { return f_1_edx[25]; } + bool SSE2(void) { return f_1_edx[26]; } + + bool FSGSBASE(void) { return f_7_ebx[0]; } + bool BMI1(void) { return f_7_ebx[3]; } + bool HLE(void) { return is_intel && f_7_ebx[4]; } + bool AVX2(void) { return f_7_ebx[5]; } + bool BMI2(void) { return f_7_ebx[8]; } + bool ERMS(void) { return f_7_ebx[9]; } + bool INVPCID(void) { return f_7_ebx[10]; } + bool RTM(void) { return is_intel && f_7_ebx[11]; } + bool AVX512F(void) { return f_7_ebx[16]; } + bool AVX512DQ(void) { return f_7_ebx[17]; } + bool RDSEED(void) { return f_7_ebx[18]; } + bool ADX(void) { return f_7_ebx[19]; } + bool AVX512PF(void) { return f_7_ebx[26]; } + bool AVX512ER(void) { return f_7_ebx[27]; } + bool AVX512CD(void) { return f_7_ebx[28]; } + bool AVX512BW(void) { return f_7_ebx[30]; } + bool AVX512VL(void) { return f_7_ebx[31]; } + + bool SHA(void) { return f_7_ebx[29]; } + + bool PREFETCHWT1(void) { return f_7_ecx[0]; } + + bool LAHF(void) { return f_81_ecx[0]; } + bool LZCNT(void) { return is_intel && f_81_ecx[5]; } + bool ABM(void) { return is_amd && f_81_ecx[5]; } + bool SSE4a(void) { return is_amd && f_81_ecx[6]; } + bool XOP(void) { return is_amd && f_81_ecx[11]; } + bool TBM(void) { return is_amd && f_81_ecx[21]; } + + bool SYSCALL(void) { return is_intel && f_81_edx[11]; } + bool MMXEXT(void) { return is_amd && f_81_edx[22]; } + bool RDTSCP(void) { return is_intel && f_81_edx[27]; } + bool _3DNOWEXT(void) { return is_amd && f_81_edx[30]; } + bool _3DNOW(void) { return is_amd && f_81_edx[31]; } + + bool AVX512_VBMI(void) { return f_7_ecx[1]; } + bool AVX512_VNNI(void) { return f_7_ecx[11]; } + bool AVX512_FP16(void) { return f_7_edx[23]; } + bool AVX512_BF16(void) { return f_7_1_eax[5]; } + bool AVX_VNNI(void) { return f_7_1_eax[4]; } + + bool AMX_TILE(void) { return f_7_edx[24]; } + bool AMX_INT8(void) { return f_7_edx[25]; } + bool AMX_FP16(void) { return f_7_1_eax[21]; } + bool AMX_BF16(void) { return f_7_edx[22]; } + +#ifdef _MSC_VER + static void cpuid(int cpu_info[4], int eax) { + __cpuid(cpu_info, eax); + } + static void cpuidex(int cpu_info[4], int eax, int ecx) { + __cpuidex(cpu_info, eax, ecx); + } +#else + static void cpuid(int cpu_info[4], int eax) { + __asm__ __volatile__( + "cpuid" + : "=a"(cpu_info[0]), "=b"(cpu_info[1]), "=c"(cpu_info[2]), "=d"(cpu_info[3]) + : "a"(eax), "c"(0)); + } + static void cpuidex(int cpu_info[4], int eax, int ecx) { + __asm__ __volatile__( + "cpuid" + : "=a"(cpu_info[0]), "=b"(cpu_info[1]), "=c"(cpu_info[2]), "=d"(cpu_info[3]) + : "a"(eax), "c"(ecx)); + } +#endif + + cpuid_x86() { + std::array cpui; + std::vector> data; + + // calling __cpuid with 0x0 as the function_id argument + // gets the number of the highest valid function ID. + cpuid(cpui.data(), 0); + int n_ids = cpui[0]; + + for (int i = 0; i <= n_ids; ++i) { + cpuidex(cpui.data(), i, 0); + data.push_back(cpui); + } + + // capture vendor string + char vendor[0x20] = {}; + *reinterpret_cast(vendor) = data[0][1]; + *reinterpret_cast(vendor + 4) = data[0][3]; + *reinterpret_cast(vendor + 8) = data[0][2]; + this->vendor = vendor; + if (this->vendor == "GenuineIntel") { + is_intel = true; + } else if (this->vendor == "AuthenticAMD") { + is_amd = true; + } + + // load bitset with flags for function 0x00000001 + if (n_ids >= 1) { + f_1_ecx = data[1][2]; + f_1_edx = data[1][3]; + } + + // load bitset with flags for function 0x00000007 + if (n_ids >= 7) { + f_7_ebx = data[7][1]; + f_7_ecx = data[7][2]; + f_7_edx = data[7][3]; + cpuidex(cpui.data(), 7, 1); + f_7_1_eax = cpui[0]; + } + + // calling __cpuid with 0x80000000 as the function_id argument + // gets the number of the highest valid extended ID. + cpuid(cpui.data(), 0x80000000); + unsigned int n_ex_ids = cpui[0]; + + std::vector> ext_data; + for (unsigned int i = 0x80000000; i <= n_ex_ids; ++i) { + cpuidex(cpui.data(), i, 0); + ext_data.push_back(cpui); + } + + // load bitset with flags for function 0x80000001 + if (n_ex_ids >= 0x80000001) { + f_81_ecx = ext_data[1][2]; + f_81_edx = ext_data[1][3]; + } + + // interpret CPU brand string if reported + char brand[0x40] = {}; + if (n_ex_ids >= 0x80000004) { + std::memcpy(brand, ext_data[2].data(), sizeof(cpui)); + std::memcpy(brand + 16, ext_data[3].data(), sizeof(cpui)); + std::memcpy(brand + 32, ext_data[4].data(), sizeof(cpui)); + this->brand = brand; + } + } + + bool is_intel = false; + bool is_amd = false; + std::string vendor; + std::string brand; + std::bitset<32> f_1_ecx; + std::bitset<32> f_1_edx; + std::bitset<32> f_7_ebx; + std::bitset<32> f_7_ecx; + std::bitset<32> f_7_edx; + std::bitset<32> f_7_1_eax; + std::bitset<32> f_81_ecx; + std::bitset<32> f_81_edx; +}; + +#if 0 +void test_x86_is() { + cpuid_x86 is; + printf("CPU Vendor: %s\n", is.vendor.c_str()); + printf("Brand: %s\n", is.brand.c_str()); + printf("is_intel: %d\n", is.is_intel); + printf("is_amd: %d\n", is.is_amd); + printf("sse3: %d\n", is.SSE3()); + printf("pclmulqdq: %d\n", is.PCLMULQDQ()); + printf("ssse3: %d\n", is.SSSE3()); + printf("fma: %d\n", is.FMA()); + printf("cmpxchg16b: %d\n", is.CMPXCHG16B()); + printf("sse41: %d\n", is.SSE41()); + printf("sse42: %d\n", is.SSE42()); + printf("movbe: %d\n", is.MOVBE()); + printf("popcnt: %d\n", is.POPCNT()); + printf("aes: %d\n", is.AES()); + printf("xsave: %d\n", is.XSAVE()); + printf("osxsave: %d\n", is.OSXSAVE()); + printf("avx: %d\n", is.AVX()); + printf("f16c: %d\n", is.F16C()); + printf("rdrand: %d\n", is.RDRAND()); + printf("msr: %d\n", is.MSR()); + printf("cx8: %d\n", is.CX8()); + printf("sep: %d\n", is.SEP()); + printf("cmov: %d\n", is.CMOV()); + printf("clflush: %d\n", is.CLFSH()); + printf("mmx: %d\n", is.MMX()); + printf("fxsr: %d\n", is.FXSR()); + printf("sse: %d\n", is.SSE()); + printf("sse2: %d\n", is.SSE2()); + printf("fsgsbase: %d\n", is.FSGSBASE()); + printf("bmi1: %d\n", is.BMI1()); + printf("hle: %d\n", is.HLE()); + printf("avx2: %d\n", is.AVX2()); + printf("bmi2: %d\n", is.BMI2()); + printf("erms: %d\n", is.ERMS()); + printf("invpcid: %d\n", is.INVPCID()); + printf("rtm: %d\n", is.RTM()); + printf("avx512f: %d\n", is.AVX512F()); + printf("rdseed: %d\n", is.RDSEED()); + printf("adx: %d\n", is.ADX()); + printf("avx512pf: %d\n", is.AVX512PF()); + printf("avx512er: %d\n", is.AVX512ER()); + printf("avx512cd: %d\n", is.AVX512CD()); + printf("sha: %d\n", is.SHA()); + printf("prefetchwt1: %d\n", is.PREFETCHWT1()); + printf("lahf: %d\n", is.LAHF()); + printf("lzcnt: %d\n", is.LZCNT()); + printf("abm: %d\n", is.ABM()); + printf("sse4a: %d\n", is.SSE4a()); + printf("xop: %d\n", is.XOP()); + printf("tbm: %d\n", is.TBM()); + printf("syscall: %d\n", is.SYSCALL()); + printf("mmxext: %d\n", is.MMXEXT()); + printf("rdtscp: %d\n", is.RDTSCP()); + printf("3dnowext: %d\n", is._3DNOWEXT()); + printf("3dnow: %d\n", is._3DNOW()); + printf("avx512_vbmi: %d\n", is.AVX512_VBMI()); + printf("avx512_vnni: %d\n", is.AVX512_VNNI()); + printf("avx512_fp16: %d\n", is.AVX512_FP16()); + printf("avx512_bf16: %d\n", is.AVX512_BF16()); + printf("amx_tile: %d\n", is.AMX_TILE()); + printf("amx_int8: %d\n", is.AMX_INT8()); + printf("amx_fp16: %d\n", is.AMX_FP16()); + printf("amx_bf16: %d\n", is.AMX_BF16()); +} +#endif + +static int ggml_backend_cpu_x86_score() { + // FIXME: this does not check for OS support + + int score = 0; + cpuid_x86 is; + +#ifdef GGML_FMA + if (!is.FMA()) { return 0; } + score += 1; +#endif +#ifdef GGML_F16C + if (!is.F16C()) { return 0; } + score += 1<<1; +#endif +#ifdef GGML_SSE42 + if (!is.SSE42()) { return 0; } + score += 1<<2; +#endif +#ifdef GGML_AVX + if (!is.AVX()) { return 0; } + score += 1<<4; +#endif +#ifdef GGML_AVX2 + if (!is.AVX2()) { return 0; } + score += 1<<5; +#endif +#ifdef GGML_AVX_VNNI + if (!is.AVX_VNNI()) { return 0; } + score += 1<<6; +#endif +#ifdef GGML_AVX512 + if (!is.AVX512F()) { return 0; } + if (!is.AVX512CD()) { return 0; } + if (!is.AVX512VL()) { return 0; } + if (!is.AVX512DQ()) { return 0; } + if (!is.AVX512BW()) { return 0; } + score += 1<<7; +#endif +#ifdef GGML_AVX512_VBMI + if (!is.AVX512_VBMI()) { return 0; } + score += 1<<8; +#endif +#ifdef GGML_AVX512_BF16 + if (!is.AVX512_BF16()) { return 0; } + score += 1<<9; +#endif +#ifdef GGML_AVX512_VNNI + if (!is.AVX512_VNNI()) { return 0; } + score += 1<<10; +#endif +#ifdef GGML_AMX_INT8 + if (!is.AMX_INT8()) { return 0; } + score += 1<<11; +#endif + + return score; +} + +GGML_BACKEND_DL_SCORE_IMPL(ggml_backend_cpu_x86_score) + +#endif // defined(__x86_64__) || (defined(_MSC_VER) && defined(_M_AMD64)) diff --git a/ggml/src/ggml-cpu/ggml-cpu-aarch64.c b/ggml/src/ggml-cpu/ggml-cpu-aarch64.c new file mode 100644 index 000000000..11152385e --- /dev/null +++ b/ggml/src/ggml-cpu/ggml-cpu-aarch64.c @@ -0,0 +1,3823 @@ +#define GGML_COMMON_IMPL_C +#include "ggml-common.h" + +#include "ggml-quants.h" +#include "ggml-impl.h" +#include "ggml-cpu.h" +#include "ggml-cpu/ggml-cpu-impl.h" + +#include +#include +#include +#include +#include // for qsort +#include // for GGML_ASSERT + +#include "ggml-cpu-aarch64.h" + +#if defined(__GNUC__) +#pragma GCC diagnostic ignored "-Woverlength-strings" +#elif defined(_MSC_VER) +#pragma warning(disable: 4244 4267) // possible loss of data +#endif + +#define UNUSED GGML_UNUSED + +// Functions to create the interleaved data layout formats + +// interleave 4 block_q4_0s in blocks of blck_size_interleave +// returns an interleaved block_q4_0x4 +// in the interleaved block_q4_0x4, place deltas for 4 block_q4_0 blocks +// first, then interleave quants from 4 block_q4_0s in blocks of blck_size_interleave +// +// - in : an array of block_q4_0 pointers +// - blck_size_interleave : the block_q4_0 quants bytes are interleaved in blocks of +// blck_size_interleave bytes +// - xor_mask : the mask to convert the nibbles in block_q4_0 quants bytes +// from bias offset form to pure sign form (this saves subtract +// operations durin unpacking) +// +#if defined(__AVX__) +#if defined(__F16C__) +#if defined(__AVX512F__) +#define GGML_F32Cx8x2_LOAD(x, y) _mm512_cvtph_ps(_mm256_set_m128i(_mm_loadu_si128((const __m128i *)(y)), _mm_loadu_si128((const __m128i *)(x)))) +#define GGML_F32Cx16_REPEAT_LOAD(x) _mm512_cvtph_ps(_mm256_set_m128i(x, x)) +#endif +// the _mm256_cvt intrinsics require F16C +#define GGML_F32Cx8_LOAD(x) _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)(x))) +#define GGML_F32Cx8_REPEAT_LOAD(x, loadMask) _mm256_cvtph_ps(_mm_shuffle_epi32(_mm_maskload_epi32((int const*)(x), loadMask), 68)) +#define GGML_F32Cx8_REARRANGE_LOAD(x, arrangeMask) _mm256_cvtph_ps(_mm_shuffle_epi8(_mm_loadu_si128((const __m128i *) x), arrangeMask)) +#else +#if defined(__AVX512F__) +static inline __m512 __avx512_f32cx8x2_load(ggml_fp16_t *x, ggml_fp16_t *y) { + float tmp[16]; + + for (int i = 0; i < 8; i++) { + tmp[i] = GGML_FP16_TO_FP32(x[i]); + } + + for (int i = 0; i < 8; i++) { + tmp[i + 8] = GGML_FP16_TO_FP32(y[i]); + } + + return _mm512_loadu_ps(tmp); +} +static inline __m512 __avx512_repeat_f32cx16_load(__m128i x) { + float tmp[16]; + uint16_t tmphalf[8]; + _mm_storeu_si128((__m128i*)tmphalf, x); + + for (int i = 0; i < 4; i++) { + tmp[i] = GGML_FP16_TO_FP32(tmphalf[i]); + tmp[i + 4] = GGML_FP16_TO_FP32(tmphalf[i]); + tmp[i + 8] = GGML_FP16_TO_FP32(tmphalf[i]); + tmp[i + 12] = GGML_FP16_TO_FP32(tmphalf[i]); + } + + return _mm512_loadu_ps(tmp); +} +#endif +static inline __m256 __avx_f32cx8_load(ggml_fp16_t *x) { + float tmp[8]; + + for (int i = 0; i < 8; i++) { + tmp[i] = GGML_FP16_TO_FP32(x[i]); + } + + return _mm256_loadu_ps(tmp); +} +static inline __m256 __avx_repeat_f32cx8_load(ggml_fp16_t *x) { + float tmp[8]; + + for (int i = 0; i < 4; i++) { + tmp[i] = GGML_FP16_TO_FP32(x[i]); + tmp[i + 4] = GGML_FP16_TO_FP32(x[i]); + } + + return _mm256_loadu_ps(tmp); +} +static inline __m256 __avx_rearranged_f32cx8_load(ggml_fp16_t *x, __m128i arrangeMask) { + uint16_t tmphalf[8]; + float tmp[8]; + + _mm_storeu_si128((__m128i*)tmphalf, _mm_shuffle_epi8(_mm_loadu_si128((const __m128i *) x), arrangeMask)); + for (int i = 0; i < 8; i++) { + tmp[i] = GGML_FP16_TO_FP32(tmphalf[i]); + } + + return _mm256_loadu_ps(tmp); +} + +#define GGML_F32Cx8_LOAD(x) __avx_f32cx8_load(x) +#define GGML_F32Cx8_REPEAT_LOAD(x, loadMask) __avx_repeat_f32cx8_load(x) +#define GGML_F32Cx8_REARRANGE_LOAD(x, arrangeMask) __avx_rearranged_f32cx8_load(x, arrangeMask) +#if defined(__AVX512F__) +#define GGML_F32Cx8x2_LOAD(x, y) __avx512_f32cx8x2_load(x, y) +#define GGML_F32Cx16_REPEAT_LOAD(x) __avx512_repeat_f32cx16_load(x) +#endif +#endif +#endif + + +#if defined(__AVX2__) || defined(__AVX512F__) +#if defined(__AVX512F__) +// add int16_t pairwise and return as 512 bit int vector +static inline __m512i sum_i16_pairs_int_32x16(const __m512i x) { + const __m512i ones = _mm512_set1_epi16(1); + return _mm512_madd_epi16(ones, x); +} + +static inline __m512i mul_sum_us8_pairs_int32x16(const __m512i ax, const __m512i sy) { +#if defined(__AVX512VNNI__) + const __m512i zero = _mm512_setzero_si512(); + return _mm512_dpbusd_epi32(zero, ax, sy); +#else + // Perform multiplication and create 16-bit values + const __m512i dot = _mm512_maddubs_epi16(ax, sy); + return sum_i16_pairs_int_32x16(dot); +#endif +} + +// multiply int8_t, add results pairwise twice and return as 512 bit int vector +static inline __m512i mul_sum_i8_pairs_int32x16(const __m512i x, const __m512i y) { + const __m512i zero = _mm512_setzero_si512(); + // Get absolute values of x vectors + const __m512i ax = _mm512_abs_epi8(x); + // Sign the values of the y vectors + __mmask64 blt0 = _mm512_movepi8_mask(x); + const __m512i sy = _mm512_mask_sub_epi8(y, blt0, zero, y); + return mul_sum_us8_pairs_int32x16(ax, sy); +} +#endif + +// add int16_t pairwise and return as 256 bit int vector +static inline __m256i sum_i16_pairs_int32x8(const __m256i x) { + const __m256i ones = _mm256_set1_epi16(1); + return _mm256_madd_epi16(ones, x); +} + +static inline __m256i mul_sum_us8_pairs_int32x8(const __m256i ax, const __m256i sy) { +#if defined(__AVXVNNI__) || (defined(__AVX512VNNI__) && defined(__AVX512VL__)) + const __m256i zero = _mm256_setzero_si256(); + return _mm256_dpbusd_epi32(zero, ax, sy); +#else + // Perform multiplication and create 16-bit values + const __m256i dot = _mm256_maddubs_epi16(ax, sy); + return sum_i16_pairs_int32x8(dot); +#endif +} + +// Integer variant of the function defined in ggml-quants.c +// multiply int8_t, add results pairwise twice and return as 256 bit int vector +static inline __m256i mul_sum_i8_pairs_int32x8(const __m256i x, const __m256i y) { +#if __AVXVNNIINT8__ + const __m256i zero = _mm256_setzero_si256(); + return _mm256_dpbssd_epi32(zero, x, y); +#else + // Get absolute values of x vectors + const __m256i ax = _mm256_sign_epi8(x, x); + // Sign the values of the y vectors + const __m256i sy = _mm256_sign_epi8(y, x); + return mul_sum_us8_pairs_int32x8(ax, sy); +#endif +} +#endif + +static const int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113}; + +static void quantize_q8_0_4x4(const float * restrict x, void * restrict vy, int64_t k) { + assert(QK8_0 == 32); + assert(k % QK8_0 == 0); + const int nb = k / QK8_0; + + block_q8_0x4 * restrict y = (block_q8_0x4 *) vy; + +#if defined(__ARM_NEON) + float32x4_t srcv[4][8]; + float id[4]; + + for (int i = 0; i < nb; i++) { + float32x4_t asrcv[8]; + float32x4_t amaxv[8]; + + for (int row_iter = 0; row_iter < 4; row_iter++) { + for (int j = 0; j < 8; j++) srcv[row_iter][j] = vld1q_f32(x + row_iter * k + i * 32 + 4 * j); + for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[row_iter][j]); + + for (int j = 0; j < 4; j++) amaxv[2 * j] = vmaxq_f32(asrcv[2 * j], asrcv[2 * j + 1]); + for (int j = 0; j < 2; j++) amaxv[4 * j] = vmaxq_f32(amaxv[4 * j], amaxv[4 * j + 2]); + for (int j = 0; j < 1; j++) amaxv[8 * j] = vmaxq_f32(amaxv[8 * j], amaxv[8 * j + 4]); + + const float amax = vmaxvq_f32(amaxv[0]); + + const float d = amax / ((1 << 7) - 1); + id[row_iter] = d ? 1.0f / d : 0.0f; + + y[i].d[row_iter] = GGML_FP32_TO_FP16(d); + } + + for (int j = 0; j < 8; j++) { + float32x4_t v = vmulq_n_f32(srcv[0][j], id[0]); + int32x4_t vi = vcvtnq_s32_f32(v); + y[i].qs[16 * j + 0] = vgetq_lane_s32(vi, 0); + y[i].qs[16 * j + 1] = vgetq_lane_s32(vi, 1); + y[i].qs[16 * j + 2] = vgetq_lane_s32(vi, 2); + y[i].qs[16 * j + 3] = vgetq_lane_s32(vi, 3); + + v = vmulq_n_f32(srcv[1][j], id[1]); + vi = vcvtnq_s32_f32(v); + y[i].qs[16 * j + 4] = vgetq_lane_s32(vi, 0); + y[i].qs[16 * j + 5] = vgetq_lane_s32(vi, 1); + y[i].qs[16 * j + 6] = vgetq_lane_s32(vi, 2); + y[i].qs[16 * j + 7] = vgetq_lane_s32(vi, 3); + + v = vmulq_n_f32(srcv[2][j], id[2]); + vi = vcvtnq_s32_f32(v); + y[i].qs[16 * j + 8] = vgetq_lane_s32(vi, 0); + y[i].qs[16 * j + 9] = vgetq_lane_s32(vi, 1); + y[i].qs[16 * j + 10] = vgetq_lane_s32(vi, 2); + y[i].qs[16 * j + 11] = vgetq_lane_s32(vi, 3); + + v = vmulq_n_f32(srcv[3][j], id[3]); + vi = vcvtnq_s32_f32(v); + y[i].qs[16 * j + 12] = vgetq_lane_s32(vi, 0); + y[i].qs[16 * j + 13] = vgetq_lane_s32(vi, 1); + y[i].qs[16 * j + 14] = vgetq_lane_s32(vi, 2); + y[i].qs[16 * j + 15] = vgetq_lane_s32(vi, 3); + } + } +#else + // scalar + const int blck_size_interleave = 4; + float srcv[4][QK8_0]; + float id[4]; + + for (int i = 0; i < nb; i++) { + for (int row_iter = 0; row_iter < 4; row_iter++) { + float amax = 0.0f; // absolute max + + for (int j = 0; j < QK8_0; j++) { + srcv[row_iter][j] = x[row_iter * k + i * QK8_0 + j]; + amax = MAX(amax, fabsf(srcv[row_iter][j])); + } + + const float d = amax / ((1 << 7) - 1); + id[row_iter] = d ? 1.0f / d : 0.0f; + + y[i].d[row_iter] = GGML_FP32_TO_FP16(d); + } + + for (int j = 0; j < QK8_0 * 4; j++) { + int src_offset = (j / (4 * blck_size_interleave)) * blck_size_interleave; + int src_id = (j % (4 * blck_size_interleave)) / blck_size_interleave; + src_offset += (j % blck_size_interleave); + + float x0 = srcv[src_id][src_offset] * id[src_id]; + y[i].qs[j] = roundf(x0); + } + } +#endif +} + +static void quantize_q8_0_4x8(const float * restrict x, void * restrict vy, int64_t k) { + assert(QK8_0 == 32); + assert(k % QK8_0 == 0); + const int nb = k / QK8_0; + + block_q8_0x4 * restrict y = (block_q8_0x4 *) vy; + +#if defined(__ARM_NEON) + float32x4_t srcv[4][8]; + float id[4]; + + for (int i = 0; i < nb; i++) { + float32x4_t asrcv[8]; + float32x4_t amaxv[8]; + + for (int row_iter = 0; row_iter < 4; row_iter++) { + for (int j = 0; j < 8; j++) srcv[row_iter][j] = vld1q_f32(x + row_iter * k + i * 32 + 4 * j); + for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[row_iter][j]); + + for (int j = 0; j < 4; j++) amaxv[2 * j] = vmaxq_f32(asrcv[2 * j], asrcv[2 * j + 1]); + for (int j = 0; j < 2; j++) amaxv[4 * j] = vmaxq_f32(amaxv[4 * j], amaxv[4 * j + 2]); + for (int j = 0; j < 1; j++) amaxv[8 * j] = vmaxq_f32(amaxv[8 * j], amaxv[8 * j + 4]); + + const float amax = vmaxvq_f32(amaxv[0]); + + const float d = amax / ((1 << 7) - 1); + id[row_iter] = d ? 1.0f / d : 0.0f; + + y[i].d[row_iter] = GGML_FP32_TO_FP16(d); + } + + for (int j = 0; j < 4; j++) { + float32x4_t v = vmulq_n_f32(srcv[0][2 * j], id[0]); + int32x4_t vi = vcvtnq_s32_f32(v); + y[i].qs[32 * j + 0] = vgetq_lane_s32(vi, 0); + y[i].qs[32 * j + 1] = vgetq_lane_s32(vi, 1); + y[i].qs[32 * j + 2] = vgetq_lane_s32(vi, 2); + y[i].qs[32 * j + 3] = vgetq_lane_s32(vi, 3); + v = vmulq_n_f32(srcv[0][2 * j + 1], id[0]); + vi = vcvtnq_s32_f32(v); + y[i].qs[32 * j + 4] = vgetq_lane_s32(vi, 0); + y[i].qs[32 * j + 5] = vgetq_lane_s32(vi, 1); + y[i].qs[32 * j + 6] = vgetq_lane_s32(vi, 2); + y[i].qs[32 * j + 7] = vgetq_lane_s32(vi, 3); + + v = vmulq_n_f32(srcv[1][2 * j], id[1]); + vi = vcvtnq_s32_f32(v); + y[i].qs[32 * j + 8] = vgetq_lane_s32(vi, 0); + y[i].qs[32 * j + 9] = vgetq_lane_s32(vi, 1); + y[i].qs[32 * j + 10] = vgetq_lane_s32(vi, 2); + y[i].qs[32 * j + 11] = vgetq_lane_s32(vi, 3); + v = vmulq_n_f32(srcv[1][2 * j + 1], id[1]); + vi = vcvtnq_s32_f32(v); + y[i].qs[32 * j + 12] = vgetq_lane_s32(vi, 0); + y[i].qs[32 * j + 13] = vgetq_lane_s32(vi, 1); + y[i].qs[32 * j + 14] = vgetq_lane_s32(vi, 2); + y[i].qs[32 * j + 15] = vgetq_lane_s32(vi, 3); + + v = vmulq_n_f32(srcv[2][2 * j], id[2]); + vi = vcvtnq_s32_f32(v); + y[i].qs[32 * j + 16] = vgetq_lane_s32(vi, 0); + y[i].qs[32 * j + 17] = vgetq_lane_s32(vi, 1); + y[i].qs[32 * j + 18] = vgetq_lane_s32(vi, 2); + y[i].qs[32 * j + 19] = vgetq_lane_s32(vi, 3); + v = vmulq_n_f32(srcv[2][2 * j + 1], id[2]); + vi = vcvtnq_s32_f32(v); + y[i].qs[32 * j + 20] = vgetq_lane_s32(vi, 0); + y[i].qs[32 * j + 21] = vgetq_lane_s32(vi, 1); + y[i].qs[32 * j + 22] = vgetq_lane_s32(vi, 2); + y[i].qs[32 * j + 23] = vgetq_lane_s32(vi, 3); + + v = vmulq_n_f32(srcv[3][2 * j], id[3]); + vi = vcvtnq_s32_f32(v); + y[i].qs[32 * j + 24] = vgetq_lane_s32(vi, 0); + y[i].qs[32 * j + 25] = vgetq_lane_s32(vi, 1); + y[i].qs[32 * j + 26] = vgetq_lane_s32(vi, 2); + y[i].qs[32 * j + 27] = vgetq_lane_s32(vi, 3); + v = vmulq_n_f32(srcv[3][2 * j + 1], id[3]); + vi = vcvtnq_s32_f32(v); + y[i].qs[32 * j + 28] = vgetq_lane_s32(vi, 0); + y[i].qs[32 * j + 29] = vgetq_lane_s32(vi, 1); + y[i].qs[32 * j + 30] = vgetq_lane_s32(vi, 2); + y[i].qs[32 * j + 31] = vgetq_lane_s32(vi, 3); + } + } +#elif defined(__AVX2__) || defined(__AVX__) + float id[4]; + __m256 srcv[4][4]; + __m256 idvec[4]; + + for (int i = 0; i < nb; i++) { + for (int row_iter = 0; row_iter < 4; row_iter++) { + // Load elements into 4 AVX vectors + __m256 v0 = _mm256_loadu_ps( x + row_iter * k + i * 32 ); + __m256 v1 = _mm256_loadu_ps( x + row_iter * k + i * 32 + 8 ); + __m256 v2 = _mm256_loadu_ps( x + row_iter * k + i * 32 + 16 ); + __m256 v3 = _mm256_loadu_ps( x + row_iter * k + i * 32 + 24 ); + + // Compute max(abs(e)) for the block + const __m256 signBit = _mm256_set1_ps( -0.0f ); + __m256 maxAbs = _mm256_andnot_ps( signBit, v0 ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) ); + + __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) ); + max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) ); + max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) ); + const float maxScalar = _mm_cvtss_f32( max4 ); + + // Divided by 127.f to mirror results in quantize_row_q8_0 + const float d = maxScalar / 127.f; + id[row_iter] = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f; //d ? 1.0f / d : 0.0f; + + // Store the scale for the individual block + y[i].d[row_iter] = GGML_FP32_TO_FP16(d); + + // Store the values in blocks of eight values - Aim is to use these later for block interleaving + srcv[row_iter][0] = v0; + srcv[row_iter][1] = v1; + srcv[row_iter][2] = v2; + srcv[row_iter][3] = v3; + idvec[row_iter] = _mm256_set1_ps(id[row_iter]); + } + + // The loop iterates four times - The aim is to get 4 corresponding chunks of eight bytes from the original weight blocks that are interleaved + for (int j = 0; j < 4; j++) { + // Apply the multiplier + __m256 v0 = _mm256_mul_ps(srcv[0][j], idvec[0]); + __m256 v1 = _mm256_mul_ps(srcv[1][j], idvec[1]); + __m256 v2 = _mm256_mul_ps(srcv[2][j], idvec[2]); + __m256 v3 = _mm256_mul_ps(srcv[3][j], idvec[3]); + + // Round to nearest integer + v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST ); + v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST ); + v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST ); + v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST ); + + // Convert floats to integers + __m256i i0 = _mm256_cvtps_epi32( v0 ); + __m256i i1 = _mm256_cvtps_epi32( v1 ); + __m256i i2 = _mm256_cvtps_epi32( v2 ); + __m256i i3 = _mm256_cvtps_epi32( v3 ); + +#if defined(__AVX2__) + // Convert int32 to int16 + i0 = _mm256_packs_epi32( i0, i1 ); + i2 = _mm256_packs_epi32( i2, i3 ); + // Convert int16 to int8 + i0 = _mm256_packs_epi16( i0, i2 ); + + // Permute and store the quantized weights in the required order after the pack instruction + const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 ); + i0 = _mm256_permutevar8x32_epi32( i0, perm ); + + _mm256_storeu_si256((__m256i *)(y[i].qs + 32 * j), i0); +#else + // Since we don't have in AVX some necessary functions, + // we split the registers in half and call AVX2 analogs from SSE + __m128i ni0 = _mm256_castsi256_si128( i0 ); + __m128i ni1 = _mm256_extractf128_si256( i0, 1); + __m128i ni2 = _mm256_castsi256_si128( i1 ); + __m128i ni3 = _mm256_extractf128_si256( i1, 1); + __m128i ni4 = _mm256_castsi256_si128( i2 ); + __m128i ni5 = _mm256_extractf128_si256( i2, 1); + __m128i ni6 = _mm256_castsi256_si128( i3 ); + __m128i ni7 = _mm256_extractf128_si256( i3, 1); + + // Convert int32 to int16 + ni0 = _mm_packs_epi32( ni0, ni1 ); + ni2 = _mm_packs_epi32( ni2, ni3 ); + ni4 = _mm_packs_epi32( ni4, ni5 ); + ni6 = _mm_packs_epi32( ni6, ni7 ); + // Convert int16 to int8 + ni0 = _mm_packs_epi16( ni0, ni2 ); + ni4 = _mm_packs_epi16( ni4, ni6 ); + _mm_storeu_si128((__m128i *)(y[i].qs + 32 * j), ni0); + _mm_storeu_si128((__m128i *)(y[i].qs + 32 * j + 16), ni4); +#endif + } + } +#else + // scalar + const int blck_size_interleave = 8; + float srcv[4][QK8_0]; + float id[4]; + + for (int i = 0; i < nb; i++) { + for (int row_iter = 0; row_iter < 4; row_iter++) { + float amax = 0.0f; // absolute max + + for (int j = 0; j < QK8_0; j++) { + srcv[row_iter][j] = x[row_iter * k + i * QK8_0 + j]; + amax = MAX(amax, fabsf(srcv[row_iter][j])); + } + + const float d = amax / ((1 << 7) - 1); + id[row_iter] = d ? 1.0f / d : 0.0f; + + y[i].d[row_iter] = GGML_FP32_TO_FP16(d); + } + + for (int j = 0; j < QK8_0 * 4; j++) { + int src_offset = (j / (4 * blck_size_interleave)) * blck_size_interleave; + int src_id = (j % (4 * blck_size_interleave)) / blck_size_interleave; + src_offset += (j % blck_size_interleave); + + float x0 = srcv[src_id][src_offset] * id[src_id]; + y[i].qs[j] = roundf(x0); + } + } +#endif +} + +void quantize_mat_q8_0(const float * restrict x, void * restrict vy, int64_t nrow, int64_t n_per_row, int64_t blck_size_interleave) { + assert(nrow == 4); + UNUSED(nrow); + if (blck_size_interleave == 4) { + quantize_q8_0_4x4(x, vy, n_per_row); + } else if (blck_size_interleave == 8) { + quantize_q8_0_4x8(x, vy, n_per_row); + } else { + assert(false); + } +} + +void ggml_gemv_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 4; + const int blocklen = 4; + + assert (n % qk == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD) + if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) { + const block_q4_0x4 * b_ptr = (const block_q4_0x4 *)vx; + + for (int c = 0; c < nc; c += ncols_interleaved) { + const block_q8_0 * a_ptr = (const block_q8_0 *)vy; + float32x4_t acc = vdupq_n_f32(0); + for (int b = 0; b < nb; b++) { + int8x16_t b0 = vld1q_s8((const int8_t *)b_ptr->qs); + int8x16_t b1 = vld1q_s8((const int8_t *)b_ptr->qs + 16); + int8x16_t b2 = vld1q_s8((const int8_t *)b_ptr->qs + 32); + int8x16_t b3 = vld1q_s8((const int8_t *)b_ptr->qs + 48); + float16x4_t bd = vld1_f16((const __fp16 *)b_ptr->d); + + int8x16_t a0 = vld1q_s8(a_ptr->qs); + int8x16_t a1 = vld1q_s8(a_ptr->qs + qk/2); + float16x4_t ad = vld1_dup_f16((const __fp16 *)&a_ptr->d); + + int32x4_t ret = vdupq_n_s32(0); + + ret = vdotq_laneq_s32(ret, b0 << 4, a0, 0); + ret = vdotq_laneq_s32(ret, b1 << 4, a0, 1); + ret = vdotq_laneq_s32(ret, b2 << 4, a0, 2); + ret = vdotq_laneq_s32(ret, b3 << 4, a0, 3); + + ret = vdotq_laneq_s32(ret, b0 & 0xf0U, a1, 0); + ret = vdotq_laneq_s32(ret, b1 & 0xf0U, a1, 1); + ret = vdotq_laneq_s32(ret, b2 & 0xf0U, a1, 2); + ret = vdotq_laneq_s32(ret, b3 & 0xf0U, a1, 3); + + acc = vfmaq_f32(acc, vcvtq_n_f32_s32(ret, 4), + vmulq_f32(vcvt_f32_f16(ad), vcvt_f32_f16(bd))); + a_ptr++; + b_ptr++; + } + vst1q_f32(s, acc); + s += ncols_interleaved; + } + return; + } +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD) + float sumf[4]; + int sumi; + + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb); + + for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0; + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); + const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); + sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4; + } + sumf[j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d); + } + } + } + for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j]; + } +} + +void ggml_gemv_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 4; + const int blocklen = 8; + + assert (n % qk == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) + if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) { + const void * b_ptr = vx; + const void * a_ptr = vy; + float * res_ptr = s; + + __asm__ __volatile__( + "movi v2.16b, #0x4\n" + "movi v1.16b, #0xf0\n" + "add %x[b_ptr], %x[b_ptr], #0x8\n" + "1:" // Column loop + "add x23, %x[a_ptr], #0x2\n" + "movi v0.16b, #0x0\n" + "mov x22, %x[nb]\n" + "2:" // Block loop + "ldr q31, [%x[b_ptr], #0x0]\n" + "ldr q30, [%x[b_ptr], #0x10]\n" + "mov x21, x23\n" + "movi v29.4s, #0x0\n" + "ldr q28, [%x[b_ptr], #0x20]\n" + "ldr q27, [%x[b_ptr], #0x30]\n" + "movi v26.4s, #0x0\n" + "sub x20, x23, #0x2\n" + "ld1r { v25.8h }, [x20]\n" + "ldr q24, [%x[b_ptr], #-0x8]\n" + "sub x22, x22, #0x1\n" + "add x23, x23, #0x22\n" + "ld1r { v23.2d }, [x21], #0x8\n" + "sshl v22.16b, v31.16b, v2.16b\n" + "sshl v16.16b, v30.16b, v2.16b\n" + "add %x[b_ptr], %x[b_ptr], #0x48\n" + "ld1r { v21.2d }, [x21], #0x8\n" + "sshl v20.16b, v28.16b, v2.16b\n" + "sshl v19.16b, v27.16b, v2.16b\n" + "ld1r { v18.2d }, [x21], #0x8\n" + "ld1r { v17.2d }, [x21], #0x8\n" + "and v31.16b, v31.16b, v1.16b\n" + "and v30.16b, v30.16b, v1.16b\n" + ".inst 0x4e9796dd // sdot v29.4s, v22.16b, v23.16b\n" + ".inst 0x4e97961a // sdot v26.4s, v16.16b, v23.16b\n" + "and v28.16b, v28.16b, v1.16b\n" + "and v27.16b, v27.16b, v1.16b\n" + "fcvtl v25.4s, v25.4h\n" + "fcvtl v16.4s, v24.4h\n" + ".inst 0x4e95969d // sdot v29.4s, v20.16b, v21.16b\n" + ".inst 0x4e95967a // sdot v26.4s, v19.16b, v21.16b\n" + "fmul v16.4s, v16.4s, v25.4s\n" + ".inst 0x4e9297fd // sdot v29.4s, v31.16b, v18.16b\n" + ".inst 0x4e9297da // sdot v26.4s, v30.16b, v18.16b\n" + ".inst 0x4e91979d // sdot v29.4s, v28.16b, v17.16b\n" + ".inst 0x4e91977a // sdot v26.4s, v27.16b, v17.16b\n" + "addp v29.4s, v29.4s, v26.4s\n" + "scvtf v29.4s, v29.4s, #0x4\n" + "fmla v0.4s, v29.4s, v16.4s\n" + "cbnz x22, 2b\n" + "sub %x[nc], %x[nc], #0x4\n" + "str q0, [%x[res_ptr], #0x0]\n" + "add %x[res_ptr], %x[res_ptr], #0x10\n" + "cbnz %x[nc], 1b\n" + : [b_ptr] "+&r" (b_ptr), [res_ptr] "+&r" (res_ptr), [nc] "+&r" (nc) + : [a_ptr] "r" (a_ptr), [nb] "r" (nb) + : "memory", "v0", "v1", "v2", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x20", "x21", "x22", "x23" + ); + return; + } +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) + float sumf[4]; + int sumi; + + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb); + + for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0; + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); + const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); + sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4; + } + sumf[j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d); + } + } + } + for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j]; + } +} + +void ggml_gemv_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 8; + const int blocklen = 8; + + assert (n % qk == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) +#if defined(__ARM_FEATURE_SVE) + if (ggml_cpu_has_sve() && ggml_cpu_get_sve_cnt() == QK8_0) { + const void * b_ptr = vx; + const void * a_ptr = vy; + float * res_ptr = s; + + __asm__ __volatile__( + "ptrue p0.b\n" + "add %x[b_ptr], %x[b_ptr], #0x10\n" + "1:" // Column loop + "add x22, %x[a_ptr], #0x2\n" + "mov z31.b, #0x0\n" + "mov x21, %x[nb]\n" + "2:" // Block loop + "ld1b { z30.b }, p0/Z, [%x[b_ptr]]\n" + "ld1b { z29.b }, p0/Z, [%x[b_ptr], #1, MUL VL]\n" + "mov z28.s, #0x0\n" + "mov z27.s, #0x0\n" + "ld1rd { z26.d }, p0/Z, [x22]\n" + "ld1b { z25.b }, p0/Z, [%x[b_ptr], #2, MUL VL]\n" + "sub x20, x22, #0x2\n" + "sub x21, x21, #0x1\n" + "ld1b { z24.b }, p0/Z, [%x[b_ptr], #3, MUL VL]\n" + "ld1rd { z23.d }, p0/Z, [x22, #8]\n" + "lsl z22.b, z30.b, #0x4\n" + "lsl z16.b, z29.b, #0x4\n" + "and z30.b, z30.b, #0xf0\n" + "and z29.b, z29.b, #0xf0\n" + "ld1rd { z21.d }, p0/Z, [x22, #16]\n" + "ld1rd { z20.d }, p0/Z, [x22, #24]\n" + "lsl z19.b, z25.b, #0x4\n" + "and z25.b, z25.b, #0xf0\n" + "ld1rh { z17.h }, p0/Z, [x20]\n" + "ld1h { z18.s }, p0/Z, [%x[b_ptr], #-1, MUL VL]\n" + "sdot z28.s, z22.b, z26.b\n" + "sdot z27.s, z16.b, z26.b\n" + "lsl z16.b, z24.b, #0x4\n" + "add x22, x22, #0x22\n" + "and z24.b, z24.b, #0xf0\n" + "add %x[b_ptr], %x[b_ptr], #0x90\n" + "fcvt z17.s, p0/m, z17.h\n" + "fcvt z18.s, p0/m, z18.h\n" + "sdot z28.s, z19.b, z23.b\n" + "sdot z27.s, z16.b, z23.b\n" + "fmul z18.s, z18.s, z17.s\n" + "sdot z28.s, z30.b, z21.b\n" + "sdot z27.s, z29.b, z21.b\n" + "sdot z28.s, z25.b, z20.b\n" + "sdot z27.s, z24.b, z20.b\n" + "uzp1 z17.s, z28.s, z27.s\n" + "uzp2 z16.s, z28.s, z27.s\n" + "add z17.s, z17.s, z16.s\n" + "asr z17.s, z17.s, #0x4\n" + "scvtf z17.s, p0/m, z17.s\n" + "fmla z31.s, p0/M, z17.s, z18.s\n" + "cbnz x21, 2b\n" + "sub %x[nc], %x[nc], #0x8\n" + "st1w { z31.s }, p0, [%x[res_ptr]]\n" + "add %x[res_ptr], %x[res_ptr], #0x20\n" + "cbnz %x[nc], 1b\n" + : [b_ptr] "+&r" (b_ptr), [res_ptr] "+&r" (res_ptr), [nc] "+&r" (nc) + : [a_ptr] "r" (a_ptr), [nb] "r" (nb) + : "memory", "p0", "x20", "x21", "x22", "z16", "z17", "z18", "z19", "z20", "z21", "z22", "z23", "z24", "z25", "z26", "z27", "z28", "z29", "z30", "z31" + ); + return; + } +#endif // #if defined(__ARM_FEATURE_SVE) +#elif defined(__AVX2__) + // Lookup table to convert signed nibbles to signed bytes + __m256i signextendlut = _mm256_castsi128_si256(_mm_set_epi8(-1, -2, -3, -4, -5, -6, -7, -8, 7, 6, 5, 4, 3, 2, 1, 0)); + signextendlut = _mm256_permute2f128_si256(signextendlut, signextendlut, 0); + __m128i changemask = _mm_set_epi8(15, 14, 7, 6, 13, 12, 5, 4, 11, 10, 3, 2, 9, 8, 1, 0); + __m256i finalpermutemask = _mm256_set_epi32(7, 5, 3, 1, 6, 4, 2, 0); + + // Permute mask used for easier vector processing at later stages + const __m256i m4b = _mm256_set1_epi8(0x0F); + + int64_t b_nb = n / QK4_0; + + const block_q4_0x8 * b_ptr_start = (const block_q4_0x8 *)vx; + const block_q8_0 * a_ptr_start = (const block_q8_0 *)vy; + + // Process Q8_0 blocks one by one + for (int64_t y = 0; y < nr; y++) { + + // Pointers to LHS blocks of block_q8_0 format + const block_q8_0 * a_ptr = a_ptr_start + (y * nb); + + // Take group of eight block_q4_0x8 structures at each pass of the loop and perform dot product operation + for (int64_t x = 0; x < nc / 8; x++) { + + // Pointers to RHS blocks + const block_q4_0x8 * b_ptr = b_ptr_start + (x * b_nb); + + // Master FP accumulator + __m256 acc_row = _mm256_setzero_ps(); + + for (int64_t b = 0; b < nb; b++) { + // Load 8 blocks of Q4_0 interleaved as 8 bytes (B0 - B7) + const __m256i rhs_raw_vec_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs)); + const __m256i rhs_raw_vec_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs) + 1); + const __m256i rhs_raw_vec_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs) + 2); + const __m256i rhs_raw_vec_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs) + 3); + + // 4-bit -> 8-bit - Sign is maintained + const __m256i rhs_vec_0123_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_vec_0123_0, m4b)); // B0(0-7) B1(0-7) B2(0-7) B3(0-7) + const __m256i rhs_vec_4567_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_vec_4567_0, m4b)); // B4(0-7) B5(0-7) B6(0-7) B7(0-7) + const __m256i rhs_vec_0123_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_vec_0123_1, m4b)); // B0(8-15) B1(8-15) B2(8-15) B3(8-15) + const __m256i rhs_vec_4567_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_vec_4567_1, m4b)); // B0(8-15) B1(8-15) B2(8-15) B3(8-15) + + const __m256i rhs_vec_0123_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_vec_0123_0, 4), m4b)); // B0(16-23) B1(16-23) B2(16-23) B3(16-23) + const __m256i rhs_vec_4567_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_vec_4567_0, 4), m4b)); // B4(16-23) B5(16-23) B6(16-23) B7(16-23) + const __m256i rhs_vec_0123_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_vec_0123_1, 4), m4b)); // B0(24-31) B1(24-31) B2(24-31) B3(24-31) + const __m256i rhs_vec_4567_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_vec_4567_1, 4), m4b)); // B4(24-31) B5(24-31) B6(24-31) B7(24-31) + + // Load the scale values for the 8 blocks interleaved in block_q4_0x8 + const __m256 col_scale_f32 = GGML_F32Cx8_REARRANGE_LOAD(b_ptr[b].d, changemask); + + // Load and convert to FP32 scale from block_q8_0 + const __m256 row_scale_f32 = _mm256_set1_ps(GGML_FP16_TO_FP32(a_ptr[b].d)); + + // Load the block values in block_q8_0 in batches of 16 bytes and replicate the same across 256 bit vector + __m256i lhs_vec_0 = _mm256_castsi128_si256(_mm_loadu_si128((const __m128i *)a_ptr[b].qs)); + __m256i lhs_vec_1 = _mm256_castsi128_si256(_mm_loadu_si128((const __m128i *)(a_ptr[b].qs + 16))); + + lhs_vec_0 = _mm256_permute2f128_si256(lhs_vec_0, lhs_vec_0, 0); // A0 (0-15) A0(0-15) + lhs_vec_1 = _mm256_permute2f128_si256(lhs_vec_1, lhs_vec_1, 0); // A0 (16-31) A0(16-31)) + + __m256i iacc = _mm256_setzero_si256(); + + // Dot product done within 32 bit lanes and accumulated in the same vector + // B0(0-3) B4(0-3) B1(0-3) B5(0-3) B2(0-3) B6(0-3) B3(0-3) B7(0-3) with A0(0-3) + // B0(4-7) B4(4-7) B1(4-7) B5(4-7) B2(4-7) B6(4-7) B3(4-7) B7(4-7) with A0(4-7) + // ........................................................................... + // B0(28-31) B4(28-31) B1(28-31) B5(28-31) B2(28-31) B6(28-31) B3(28-31) B7(28-31) with A0(28-31) + + iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(rhs_vec_0123_0 ,_mm256_shuffle_epi32(rhs_vec_4567_0, 177), 170), _mm256_shuffle_epi32(lhs_vec_0, 0))); + iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(_mm256_shuffle_epi32(rhs_vec_0123_0, 177) ,rhs_vec_4567_0, 170), _mm256_shuffle_epi32(lhs_vec_0, 85))); + + iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(rhs_vec_0123_1 ,_mm256_shuffle_epi32(rhs_vec_4567_1, 177), 170), _mm256_shuffle_epi32(lhs_vec_0, 170))); + iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(_mm256_shuffle_epi32(rhs_vec_0123_1, 177) ,rhs_vec_4567_1, 170), _mm256_shuffle_epi32(lhs_vec_0, 255))); + + iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(rhs_vec_0123_2 ,_mm256_shuffle_epi32(rhs_vec_4567_2, 177), 170), _mm256_shuffle_epi32(lhs_vec_1, 0))); + iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(_mm256_shuffle_epi32(rhs_vec_0123_2, 177) ,rhs_vec_4567_2, 170), _mm256_shuffle_epi32(lhs_vec_1, 85))); + + iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(rhs_vec_0123_3 ,_mm256_shuffle_epi32(rhs_vec_4567_3, 177), 170), _mm256_shuffle_epi32(lhs_vec_1, 170))); + iacc = _mm256_add_epi32(iacc, mul_sum_i8_pairs_int32x8(_mm256_blend_epi32(_mm256_shuffle_epi32(rhs_vec_0123_3, 177) ,rhs_vec_4567_3, 170), _mm256_shuffle_epi32(lhs_vec_1, 255))); + + // Accumulated values multipled with appropriate scales + acc_row = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc), _mm256_mul_ps(col_scale_f32, row_scale_f32), acc_row); + } + + // Accumulated output values permuted so as to be stored in appropriate order post accumulation + acc_row = _mm256_permutevar8x32_ps(acc_row, finalpermutemask); + _mm256_storeu_ps(s + (y * nr + x * 8), acc_row); + } + } + return; +#elif defined(__riscv_v_intrinsic) + if (__riscv_vlenb() >= QK4_0) { + const size_t vl = QK4_0; + + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb); + + vfloat32m1_t sumf = __riscv_vfmv_v_f_f32m1(0.0, vl / 4); + for (int l = 0; l < nb; l++) { + const int64_t a0 = *(const int64_t *)&a_ptr[l].qs[0]; + const int64_t a1 = *(const int64_t *)&a_ptr[l].qs[8]; + const int64_t a2 = *(const int64_t *)&a_ptr[l].qs[16]; + const int64_t a3 = *(const int64_t *)&a_ptr[l].qs[24]; + __asm__ __volatile__("" ::: "memory"); // prevent gcc from emitting fused vlse64, violating alignment + const vint8m2_t lhs_0_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(a0, vl / 4)); + const vint8m2_t lhs_1_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(a1, vl / 4)); + const vint8m2_t lhs_2_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(a2, vl / 4)); + const vint8m2_t lhs_3_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(a3, vl / 4)); + + const vint8m4_t rhs_raw_vec = __riscv_vle8_v_i8m4((const int8_t *)b_ptr[l].qs, vl * 4); + const vint8m4_t rhs_vec_lo = __riscv_vsra_vx_i8m4(__riscv_vsll_vx_i8m4(rhs_raw_vec, 4, vl * 4), 4, vl * 4); + const vint8m4_t rhs_vec_hi = __riscv_vsra_vx_i8m4(rhs_raw_vec, 4, vl * 4); + const vint8m2_t rhs_vec_lo_0 = __riscv_vget_v_i8m4_i8m2(rhs_vec_lo, 0); + const vint8m2_t rhs_vec_lo_1 = __riscv_vget_v_i8m4_i8m2(rhs_vec_lo, 1); + const vint8m2_t rhs_vec_hi_0 = __riscv_vget_v_i8m4_i8m2(rhs_vec_hi, 0); + const vint8m2_t rhs_vec_hi_1 = __riscv_vget_v_i8m4_i8m2(rhs_vec_hi, 1); + + const vint16m4_t sumi_lo_0 = __riscv_vwmul_vv_i16m4(rhs_vec_lo_0, lhs_0_8, vl * 2); + const vint16m4_t sumi_lo_1 = __riscv_vwmacc_vv_i16m4(sumi_lo_0, rhs_vec_lo_1, lhs_1_8, vl * 2); + const vint16m4_t sumi_hi_0 = __riscv_vwmacc_vv_i16m4(sumi_lo_1, rhs_vec_hi_0, lhs_2_8, vl * 2); + const vint16m4_t sumi_hi_m = __riscv_vwmacc_vv_i16m4(sumi_hi_0, rhs_vec_hi_1, lhs_3_8, vl * 2); + + const vuint32m4_t sumi_i32 = __riscv_vreinterpret_v_i32m4_u32m4(__riscv_vreinterpret_v_i16m4_i32m4(sumi_hi_m)); + const vuint16m2_t sumi_h2_0 = __riscv_vnsrl_wx_u16m2(sumi_i32, 0, vl); + const vuint16m2_t sumi_h2_1 = __riscv_vnsrl_wx_u16m2(sumi_i32, 16, vl); + const vuint16m2_t sumi_h2 = __riscv_vadd_vv_u16m2(sumi_h2_0, sumi_h2_1, vl); + const vuint32m2_t sumi_h2_i32 = __riscv_vreinterpret_v_u16m2_u32m2(sumi_h2); + const vuint16m1_t sumi_h4_0 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 0, vl / 2); + const vuint16m1_t sumi_h4_1 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 16, vl / 2); + const vuint16m1_t sumi_h4 = __riscv_vadd_vv_u16m1(sumi_h4_0, sumi_h4_1, vl / 2); + const vuint32m1_t sumi_h4_i32 = __riscv_vreinterpret_v_u16m1_u32m1(sumi_h4); + const vint16mf2_t sumi_h8_0 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 0, vl / 4)); + const vint16mf2_t sumi_h8_1 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 16, vl / 4)); + const vint32m1_t sumi_h8 = __riscv_vwadd_vv_i32m1(sumi_h8_0, sumi_h8_1, vl / 4); + const vfloat32m1_t facc = __riscv_vfcvt_f_x_v_f32m1(sumi_h8, vl / 4); + + // vector version needs Zvfhmin extension + const float a_scale = GGML_FP16_TO_FP32(a_ptr[l].d); + const float b_scales[8] = { + GGML_FP16_TO_FP32(b_ptr[l].d[0]), + GGML_FP16_TO_FP32(b_ptr[l].d[1]), + GGML_FP16_TO_FP32(b_ptr[l].d[2]), + GGML_FP16_TO_FP32(b_ptr[l].d[3]), + GGML_FP16_TO_FP32(b_ptr[l].d[4]), + GGML_FP16_TO_FP32(b_ptr[l].d[5]), + GGML_FP16_TO_FP32(b_ptr[l].d[6]), + GGML_FP16_TO_FP32(b_ptr[l].d[7]) + }; + const vfloat32m1_t b_scales_vec = __riscv_vle32_v_f32m1(b_scales, vl / 4); + const vfloat32m1_t tmp1 = __riscv_vfmul_vf_f32m1(facc, a_scale, vl / 4); + sumf = __riscv_vfmacc_vv_f32m1(sumf, tmp1, b_scales_vec, vl / 4); + } + __riscv_vse32_v_f32m1(s + x * ncols_interleaved, sumf, vl / 4); + } + return; + } +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) + { + float sumf[8]; + int sumi; + + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb); + + for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0; + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); + const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); + sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4; + } + sumf[j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d); + } + } + } + for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j]; + } + } +} + +void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 4; + const int blocklen = 4; + + assert (n % qk == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD) + if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) { + const int8x16_t kvalues = vld1q_s8(kvalues_iq4nl); + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + float * res_ptr = s; + + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_iq4_nlx4 * b_ptr = (const block_iq4_nlx4 *) vx + (x * nb); + + float32x4_t sumf = vdupq_n_f32(0); + for (int l = 0; l < nb; l++) { + uint8x16_t b_0 = vld1q_u8(b_ptr[l].qs + 0); + uint8x16_t b_1 = vld1q_u8(b_ptr[l].qs + 16); + uint8x16_t b_2 = vld1q_u8(b_ptr[l].qs + 32); + uint8x16_t b_3 = vld1q_u8(b_ptr[l].qs + 48); + + int8x16_t b_0_hi = vqtbl1q_s8(kvalues, b_0 >> 4); + int8x16_t b_0_lo = vqtbl1q_s8(kvalues, b_0 & 0x0F); + int8x16_t b_1_hi = vqtbl1q_s8(kvalues, b_1 >> 4); + int8x16_t b_1_lo = vqtbl1q_s8(kvalues, b_1 & 0x0F); + int8x16_t b_2_hi = vqtbl1q_s8(kvalues, b_2 >> 4); + int8x16_t b_2_lo = vqtbl1q_s8(kvalues, b_2 & 0x0F); + int8x16_t b_3_hi = vqtbl1q_s8(kvalues, b_3 >> 4); + int8x16_t b_3_lo = vqtbl1q_s8(kvalues, b_3 & 0x0F); + + int8x16_t a_0 = vld1q_s8(a_ptr[l].qs + 0); + int8x16_t a_1 = vld1q_s8(a_ptr[l].qs + 16); + + int32x4_t sumi = vdupq_n_s32(0); + sumi = vdotq_laneq_s32(sumi, b_0_lo, a_0, 0); + sumi = vdotq_laneq_s32(sumi, b_0_hi, a_1, 0); + sumi = vdotq_laneq_s32(sumi, b_1_lo, a_0, 1); + sumi = vdotq_laneq_s32(sumi, b_1_hi, a_1, 1); + sumi = vdotq_laneq_s32(sumi, b_2_lo, a_0, 2); + sumi = vdotq_laneq_s32(sumi, b_2_hi, a_1, 2); + sumi = vdotq_laneq_s32(sumi, b_3_lo, a_0, 3); + sumi = vdotq_laneq_s32(sumi, b_3_hi, a_1, 3); + + float32x4_t a_d = vcvt_f32_f16(vld1_dup_f16((const float16_t *)&a_ptr[l].d)); + float32x4_t b_d = vcvt_f32_f16(vld1_f16((const float16_t *)b_ptr[l].d)); + float32x4_t d = a_d * b_d; + + sumf = vmlaq_f32(sumf, d, vcvtq_f32_s32(sumi)); + } + + vst1q_f32(res_ptr + x * 4, sumf); + } + return; + } +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) + { + float sumf[4]; + int sumi; + + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_iq4_nlx4 * b_ptr = (const block_iq4_nlx4 *) vx + (x * nb); + + for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0; + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F]; + const int v1 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4]; + sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])); + } + sumf[j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d); + } + } + } + for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j]; + } + } +} + +void ggml_gemm_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 4; + const int blocklen = 4; + + assert (n % qk == 0); + assert (nr % 4 == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) + if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) { + const void * b_ptr = vx; + const void * a_ptr = vy; + float * res_ptr = s; + size_t res_stride = bs * sizeof(float); + + __asm__ __volatile__( + "mov x10, %x[nr]\n" + "mov x9, #0x88\n" + "cmp x10, #0x10\n" + "mul x9, %x[nb], x9\n" + "blt 4f\n" + "1:" // Row loop + "add x28, %x[b_ptr], #0x8\n" + "mov x27, %x[nc]\n" + "add x26, %x[res_ptr], %x[res_stride], LSL #4\n" + "2:" // Column loop + "add x25, %x[a_ptr], #0x8\n" + "movi v15.16b, #0x0\n" + "movi v19.16b, #0x0\n" + "mov x24, %x[nb]\n" + "add x23, x25, x9\n" + "movi v18.16b, #0x0\n" + "movi v14.16b, #0x0\n" + "add x22, x23, x9\n" + "movi v11.16b, #0x0\n" + "movi v13.16b, #0x0\n" + "add x21, x22, x9\n" + "movi v23.16b, #0x0\n" + "movi v16.16b, #0x0\n" + "movi v25.16b, #0x0\n" + "movi v7.16b, #0x0\n" + "movi v0.16b, #0x0\n" + "movi v4.16b, #0x0\n" + "movi v5.16b, #0x0\n" + "movi v21.16b, #0x0\n" + "movi v8.16b, #0x0\n" + "movi v1.16b, #0x0\n" + "3:" // Block loop + "ldr q3, [x28, #0x0]\n" + "ldr q31, [x25, #0x0]\n" + "movi v28.16b, #0x4\n" + "movi v10.4s, #0x0\n" + "ldr q22, [x28, #0x10]\n" + "ldr q6, [x25, #0x10]\n" + "movi v29.4s, #0x0\n" + "movi v9.4s, #0x0\n" + "ldr q27, [x28, #0x20]\n" + "ldr q30, [x28, #0x30]\n" + "movi v20.4s, #0x0\n" + "movi v24.16b, #0xf0\n" + "ldr d2, [x25, #-0x8]\n" + "ldr d26, [x23, #-0x8]\n" + "sshl v12.16b, v3.16b, v28.16b\n" + "sub x20, x28, #0x8\n" + "ldr d17, [x20, #0x0]\n" + "and v3.16b, v3.16b, v24.16b\n" + "subs x24, x24, #0x1\n" + "add x28, x28, #0x48\n" + ".inst 0x4f9fe18a // sdot v10.4s, v12.16b, v31.4b[0]\n" + ".inst 0x4fbfe19d // sdot v29.4s, v12.16b, v31.4b[1]\n" + ".inst 0x4f9fe989 // sdot v9.4s, v12.16b, v31.4b[2]\n" + ".inst 0x4fbfe994 // sdot v20.4s, v12.16b, v31.4b[3]\n" + "sshl v31.16b, v22.16b, v28.16b\n" + "and v22.16b, v22.16b, v24.16b\n" + "fcvtl v17.4s, v17.4h\n" + "fcvtl v2.4s, v2.4h\n" + "fcvtl v26.4s, v26.4h\n" + ".inst 0x4f86e3ea // sdot v10.4s, v31.16b, v6.4b[0]\n" + ".inst 0x4fa6e3fd // sdot v29.4s, v31.16b, v6.4b[1]\n" + ".inst 0x4f86ebe9 // sdot v9.4s, v31.16b, v6.4b[2]\n" + ".inst 0x4fa6ebf4 // sdot v20.4s, v31.16b, v6.4b[3]\n" + "sshl v6.16b, v27.16b, v28.16b\n" + "sshl v28.16b, v30.16b, v28.16b\n" + "and v27.16b, v27.16b, v24.16b\n" + "and v30.16b, v30.16b, v24.16b\n" + "ldr q24, [x25, #0x20]\n" + ".inst 0x4f98e0ca // sdot v10.4s, v6.16b, v24.4b[0]\n" + ".inst 0x4fb8e0dd // sdot v29.4s, v6.16b, v24.4b[1]\n" + ".inst 0x4f98e8c9 // sdot v9.4s, v6.16b, v24.4b[2]\n" + ".inst 0x4fb8e8d4 // sdot v20.4s, v6.16b, v24.4b[3]\n" + "ldr q24, [x25, #0x30]\n" + ".inst 0x4f98e38a // sdot v10.4s, v28.16b, v24.4b[0]\n" + ".inst 0x4fb8e39d // sdot v29.4s, v28.16b, v24.4b[1]\n" + ".inst 0x4f98eb89 // sdot v9.4s, v28.16b, v24.4b[2]\n" + ".inst 0x4fb8eb94 // sdot v20.4s, v28.16b, v24.4b[3]\n" + "ldr q24, [x25, #0x40]\n" + ".inst 0x4f98e06a // sdot v10.4s, v3.16b, v24.4b[0]\n" + ".inst 0x4fb8e07d // sdot v29.4s, v3.16b, v24.4b[1]\n" + ".inst 0x4f98e869 // sdot v9.4s, v3.16b, v24.4b[2]\n" + ".inst 0x4fb8e874 // sdot v20.4s, v3.16b, v24.4b[3]\n" + "ldr q24, [x25, #0x50]\n" + ".inst 0x4f98e2ca // sdot v10.4s, v22.16b, v24.4b[0]\n" + ".inst 0x4fb8e2dd // sdot v29.4s, v22.16b, v24.4b[1]\n" + ".inst 0x4f98eac9 // sdot v9.4s, v22.16b, v24.4b[2]\n" + ".inst 0x4fb8ead4 // sdot v20.4s, v22.16b, v24.4b[3]\n" + "ldr q24, [x25, #0x60]\n" + ".inst 0x4f98e36a // sdot v10.4s, v27.16b, v24.4b[0]\n" + ".inst 0x4fb8e37d // sdot v29.4s, v27.16b, v24.4b[1]\n" + ".inst 0x4f98eb69 // sdot v9.4s, v27.16b, v24.4b[2]\n" + ".inst 0x4fb8eb74 // sdot v20.4s, v27.16b, v24.4b[3]\n" + "ldr q24, [x25, #0x70]\n" + "add x25, x25, #0x88\n" + ".inst 0x4f98e3ca // sdot v10.4s, v30.16b, v24.4b[0]\n" + ".inst 0x4fb8e3dd // sdot v29.4s, v30.16b, v24.4b[1]\n" + ".inst 0x4f98ebc9 // sdot v9.4s, v30.16b, v24.4b[2]\n" + ".inst 0x4fb8ebd4 // sdot v20.4s, v30.16b, v24.4b[3]\n" + "fmul v24.4s, v17.4s, v2.s[0]\n" + "scvtf v10.4s, v10.4s, #0x4\n" + "scvtf v29.4s, v29.4s, #0x4\n" + "scvtf v9.4s, v9.4s, #0x4\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "fmla v15.4s, v10.4s, v24.4s\n" + "ldr q24, [x23, #0x0]\n" + "fmul v10.4s, v17.4s, v2.s[1]\n" + "fmla v19.4s, v29.4s, v10.4s\n" + "ldr q10, [x23, #0x10]\n" + "fmul v29.4s, v17.4s, v2.s[2]\n" + "fmul v2.4s, v17.4s, v2.s[3]\n" + "fmla v18.4s, v9.4s, v29.4s\n" + "movi v9.4s, #0x0\n" + "movi v29.4s, #0x0\n" + ".inst 0x4f98e189 // sdot v9.4s, v12.16b, v24.4b[0]\n" + ".inst 0x4fb8e19d // sdot v29.4s, v12.16b, v24.4b[1]\n" + "fmla v14.4s, v20.4s, v2.4s\n" + "movi v20.4s, #0x0\n" + "movi v2.4s, #0x0\n" + ".inst 0x4f98e994 // sdot v20.4s, v12.16b, v24.4b[2]\n" + ".inst 0x4fb8e982 // sdot v2.4s, v12.16b, v24.4b[3]\n" + "ldr q24, [x23, #0x20]\n" + ".inst 0x4f8ae3e9 // sdot v9.4s, v31.16b, v10.4b[0]\n" + ".inst 0x4faae3fd // sdot v29.4s, v31.16b, v10.4b[1]\n" + ".inst 0x4f8aebf4 // sdot v20.4s, v31.16b, v10.4b[2]\n" + ".inst 0x4faaebe2 // sdot v2.4s, v31.16b, v10.4b[3]\n" + "ldr q10, [x23, #0x30]\n" + ".inst 0x4f98e0c9 // sdot v9.4s, v6.16b, v24.4b[0]\n" + ".inst 0x4fb8e0dd // sdot v29.4s, v6.16b, v24.4b[1]\n" + ".inst 0x4f98e8d4 // sdot v20.4s, v6.16b, v24.4b[2]\n" + ".inst 0x4fb8e8c2 // sdot v2.4s, v6.16b, v24.4b[3]\n" + "ldr q24, [x23, #0x40]\n" + ".inst 0x4f8ae389 // sdot v9.4s, v28.16b, v10.4b[0]\n" + ".inst 0x4faae39d // sdot v29.4s, v28.16b, v10.4b[1]\n" + ".inst 0x4f8aeb94 // sdot v20.4s, v28.16b, v10.4b[2]\n" + ".inst 0x4faaeb82 // sdot v2.4s, v28.16b, v10.4b[3]\n" + "ldr q10, [x23, #0x50]\n" + ".inst 0x4f98e069 // sdot v9.4s, v3.16b, v24.4b[0]\n" + ".inst 0x4fb8e07d // sdot v29.4s, v3.16b, v24.4b[1]\n" + ".inst 0x4f98e874 // sdot v20.4s, v3.16b, v24.4b[2]\n" + ".inst 0x4fb8e862 // sdot v2.4s, v3.16b, v24.4b[3]\n" + "ldr q24, [x23, #0x60]\n" + ".inst 0x4f8ae2c9 // sdot v9.4s, v22.16b, v10.4b[0]\n" + ".inst 0x4faae2dd // sdot v29.4s, v22.16b, v10.4b[1]\n" + ".inst 0x4f8aead4 // sdot v20.4s, v22.16b, v10.4b[2]\n" + ".inst 0x4faaeac2 // sdot v2.4s, v22.16b, v10.4b[3]\n" + "ldr q10, [x23, #0x70]\n" + "add x23, x23, #0x88\n" + ".inst 0x4f98e369 // sdot v9.4s, v27.16b, v24.4b[0]\n" + ".inst 0x4fb8e37d // sdot v29.4s, v27.16b, v24.4b[1]\n" + ".inst 0x4f98eb74 // sdot v20.4s, v27.16b, v24.4b[2]\n" + ".inst 0x4fb8eb62 // sdot v2.4s, v27.16b, v24.4b[3]\n" + "ldr q24, [x22, #0x0]\n" + ".inst 0x4f8ae3c9 // sdot v9.4s, v30.16b, v10.4b[0]\n" + ".inst 0x4faae3dd // sdot v29.4s, v30.16b, v10.4b[1]\n" + ".inst 0x4f8aebd4 // sdot v20.4s, v30.16b, v10.4b[2]\n" + ".inst 0x4faaebc2 // sdot v2.4s, v30.16b, v10.4b[3]\n" + "fmul v10.4s, v17.4s, v26.s[0]\n" + "scvtf v9.4s, v9.4s, #0x4\n" + "scvtf v29.4s, v29.4s, #0x4\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "scvtf v2.4s, v2.4s, #0x4\n" + "fmla v11.4s, v9.4s, v10.4s\n" + "ldr q9, [x22, #0x10]\n" + "fmul v10.4s, v17.4s, v26.s[1]\n" + "fmla v13.4s, v29.4s, v10.4s\n" + "ldr d29, [x22, #-0x8]\n" + "fmul v10.4s, v17.4s, v26.s[2]\n" + "fmul v26.4s, v17.4s, v26.s[3]\n" + "fcvtl v29.4s, v29.4h\n" + "fmla v23.4s, v20.4s, v10.4s\n" + "movi v20.4s, #0x0\n" + "movi v10.4s, #0x0\n" + "fmla v16.4s, v2.4s, v26.4s\n" + "movi v26.4s, #0x0\n" + "movi v2.4s, #0x0\n" + ".inst 0x4f98e194 // sdot v20.4s, v12.16b, v24.4b[0]\n" + ".inst 0x4fb8e18a // sdot v10.4s, v12.16b, v24.4b[1]\n" + ".inst 0x4f98e99a // sdot v26.4s, v12.16b, v24.4b[2]\n" + ".inst 0x4fb8e982 // sdot v2.4s, v12.16b, v24.4b[3]\n" + "ldr q24, [x22, #0x20]\n" + ".inst 0x4f89e3f4 // sdot v20.4s, v31.16b, v9.4b[0]\n" + ".inst 0x4fa9e3ea // sdot v10.4s, v31.16b, v9.4b[1]\n" + ".inst 0x4f89ebfa // sdot v26.4s, v31.16b, v9.4b[2]\n" + ".inst 0x4fa9ebe2 // sdot v2.4s, v31.16b, v9.4b[3]\n" + "ldr q9, [x22, #0x30]\n" + ".inst 0x4f98e0d4 // sdot v20.4s, v6.16b, v24.4b[0]\n" + ".inst 0x4fb8e0ca // sdot v10.4s, v6.16b, v24.4b[1]\n" + ".inst 0x4f98e8da // sdot v26.4s, v6.16b, v24.4b[2]\n" + ".inst 0x4fb8e8c2 // sdot v2.4s, v6.16b, v24.4b[3]\n" + "ldr q24, [x22, #0x40]\n" + ".inst 0x4f89e394 // sdot v20.4s, v28.16b, v9.4b[0]\n" + ".inst 0x4fa9e38a // sdot v10.4s, v28.16b, v9.4b[1]\n" + ".inst 0x4f89eb9a // sdot v26.4s, v28.16b, v9.4b[2]\n" + ".inst 0x4fa9eb82 // sdot v2.4s, v28.16b, v9.4b[3]\n" + "ldr q9, [x22, #0x50]\n" + ".inst 0x4f98e074 // sdot v20.4s, v3.16b, v24.4b[0]\n" + ".inst 0x4fb8e06a // sdot v10.4s, v3.16b, v24.4b[1]\n" + ".inst 0x4f98e87a // sdot v26.4s, v3.16b, v24.4b[2]\n" + ".inst 0x4fb8e862 // sdot v2.4s, v3.16b, v24.4b[3]\n" + "ldr q24, [x22, #0x60]\n" + ".inst 0x4f89e2d4 // sdot v20.4s, v22.16b, v9.4b[0]\n" + ".inst 0x4fa9e2ca // sdot v10.4s, v22.16b, v9.4b[1]\n" + ".inst 0x4f89eada // sdot v26.4s, v22.16b, v9.4b[2]\n" + ".inst 0x4fa9eac2 // sdot v2.4s, v22.16b, v9.4b[3]\n" + "ldr q9, [x22, #0x70]\n" + "add x22, x22, #0x88\n" + ".inst 0x4f98e374 // sdot v20.4s, v27.16b, v24.4b[0]\n" + ".inst 0x4fb8e36a // sdot v10.4s, v27.16b, v24.4b[1]\n" + ".inst 0x4f98eb7a // sdot v26.4s, v27.16b, v24.4b[2]\n" + ".inst 0x4fb8eb62 // sdot v2.4s, v27.16b, v24.4b[3]\n" + "ldr q24, [x21, #0x0]\n" + ".inst 0x4f89e3d4 // sdot v20.4s, v30.16b, v9.4b[0]\n" + ".inst 0x4fa9e3ca // sdot v10.4s, v30.16b, v9.4b[1]\n" + ".inst 0x4f89ebda // sdot v26.4s, v30.16b, v9.4b[2]\n" + ".inst 0x4fa9ebc2 // sdot v2.4s, v30.16b, v9.4b[3]\n" + "fmul v9.4s, v17.4s, v29.s[0]\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "scvtf v10.4s, v10.4s, #0x4\n" + "scvtf v26.4s, v26.4s, #0x4\n" + "scvtf v2.4s, v2.4s, #0x4\n" + "fmla v25.4s, v20.4s, v9.4s\n" + "ldr q9, [x21, #0x10]\n" + "fmul v20.4s, v17.4s, v29.s[1]\n" + "fmla v7.4s, v10.4s, v20.4s\n" + "ldr d20, [x21, #-0x8]\n" + "fmul v10.4s, v17.4s, v29.s[2]\n" + "fmul v29.4s, v17.4s, v29.s[3]\n" + "fcvtl v20.4s, v20.4h\n" + "fmla v0.4s, v26.4s, v10.4s\n" + "movi v26.4s, #0x0\n" + "movi v10.4s, #0x0\n" + "fmla v4.4s, v2.4s, v29.4s\n" + "movi v2.4s, #0x0\n" + "movi v29.4s, #0x0\n" + ".inst 0x4f98e19a // sdot v26.4s, v12.16b, v24.4b[0]\n" + ".inst 0x4fb8e18a // sdot v10.4s, v12.16b, v24.4b[1]\n" + ".inst 0x4f98e982 // sdot v2.4s, v12.16b, v24.4b[2]\n" + ".inst 0x4fb8e99d // sdot v29.4s, v12.16b, v24.4b[3]\n" + "ldr q12, [x21, #0x20]\n" + "fmul v24.4s, v17.4s, v20.s[0]\n" + ".inst 0x4f89e3fa // sdot v26.4s, v31.16b, v9.4b[0]\n" + ".inst 0x4fa9e3ea // sdot v10.4s, v31.16b, v9.4b[1]\n" + ".inst 0x4f89ebe2 // sdot v2.4s, v31.16b, v9.4b[2]\n" + ".inst 0x4fa9ebfd // sdot v29.4s, v31.16b, v9.4b[3]\n" + "ldr q9, [x21, #0x30]\n" + "fmul v31.4s, v17.4s, v20.s[1]\n" + ".inst 0x4f8ce0da // sdot v26.4s, v6.16b, v12.4b[0]\n" + ".inst 0x4face0ca // sdot v10.4s, v6.16b, v12.4b[1]\n" + ".inst 0x4f8ce8c2 // sdot v2.4s, v6.16b, v12.4b[2]\n" + ".inst 0x4face8dd // sdot v29.4s, v6.16b, v12.4b[3]\n" + "ldr q12, [x21, #0x40]\n" + "fmul v6.4s, v17.4s, v20.s[2]\n" + "fmul v20.4s, v17.4s, v20.s[3]\n" + ".inst 0x4f89e39a // sdot v26.4s, v28.16b, v9.4b[0]\n" + ".inst 0x4fa9e38a // sdot v10.4s, v28.16b, v9.4b[1]\n" + ".inst 0x4f89eb82 // sdot v2.4s, v28.16b, v9.4b[2]\n" + ".inst 0x4fa9eb9d // sdot v29.4s, v28.16b, v9.4b[3]\n" + "ldr q9, [x21, #0x50]\n" + ".inst 0x4f8ce07a // sdot v26.4s, v3.16b, v12.4b[0]\n" + ".inst 0x4face06a // sdot v10.4s, v3.16b, v12.4b[1]\n" + ".inst 0x4f8ce862 // sdot v2.4s, v3.16b, v12.4b[2]\n" + ".inst 0x4face87d // sdot v29.4s, v3.16b, v12.4b[3]\n" + "ldr q12, [x21, #0x60]\n" + ".inst 0x4f89e2da // sdot v26.4s, v22.16b, v9.4b[0]\n" + ".inst 0x4fa9e2ca // sdot v10.4s, v22.16b, v9.4b[1]\n" + ".inst 0x4f89eac2 // sdot v2.4s, v22.16b, v9.4b[2]\n" + ".inst 0x4fa9eadd // sdot v29.4s, v22.16b, v9.4b[3]\n" + "ldr q17, [x21, #0x70]\n" + "add x21, x21, #0x88\n" + ".inst 0x4f8ce37a // sdot v26.4s, v27.16b, v12.4b[0]\n" + ".inst 0x4face36a // sdot v10.4s, v27.16b, v12.4b[1]\n" + ".inst 0x4f8ceb62 // sdot v2.4s, v27.16b, v12.4b[2]\n" + ".inst 0x4faceb7d // sdot v29.4s, v27.16b, v12.4b[3]\n" + ".inst 0x4f91e3da // sdot v26.4s, v30.16b, v17.4b[0]\n" + ".inst 0x4fb1e3ca // sdot v10.4s, v30.16b, v17.4b[1]\n" + ".inst 0x4f91ebc2 // sdot v2.4s, v30.16b, v17.4b[2]\n" + ".inst 0x4fb1ebdd // sdot v29.4s, v30.16b, v17.4b[3]\n" + "scvtf v26.4s, v26.4s, #0x4\n" + "scvtf v10.4s, v10.4s, #0x4\n" + "fmla v5.4s, v26.4s, v24.4s\n" + "scvtf v2.4s, v2.4s, #0x4\n" + "scvtf v29.4s, v29.4s, #0x4\n" + "fmla v21.4s, v10.4s, v31.4s\n" + "fmla v8.4s, v2.4s, v6.4s\n" + "fmla v1.4s, v29.4s, v20.4s\n" + "bgt 3b\n" + "mov x20, %x[res_ptr]\n" + "subs x27, x27, #0x4\n" + "add %x[res_ptr], %x[res_ptr], #0x10\n" + "str q15, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q19, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q18, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q14, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q11, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q13, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q23, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q16, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q25, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q7, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q0, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q4, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q5, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q21, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q8, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q1, [x20, #0x0]\n" + "bne 2b\n" + "mov x20, #0x4\n" + "sub x10, x10, #0x10\n" + "cmp x10, #0x10\n" + "mov %x[res_ptr], x26\n" + "madd %x[a_ptr], x20, x9, %x[a_ptr]\n" + "bge 1b\n" + "4:" // Row loop skip + "cbz x10, 9f\n" + "5:" // Row tail: Row loop + "add x24, %x[b_ptr], #0x8\n" + "mov x23, %x[nc]\n" + "add x22, %x[res_ptr], %x[res_stride], LSL #2\n" + "6:" // Row tail: Column loop + "movi v15.16b, #0x0\n" + "movi v19.16b, #0x0\n" + "add x25, %x[a_ptr], #0x8\n" + "mov x21, %x[nb]\n" + "movi v18.16b, #0x0\n" + "movi v14.16b, #0x0\n" + "7:" // Row tail: Block loop + "ldr q7, [x24, #0x0]\n" + "ldr q5, [x25, #0x0]\n" + "movi v9.16b, #0x4\n" + "movi v4.4s, #0x0\n" + "ldr q3, [x24, #0x10]\n" + "ldr q2, [x25, #0x10]\n" + "movi v1.4s, #0x0\n" + "movi v0.4s, #0x0\n" + "ldr q13, [x24, #0x20]\n" + "ldr q31, [x25, #0x20]\n" + "movi v30.4s, #0x0\n" + "movi v29.16b, #0xf0\n" + "ldr q28, [x24, #0x30]\n" + "ldr q27, [x25, #0x30]\n" + "sshl v20.16b, v7.16b, v9.16b\n" + "sub x20, x24, #0x8\n" + "ldr q26, [x25, #0x40]\n" + "ldr q25, [x25, #0x50]\n" + "sshl v17.16b, v3.16b, v9.16b\n" + "and v7.16b, v7.16b, v29.16b\n" + "ldr q24, [x25, #0x60]\n" + "ldr q16, [x25, #0x70]\n" + "sshl v22.16b, v13.16b, v9.16b\n" + "and v3.16b, v3.16b, v29.16b\n" + "ldr d21, [x20, #0x0]\n" + "ldr d12, [x25, #-0x8]\n" + ".inst 0x4f85e284 // sdot v4.4s, v20.16b, v5.4b[0]\n" + ".inst 0x4fa5e281 // sdot v1.4s, v20.16b, v5.4b[1]\n" + ".inst 0x4f85ea80 // sdot v0.4s, v20.16b, v5.4b[2]\n" + ".inst 0x4fa5ea9e // sdot v30.4s, v20.16b, v5.4b[3]\n" + "sshl v9.16b, v28.16b, v9.16b\n" + "subs x21, x21, #0x1\n" + "and v13.16b, v13.16b, v29.16b\n" + "and v28.16b, v28.16b, v29.16b\n" + "add x25, x25, #0x88\n" + "add x24, x24, #0x48\n" + "fcvtl v21.4s, v21.4h\n" + "fcvtl v12.4s, v12.4h\n" + ".inst 0x4f82e224 // sdot v4.4s, v17.16b, v2.4b[0]\n" + ".inst 0x4fa2e221 // sdot v1.4s, v17.16b, v2.4b[1]\n" + ".inst 0x4f82ea20 // sdot v0.4s, v17.16b, v2.4b[2]\n" + ".inst 0x4fa2ea3e // sdot v30.4s, v17.16b, v2.4b[3]\n" + "fmul v11.4s, v21.4s, v12.s[0]\n" + "fmul v23.4s, v21.4s, v12.s[1]\n" + "fmul v17.4s, v21.4s, v12.s[2]\n" + ".inst 0x4f9fe2c4 // sdot v4.4s, v22.16b, v31.4b[0]\n" + "fmul v6.4s, v21.4s, v12.s[3]\n" + ".inst 0x4fbfe2c1 // sdot v1.4s, v22.16b, v31.4b[1]\n" + ".inst 0x4f9feac0 // sdot v0.4s, v22.16b, v31.4b[2]\n" + ".inst 0x4fbfeade // sdot v30.4s, v22.16b, v31.4b[3]\n" + ".inst 0x4f9be124 // sdot v4.4s, v9.16b, v27.4b[0]\n" + ".inst 0x4fbbe121 // sdot v1.4s, v9.16b, v27.4b[1]\n" + ".inst 0x4f9be920 // sdot v0.4s, v9.16b, v27.4b[2]\n" + ".inst 0x4fbbe93e // sdot v30.4s, v9.16b, v27.4b[3]\n" + ".inst 0x4f9ae0e4 // sdot v4.4s, v7.16b, v26.4b[0]\n" + ".inst 0x4fbae0e1 // sdot v1.4s, v7.16b, v26.4b[1]\n" + ".inst 0x4f9ae8e0 // sdot v0.4s, v7.16b, v26.4b[2]\n" + ".inst 0x4fbae8fe // sdot v30.4s, v7.16b, v26.4b[3]\n" + ".inst 0x4f99e064 // sdot v4.4s, v3.16b, v25.4b[0]\n" + ".inst 0x4fb9e061 // sdot v1.4s, v3.16b, v25.4b[1]\n" + ".inst 0x4f99e860 // sdot v0.4s, v3.16b, v25.4b[2]\n" + ".inst 0x4fb9e87e // sdot v30.4s, v3.16b, v25.4b[3]\n" + ".inst 0x4f98e1a4 // sdot v4.4s, v13.16b, v24.4b[0]\n" + ".inst 0x4fb8e1a1 // sdot v1.4s, v13.16b, v24.4b[1]\n" + ".inst 0x4f98e9a0 // sdot v0.4s, v13.16b, v24.4b[2]\n" + ".inst 0x4fb8e9be // sdot v30.4s, v13.16b, v24.4b[3]\n" + ".inst 0x4f90e384 // sdot v4.4s, v28.16b, v16.4b[0]\n" + ".inst 0x4fb0e381 // sdot v1.4s, v28.16b, v16.4b[1]\n" + ".inst 0x4f90eb80 // sdot v0.4s, v28.16b, v16.4b[2]\n" + ".inst 0x4fb0eb9e // sdot v30.4s, v28.16b, v16.4b[3]\n" + "scvtf v4.4s, v4.4s, #0x4\n" + "scvtf v1.4s, v1.4s, #0x4\n" + "scvtf v0.4s, v0.4s, #0x4\n" + "fmla v15.4s, v4.4s, v11.4s\n" + "scvtf v30.4s, v30.4s, #0x4\n" + "fmla v19.4s, v1.4s, v23.4s\n" + "fmla v18.4s, v0.4s, v17.4s\n" + "fmla v14.4s, v30.4s, v6.4s\n" + "bgt 7b\n" + "mov x20, %x[res_ptr]\n" + "cmp x10, #0x1\n" + "str q15, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "cmp x10, #0x2\n" + "str q19, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "cmp x10, #0x3\n" + "str q18, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "str q14, [x20, #0x0]\n" + "8:" // Row tail: Accumulator store skip + "subs x23, x23, #0x4\n" + "add %x[res_ptr], %x[res_ptr], #0x10\n" + "bne 6b\n" + "subs x10, x10, #0x4\n" + "add %x[a_ptr], %x[a_ptr], x9\n" + "mov %x[res_ptr], x22\n" + "bgt 5b\n" + "9:" // Row tail: Row loop skip + : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr) + : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc) + : "cc", "memory", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x9", "x10", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28" + ); + return; + } +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) + { + float sumf[4][4]; + int sumi; + + for (int y = 0; y < nr / 4; y++) { + const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb); + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0; + } + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); + const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); + sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) + + (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4; + } + sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d[m]); + } + } + } + } + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) + s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j]; + } + } + } + } +} + +void ggml_gemm_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 4; + const int blocklen = 8; + + assert (n % qk == 0); + assert (nr % 4 == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) + if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) { + const void * b_ptr = vx; + const void * a_ptr = vy; + float * res_ptr = s; + size_t res_stride = bs * sizeof(float); + + __asm__ __volatile__( + "mov x10, %x[nr]\n" + "mov x9, #0x88\n" + "cmp x10, #0x10\n" + "mul x9, %x[nb], x9\n" + "blt 4f\n" + "1:" // Row loop + "add x28, %x[b_ptr], #0x8\n" + "mov x27, %x[nc]\n" + "add x26, %x[res_ptr], %x[res_stride], LSL #4\n" + "2:" // Column loop + "add x25, %x[a_ptr], #0x8\n" + "movi v2.16b, #0x0\n" + "movi v10.16b, #0x0\n" + "mov x24, %x[nb]\n" + "add x23, x25, x9\n" + "movi v12.16b, #0x0\n" + "movi v28.16b, #0x0\n" + "add x22, x23, x9\n" + "movi v11.16b, #0x0\n" + "movi v13.16b, #0x0\n" + "add x21, x22, x9\n" + "movi v22.16b, #0x0\n" + "movi v23.16b, #0x0\n" + "movi v25.16b, #0x0\n" + "movi v5.16b, #0x0\n" + "movi v7.16b, #0x0\n" + "movi v4.16b, #0x0\n" + "movi v6.16b, #0x0\n" + "movi v30.16b, #0x0\n" + "movi v24.16b, #0x0\n" + "movi v14.16b, #0x0\n" + "3:" // Block loop + "ldr q21, [x28, #0x0]\n" + "ldr q16, [x28, #0x10]\n" + "movi v1.16b, #0x4\n" + "movi v19.4s, #0x0\n" + "ldr q27, [x25, #0x0]\n" + "ldr q15, [x25, #0x10]\n" + "movi v26.4s, #0x0\n" + "movi v18.4s, #0x0\n" + "ldr q29, [x28, #0x20]\n" + "ldr q3, [x28, #0x30]\n" + "movi v17.4s, #0x0\n" + "movi v0.16b, #0xf0\n" + "ldr d20, [x25, #-0x8]\n" + "ldr d9, [x23, #-0x8]\n" + "sshl v8.16b, v21.16b, v1.16b\n" + "sshl v31.16b, v16.16b, v1.16b\n" + "and v21.16b, v21.16b, v0.16b\n" + "and v16.16b, v16.16b, v0.16b\n" + "sub x20, x28, #0x8\n" + "subs x24, x24, #0x1\n" + "add x28, x28, #0x48\n" + ".inst 0x4e88a773 // smmla v19.4s, v27.16b, v8.16b\n" + ".inst 0x4e9fa77a // smmla v26.4s, v27.16b, v31.16b\n" + "ldr q27, [x25, #0x20]\n" + ".inst 0x4e88a5f2 // smmla v18.4s, v15.16b, v8.16b\n" + ".inst 0x4e9fa5f1 // smmla v17.4s, v15.16b, v31.16b\n" + "sshl v15.16b, v29.16b, v1.16b\n" + "sshl v1.16b, v3.16b, v1.16b\n" + "and v29.16b, v29.16b, v0.16b\n" + "and v3.16b, v3.16b, v0.16b\n" + "ldr q0, [x25, #0x30]\n" + "fcvtl v20.4s, v20.4h\n" + ".inst 0x4e8fa773 // smmla v19.4s, v27.16b, v15.16b\n" + "fcvtl v9.4s, v9.4h\n" + ".inst 0x4e81a77a // smmla v26.4s, v27.16b, v1.16b\n" + "ldr q27, [x25, #0x40]\n" + ".inst 0x4e8fa412 // smmla v18.4s, v0.16b, v15.16b\n" + ".inst 0x4e81a411 // smmla v17.4s, v0.16b, v1.16b\n" + "ldr q0, [x25, #0x50]\n" + ".inst 0x4e95a773 // smmla v19.4s, v27.16b, v21.16b\n" + ".inst 0x4e90a77a // smmla v26.4s, v27.16b, v16.16b\n" + "ldr q27, [x25, #0x60]\n" + ".inst 0x4e95a412 // smmla v18.4s, v0.16b, v21.16b\n" + ".inst 0x4e90a411 // smmla v17.4s, v0.16b, v16.16b\n" + "ldr q0, [x25, #0x70]\n" + "add x25, x25, #0x88\n" + ".inst 0x4e9da773 // smmla v19.4s, v27.16b, v29.16b\n" + ".inst 0x4e83a77a // smmla v26.4s, v27.16b, v3.16b\n" + "ldr d27, [x20, #0x0]\n" + ".inst 0x4e9da412 // smmla v18.4s, v0.16b, v29.16b\n" + ".inst 0x4e83a411 // smmla v17.4s, v0.16b, v3.16b\n" + "fcvtl v27.4s, v27.4h\n" + "uzp1 v0.2d, v19.2d, v26.2d\n" + "uzp2 v26.2d, v19.2d, v26.2d\n" + "fmul v19.4s, v27.4s, v20.s[0]\n" + "scvtf v0.4s, v0.4s, #0x4\n" + "scvtf v26.4s, v26.4s, #0x4\n" + "fmla v2.4s, v0.4s, v19.4s\n" + "ldr q19, [x23, #0x0]\n" + "uzp1 v0.2d, v18.2d, v17.2d\n" + "uzp2 v18.2d, v18.2d, v17.2d\n" + "fmul v17.4s, v27.4s, v20.s[1]\n" + "scvtf v0.4s, v0.4s, #0x4\n" + "scvtf v18.4s, v18.4s, #0x4\n" + "fmla v10.4s, v26.4s, v17.4s\n" + "ldr q17, [x23, #0x10]\n" + "fmul v26.4s, v27.4s, v20.s[2]\n" + "fmul v20.4s, v27.4s, v20.s[3]\n" + "fmla v12.4s, v0.4s, v26.4s\n" + "ldr d0, [x22, #-0x8]\n" + "ldr d26, [x21, #-0x8]\n" + "fcvtl v0.4s, v0.4h\n" + "fmla v28.4s, v18.4s, v20.4s\n" + "movi v20.4s, #0x0\n" + "movi v18.4s, #0x0\n" + ".inst 0x4e88a674 // smmla v20.4s, v19.16b, v8.16b\n" + ".inst 0x4e9fa672 // smmla v18.4s, v19.16b, v31.16b\n" + "ldr q19, [x23, #0x20]\n" + "fcvtl v26.4s, v26.4h\n" + ".inst 0x4e8fa674 // smmla v20.4s, v19.16b, v15.16b\n" + ".inst 0x4e81a672 // smmla v18.4s, v19.16b, v1.16b\n" + "ldr q19, [x23, #0x40]\n" + ".inst 0x4e95a674 // smmla v20.4s, v19.16b, v21.16b\n" + ".inst 0x4e90a672 // smmla v18.4s, v19.16b, v16.16b\n" + "ldr q19, [x23, #0x60]\n" + ".inst 0x4e9da674 // smmla v20.4s, v19.16b, v29.16b\n" + ".inst 0x4e83a672 // smmla v18.4s, v19.16b, v3.16b\n" + "uzp1 v19.2d, v20.2d, v18.2d\n" + "scvtf v19.4s, v19.4s, #0x4\n" + "uzp2 v20.2d, v20.2d, v18.2d\n" + "fmul v18.4s, v27.4s, v9.s[0]\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "fmla v11.4s, v19.4s, v18.4s\n" + "ldr q18, [x22, #0x0]\n" + "fmul v19.4s, v27.4s, v9.s[1]\n" + "fmla v13.4s, v20.4s, v19.4s\n" + "movi v19.4s, #0x0\n" + "movi v20.4s, #0x0\n" + ".inst 0x4e88a633 // smmla v19.4s, v17.16b, v8.16b\n" + ".inst 0x4e9fa634 // smmla v20.4s, v17.16b, v31.16b\n" + "ldr q17, [x23, #0x30]\n" + ".inst 0x4e8fa633 // smmla v19.4s, v17.16b, v15.16b\n" + ".inst 0x4e81a634 // smmla v20.4s, v17.16b, v1.16b\n" + "ldr q17, [x23, #0x50]\n" + ".inst 0x4e95a633 // smmla v19.4s, v17.16b, v21.16b\n" + ".inst 0x4e90a634 // smmla v20.4s, v17.16b, v16.16b\n" + "ldr q17, [x23, #0x70]\n" + "add x23, x23, #0x88\n" + ".inst 0x4e9da633 // smmla v19.4s, v17.16b, v29.16b\n" + ".inst 0x4e83a634 // smmla v20.4s, v17.16b, v3.16b\n" + "uzp1 v17.2d, v19.2d, v20.2d\n" + "scvtf v17.4s, v17.4s, #0x4\n" + "uzp2 v20.2d, v19.2d, v20.2d\n" + "fmul v19.4s, v27.4s, v9.s[2]\n" + "fmul v9.4s, v27.4s, v9.s[3]\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "fmla v22.4s, v17.4s, v19.4s\n" + "ldr q17, [x22, #0x10]\n" + "movi v19.4s, #0x0\n" + ".inst 0x4e88a653 // smmla v19.4s, v18.16b, v8.16b\n" + "fmla v23.4s, v20.4s, v9.4s\n" + "movi v20.4s, #0x0\n" + "movi v9.4s, #0x0\n" + ".inst 0x4e9fa654 // smmla v20.4s, v18.16b, v31.16b\n" + "ldr q18, [x22, #0x20]\n" + ".inst 0x4e88a629 // smmla v9.4s, v17.16b, v8.16b\n" + ".inst 0x4e8fa653 // smmla v19.4s, v18.16b, v15.16b\n" + ".inst 0x4e81a654 // smmla v20.4s, v18.16b, v1.16b\n" + "ldr q18, [x22, #0x40]\n" + ".inst 0x4e95a653 // smmla v19.4s, v18.16b, v21.16b\n" + ".inst 0x4e90a654 // smmla v20.4s, v18.16b, v16.16b\n" + "ldr q18, [x22, #0x60]\n" + ".inst 0x4e9da653 // smmla v19.4s, v18.16b, v29.16b\n" + ".inst 0x4e83a654 // smmla v20.4s, v18.16b, v3.16b\n" + "movi v18.4s, #0x0\n" + ".inst 0x4e9fa632 // smmla v18.4s, v17.16b, v31.16b\n" + "ldr q17, [x22, #0x30]\n" + ".inst 0x4e8fa629 // smmla v9.4s, v17.16b, v15.16b\n" + ".inst 0x4e81a632 // smmla v18.4s, v17.16b, v1.16b\n" + "ldr q17, [x22, #0x50]\n" + ".inst 0x4e95a629 // smmla v9.4s, v17.16b, v21.16b\n" + ".inst 0x4e90a632 // smmla v18.4s, v17.16b, v16.16b\n" + "ldr q17, [x22, #0x70]\n" + "add x22, x22, #0x88\n" + ".inst 0x4e9da629 // smmla v9.4s, v17.16b, v29.16b\n" + ".inst 0x4e83a632 // smmla v18.4s, v17.16b, v3.16b\n" + "uzp1 v17.2d, v19.2d, v20.2d\n" + "uzp2 v20.2d, v19.2d, v20.2d\n" + "fmul v19.4s, v27.4s, v0.s[0]\n" + "scvtf v17.4s, v17.4s, #0x4\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "fmla v25.4s, v17.4s, v19.4s\n" + "ldr q19, [x21, #0x0]\n" + "fmul v17.4s, v27.4s, v0.s[1]\n" + "fmla v5.4s, v20.4s, v17.4s\n" + "ldr q17, [x21, #0x10]\n" + "uzp1 v20.2d, v9.2d, v18.2d\n" + "uzp2 v9.2d, v9.2d, v18.2d\n" + "fmul v18.4s, v27.4s, v0.s[2]\n" + "fmul v0.4s, v27.4s, v0.s[3]\n" + "scvtf v20.4s, v20.4s, #0x4\n" + "scvtf v9.4s, v9.4s, #0x4\n" + "fmla v7.4s, v20.4s, v18.4s\n" + "movi v20.4s, #0x0\n" + "movi v18.4s, #0x0\n" + ".inst 0x4e88a674 // smmla v20.4s, v19.16b, v8.16b\n" + ".inst 0x4e9fa672 // smmla v18.4s, v19.16b, v31.16b\n" + "ldr q19, [x21, #0x20]\n" + "fmla v4.4s, v9.4s, v0.4s\n" + "movi v9.4s, #0x0\n" + "movi v0.4s, #0x0\n" + ".inst 0x4e88a629 // smmla v9.4s, v17.16b, v8.16b\n" + "fmul v8.4s, v27.4s, v26.s[0]\n" + ".inst 0x4e9fa620 // smmla v0.4s, v17.16b, v31.16b\n" + "ldr q17, [x21, #0x30]\n" + ".inst 0x4e8fa674 // smmla v20.4s, v19.16b, v15.16b\n" + "fmul v31.4s, v27.4s, v26.s[1]\n" + ".inst 0x4e81a672 // smmla v18.4s, v19.16b, v1.16b\n" + "ldr q19, [x21, #0x40]\n" + ".inst 0x4e8fa629 // smmla v9.4s, v17.16b, v15.16b\n" + "fmul v15.4s, v27.4s, v26.s[2]\n" + "fmul v27.4s, v27.4s, v26.s[3]\n" + ".inst 0x4e81a620 // smmla v0.4s, v17.16b, v1.16b\n" + "ldr q1, [x21, #0x50]\n" + ".inst 0x4e95a674 // smmla v20.4s, v19.16b, v21.16b\n" + ".inst 0x4e90a672 // smmla v18.4s, v19.16b, v16.16b\n" + "ldr q26, [x21, #0x60]\n" + ".inst 0x4e95a429 // smmla v9.4s, v1.16b, v21.16b\n" + ".inst 0x4e90a420 // smmla v0.4s, v1.16b, v16.16b\n" + "ldr q21, [x21, #0x70]\n" + "add x21, x21, #0x88\n" + ".inst 0x4e9da754 // smmla v20.4s, v26.16b, v29.16b\n" + ".inst 0x4e83a752 // smmla v18.4s, v26.16b, v3.16b\n" + ".inst 0x4e9da6a9 // smmla v9.4s, v21.16b, v29.16b\n" + ".inst 0x4e83a6a0 // smmla v0.4s, v21.16b, v3.16b\n" + "uzp1 v29.2d, v20.2d, v18.2d\n" + "uzp2 v21.2d, v20.2d, v18.2d\n" + "scvtf v29.4s, v29.4s, #0x4\n" + "uzp1 v18.2d, v9.2d, v0.2d\n" + "uzp2 v16.2d, v9.2d, v0.2d\n" + "scvtf v21.4s, v21.4s, #0x4\n" + "fmla v6.4s, v29.4s, v8.4s\n" + "scvtf v18.4s, v18.4s, #0x4\n" + "scvtf v16.4s, v16.4s, #0x4\n" + "fmla v30.4s, v21.4s, v31.4s\n" + "fmla v24.4s, v18.4s, v15.4s\n" + "fmla v14.4s, v16.4s, v27.4s\n" + "bgt 3b\n" + "mov x20, %x[res_ptr]\n" + "subs x27, x27, #0x4\n" + "add %x[res_ptr], %x[res_ptr], #0x10\n" + "str q2, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q10, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q12, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q28, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q11, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q13, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q22, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q23, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q25, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q5, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q7, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q4, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q6, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q30, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q24, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "str q14, [x20, #0x0]\n" + "bne 2b\n" + "mov x20, #0x4\n" + "sub x10, x10, #0x10\n" + "cmp x10, #0x10\n" + "mov %x[res_ptr], x26\n" + "madd %x[a_ptr], x20, x9, %x[a_ptr]\n" + "bge 1b\n" + "4:" // Row loop skip + "cbz x10, 9f\n" + "5:" // Row tail: Row loop + "add x24, %x[b_ptr], #0x8\n" + "mov x23, %x[nc]\n" + "add x22, %x[res_ptr], %x[res_stride], LSL #2\n" + "6:" // Row tail: Column loop + "movi v2.16b, #0x0\n" + "movi v10.16b, #0x0\n" + "add x25, %x[a_ptr], #0x8\n" + "mov x21, %x[nb]\n" + "movi v12.16b, #0x0\n" + "movi v28.16b, #0x0\n" + "7:" // Row tail: Block loop + "ldr q6, [x24, #0x0]\n" + "ldr q5, [x24, #0x10]\n" + "movi v17.16b, #0x4\n" + "movi v8.4s, #0x0\n" + "ldr q4, [x25, #0x0]\n" + "ldr q13, [x25, #0x10]\n" + "movi v27.4s, #0x0\n" + "movi v0.4s, #0x0\n" + "ldr q31, [x24, #0x20]\n" + "ldr q14, [x24, #0x30]\n" + "movi v29.4s, #0x0\n" + "movi v22.16b, #0xf0\n" + "ldr q11, [x25, #0x20]\n" + "ldr q23, [x25, #0x30]\n" + "sshl v21.16b, v6.16b, v17.16b\n" + "sshl v16.16b, v5.16b, v17.16b\n" + "ldr q20, [x25, #0x40]\n" + "ldr q26, [x25, #0x50]\n" + "and v6.16b, v6.16b, v22.16b\n" + "and v5.16b, v5.16b, v22.16b\n" + "ldr q25, [x25, #0x60]\n" + "ldr q3, [x25, #0x70]\n" + "sshl v19.16b, v31.16b, v17.16b\n" + "sshl v18.16b, v14.16b, v17.16b\n" + "ldr d17, [x25, #-0x8]\n" + ".inst 0x4e95a488 // smmla v8.4s, v4.16b, v21.16b\n" + ".inst 0x4e90a49b // smmla v27.4s, v4.16b, v16.16b\n" + "and v31.16b, v31.16b, v22.16b\n" + ".inst 0x4e95a5a0 // smmla v0.4s, v13.16b, v21.16b\n" + ".inst 0x4e90a5bd // smmla v29.4s, v13.16b, v16.16b\n" + "and v14.16b, v14.16b, v22.16b\n" + "sub x20, x24, #0x8\n" + "ldr d16, [x20, #0x0]\n" + "subs x21, x21, #0x1\n" + "add x25, x25, #0x88\n" + "fcvtl v17.4s, v17.4h\n" + "add x24, x24, #0x48\n" + ".inst 0x4e93a568 // smmla v8.4s, v11.16b, v19.16b\n" + ".inst 0x4e92a57b // smmla v27.4s, v11.16b, v18.16b\n" + ".inst 0x4e93a6e0 // smmla v0.4s, v23.16b, v19.16b\n" + ".inst 0x4e92a6fd // smmla v29.4s, v23.16b, v18.16b\n" + "fcvtl v16.4s, v16.4h\n" + ".inst 0x4e86a688 // smmla v8.4s, v20.16b, v6.16b\n" + ".inst 0x4e85a69b // smmla v27.4s, v20.16b, v5.16b\n" + "fmul v23.4s, v16.4s, v17.s[0]\n" + "fmul v21.4s, v16.4s, v17.s[1]\n" + "fmul v1.4s, v16.4s, v17.s[2]\n" + "fmul v20.4s, v16.4s, v17.s[3]\n" + ".inst 0x4e86a740 // smmla v0.4s, v26.16b, v6.16b\n" + ".inst 0x4e85a75d // smmla v29.4s, v26.16b, v5.16b\n" + ".inst 0x4e9fa728 // smmla v8.4s, v25.16b, v31.16b\n" + ".inst 0x4e8ea73b // smmla v27.4s, v25.16b, v14.16b\n" + ".inst 0x4e9fa460 // smmla v0.4s, v3.16b, v31.16b\n" + ".inst 0x4e8ea47d // smmla v29.4s, v3.16b, v14.16b\n" + "uzp1 v19.2d, v8.2d, v27.2d\n" + "uzp2 v18.2d, v8.2d, v27.2d\n" + "scvtf v19.4s, v19.4s, #0x4\n" + "uzp1 v17.2d, v0.2d, v29.2d\n" + "uzp2 v16.2d, v0.2d, v29.2d\n" + "scvtf v18.4s, v18.4s, #0x4\n" + "fmla v2.4s, v19.4s, v23.4s\n" + "scvtf v17.4s, v17.4s, #0x4\n" + "scvtf v16.4s, v16.4s, #0x4\n" + "fmla v10.4s, v18.4s, v21.4s\n" + "fmla v12.4s, v17.4s, v1.4s\n" + "fmla v28.4s, v16.4s, v20.4s\n" + "bgt 7b\n" + "mov x20, %x[res_ptr]\n" + "cmp x10, #0x1\n" + "str q2, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "cmp x10, #0x2\n" + "str q10, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "cmp x10, #0x3\n" + "str q12, [x20, #0x0]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "str q28, [x20, #0x0]\n" + "8:" // Row tail: Accumulator store skip + "subs x23, x23, #0x4\n" + "add %x[res_ptr], %x[res_ptr], #0x10\n" + "bne 6b\n" + "subs x10, x10, #0x4\n" + "add %x[a_ptr], %x[a_ptr], x9\n" + "mov %x[res_ptr], x22\n" + "bgt 5b\n" + "9:" // Row tail: Row loop skip + : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr) + : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc) + : "cc", "memory", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x9", "x10", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28" + ); + return; + } +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) + float sumf[4][4]; + int sumi; + + for (int y = 0; y < nr / 4; y++) { + const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb); + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0; + } + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); + const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); + sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) + + (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4; + } + sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d[m]); + } + } + } + } + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) + s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j]; + } + } + } +} + +void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 8; + const int blocklen = 8; + + assert (n % qk == 0); + assert (nr % 4 == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) +#if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8) + if (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0) { + const void * b_ptr = vx; + const void * a_ptr = vy; + float * res_ptr = s; + size_t res_stride = bs * sizeof(float); + + __asm__ __volatile__( + "mov x20, #0x4\n" + "mov x13, %x[nr]\n" + "mov z28.s, #-0x4\n" + "mov x12, #0x88\n" + "ptrue p1.b\n" + "whilelt p0.s, XZR, x20\n" + "cmp x13, #0x10\n" + "mul x12, %x[nb], x12\n" + "blt 4f\n" + "1:" // Row loop + "add x11, %x[b_ptr], #0x10\n" + "mov x10, %x[nc]\n" + "add x9, %x[res_ptr], %x[res_stride], LSL #4\n" + "2:" // Column loop + "add x28, %x[a_ptr], #0x8\n" + "mov z24.b, #0x0\n" + "mov z15.b, #0x0\n" + "mov x27, %x[nb]\n" + "add x26, x28, x12\n" + "mov z12.b, #0x0\n" + "mov z0.b, #0x0\n" + "add x25, x26, x12\n" + "mov z13.b, #0x0\n" + "mov z1.b, #0x0\n" + "add x24, x25, x12\n" + "mov z20.b, #0x0\n" + "mov z25.b, #0x0\n" + "mov z11.b, #0x0\n" + "mov z16.b, #0x0\n" + "mov z19.b, #0x0\n" + "mov z26.b, #0x0\n" + "mov z8.b, #0x0\n" + "mov z29.b, #0x0\n" + "mov z27.b, #0x0\n" + "mov z10.b, #0x0\n" + "3:" // Block loop + "ld1b { z30.b }, p1/Z, [x11]\n" + "ld1b { z21.b }, p1/Z, [x11, #1, MUL VL]\n" + "mov z18.s, #0x0\n" + "mov z7.s, #0x0\n" + "ld1rqb { z3.b }, p1/Z, [x28]\n" + "ld1rqb { z5.b }, p1/Z, [x28, #16]\n" + "mov z9.s, #0x0\n" + "mov z22.s, #0x0\n" + "ld1b { z4.b }, p1/Z, [x11, #2, MUL VL]\n" + "ld1b { z17.b }, p1/Z, [x11, #3, MUL VL]\n" + "sub x20, x11, #0x10\n" + "sub x23, x28, #0x8\n" + "lsl z31.b, z30.b, #0x4\n" + "lsl z6.b, z21.b, #0x4\n" + "ld1h { z23.s }, p1/Z, [x20]\n" + "sub x22, x26, #0x8\n" + "and z30.b, z30.b, #0xf0\n" + "and z21.b, z21.b, #0xf0\n" + "sub x21, x25, #0x8\n" + "sub x20, x24, #0x8\n" + "lsl z14.b, z4.b, #0x4\n" + "lsl z2.b, z17.b, #0x4\n" + "subs x27, x27, #0x1\n" + "add x11, x11, #0x90\n" + ".inst 0x451f9872 // smmla z18.s, z3.b, z31.b\n" + ".inst 0x45069867 // smmla z7.s, z3.b, z6.b\n" + "ld1rqb { z3.b }, p1/Z, [x28, #32]\n" + "and z4.b, z4.b, #0xf0\n" + ".inst 0x451f98a9 // smmla z9.s, z5.b, z31.b\n" + ".inst 0x450698b6 // smmla z22.s, z5.b, z6.b\n" + "ld1rqb { z5.b }, p1/Z, [x28, #48]\n" + "and z17.b, z17.b, #0xf0\n" + "fcvt z23.s, p1/m, z23.h\n" + ".inst 0x450e9872 // smmla z18.s, z3.b, z14.b\n" + ".inst 0x45029867 // smmla z7.s, z3.b, z2.b\n" + "ld1rqb { z3.b }, p1/Z, [x28, #64]\n" + ".inst 0x450e98a9 // smmla z9.s, z5.b, z14.b\n" + ".inst 0x450298b6 // smmla z22.s, z5.b, z2.b\n" + "ld1rqb { z5.b }, p1/Z, [x28, #80]\n" + "fscale z23.s, p1/m, z23.s, z28.s\n" + ".inst 0x451e9872 // smmla z18.s, z3.b, z30.b\n" + ".inst 0x45159867 // smmla z7.s, z3.b, z21.b\n" + "ld1rqb { z3.b }, p1/Z, [x28, #96]\n" + ".inst 0x451e98a9 // smmla z9.s, z5.b, z30.b\n" + ".inst 0x451598b6 // smmla z22.s, z5.b, z21.b\n" + "ld1rqb { z5.b }, p1/Z, [x28, #112]\n" + "add x28, x28, #0x88\n" + ".inst 0x45049872 // smmla z18.s, z3.b, z4.b\n" + ".inst 0x45119867 // smmla z7.s, z3.b, z17.b\n" + "ld1h { z3.s }, p0/Z, [x23]\n" + ".inst 0x450498a9 // smmla z9.s, z5.b, z4.b\n" + ".inst 0x451198b6 // smmla z22.s, z5.b, z17.b\n" + "fcvt z3.s, p1/m, z3.h\n" + "uzp1 z5.d, z18.d, z7.d\n" + "uzp2 z18.d, z18.d, z7.d\n" + "mov z3.q, z3.q[0]\n" + "uzp1 z7.d, z9.d, z22.d\n" + "uzp2 z22.d, z9.d, z22.d\n" + "fmul z9.s, z23.s, z3.s[0]\n" + "scvtf z5.s, p1/m, z5.s\n" + "scvtf z18.s, p1/m, z18.s\n" + "scvtf z7.s, p1/m, z7.s\n" + "scvtf z22.s, p1/m, z22.s\n" + "fmla z24.s, p1/M, z5.s, z9.s\n" + "ld1rqb { z5.b }, p1/Z, [x26]\n" + "fmul z9.s, z23.s, z3.s[1]\n" + "fmla z15.s, p1/M, z18.s, z9.s\n" + "ld1rqb { z18.b }, p1/Z, [x26, #16]\n" + "fmul z9.s, z23.s, z3.s[2]\n" + "fmul z3.s, z23.s, z3.s[3]\n" + "fmla z12.s, p1/M, z7.s, z9.s\n" + "mov z9.s, #0x0\n" + "ld1h { z7.s }, p0/Z, [x22]\n" + ".inst 0x451f98a9 // smmla z9.s, z5.b, z31.b\n" + "fmla z0.s, p1/M, z22.s, z3.s\n" + "mov z22.s, #0x0\n" + "ld1h { z3.s }, p0/Z, [x21]\n" + ".inst 0x450698b6 // smmla z22.s, z5.b, z6.b\n" + "ld1rqb { z5.b }, p1/Z, [x26, #32]\n" + "fcvt z7.s, p1/m, z7.h\n" + "fcvt z3.s, p1/m, z3.h\n" + ".inst 0x450e98a9 // smmla z9.s, z5.b, z14.b\n" + ".inst 0x450298b6 // smmla z22.s, z5.b, z2.b\n" + "ld1rqb { z5.b }, p1/Z, [x26, #64]\n" + "mov z7.q, z7.q[0]\n" + "mov z3.q, z3.q[0]\n" + ".inst 0x451e98a9 // smmla z9.s, z5.b, z30.b\n" + ".inst 0x451598b6 // smmla z22.s, z5.b, z21.b\n" + "ld1rqb { z5.b }, p1/Z, [x26, #96]\n" + ".inst 0x450498a9 // smmla z9.s, z5.b, z4.b\n" + ".inst 0x451198b6 // smmla z22.s, z5.b, z17.b\n" + "uzp1 z5.d, z9.d, z22.d\n" + "scvtf z5.s, p1/m, z5.s\n" + "uzp2 z22.d, z9.d, z22.d\n" + "fmul z9.s, z23.s, z7.s[0]\n" + "scvtf z22.s, p1/m, z22.s\n" + "fmla z13.s, p1/M, z5.s, z9.s\n" + "ld1rqb { z9.b }, p1/Z, [x25]\n" + "fmul z5.s, z23.s, z7.s[1]\n" + "fmla z1.s, p1/M, z22.s, z5.s\n" + "mov z5.s, #0x0\n" + "mov z22.s, #0x0\n" + ".inst 0x451f9a45 // smmla z5.s, z18.b, z31.b\n" + ".inst 0x45069a56 // smmla z22.s, z18.b, z6.b\n" + "ld1rqb { z18.b }, p1/Z, [x26, #48]\n" + ".inst 0x450e9a45 // smmla z5.s, z18.b, z14.b\n" + ".inst 0x45029a56 // smmla z22.s, z18.b, z2.b\n" + "ld1rqb { z18.b }, p1/Z, [x26, #80]\n" + ".inst 0x451e9a45 // smmla z5.s, z18.b, z30.b\n" + ".inst 0x45159a56 // smmla z22.s, z18.b, z21.b\n" + "ld1rqb { z18.b }, p1/Z, [x26, #112]\n" + "add x26, x26, #0x88\n" + ".inst 0x45049a45 // smmla z5.s, z18.b, z4.b\n" + ".inst 0x45119a56 // smmla z22.s, z18.b, z17.b\n" + "uzp1 z18.d, z5.d, z22.d\n" + "scvtf z18.s, p1/m, z18.s\n" + "uzp2 z22.d, z5.d, z22.d\n" + "fmul z5.s, z23.s, z7.s[2]\n" + "fmul z7.s, z23.s, z7.s[3]\n" + "scvtf z22.s, p1/m, z22.s\n" + "fmla z20.s, p1/M, z18.s, z5.s\n" + "ld1rqb { z18.b }, p1/Z, [x25, #16]\n" + "ld1h { z5.s }, p0/Z, [x20]\n" + "fcvt z5.s, p1/m, z5.h\n" + "fmla z25.s, p1/M, z22.s, z7.s\n" + "mov z22.s, #0x0\n" + "mov z7.s, #0x0\n" + ".inst 0x451f9936 // smmla z22.s, z9.b, z31.b\n" + ".inst 0x45069927 // smmla z7.s, z9.b, z6.b\n" + "ld1rqb { z9.b }, p1/Z, [x25, #32]\n" + "mov z5.q, z5.q[0]\n" + ".inst 0x450e9936 // smmla z22.s, z9.b, z14.b\n" + ".inst 0x45029927 // smmla z7.s, z9.b, z2.b\n" + "ld1rqb { z9.b }, p1/Z, [x25, #64]\n" + ".inst 0x451e9936 // smmla z22.s, z9.b, z30.b\n" + ".inst 0x45159927 // smmla z7.s, z9.b, z21.b\n" + "ld1rqb { z9.b }, p1/Z, [x25, #96]\n" + ".inst 0x45049936 // smmla z22.s, z9.b, z4.b\n" + ".inst 0x45119927 // smmla z7.s, z9.b, z17.b\n" + "uzp1 z9.d, z22.d, z7.d\n" + "scvtf z9.s, p1/m, z9.s\n" + "uzp2 z22.d, z22.d, z7.d\n" + "fmul z7.s, z23.s, z3.s[0]\n" + "scvtf z22.s, p1/m, z22.s\n" + "fmla z11.s, p1/M, z9.s, z7.s\n" + "ld1rqb { z9.b }, p1/Z, [x24]\n" + "fmul z7.s, z23.s, z3.s[1]\n" + "fmla z16.s, p1/M, z22.s, z7.s\n" + "mov z22.s, #0x0\n" + "mov z7.s, #0x0\n" + ".inst 0x451f9a56 // smmla z22.s, z18.b, z31.b\n" + ".inst 0x45069a47 // smmla z7.s, z18.b, z6.b\n" + "ld1rqb { z18.b }, p1/Z, [x25, #48]\n" + ".inst 0x450e9a56 // smmla z22.s, z18.b, z14.b\n" + ".inst 0x45029a47 // smmla z7.s, z18.b, z2.b\n" + "ld1rqb { z18.b }, p1/Z, [x25, #80]\n" + ".inst 0x451e9a56 // smmla z22.s, z18.b, z30.b\n" + ".inst 0x45159a47 // smmla z7.s, z18.b, z21.b\n" + "ld1rqb { z18.b }, p1/Z, [x25, #112]\n" + "add x25, x25, #0x88\n" + ".inst 0x45049a56 // smmla z22.s, z18.b, z4.b\n" + ".inst 0x45119a47 // smmla z7.s, z18.b, z17.b\n" + "uzp1 z18.d, z22.d, z7.d\n" + "scvtf z18.s, p1/m, z18.s\n" + "uzp2 z7.d, z22.d, z7.d\n" + "fmul z22.s, z23.s, z3.s[2]\n" + "fmul z3.s, z23.s, z3.s[3]\n" + "scvtf z7.s, p1/m, z7.s\n" + "fmla z19.s, p1/M, z18.s, z22.s\n" + "ld1rqb { z18.b }, p1/Z, [x24, #16]\n" + "fmul z22.s, z23.s, z5.s[0]\n" + "fmla z26.s, p1/M, z7.s, z3.s\n" + "mov z3.s, #0x0\n" + "mov z7.s, #0x0\n" + ".inst 0x451f9923 // smmla z3.s, z9.b, z31.b\n" + ".inst 0x45069927 // smmla z7.s, z9.b, z6.b\n" + "ld1rqb { z9.b }, p1/Z, [x24, #32]\n" + ".inst 0x450e9923 // smmla z3.s, z9.b, z14.b\n" + ".inst 0x45029927 // smmla z7.s, z9.b, z2.b\n" + "mov z9.s, #0x0\n" + ".inst 0x451f9a49 // smmla z9.s, z18.b, z31.b\n" + "mov z31.s, #0x0\n" + ".inst 0x45069a5f // smmla z31.s, z18.b, z6.b\n" + "ld1rqb { z6.b }, p1/Z, [x24, #48]\n" + "ld1rqb { z18.b }, p1/Z, [x24, #64]\n" + ".inst 0x450e98c9 // smmla z9.s, z6.b, z14.b\n" + "fmul z14.s, z23.s, z5.s[1]\n" + ".inst 0x450298df // smmla z31.s, z6.b, z2.b\n" + "ld1rqb { z6.b }, p1/Z, [x24, #80]\n" + "fmul z2.s, z23.s, z5.s[2]\n" + "fmul z23.s, z23.s, z5.s[3]\n" + ".inst 0x451e9a43 // smmla z3.s, z18.b, z30.b\n" + ".inst 0x45159a47 // smmla z7.s, z18.b, z21.b\n" + "ld1rqb { z5.b }, p1/Z, [x24, #96]\n" + ".inst 0x451e98c9 // smmla z9.s, z6.b, z30.b\n" + ".inst 0x451598df // smmla z31.s, z6.b, z21.b\n" + "ld1rqb { z18.b }, p1/Z, [x24, #112]\n" + "add x24, x24, #0x88\n" + ".inst 0x450498a3 // smmla z3.s, z5.b, z4.b\n" + ".inst 0x451198a7 // smmla z7.s, z5.b, z17.b\n" + ".inst 0x45049a49 // smmla z9.s, z18.b, z4.b\n" + ".inst 0x45119a5f // smmla z31.s, z18.b, z17.b\n" + "uzp1 z18.d, z3.d, z7.d\n" + "uzp2 z5.d, z3.d, z7.d\n" + "scvtf z18.s, p1/m, z18.s\n" + "uzp1 z6.d, z9.d, z31.d\n" + "uzp2 z9.d, z9.d, z31.d\n" + "scvtf z5.s, p1/m, z5.s\n" + "fmla z8.s, p1/M, z18.s, z22.s\n" + "scvtf z6.s, p1/m, z6.s\n" + "scvtf z9.s, p1/m, z9.s\n" + "fmla z29.s, p1/M, z5.s, z14.s\n" + "fmla z27.s, p1/M, z6.s, z2.s\n" + "fmla z10.s, p1/M, z9.s, z23.s\n" + "bgt 3b\n" + "mov x20, %x[res_ptr]\n" + "subs x10, x10, #0x8\n" + "add %x[res_ptr], %x[res_ptr], #0x20\n" + "st1w { z24.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z15.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z12.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z0.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z13.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z1.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z20.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z25.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z11.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z16.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z19.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z26.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z8.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z29.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z27.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "st1w { z10.s }, p1, [x20]\n" + "bne 2b\n" + "mov x20, #0x4\n" + "sub x13, x13, #0x10\n" + "cmp x13, #0x10\n" + "mov %x[res_ptr], x9\n" + "madd %x[a_ptr], x20, x12, %x[a_ptr]\n" + "bge 1b\n" + "4:" // Row loop skip + "cbz x13, 9f\n" + "5:" // Row tail: Row loop + "add x25, %x[b_ptr], #0x10\n" + "mov x24, %x[nc]\n" + "add x23, %x[res_ptr], %x[res_stride], LSL #2\n" + "6:" // Row tail: Column loop + "mov z24.b, #0x0\n" + "mov z15.b, #0x0\n" + "add x28, %x[a_ptr], #0x8\n" + "mov x22, %x[nb]\n" + "mov z12.b, #0x0\n" + "mov z0.b, #0x0\n" + "7:" // Row tail: Block loop + "ld1b { z3.b }, p1/Z, [x25]\n" + "ld1b { z6.b }, p1/Z, [x25, #1, MUL VL]\n" + "mov z2.s, #0x0\n" + "mov z25.s, #0x0\n" + "ld1rqb { z26.b }, p1/Z, [x28]\n" + "ld1rqb { z21.b }, p1/Z, [x28, #16]\n" + "mov z27.s, #0x0\n" + "mov z19.s, #0x0\n" + "ld1b { z29.b }, p1/Z, [x25, #2, MUL VL]\n" + "ld1b { z16.b }, p1/Z, [x25, #3, MUL VL]\n" + "sub x21, x25, #0x10\n" + "sub x20, x28, #0x8\n" + "lsl z20.b, z3.b, #0x4\n" + "lsl z4.b, z6.b, #0x4\n" + "ld1rqb { z10.b }, p1/Z, [x28, #32]\n" + "ld1rqb { z23.b }, p1/Z, [x28, #48]\n" + "and z3.b, z3.b, #0xf0\n" + "and z6.b, z6.b, #0xf0\n" + "ld1rqb { z11.b }, p1/Z, [x28, #64]\n" + "ld1rqb { z7.b }, p1/Z, [x28, #80]\n" + "lsl z8.b, z29.b, #0x4\n" + "lsl z14.b, z16.b, #0x4\n" + "ld1rqb { z18.b }, p1/Z, [x28, #96]\n" + "ld1rqb { z30.b }, p1/Z, [x28, #112]\n" + ".inst 0x45149b42 // smmla z2.s, z26.b, z20.b\n" + ".inst 0x45049b59 // smmla z25.s, z26.b, z4.b\n" + "and z29.b, z29.b, #0xf0\n" + "ld1h { z17.s }, p1/Z, [x21]\n" + ".inst 0x45149abb // smmla z27.s, z21.b, z20.b\n" + ".inst 0x45049ab3 // smmla z19.s, z21.b, z4.b\n" + "and z16.b, z16.b, #0xf0\n" + "ld1h { z4.s }, p0/Z, [x20]\n" + "subs x22, x22, #0x1\n" + "add x28, x28, #0x88\n" + "fcvt z17.s, p1/m, z17.h\n" + "add x25, x25, #0x90\n" + ".inst 0x45089942 // smmla z2.s, z10.b, z8.b\n" + ".inst 0x450e9959 // smmla z25.s, z10.b, z14.b\n" + "fcvt z4.s, p1/m, z4.h\n" + ".inst 0x45089afb // smmla z27.s, z23.b, z8.b\n" + ".inst 0x450e9af3 // smmla z19.s, z23.b, z14.b\n" + "fscale z17.s, p1/m, z17.s, z28.s\n" + "mov z4.q, z4.q[0]\n" + ".inst 0x45039962 // smmla z2.s, z11.b, z3.b\n" + ".inst 0x45069979 // smmla z25.s, z11.b, z6.b\n" + "fmul z23.s, z17.s, z4.s[0]\n" + "fmul z9.s, z17.s, z4.s[1]\n" + "fmul z21.s, z17.s, z4.s[2]\n" + "fmul z4.s, z17.s, z4.s[3]\n" + ".inst 0x450398fb // smmla z27.s, z7.b, z3.b\n" + ".inst 0x450698f3 // smmla z19.s, z7.b, z6.b\n" + ".inst 0x451d9a42 // smmla z2.s, z18.b, z29.b\n" + ".inst 0x45109a59 // smmla z25.s, z18.b, z16.b\n" + ".inst 0x451d9bdb // smmla z27.s, z30.b, z29.b\n" + ".inst 0x45109bd3 // smmla z19.s, z30.b, z16.b\n" + "uzp1 z31.d, z2.d, z25.d\n" + "uzp2 z13.d, z2.d, z25.d\n" + "scvtf z31.s, p1/m, z31.s\n" + "uzp1 z17.d, z27.d, z19.d\n" + "uzp2 z18.d, z27.d, z19.d\n" + "scvtf z13.s, p1/m, z13.s\n" + "fmla z24.s, p1/M, z31.s, z23.s\n" + "scvtf z17.s, p1/m, z17.s\n" + "scvtf z18.s, p1/m, z18.s\n" + "fmla z15.s, p1/M, z13.s, z9.s\n" + "fmla z12.s, p1/M, z17.s, z21.s\n" + "fmla z0.s, p1/M, z18.s, z4.s\n" + "bgt 7b\n" + "mov x20, %x[res_ptr]\n" + "cmp x13, #0x1\n" + "st1w { z24.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "cmp x13, #0x2\n" + "st1w { z15.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "cmp x13, #0x3\n" + "st1w { z12.s }, p1, [x20]\n" + "add x20, x20, %x[res_stride]\n" + "ble 8f\n" + "st1w { z0.s }, p1, [x20]\n" + "8:" // Row tail: Accumulator store skip + "subs x24, x24, #0x8\n" + "add %x[res_ptr], %x[res_ptr], #0x20\n" + "bne 6b\n" + "subs x13, x13, #0x4\n" + "add %x[a_ptr], %x[a_ptr], x12\n" + "mov %x[res_ptr], x23\n" + "bgt 5b\n" + "9:" // Row tail: Row loop skip + : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr) + : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc) + : "cc", "memory", "p0", "p1", "x9", "x10", "x11", "x12", "x13", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28", "z0", "z1", "z2", "z3", "z4", "z5", "z6", "z7", "z8", "z9", "z10", "z11", "z12", "z13", "z14", "z15", "z16", "z17", "z18", "z19", "z20", "z21", "z22", "z23", "z24", "z25", "z26", "z27", "z28", "z29", "z30", "z31" + ); + return; + } +#endif // #if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8) +#elif defined(__AVX2__) || defined(__AVX512F__) + { + const block_q4_0x8 * b_ptr_start = (const block_q4_0x8 *)vx; + const block_q8_0x4 * a_ptr_start = (const block_q8_0x4 *)vy; + int64_t b_nb = n / QK4_0; + int64_t y = 0; + // Mask to mask out nibbles from packed bytes + const __m256i m4b = _mm256_set1_epi8(0x0F); + const __m128i loadMask = _mm_blend_epi32(_mm_setzero_si128(), _mm_set1_epi32(0xFFFFFFFF), 3); + // Lookup table to convert signed nibbles to signed bytes + __m256i signextendlut = _mm256_castsi128_si256(_mm_set_epi8(-1, -2, -3, -4, -5, -6, -7, -8, 7, 6, 5, 4, 3, 2, 1, 0)); + signextendlut = _mm256_permute2f128_si256(signextendlut, signextendlut, 0); + // Permute mask used for easier vector processing at later stages + __m256i requiredOrder = _mm256_set_epi32(3, 2, 1, 0, 7, 6, 5, 4); + int64_t xstart = 0; + int anr = nr - nr%16; // Used to align nr with boundary of 16 + #ifdef __AVX512F__ + int anc = nc - nc%16; // Used to align nc with boundary of 16 + // Mask to mask out nibbles from packed bytes expanded to 512 bit length + const __m512i m4bexpanded = _mm512_set1_epi8(0x0F); + // Lookup table to convert signed nibbles to signed bytes expanded to 512 bit length + __m512i signextendlutexpanded = _mm512_inserti32x8(_mm512_castsi256_si512(signextendlut), signextendlut, 1); + + // Take group of four block_q8_0x4 structures at each pass of the loop and perform dot product operation + for (; y < anr / 4; y += 4) { + + const block_q8_0x4 * a_ptrs[4]; + + a_ptrs[0] = a_ptr_start + (y * nb); + for (int i = 0; i < 3; ++i) { + a_ptrs[i + 1] = a_ptrs[i] + nb; + } + + // Take group of two block_q4_0x8 structures at each pass of the loop and perform dot product operation + for (int64_t x = 0; x < anc / 8; x += 2) { + + const block_q4_0x8 * b_ptr_0 = b_ptr_start + ((x) * b_nb); + const block_q4_0x8 * b_ptr_1 = b_ptr_start + ((x + 1) * b_nb); + + // Master FP accumulators + __m512 acc_rows[16]; + for (int i = 0; i < 16; i++) { + acc_rows[i] = _mm512_setzero_ps(); + } + + for (int64_t b = 0; b < nb; b++) { + // Load the sixteen block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....BE,BF + const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs)); + const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 32)); + const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 64)); + const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 96)); + + const __m256i rhs_raw_mat_89AB_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs)); + const __m256i rhs_raw_mat_CDEF_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 32)); + const __m256i rhs_raw_mat_89AB_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 64)); + const __m256i rhs_raw_mat_CDEF_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 96)); + + // Save the values in the following vectors in the formats B0B1B4B5B8B9BCBD, B2B3B6B7BABBBEBF for further processing and storing of values + const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240); + const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240); + const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240); + const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240); + + const __m256i rhs_raw_mat_89CD_0 = _mm256_blend_epi32(rhs_raw_mat_89AB_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_0, requiredOrder), 240); + const __m256i rhs_raw_mat_ABEF_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_0, requiredOrder), rhs_raw_mat_CDEF_0, 240); + const __m256i rhs_raw_mat_89CD_1 = _mm256_blend_epi32(rhs_raw_mat_89AB_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_1, requiredOrder), 240); + const __m256i rhs_raw_mat_ABEF_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_1, requiredOrder), rhs_raw_mat_CDEF_1, 240); + + const __m512i rhs_raw_mat_014589CD_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_0), rhs_raw_mat_89CD_0, 1); + const __m512i rhs_raw_mat_2367ABEF_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_0), rhs_raw_mat_ABEF_0, 1); + const __m512i rhs_raw_mat_014589CD_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_1), rhs_raw_mat_89CD_1, 1); + const __m512i rhs_raw_mat_2367ABEF_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_1), rhs_raw_mat_ABEF_1, 1); + + // 4-bit -> 8-bit - Sign is maintained + const __m512i rhs_mat_014589CD_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_0, m4bexpanded)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) B8(0-7) B9(0-7) BC(0-7) BD(0-7) + const __m512i rhs_mat_2367ABEF_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_0, m4bexpanded)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) BA(0-7) BB(0-7) BE(0-7) BF(0-7) + + const __m512i rhs_mat_014589CD_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_1, m4bexpanded)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) B8(8-15) B9(8-15) BC(8-15) BD(8-15) + const __m512i rhs_mat_2367ABEF_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_1, m4bexpanded)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) BA(8-15) BB(8-15) BE(8-15) BF(8-15) + + const __m512i rhs_mat_014589CD_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_0, 4), m4bexpanded)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) B8(16-23) B9(16-23) BC(16-23) BD(16-23) + const __m512i rhs_mat_2367ABEF_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_0, 4), m4bexpanded)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) BA(16-23) BB(16-23) BE(16-23) BF(16-23) + + const __m512i rhs_mat_014589CD_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_1, 4), m4bexpanded)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) B8(24-31) B9(24-31) BC(24-31) BD(24-31) + const __m512i rhs_mat_2367ABEF_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_1, 4), m4bexpanded)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) BA(24-31) BB(24-31) BE(24-31) BF(24-31) + + // Shuffle pattern one - right side input + const __m512i rhs_mat_014589CD_0_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) B8(0-3) B9(0-3) B8(0-3) B9(0-3) BC(0-3) BD(0-3) BC(0-3) BD(0-3) + const __m512i rhs_mat_2367ABEF_0_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) BA(0-3) BB(0-3) BA(0-3) BB(0-3) BE(0-3) BF(0-3) BE(0-3) BF(0-3) + + const __m512i rhs_mat_014589CD_1_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) B8(8-11) B9(8-11) B8(8-11) B9(8-11) BC(8-11) BD(8-11) BC(8-11) BD(8-11) + const __m512i rhs_mat_2367ABEF_1_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) BA(8-11) BB(8-11) BA(8-11) BB(8-11) BE(8-11) BF(8-11) BE(8-11) BF(8-11) + + const __m512i rhs_mat_014589CD_2_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) B8(16-19) B9(16-19) B8(16-19) B9(16-19) BC(16-19) BD(16-19) BC(16-19) BD(16-19) + const __m512i rhs_mat_2367ABEF_2_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) BA(16-19) BB(16-19) BA(16-19) BB(16-19) BE(16-19) BF(16-19) BE(16-19) BF(16-19) + + const __m512i rhs_mat_014589CD_3_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) B8(24-27) B9(24-27) B8(24-27) B9(24-27) BC(24-27) BD(24-27) BC(24-27) BD(24-27) + const __m512i rhs_mat_2367ABEF_3_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) BA(24-27) BB(24-27) BA(24-27) BB(24-27) BE(24-27) BF(24-27) BE(24-27) BF(24-27) + + // Shuffle pattern two - right side input + + const __m512i rhs_mat_014589CD_0_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) B8(4-7) B9(4-7) B8(4-7) B9(4-7) BC(4-7) BD(4-7) BC(4-7) BD(4-7) + const __m512i rhs_mat_2367ABEF_0_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) BA(4-7) BB(4-7) BA(4-7) BB(4-7) BE(4-7) BF(4-7) BE(4-7) BF(4-7) + + const __m512i rhs_mat_014589CD_1_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) B8(12-15) B9(12-15) B8(12-15) B9(12-15) BC(12-15) BD(12-15) BC(12-15) BD(12-15) + const __m512i rhs_mat_2367ABEF_1_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) BA(12-15) BB(12-15) BA(12-15) BB(12-15) BE(12-15) BF(12-15) BE(12-15) BF(12-15) + + const __m512i rhs_mat_014589CD_2_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) B8(20-23) B9(20-23) B8(20-23) B9(20-23) BC(20-23) BD(20-23) BC(20-23) BD(20-23) + const __m512i rhs_mat_2367ABEF_2_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) BA(20-23) BB(20-23) BA(20-23) BB(20-23) BE(20-23) BF(20-23) BE(20-23) BF(20-23) + + const __m512i rhs_mat_014589CD_3_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) B8(28-31) B9(28-31) B8(28-31) B9(28-31) BC(28-31) BD(28-31) BC(28-31) BD(28-31) + const __m512i rhs_mat_2367ABEF_3_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) BA(28-31) BB(28-31) BA(28-31) BB(28-31) BE(28-31) BF(28-31) BE(28-31) BF(28-31) + + // Scale values - Load the weight scale values of two block_q4_0x8 + const __m512 col_scale_f32 = GGML_F32Cx8x2_LOAD(b_ptr_0[b].d, b_ptr_1[b].d); + + // Process LHS in pairs of rows + for (int rp = 0; rp < 4; rp++) { + + // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3 + // Loaded as set of 128 bit vectors and repeated and stored into a 256 bit vector before again repeating into 512 bit vector + __m256i lhs_mat_ymm_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs))); + __m256i lhs_mat_ymm_01_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 0); + __m256i lhs_mat_ymm_23_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 17); + __m256i lhs_mat_ymm_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 32))); + __m256i lhs_mat_ymm_01_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 0); + __m256i lhs_mat_ymm_23_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 17); + __m256i lhs_mat_ymm_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 64))); + __m256i lhs_mat_ymm_01_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 0); + __m256i lhs_mat_ymm_23_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 17); + __m256i lhs_mat_ymm_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 96))); + __m256i lhs_mat_ymm_01_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 0); + __m256i lhs_mat_ymm_23_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 17); + + __m512i lhs_mat_01_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_0), lhs_mat_ymm_01_0, 1); + __m512i lhs_mat_23_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_0), lhs_mat_ymm_23_0, 1); + __m512i lhs_mat_01_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_1), lhs_mat_ymm_01_1, 1); + __m512i lhs_mat_23_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_1), lhs_mat_ymm_23_1, 1); + __m512i lhs_mat_01_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_2), lhs_mat_ymm_01_2, 1); + __m512i lhs_mat_23_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_2), lhs_mat_ymm_23_2, 1); + __m512i lhs_mat_01_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_3), lhs_mat_ymm_01_3, 1); + __m512i lhs_mat_23_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_3), lhs_mat_ymm_23_3, 1); + + // Shuffle pattern one - left side input + + const __m512i lhs_mat_01_0_sp1 = _mm512_shuffle_epi32(lhs_mat_01_0, 160); //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) + const __m512i lhs_mat_23_0_sp1 = _mm512_shuffle_epi32(lhs_mat_23_0, 160); //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) + + const __m512i lhs_mat_01_1_sp1 = _mm512_shuffle_epi32(lhs_mat_01_1, 160); //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) + const __m512i lhs_mat_23_1_sp1 = _mm512_shuffle_epi32(lhs_mat_23_1, 160); //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) + + const __m512i lhs_mat_01_2_sp1 = _mm512_shuffle_epi32(lhs_mat_01_2, 160); //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) + const __m512i lhs_mat_23_2_sp1 = _mm512_shuffle_epi32(lhs_mat_23_2, 160); //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) + + const __m512i lhs_mat_01_3_sp1 = _mm512_shuffle_epi32(lhs_mat_01_3, 160); //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) + const __m512i lhs_mat_23_3_sp1 = _mm512_shuffle_epi32(lhs_mat_23_3, 160); //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) + + // Shuffle pattern two - left side input + + const __m512i lhs_mat_01_0_sp2 = _mm512_shuffle_epi32(lhs_mat_01_0, 245); //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) + const __m512i lhs_mat_23_0_sp2 = _mm512_shuffle_epi32(lhs_mat_23_0, 245); //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) + + const __m512i lhs_mat_01_1_sp2 = _mm512_shuffle_epi32(lhs_mat_01_1, 245); //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) + const __m512i lhs_mat_23_1_sp2 = _mm512_shuffle_epi32(lhs_mat_23_1, 245); //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) + + const __m512i lhs_mat_01_2_sp2 = _mm512_shuffle_epi32(lhs_mat_01_2, 245); //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) + const __m512i lhs_mat_23_2_sp2 = _mm512_shuffle_epi32(lhs_mat_23_2, 245); //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) + + const __m512i lhs_mat_01_3_sp2 = _mm512_shuffle_epi32(lhs_mat_01_3, 245); //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) + const __m512i lhs_mat_23_3_sp2 = _mm512_shuffle_epi32(lhs_mat_23_3, 245); //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) + + // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane + // Resembles MMLAs into 2x2 matrices in ARM Version + __m512i iacc_mat_00_sp1 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp1, rhs_mat_014589CD_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp1, rhs_mat_014589CD_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp1, rhs_mat_014589CD_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp1, rhs_mat_014589CD_0_sp1)); + __m512i iacc_mat_01_sp1 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp1, rhs_mat_2367ABEF_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp1, rhs_mat_2367ABEF_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp1, rhs_mat_2367ABEF_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp1, rhs_mat_2367ABEF_0_sp1)); + __m512i iacc_mat_10_sp1 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp1, rhs_mat_014589CD_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp1, rhs_mat_014589CD_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp1, rhs_mat_014589CD_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp1, rhs_mat_014589CD_0_sp1)); + __m512i iacc_mat_11_sp1 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp1, rhs_mat_2367ABEF_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp1, rhs_mat_2367ABEF_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp1, rhs_mat_2367ABEF_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp1, rhs_mat_2367ABEF_0_sp1)); + __m512i iacc_mat_00_sp2 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp2, rhs_mat_014589CD_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp2, rhs_mat_014589CD_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp2, rhs_mat_014589CD_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp2, rhs_mat_014589CD_0_sp2)); + __m512i iacc_mat_01_sp2 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp2, rhs_mat_2367ABEF_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp2, rhs_mat_2367ABEF_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp2, rhs_mat_2367ABEF_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp2, rhs_mat_2367ABEF_0_sp2)); + __m512i iacc_mat_10_sp2 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp2, rhs_mat_014589CD_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp2, rhs_mat_014589CD_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp2, rhs_mat_014589CD_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp2, rhs_mat_014589CD_0_sp2)); + __m512i iacc_mat_11_sp2 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp2, rhs_mat_2367ABEF_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp2, rhs_mat_2367ABEF_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp2, rhs_mat_2367ABEF_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp2, rhs_mat_2367ABEF_0_sp2)); + + // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block + __m512i iacc_mat_00 = _mm512_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2); + __m512i iacc_mat_01 = _mm512_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2); + __m512i iacc_mat_10 = _mm512_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2); + __m512i iacc_mat_11 = _mm512_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2); + + + // Straighten out to make 4 row vectors + __m512i iacc_row_0 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_00, _mm512_shuffle_epi32(iacc_mat_01, 78)); + __m512i iacc_row_1 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01); + __m512i iacc_row_2 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_10, _mm512_shuffle_epi32(iacc_mat_11, 78)); + __m512i iacc_row_3 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11); + + // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes + const __m128i row_scale_f16 = _mm_shuffle_epi32(_mm_maskload_epi32((int const*)(a_ptrs[rp][b].d), loadMask), 68); + const __m512 row_scale_f32 = GGML_F32Cx16_REPEAT_LOAD(row_scale_f16); + + // Multiply with appropiate scales and accumulate + acc_rows[rp * 4] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_0), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[rp * 4]); + acc_rows[rp * 4 + 1] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_1), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[rp * 4 + 1]); + acc_rows[rp * 4 + 2] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_2), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[rp * 4 + 2]); + acc_rows[rp * 4 + 3] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_3), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[rp * 4 + 3]); + } + } + + // Store the accumulated values + for (int i = 0; i < 16; i++) { + _mm512_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]); + } + } + } + // Take a block_q8_0x4 structures at each pass of the loop and perform dot product operation + for (; y < nr / 4; y ++) { + + const block_q8_0x4 * a_ptr = a_ptr_start + (y * nb); + + // Take group of two block_q4_0x8 structures at each pass of the loop and perform dot product operation + for (int64_t x = 0; x < anc / 8; x += 2) { + + const block_q4_0x8 * b_ptr_0 = b_ptr_start + ((x) * b_nb); + const block_q4_0x8 * b_ptr_1 = b_ptr_start + ((x + 1) * b_nb); + + // Master FP accumulators + __m512 acc_rows[4]; + for (int i = 0; i < 4; i++) { + acc_rows[i] = _mm512_setzero_ps(); + } + + for (int64_t b = 0; b < nb; b++) { + // Load the sixteen block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....BE,BF + const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs)); + const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 32)); + const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 64)); + const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_0[b].qs + 96)); + + const __m256i rhs_raw_mat_89AB_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs)); + const __m256i rhs_raw_mat_CDEF_0 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 32)); + const __m256i rhs_raw_mat_89AB_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 64)); + const __m256i rhs_raw_mat_CDEF_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 96)); + + // Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of valuess + const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240); + const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240); + const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240); + const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240); + + const __m256i rhs_raw_mat_89CD_0 = _mm256_blend_epi32(rhs_raw_mat_89AB_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_0, requiredOrder), 240); + const __m256i rhs_raw_mat_ABEF_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_0, requiredOrder), rhs_raw_mat_CDEF_0, 240); + const __m256i rhs_raw_mat_89CD_1 = _mm256_blend_epi32(rhs_raw_mat_89AB_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_CDEF_1, requiredOrder), 240); + const __m256i rhs_raw_mat_ABEF_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_89AB_1, requiredOrder), rhs_raw_mat_CDEF_1, 240); + + const __m512i rhs_raw_mat_014589CD_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_0), rhs_raw_mat_89CD_0, 1); + const __m512i rhs_raw_mat_2367ABEF_0 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_0), rhs_raw_mat_ABEF_0, 1); + const __m512i rhs_raw_mat_014589CD_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_0145_1), rhs_raw_mat_89CD_1, 1); + const __m512i rhs_raw_mat_2367ABEF_1 = _mm512_inserti32x8(_mm512_castsi256_si512(rhs_raw_mat_2367_1), rhs_raw_mat_ABEF_1, 1); + + // 4-bit -> 8-bit - Sign is maintained + const __m512i rhs_mat_014589CD_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_0, m4bexpanded)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) B8(0-7) B9(0-7) BC(0-7) BD(0-7) + const __m512i rhs_mat_2367ABEF_0 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_0, m4bexpanded)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) BA(0-7) BB(0-7) BE(0-7) BF(0-7) + + const __m512i rhs_mat_014589CD_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_014589CD_1, m4bexpanded)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) B8(8-15) B9(8-15) BC(8-15) BD(8-15) + const __m512i rhs_mat_2367ABEF_1 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(rhs_raw_mat_2367ABEF_1, m4bexpanded)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) BA(8-15) BB(8-15) BE(8-15) BF(8-15) + + const __m512i rhs_mat_014589CD_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_0, 4), m4bexpanded)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) B8(16-23) B9(16-23) BC(16-23) BD(16-23) + const __m512i rhs_mat_2367ABEF_2 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_0, 4), m4bexpanded)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) BA(16-23) BB(16-23) BE(16-23) BF(16-23) + + const __m512i rhs_mat_014589CD_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_014589CD_1, 4), m4bexpanded)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) B8(24-31) B9(24-31) BC(24-31) BD(24-31) + const __m512i rhs_mat_2367ABEF_3 = _mm512_shuffle_epi8(signextendlutexpanded, _mm512_and_si512(_mm512_srli_epi16(rhs_raw_mat_2367ABEF_1, 4), m4bexpanded)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) BA(24-31) BB(24-31) BE(24-31) BF(24-31) + + // Shuffle pattern one - right side input + const __m512i rhs_mat_014589CD_0_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) B8(0-3) B9(0-3) B8(0-3) B9(0-3) BC(0-3) BD(0-3) BC(0-3) BD(0-3) + const __m512i rhs_mat_2367ABEF_0_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) BA(0-3) BB(0-3) BA(0-3) BB(0-3) BE(0-3) BF(0-3) BE(0-3) BF(0-3) + + const __m512i rhs_mat_014589CD_1_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) B8(8-11) B9(8-11) B8(8-11) B9(8-11) BC(8-11) BD(8-11) BC(8-11) BD(8-11) + const __m512i rhs_mat_2367ABEF_1_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) BA(8-11) BB(8-11) BA(8-11) BB(8-11) BE(8-11) BF(8-11) BE(8-11) BF(8-11) + + const __m512i rhs_mat_014589CD_2_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) B8(16-19) B9(16-19) B8(16-19) B9(16-19) BC(16-19) BD(16-19) BC(16-19) BD(16-19) + const __m512i rhs_mat_2367ABEF_2_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) BA(16-19) BB(16-19) BA(16-19) BB(16-19) BE(16-19) BF(16-19) BE(16-19) BF(16-19) + + const __m512i rhs_mat_014589CD_3_sp1 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) B8(24-27) B9(24-27) B8(24-27) B9(24-27) BC(24-27) BD(24-27) BC(24-27) BD(24-27) + const __m512i rhs_mat_2367ABEF_3_sp1 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) BA(24-27) BB(24-27) BA(24-27) BB(24-27) BE(24-27) BF(24-27) BE(24-27) BF(24-27) + + // Shuffle pattern two - right side input + + const __m512i rhs_mat_014589CD_0_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) B8(4-7) B9(4-7) B8(4-7) B9(4-7) BC(4-7) BD(4-7) BC(4-7) BD(4-7) + const __m512i rhs_mat_2367ABEF_0_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) BA(4-7) BB(4-7) BA(4-7) BB(4-7) BE(4-7) BF(4-7) BE(4-7) BF(4-7) + + const __m512i rhs_mat_014589CD_1_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) B8(12-15) B9(12-15) B8(12-15) B9(12-15) BC(12-15) BD(12-15) BC(12-15) BD(12-15) + const __m512i rhs_mat_2367ABEF_1_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) BA(12-15) BB(12-15) BA(12-15) BB(12-15) BE(12-15) BF(12-15) BE(12-15) BF(12-15) + + const __m512i rhs_mat_014589CD_2_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) B8(20-23) B9(20-23) B8(20-23) B9(20-23) BC(20-23) BD(20-23) BC(20-23) BD(20-23) + const __m512i rhs_mat_2367ABEF_2_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) BA(20-23) BB(20-23) BA(20-23) BB(20-23) BE(20-23) BF(20-23) BE(20-23) BF(20-23) + + const __m512i rhs_mat_014589CD_3_sp2 = _mm512_shuffle_epi32(rhs_mat_014589CD_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) B8(28-31) B9(28-31) B8(28-31) B9(28-31) BC(28-31) BD(28-31) BC(28-31) BD(28-31) + const __m512i rhs_mat_2367ABEF_3_sp2 = _mm512_shuffle_epi32(rhs_mat_2367ABEF_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) BA(28-31) BB(28-31) BA(28-31) BB(28-31) BE(28-31) BF(28-31) BE(28-31) BF(28-31) + + + // Scale values - Load the weight scale values of two block_q4_0x8 + const __m512 col_scale_f32 = GGML_F32Cx8x2_LOAD(b_ptr_0[b].d, b_ptr_1[b].d); + + // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3 + // Loaded as set of 128 bit vectors and repeated and stored into a 256 bit vector before again repeating into 512 bit vector + __m256i lhs_mat_ymm_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs))); + __m256i lhs_mat_ymm_01_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 0); + __m256i lhs_mat_ymm_23_0 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_0, lhs_mat_ymm_0123_0, 17); + __m256i lhs_mat_ymm_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 32))); + __m256i lhs_mat_ymm_01_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 0); + __m256i lhs_mat_ymm_23_1 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_1, lhs_mat_ymm_0123_1, 17); + __m256i lhs_mat_ymm_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 64))); + __m256i lhs_mat_ymm_01_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 0); + __m256i lhs_mat_ymm_23_2 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_2, lhs_mat_ymm_0123_2, 17); + __m256i lhs_mat_ymm_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 96))); + __m256i lhs_mat_ymm_01_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 0); + __m256i lhs_mat_ymm_23_3 = _mm256_permute2f128_si256(lhs_mat_ymm_0123_3, lhs_mat_ymm_0123_3, 17); + + __m512i lhs_mat_01_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_0), lhs_mat_ymm_01_0, 1); + __m512i lhs_mat_23_0 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_0), lhs_mat_ymm_23_0, 1); + __m512i lhs_mat_01_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_1), lhs_mat_ymm_01_1, 1); + __m512i lhs_mat_23_1 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_1), lhs_mat_ymm_23_1, 1); + __m512i lhs_mat_01_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_2), lhs_mat_ymm_01_2, 1); + __m512i lhs_mat_23_2 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_2), lhs_mat_ymm_23_2, 1); + __m512i lhs_mat_01_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_01_3), lhs_mat_ymm_01_3, 1); + __m512i lhs_mat_23_3 = _mm512_inserti32x8(_mm512_castsi256_si512(lhs_mat_ymm_23_3), lhs_mat_ymm_23_3, 1); + + // Shuffle pattern one - left side input + + const __m512i lhs_mat_01_0_sp1 = _mm512_shuffle_epi32(lhs_mat_01_0, 160); //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) + const __m512i lhs_mat_23_0_sp1 = _mm512_shuffle_epi32(lhs_mat_23_0, 160); //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) + + const __m512i lhs_mat_01_1_sp1 = _mm512_shuffle_epi32(lhs_mat_01_1, 160); //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) + const __m512i lhs_mat_23_1_sp1 = _mm512_shuffle_epi32(lhs_mat_23_1, 160); //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) + + const __m512i lhs_mat_01_2_sp1 = _mm512_shuffle_epi32(lhs_mat_01_2, 160); //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) + const __m512i lhs_mat_23_2_sp1 = _mm512_shuffle_epi32(lhs_mat_23_2, 160); //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) + + const __m512i lhs_mat_01_3_sp1 = _mm512_shuffle_epi32(lhs_mat_01_3, 160); //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) + const __m512i lhs_mat_23_3_sp1 = _mm512_shuffle_epi32(lhs_mat_23_3, 160); //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) + + // Shuffle pattern two - left side input + + const __m512i lhs_mat_01_0_sp2 = _mm512_shuffle_epi32(lhs_mat_01_0, 245); //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) + const __m512i lhs_mat_23_0_sp2 = _mm512_shuffle_epi32(lhs_mat_23_0, 245); //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) + + const __m512i lhs_mat_01_1_sp2 = _mm512_shuffle_epi32(lhs_mat_01_1, 245); //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) + const __m512i lhs_mat_23_1_sp2 = _mm512_shuffle_epi32(lhs_mat_23_1, 245); //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) + + const __m512i lhs_mat_01_2_sp2 = _mm512_shuffle_epi32(lhs_mat_01_2, 245); //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) + const __m512i lhs_mat_23_2_sp2 = _mm512_shuffle_epi32(lhs_mat_23_2, 245); //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) + + const __m512i lhs_mat_01_3_sp2 = _mm512_shuffle_epi32(lhs_mat_01_3, 245); //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) + const __m512i lhs_mat_23_3_sp2 = _mm512_shuffle_epi32(lhs_mat_23_3, 245); //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) + + // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane + // Resembles MMLAs into 2x2 matrices in ARM Version + __m512i iacc_mat_00_sp1 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp1, rhs_mat_014589CD_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp1, rhs_mat_014589CD_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp1, rhs_mat_014589CD_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp1, rhs_mat_014589CD_0_sp1)); + __m512i iacc_mat_01_sp1 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp1, rhs_mat_2367ABEF_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp1, rhs_mat_2367ABEF_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp1, rhs_mat_2367ABEF_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp1, rhs_mat_2367ABEF_0_sp1)); + __m512i iacc_mat_10_sp1 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp1, rhs_mat_014589CD_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp1, rhs_mat_014589CD_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp1, rhs_mat_014589CD_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp1, rhs_mat_014589CD_0_sp1)); + __m512i iacc_mat_11_sp1 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp1, rhs_mat_2367ABEF_3_sp1), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp1, rhs_mat_2367ABEF_2_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp1, rhs_mat_2367ABEF_1_sp1)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp1, rhs_mat_2367ABEF_0_sp1)); + __m512i iacc_mat_00_sp2 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp2, rhs_mat_014589CD_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp2, rhs_mat_014589CD_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp2, rhs_mat_014589CD_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp2, rhs_mat_014589CD_0_sp2)); + __m512i iacc_mat_01_sp2 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_01_3_sp2, rhs_mat_2367ABEF_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_01_2_sp2, rhs_mat_2367ABEF_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_1_sp2, rhs_mat_2367ABEF_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_01_0_sp2, rhs_mat_2367ABEF_0_sp2)); + __m512i iacc_mat_10_sp2 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp2, rhs_mat_014589CD_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp2, rhs_mat_014589CD_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp2, rhs_mat_014589CD_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp2, rhs_mat_014589CD_0_sp2)); + __m512i iacc_mat_11_sp2 = + _mm512_add_epi32(_mm512_add_epi32(_mm512_add_epi32(mul_sum_i8_pairs_int32x16(lhs_mat_23_3_sp2, rhs_mat_2367ABEF_3_sp2), mul_sum_i8_pairs_int32x16(lhs_mat_23_2_sp2, rhs_mat_2367ABEF_2_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_1_sp2, rhs_mat_2367ABEF_1_sp2)), mul_sum_i8_pairs_int32x16(lhs_mat_23_0_sp2, rhs_mat_2367ABEF_0_sp2)); + + // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block + __m512i iacc_mat_00 = _mm512_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2); + __m512i iacc_mat_01 = _mm512_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2); + __m512i iacc_mat_10 = _mm512_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2); + __m512i iacc_mat_11 = _mm512_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2); + + + // Straighten out to make 4 row vectors + __m512i iacc_row_0 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_00, _mm512_shuffle_epi32(iacc_mat_01, 78)); + __m512i iacc_row_1 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01); + __m512i iacc_row_2 = _mm512_mask_blend_epi32(0xCCCC, iacc_mat_10, _mm512_shuffle_epi32(iacc_mat_11, 78)); + __m512i iacc_row_3 = _mm512_mask_blend_epi32(0xCCCC, _mm512_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11); + + // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes + const __m128i row_scale_f16 = _mm_shuffle_epi32(_mm_maskload_epi32((int const*)(a_ptr[b].d), loadMask), 68); + const __m512 row_scale_f32 = GGML_F32Cx16_REPEAT_LOAD(row_scale_f16); + + // Multiply with appropiate scales and accumulate + acc_rows[0] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_0), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[0]); + acc_rows[1] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_1), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[1]); + acc_rows[2] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_2), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[2]); + acc_rows[3] = _mm512_fmadd_ps(_mm512_cvtepi32_ps(iacc_row_3), _mm512_mul_ps(col_scale_f32, _mm512_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[3]); + } + + // Store the accumulated values + for (int i = 0; i < 4; i++) { + _mm512_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]); + } + } + } + if (anc != nc) { + xstart = anc/8; + y = 0; + } + #endif // __AVX512F__ + + // Take group of four block_q8_0x4 structures at each pass of the loop and perform dot product operation + + for (; y < anr / 4; y += 4) { + const block_q8_0x4 * a_ptrs[4]; + + a_ptrs[0] = a_ptr_start + (y * nb); + for (int i = 0; i < 3; ++i) { + a_ptrs[i + 1] = a_ptrs[i] + nb; + } + + // Take group of eight block_q4_0x8 structures at each pass of the loop and perform dot product operation + for (int64_t x = xstart; x < nc / 8; x++) { + + const block_q4_0x8 * b_ptr = b_ptr_start + (x * b_nb); + + // Master FP accumulators + __m256 acc_rows[16]; + for (int i = 0; i < 16; i++) { + acc_rows[i] = _mm256_setzero_ps(); + } + + for (int64_t b = 0; b < nb; b++) { + // Load the eight block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7 + const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs)); + const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 32)); + const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 64)); + const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 96)); + + // Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of values + const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240); + const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240); + const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240); + const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240); + + // 4-bit -> 8-bit - Sign is maintained + const __m256i rhs_mat_0145_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_0, m4b)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) + const __m256i rhs_mat_2367_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_0, m4b)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) + + const __m256i rhs_mat_0145_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_1, m4b)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) + const __m256i rhs_mat_2367_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_1, m4b)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) + + const __m256i rhs_mat_0145_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_0, 4), m4b)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) + const __m256i rhs_mat_2367_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_0, 4), m4b)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) + + const __m256i rhs_mat_0145_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_1, 4), m4b)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) + const __m256i rhs_mat_2367_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_1, 4), m4b)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) + + // Shuffle pattern one - right side input + const __m256i rhs_mat_0145_0_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) + const __m256i rhs_mat_2367_0_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) + + const __m256i rhs_mat_0145_1_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) + const __m256i rhs_mat_2367_1_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) + + const __m256i rhs_mat_0145_2_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) + const __m256i rhs_mat_2367_2_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) + + const __m256i rhs_mat_0145_3_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) + const __m256i rhs_mat_2367_3_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) + + // Shuffle pattern two - right side input + + const __m256i rhs_mat_0145_0_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) + const __m256i rhs_mat_2367_0_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) + + const __m256i rhs_mat_0145_1_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) + const __m256i rhs_mat_2367_1_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) + + const __m256i rhs_mat_0145_2_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) + const __m256i rhs_mat_2367_2_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) + + const __m256i rhs_mat_0145_3_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) + const __m256i rhs_mat_2367_3_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) + + // Scale values - Load the wight scale values of block_q4_0x8 + const __m256 col_scale_f32 = GGML_F32Cx8_LOAD(b_ptr[b].d); + + // Process LHS in groups of four + for (int rp = 0; rp < 4; rp++) { + // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3 + // Loaded as set of 128 bit vectors and repeated into a 256 bit vector + __m256i lhs_mat_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs))); + __m256i lhs_mat_01_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 0); + __m256i lhs_mat_23_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 17); + __m256i lhs_mat_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 32))); + __m256i lhs_mat_01_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 0); + __m256i lhs_mat_23_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 17); + __m256i lhs_mat_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 64))); + __m256i lhs_mat_01_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 0); + __m256i lhs_mat_23_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 17); + __m256i lhs_mat_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptrs[rp][b].qs + 96))); + __m256i lhs_mat_01_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 0); + __m256i lhs_mat_23_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 17); + + // Shuffle pattern one - left side input + const __m256i lhs_mat_01_0_sp1 = _mm256_shuffle_epi32(lhs_mat_01_0, 160); //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) + const __m256i lhs_mat_23_0_sp1 = _mm256_shuffle_epi32(lhs_mat_23_0, 160); //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) + + const __m256i lhs_mat_01_1_sp1 = _mm256_shuffle_epi32(lhs_mat_01_1, 160); //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) + const __m256i lhs_mat_23_1_sp1 = _mm256_shuffle_epi32(lhs_mat_23_1, 160); //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) + + const __m256i lhs_mat_01_2_sp1 = _mm256_shuffle_epi32(lhs_mat_01_2, 160); //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) + const __m256i lhs_mat_23_2_sp1 = _mm256_shuffle_epi32(lhs_mat_23_2, 160); //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) + + const __m256i lhs_mat_01_3_sp1 = _mm256_shuffle_epi32(lhs_mat_01_3, 160); //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) + const __m256i lhs_mat_23_3_sp1 = _mm256_shuffle_epi32(lhs_mat_23_3, 160); //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) + + // Shuffle pattern two - left side input + const __m256i lhs_mat_01_0_sp2 = _mm256_shuffle_epi32(lhs_mat_01_0, 245); //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) + const __m256i lhs_mat_23_0_sp2 = _mm256_shuffle_epi32(lhs_mat_23_0, 245); //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) + + const __m256i lhs_mat_01_1_sp2 = _mm256_shuffle_epi32(lhs_mat_01_1, 245); //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) + const __m256i lhs_mat_23_1_sp2 = _mm256_shuffle_epi32(lhs_mat_23_1, 245); //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) + + const __m256i lhs_mat_01_2_sp2 = _mm256_shuffle_epi32(lhs_mat_01_2, 245); //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) + const __m256i lhs_mat_23_2_sp2 = _mm256_shuffle_epi32(lhs_mat_23_2, 245); //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) + + const __m256i lhs_mat_01_3_sp2 = _mm256_shuffle_epi32(lhs_mat_01_3, 245); //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) + const __m256i lhs_mat_23_3_sp2 = _mm256_shuffle_epi32(lhs_mat_23_3, 245); //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) + + // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane + // Resembles MMLAs into 2x2 matrices in ARM Version + __m256i iacc_mat_00_sp1 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp1, rhs_mat_0145_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp1, rhs_mat_0145_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp1, rhs_mat_0145_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp1, rhs_mat_0145_0_sp1)); + __m256i iacc_mat_01_sp1 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp1, rhs_mat_2367_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp1, rhs_mat_2367_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp1, rhs_mat_2367_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp1, rhs_mat_2367_0_sp1)); + __m256i iacc_mat_10_sp1 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp1, rhs_mat_0145_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp1, rhs_mat_0145_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp1, rhs_mat_0145_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp1, rhs_mat_0145_0_sp1)); + __m256i iacc_mat_11_sp1 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp1, rhs_mat_2367_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp1, rhs_mat_2367_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp1, rhs_mat_2367_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp1, rhs_mat_2367_0_sp1)); + __m256i iacc_mat_00_sp2 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp2, rhs_mat_0145_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp2, rhs_mat_0145_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp2, rhs_mat_0145_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp2, rhs_mat_0145_0_sp2)); + __m256i iacc_mat_01_sp2 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp2, rhs_mat_2367_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp2, rhs_mat_2367_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp2, rhs_mat_2367_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp2, rhs_mat_2367_0_sp2)); + __m256i iacc_mat_10_sp2 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp2, rhs_mat_0145_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp2, rhs_mat_0145_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp2, rhs_mat_0145_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp2, rhs_mat_0145_0_sp2)); + __m256i iacc_mat_11_sp2 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp2, rhs_mat_2367_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp2, rhs_mat_2367_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp2, rhs_mat_2367_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp2, rhs_mat_2367_0_sp2)); + + // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block + __m256i iacc_mat_00 = _mm256_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2); + __m256i iacc_mat_01 = _mm256_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2); + __m256i iacc_mat_10 = _mm256_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2); + __m256i iacc_mat_11 = _mm256_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2); + + // Straighten out to make 4 row vectors + __m256i iacc_row_0 = _mm256_blend_epi32(iacc_mat_00, _mm256_shuffle_epi32(iacc_mat_01, 78), 204); + __m256i iacc_row_1 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01, 204); + __m256i iacc_row_2 = _mm256_blend_epi32(iacc_mat_10, _mm256_shuffle_epi32(iacc_mat_11, 78), 204); + __m256i iacc_row_3 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11, 204); + + // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes + const __m256 row_scale_f32 = GGML_F32Cx8_REPEAT_LOAD(a_ptrs[rp][b].d, loadMask); + + // Multiply with appropiate scales and accumulate + acc_rows[rp * 4] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_0), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[rp * 4]); + acc_rows[rp * 4 + 1] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_1), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[rp * 4 + 1]); + acc_rows[rp * 4 + 2] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_2), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[rp * 4 + 2]); + acc_rows[rp * 4 + 3] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_3), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[rp * 4 + 3]); + } + } + + // Store the accumulated values + for (int i = 0; i < 16; i++) { + _mm256_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]); + } + } + } + + // Take a block_q8_0x4 structures at each pass of the loop and perform dot product operation + for (; y < nr / 4; y ++) { + + const block_q8_0x4 * a_ptr = a_ptr_start + (y * nb); + + // Load the eight block_q4_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7 + for (int64_t x = xstart; x < nc / 8; x++) { + + const block_q4_0x8 * b_ptr = b_ptr_start + (x * b_nb); + + // Master FP accumulators + __m256 acc_rows[4]; + for (int i = 0; i < 4; i++) { + acc_rows[i] = _mm256_setzero_ps(); + } + + for (int64_t b = 0; b < nb; b++) { + // Load the eight block_q8_0 quantized values interleaved with each other in chunks of eight - B0,B1 ....B6,B7 + const __m256i rhs_raw_mat_0123_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs)); + const __m256i rhs_raw_mat_4567_0 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 32)); + const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 64)); + const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 96)); + + // Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of valuess + const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240); + const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240); + const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240); + const __m256i rhs_raw_mat_2367_1 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_1, requiredOrder), rhs_raw_mat_4567_1, 240); + + // 4-bit -> 8-bit - Sign is maintained + const __m256i rhs_mat_0145_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_0, m4b)); //B0(0-7) B1(0-7) B4(0-7) B5(0-7) + const __m256i rhs_mat_2367_0 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_0, m4b)); //B2(0-7) B3(0-7) B6(0-7) B7(0-7) + + const __m256i rhs_mat_0145_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_0145_1, m4b)); //B0(8-15) B1(8-15) B4(8-15) B5(8-15) + const __m256i rhs_mat_2367_1 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(rhs_raw_mat_2367_1, m4b)); //B2(8-15) B3(8-15) B6(8-15) B7(8-15) + + const __m256i rhs_mat_0145_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_0, 4), m4b)); //B0(16-23) B1(16-23) B4(16-23) B5(16-23) + const __m256i rhs_mat_2367_2 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_0, 4), m4b)); //B2(16-23) B3(16-23) B6(16-23) B7(16-23) + + const __m256i rhs_mat_0145_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_0145_1, 4), m4b)); //B0(24-31) B1(24-31) B4(24-31) B5(24-31) + const __m256i rhs_mat_2367_3 = _mm256_shuffle_epi8(signextendlut, _mm256_and_si256(_mm256_srli_epi16(rhs_raw_mat_2367_1, 4), m4b)); //B2(24-31) B3(24-31) B6(24-31) B7(24-31) + + // Shuffle pattern one - right side input + const __m256i rhs_mat_0145_0_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_0, 136); //B0(0-3) B1(0-3) B0(0-3) B1(0-3) B4(0-3) B5(0-3) B4(0-3) B5(0-3) + const __m256i rhs_mat_2367_0_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_0, 136); //B2(0-3) B3(0-3) B2(0-3) B3(0-3) B6(0-3) B7(0-3) B6(0-3) B7(0-3) + + const __m256i rhs_mat_0145_1_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_1, 136); //B0(8-11) B1(8-11) B0(8-11) B1(8-11) B4(8-11) B5(8-11) B4(8-11) B5(8-11) + const __m256i rhs_mat_2367_1_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_1, 136); //B2(8-11) B3(8-11) B2(8-11) B3(8-11) B6(8-11) B7(8-11) B6(8-11) B7(8-11) + + const __m256i rhs_mat_0145_2_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_2, 136); //B0(16-19) B1(16-19) B0(16-19) B1(16-19) B4(16-19) B5(16-19) B4(16-19) B5(16-19) + const __m256i rhs_mat_2367_2_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_2, 136); //B2(16-19) B3(16-19) B2(16-19) B3(16-19) B6(16-19) B7(16-19) B6(16-19) B7(16-19) + + const __m256i rhs_mat_0145_3_sp1 = _mm256_shuffle_epi32(rhs_mat_0145_3, 136); //B0(24-27) B1(24-27) B0(24-27) B1(24-27) B4(24-27) B5(24-27) B4(24-27) B5(24-27) + const __m256i rhs_mat_2367_3_sp1 = _mm256_shuffle_epi32(rhs_mat_2367_3, 136); //B2(24-27) B3(24-27) B2(24-27) B3(24-27) B6(24-27) B7(24-27) B6(24-27) B7(24-27) + + // Shuffle pattern two - right side input + + const __m256i rhs_mat_0145_0_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_0, 221); //B0(4-7) B1(4-7) B0(4-7) B1(4-7) B4(4-7) B5(4-7) B4(4-7) B5(4-7) + const __m256i rhs_mat_2367_0_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_0, 221); //B2(4-7) B3(4-7) B2(4-7) B3(4-7) B6(4-7) B7(4-7) B6(4-7) B7(4-7) + + const __m256i rhs_mat_0145_1_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_1, 221); //B0(12-15) B1(12-15) B0(12-15) B1(12-15) B4(12-15) B5(12-15) B4(12-15) B5(12-15) + const __m256i rhs_mat_2367_1_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_1, 221); //B2(12-15) B3(12-15) B2(12-15) B3(12-15) B6(12-15) B7(12-15) B6(12-15) B7(12-15) + + const __m256i rhs_mat_0145_2_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_2, 221); //B0(20-23) B1(20-23) B0(20-23) B1(20-23) B4(20-23) B5(20-23) B4(20-23) B5(20-23) + const __m256i rhs_mat_2367_2_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_2, 221); //B2(20-23) B3(20-23) B2(20-23) B3(20-23) B6(20-23) B7(20-23) B6(20-23) B7(20-23) + + const __m256i rhs_mat_0145_3_sp2 = _mm256_shuffle_epi32(rhs_mat_0145_3, 221); //B0(28-31) B1(28-31) B0(28-31) B1(28-31) B4(28-31) B5(28-31) B4(28-31) B5(28-31) + const __m256i rhs_mat_2367_3_sp2 = _mm256_shuffle_epi32(rhs_mat_2367_3, 221); //B2(28-31) B3(28-31) B2(28-31) B3(28-31) B6(28-31) B7(28-31) B6(28-31) B7(28-31) + + // Scale values - Load the wight scale values of block_q4_0x8 + const __m256 col_scale_f32 = GGML_F32Cx8_LOAD(b_ptr[b].d); + + // Load the four block_q4_0 quantized values interleaved with each other in chunks of eight - A0,A1,A2,A3 + // Loaded as set of 128 bit vectors and repeated into a 256 bit vector + __m256i lhs_mat_0123_0 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs))); + __m256i lhs_mat_01_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 0); + __m256i lhs_mat_23_0 = _mm256_permute2f128_si256(lhs_mat_0123_0, lhs_mat_0123_0, 17); + __m256i lhs_mat_0123_1 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 32))); + __m256i lhs_mat_01_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 0); + __m256i lhs_mat_23_1 = _mm256_permute2f128_si256(lhs_mat_0123_1, lhs_mat_0123_1, 17); + __m256i lhs_mat_0123_2 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 64))); + __m256i lhs_mat_01_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 0); + __m256i lhs_mat_23_2 = _mm256_permute2f128_si256(lhs_mat_0123_2, lhs_mat_0123_2, 17); + __m256i lhs_mat_0123_3 = _mm256_loadu_si256((const __m256i *)((a_ptr[b].qs + 96))); + __m256i lhs_mat_01_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 0); + __m256i lhs_mat_23_3 = _mm256_permute2f128_si256(lhs_mat_0123_3, lhs_mat_0123_3, 17); + + // Shuffle pattern one - left side input + + const __m256i lhs_mat_01_0_sp1 = _mm256_shuffle_epi32(lhs_mat_01_0, 160); //A0(0-3) A0(0-3) A1(0-3) A1(0-3) A0(0-3) A0(0-3) A1(0-3) A1(0-3) + const __m256i lhs_mat_23_0_sp1 = _mm256_shuffle_epi32(lhs_mat_23_0, 160); //A2(0-3) A2(0-3) A3(0-3) A3(0-3) A2(0-3) A2(0-3) A3(0-3) A3(0-3) + + const __m256i lhs_mat_01_1_sp1 = _mm256_shuffle_epi32(lhs_mat_01_1, 160); //A0(8-11) A0(8-11) A1(8-11) A1(8-11) A0(8-11) A0(8-11) A1(8-11) A1(8-11) + const __m256i lhs_mat_23_1_sp1 = _mm256_shuffle_epi32(lhs_mat_23_1, 160); //A2(8-11) A2(8-11) A3(8-11) A3(8-11) A2(8-11) A2(8-11) A3(8-11) A3(8-11) + + const __m256i lhs_mat_01_2_sp1 = _mm256_shuffle_epi32(lhs_mat_01_2, 160); //A0(16-19) A0(16-19) A1(16-19) A1(16-19) A0(16-19) A0(16-19) A1(16-19) A1(16-19) + const __m256i lhs_mat_23_2_sp1 = _mm256_shuffle_epi32(lhs_mat_23_2, 160); //A2(16-19) A2(16-19) A3(16-19) A3(16-19) A2(16-19) A2(16-19) A3(16-19) A3(16-19) + + const __m256i lhs_mat_01_3_sp1 = _mm256_shuffle_epi32(lhs_mat_01_3, 160); //A0(24-27) A0(24-27) A1(24-27) A1(24-27) A0(24-27) A0(24-27) A1(24-27) A1(24-27) + const __m256i lhs_mat_23_3_sp1 = _mm256_shuffle_epi32(lhs_mat_23_3, 160); //A2(24-27) A2(24-27) A3(24-27) A3(24-27) A2(24-27) A2(24-27) A3(24-27) A3(24-27) + + // Shuffle pattern two - left side input + + const __m256i lhs_mat_01_0_sp2 = _mm256_shuffle_epi32(lhs_mat_01_0, 245); //A0(4-7) A0(4-7) A1(4-7) A1(4-7) A0(4-7) A0(4-7) A1(4-7) A1(4-7) + const __m256i lhs_mat_23_0_sp2 = _mm256_shuffle_epi32(lhs_mat_23_0, 245); //A2(4-7) A2(4-7) A3(4-7) A3(4-7) A2(4-7) A2(4-7) A3(4-7) A3(4-7) + + const __m256i lhs_mat_01_1_sp2 = _mm256_shuffle_epi32(lhs_mat_01_1, 245); //A0(12-15) A0(12-15) A1(12-15) A1(12-15) A0(12-15) A0(12-15) A1(12-15) A1(12-15) + const __m256i lhs_mat_23_1_sp2 = _mm256_shuffle_epi32(lhs_mat_23_1, 245); //A2(12-15) A2(12-15) A3(12-15) A3(12-15) A2(12-15) A2(12-15) A3(12-15) A3(12-15) + + const __m256i lhs_mat_01_2_sp2 = _mm256_shuffle_epi32(lhs_mat_01_2, 245); //A0(20-23) A0(20-23) A1(20-23) A1(20-23) A0(20-23) A0(20-23) A1(20-23) A1(20-23) + const __m256i lhs_mat_23_2_sp2 = _mm256_shuffle_epi32(lhs_mat_23_2, 245); //A2(20-23) A2(20-23) A3(20-23) A3(20-23) A2(20-23) A2(20-23) A3(20-23) A3(20-23) + + const __m256i lhs_mat_01_3_sp2 = _mm256_shuffle_epi32(lhs_mat_01_3, 245); //A0(28-31) A0(28-31) A1(28-31) A1(28-31) A0(28-31) A0(28-31) A1(28-31) A1(28-31) + const __m256i lhs_mat_23_3_sp2 = _mm256_shuffle_epi32(lhs_mat_23_3, 245); //A2(28-31) A2(28-31) A3(28-31) A3(28-31) A2(28-31) A2(28-31) A3(28-31) A3(28-31) + + // The values arranged in shuffle patterns are operated with dot product operation within 32 bit lane i.e corresponding bytes and multiplied and added into 32 bit integers within 32 bit lane + // Resembles MMLAs into 2x2 matrices in ARM Version + __m256i iacc_mat_00_sp1 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp1, rhs_mat_0145_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp1, rhs_mat_0145_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp1, rhs_mat_0145_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp1, rhs_mat_0145_0_sp1)); + __m256i iacc_mat_01_sp1 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp1, rhs_mat_2367_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp1, rhs_mat_2367_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp1, rhs_mat_2367_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp1, rhs_mat_2367_0_sp1)); + __m256i iacc_mat_10_sp1 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp1, rhs_mat_0145_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp1, rhs_mat_0145_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp1, rhs_mat_0145_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp1, rhs_mat_0145_0_sp1)); + __m256i iacc_mat_11_sp1 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp1, rhs_mat_2367_3_sp1), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp1, rhs_mat_2367_2_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp1, rhs_mat_2367_1_sp1)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp1, rhs_mat_2367_0_sp1)); + __m256i iacc_mat_00_sp2 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp2, rhs_mat_0145_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp2, rhs_mat_0145_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp2, rhs_mat_0145_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp2, rhs_mat_0145_0_sp2)); + __m256i iacc_mat_01_sp2 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_01_3_sp2, rhs_mat_2367_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_01_2_sp2, rhs_mat_2367_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_1_sp2, rhs_mat_2367_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_01_0_sp2, rhs_mat_2367_0_sp2)); + __m256i iacc_mat_10_sp2 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp2, rhs_mat_0145_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp2, rhs_mat_0145_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp2, rhs_mat_0145_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp2, rhs_mat_0145_0_sp2)); + __m256i iacc_mat_11_sp2 = + _mm256_add_epi32(_mm256_add_epi32(_mm256_add_epi32(mul_sum_i8_pairs_int32x8(lhs_mat_23_3_sp2, rhs_mat_2367_3_sp2), mul_sum_i8_pairs_int32x8(lhs_mat_23_2_sp2, rhs_mat_2367_2_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_1_sp2, rhs_mat_2367_1_sp2)), mul_sum_i8_pairs_int32x8(lhs_mat_23_0_sp2, rhs_mat_2367_0_sp2)); + + // Output of both shuffle patterns are added in order to sum dot product outputs of all 32 values in block + __m256i iacc_mat_00 = _mm256_add_epi32(iacc_mat_00_sp1, iacc_mat_00_sp2); + __m256i iacc_mat_01 = _mm256_add_epi32(iacc_mat_01_sp1, iacc_mat_01_sp2); + __m256i iacc_mat_10 = _mm256_add_epi32(iacc_mat_10_sp1, iacc_mat_10_sp2); + __m256i iacc_mat_11 = _mm256_add_epi32(iacc_mat_11_sp1, iacc_mat_11_sp2); + + + // Straighten out to make 4 row vectors + __m256i iacc_row_0 = _mm256_blend_epi32(iacc_mat_00, _mm256_shuffle_epi32(iacc_mat_01, 78), 204); + __m256i iacc_row_1 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_00, 78), iacc_mat_01, 204); + __m256i iacc_row_2 = _mm256_blend_epi32(iacc_mat_10, _mm256_shuffle_epi32(iacc_mat_11, 78), 204); + __m256i iacc_row_3 = _mm256_blend_epi32(_mm256_shuffle_epi32(iacc_mat_10, 78), iacc_mat_11, 204); + + // Load the scale(d) values for all the 4 Q8_0 blocks and repeat it across lanes + const __m256 row_scale_f32 = GGML_F32Cx8_REPEAT_LOAD(a_ptr[b].d, loadMask); + + // Multiply with appropiate scales and accumulate + acc_rows[0] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_0), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 0)), acc_rows[0]); + acc_rows[1] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_1), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 85)), acc_rows[1]); + acc_rows[2] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_2), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 170)), acc_rows[2]); + acc_rows[3] = _mm256_fmadd_ps(_mm256_cvtepi32_ps(iacc_row_3), _mm256_mul_ps(col_scale_f32, _mm256_shuffle_ps(row_scale_f32, row_scale_f32, 255)), acc_rows[3]); + } + + // Store the accumulated values + for (int i = 0; i < 4; i++) { + _mm256_storeu_ps((float *)(s + ((y * 4 + i) * bs + x * 8)), acc_rows[i]); + } + } + } + return; + } +#elif defined(__riscv_v_intrinsic) + if (__riscv_vlenb() >= QK4_0) { + const size_t vl = QK4_0; + + for (int y = 0; y < nr / 4; y++) { + const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb); + vfloat32m1_t sumf0 = __riscv_vfmv_v_f_f32m1(0.0, vl / 4); + vfloat32m1_t sumf1 = __riscv_vfmv_v_f_f32m1(0.0, vl / 4); + vfloat32m1_t sumf2 = __riscv_vfmv_v_f_f32m1(0.0, vl / 4); + vfloat32m1_t sumf3 = __riscv_vfmv_v_f_f32m1(0.0, vl / 4); + for (int l = 0; l < nb; l++) { + const vint8m4_t rhs_raw_vec = __riscv_vle8_v_i8m4((const int8_t *)b_ptr[l].qs, vl * 4); + const vint8m4_t rhs_vec_lo = __riscv_vsra_vx_i8m4(__riscv_vsll_vx_i8m4(rhs_raw_vec, 4, vl * 4), 4, vl * 4); + const vint8m4_t rhs_vec_hi = __riscv_vsra_vx_i8m4(rhs_raw_vec, 4, vl * 4); + const vint8m2_t rhs_vec_lo_0 = __riscv_vget_v_i8m4_i8m2(rhs_vec_lo, 0); + const vint8m2_t rhs_vec_lo_1 = __riscv_vget_v_i8m4_i8m2(rhs_vec_lo, 1); + const vint8m2_t rhs_vec_hi_0 = __riscv_vget_v_i8m4_i8m2(rhs_vec_hi, 0); + const vint8m2_t rhs_vec_hi_1 = __riscv_vget_v_i8m4_i8m2(rhs_vec_hi, 1); + + // vector version needs Zvfhmin extension + const float a_scales[4] = { + GGML_FP16_TO_FP32(a_ptr[l].d[0]), + GGML_FP16_TO_FP32(a_ptr[l].d[1]), + GGML_FP16_TO_FP32(a_ptr[l].d[2]), + GGML_FP16_TO_FP32(a_ptr[l].d[3]) + }; + const float b_scales[8] = { + GGML_FP16_TO_FP32(b_ptr[l].d[0]), + GGML_FP16_TO_FP32(b_ptr[l].d[1]), + GGML_FP16_TO_FP32(b_ptr[l].d[2]), + GGML_FP16_TO_FP32(b_ptr[l].d[3]), + GGML_FP16_TO_FP32(b_ptr[l].d[4]), + GGML_FP16_TO_FP32(b_ptr[l].d[5]), + GGML_FP16_TO_FP32(b_ptr[l].d[6]), + GGML_FP16_TO_FP32(b_ptr[l].d[7]) + }; + const vfloat32m1_t b_scales_vec = __riscv_vle32_v_f32m1(b_scales, vl / 4); + + const int64_t A0 = *(const int64_t *)&a_ptr[l].qs[0]; + const int64_t A4 = *(const int64_t *)&a_ptr[l].qs[32]; + const int64_t A8 = *(const int64_t *)&a_ptr[l].qs[64]; + const int64_t Ac = *(const int64_t *)&a_ptr[l].qs[96]; + __asm__ __volatile__("" ::: "memory"); // prevent gcc from emitting fused vlse64, violating alignment + vint16m4_t sumi_l0; + { + const vint8m2_t lhs_0_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A0, vl / 4)); + const vint8m2_t lhs_1_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A4, vl / 4)); + const vint8m2_t lhs_2_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A8, vl / 4)); + const vint8m2_t lhs_3_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Ac, vl / 4)); + const vint16m4_t sumi_lo_0 = __riscv_vwmul_vv_i16m4(rhs_vec_lo_0, lhs_0_8, vl * 2); + const vint16m4_t sumi_lo_1 = __riscv_vwmacc_vv_i16m4(sumi_lo_0, rhs_vec_lo_1, lhs_1_8, vl * 2); + const vint16m4_t sumi_hi_0 = __riscv_vwmacc_vv_i16m4(sumi_lo_1, rhs_vec_hi_0, lhs_2_8, vl * 2); + const vint16m4_t sumi_hi_m = __riscv_vwmacc_vv_i16m4(sumi_hi_0, rhs_vec_hi_1, lhs_3_8, vl * 2); + + sumi_l0 = sumi_hi_m; + } + + { + const vuint32m4_t sumi_i32 = __riscv_vreinterpret_v_i32m4_u32m4(__riscv_vreinterpret_v_i16m4_i32m4(sumi_l0)); + const vuint16m2_t sumi_h2_0 = __riscv_vnsrl_wx_u16m2(sumi_i32, 0, vl); + const vuint16m2_t sumi_h2_1 = __riscv_vnsrl_wx_u16m2(sumi_i32, 16, vl); + const vuint16m2_t sumi_h2 = __riscv_vadd_vv_u16m2(sumi_h2_0, sumi_h2_1, vl); + const vuint32m2_t sumi_h2_i32 = __riscv_vreinterpret_v_u16m2_u32m2(sumi_h2); + const vuint16m1_t sumi_h4_0 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 0, vl / 2); + const vuint16m1_t sumi_h4_1 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 16, vl / 2); + const vuint16m1_t sumi_h4 = __riscv_vadd_vv_u16m1(sumi_h4_0, sumi_h4_1, vl / 2); + const vuint32m1_t sumi_h4_i32 = __riscv_vreinterpret_v_u16m1_u32m1(sumi_h4); + const vint16mf2_t sumi_h8_0 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 0, vl / 4)); + const vint16mf2_t sumi_h8_1 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 16, vl / 4)); + const vint32m1_t sumi_h8 = __riscv_vwadd_vv_i32m1(sumi_h8_0, sumi_h8_1, vl / 4); + const vfloat32m1_t facc = __riscv_vfcvt_f_x_v_f32m1(sumi_h8, vl / 4); + + const vfloat32m1_t tmp1 = __riscv_vfmul_vf_f32m1(facc, a_scales[0], vl / 4); + sumf0 = __riscv_vfmacc_vv_f32m1(sumf0, tmp1, b_scales_vec, vl / 4); + } + + const int64_t A1 = *(const int64_t *)&a_ptr[l].qs[8]; + const int64_t A5 = *(const int64_t *)&a_ptr[l].qs[40]; + const int64_t A9 = *(const int64_t *)&a_ptr[l].qs[72]; + const int64_t Ad = *(const int64_t *)&a_ptr[l].qs[104]; + __asm__ __volatile__("" ::: "memory"); // prevent gcc from emitting fused vlse64, violating alignment + vint16m4_t sumi_l1; + { + const vint8m2_t lhs_0_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A1, vl / 4)); + const vint8m2_t lhs_1_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A5, vl / 4)); + const vint8m2_t lhs_2_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A9, vl / 4)); + const vint8m2_t lhs_3_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Ad, vl / 4)); + const vint16m4_t sumi_lo_0 = __riscv_vwmul_vv_i16m4(rhs_vec_lo_0, lhs_0_8, vl * 2); + const vint16m4_t sumi_lo_1 = __riscv_vwmacc_vv_i16m4(sumi_lo_0, rhs_vec_lo_1, lhs_1_8, vl * 2); + const vint16m4_t sumi_hi_0 = __riscv_vwmacc_vv_i16m4(sumi_lo_1, rhs_vec_hi_0, lhs_2_8, vl * 2); + const vint16m4_t sumi_hi_m = __riscv_vwmacc_vv_i16m4(sumi_hi_0, rhs_vec_hi_1, lhs_3_8, vl * 2); + + sumi_l1 = sumi_hi_m; + } + + { + const vuint32m4_t sumi_i32 = __riscv_vreinterpret_v_i32m4_u32m4(__riscv_vreinterpret_v_i16m4_i32m4(sumi_l1)); + const vuint16m2_t sumi_h2_0 = __riscv_vnsrl_wx_u16m2(sumi_i32, 0, vl); + const vuint16m2_t sumi_h2_1 = __riscv_vnsrl_wx_u16m2(sumi_i32, 16, vl); + const vuint16m2_t sumi_h2 = __riscv_vadd_vv_u16m2(sumi_h2_0, sumi_h2_1, vl); + const vuint32m2_t sumi_h2_i32 = __riscv_vreinterpret_v_u16m2_u32m2(sumi_h2); + const vuint16m1_t sumi_h4_0 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 0, vl / 2); + const vuint16m1_t sumi_h4_1 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 16, vl / 2); + const vuint16m1_t sumi_h4 = __riscv_vadd_vv_u16m1(sumi_h4_0, sumi_h4_1, vl / 2); + const vuint32m1_t sumi_h4_i32 = __riscv_vreinterpret_v_u16m1_u32m1(sumi_h4); + const vint16mf2_t sumi_h8_0 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 0, vl / 4)); + const vint16mf2_t sumi_h8_1 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 16, vl / 4)); + const vint32m1_t sumi_h8 = __riscv_vwadd_vv_i32m1(sumi_h8_0, sumi_h8_1, vl / 4); + const vfloat32m1_t facc = __riscv_vfcvt_f_x_v_f32m1(sumi_h8, vl / 4); + + const vfloat32m1_t tmp1 = __riscv_vfmul_vf_f32m1(facc, a_scales[1], vl / 4); + sumf1 = __riscv_vfmacc_vv_f32m1(sumf1, tmp1, b_scales_vec, vl / 4); + } + + const int64_t A2 = *(const int64_t *)&a_ptr[l].qs[16]; + const int64_t A6 = *(const int64_t *)&a_ptr[l].qs[48]; + const int64_t Aa = *(const int64_t *)&a_ptr[l].qs[80]; + const int64_t Ae = *(const int64_t *)&a_ptr[l].qs[112]; + __asm__ __volatile__("" ::: "memory"); // prevent gcc from emitting fused vlse64, violating alignment + vint16m4_t sumi_l2; + { + const vint8m2_t lhs_0_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A2, vl / 4)); + const vint8m2_t lhs_1_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A6, vl / 4)); + const vint8m2_t lhs_2_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Aa, vl / 4)); + const vint8m2_t lhs_3_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Ae, vl / 4)); + const vint16m4_t sumi_lo_0 = __riscv_vwmul_vv_i16m4(rhs_vec_lo_0, lhs_0_8, vl * 2); + const vint16m4_t sumi_lo_1 = __riscv_vwmacc_vv_i16m4(sumi_lo_0, rhs_vec_lo_1, lhs_1_8, vl * 2); + const vint16m4_t sumi_hi_0 = __riscv_vwmacc_vv_i16m4(sumi_lo_1, rhs_vec_hi_0, lhs_2_8, vl * 2); + const vint16m4_t sumi_hi_m = __riscv_vwmacc_vv_i16m4(sumi_hi_0, rhs_vec_hi_1, lhs_3_8, vl * 2); + + sumi_l2 = sumi_hi_m; + } + + { + const vuint32m4_t sumi_i32 = __riscv_vreinterpret_v_i32m4_u32m4(__riscv_vreinterpret_v_i16m4_i32m4(sumi_l2)); + const vuint16m2_t sumi_h2_0 = __riscv_vnsrl_wx_u16m2(sumi_i32, 0, vl); + const vuint16m2_t sumi_h2_1 = __riscv_vnsrl_wx_u16m2(sumi_i32, 16, vl); + const vuint16m2_t sumi_h2 = __riscv_vadd_vv_u16m2(sumi_h2_0, sumi_h2_1, vl); + const vuint32m2_t sumi_h2_i32 = __riscv_vreinterpret_v_u16m2_u32m2(sumi_h2); + const vuint16m1_t sumi_h4_0 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 0, vl / 2); + const vuint16m1_t sumi_h4_1 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 16, vl / 2); + const vuint16m1_t sumi_h4 = __riscv_vadd_vv_u16m1(sumi_h4_0, sumi_h4_1, vl / 2); + const vuint32m1_t sumi_h4_i32 = __riscv_vreinterpret_v_u16m1_u32m1(sumi_h4); + const vint16mf2_t sumi_h8_0 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 0, vl / 4)); + const vint16mf2_t sumi_h8_1 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 16, vl / 4)); + const vint32m1_t sumi_h8 = __riscv_vwadd_vv_i32m1(sumi_h8_0, sumi_h8_1, vl / 4); + const vfloat32m1_t facc = __riscv_vfcvt_f_x_v_f32m1(sumi_h8, vl / 4); + + const vfloat32m1_t tmp1 = __riscv_vfmul_vf_f32m1(facc, a_scales[2], vl / 4); + sumf2 = __riscv_vfmacc_vv_f32m1(sumf2, tmp1, b_scales_vec, vl / 4); + } + + const int64_t A3 = *(const int64_t *)&a_ptr[l].qs[24]; + const int64_t A7 = *(const int64_t *)&a_ptr[l].qs[56]; + const int64_t Ab = *(const int64_t *)&a_ptr[l].qs[88]; + const int64_t Af = *(const int64_t *)&a_ptr[l].qs[120]; + __asm__ __volatile__("" ::: "memory"); // prevent gcc from emitting fused vlse64, violating alignment + vint16m4_t sumi_l3; + { + const vint8m2_t lhs_0_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A3, vl / 4)); + const vint8m2_t lhs_1_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(A7, vl / 4)); + const vint8m2_t lhs_2_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Ab, vl / 4)); + const vint8m2_t lhs_3_8 =__riscv_vreinterpret_v_i64m2_i8m2(__riscv_vmv_v_x_i64m2(Af, vl / 4)); + const vint16m4_t sumi_lo_0 = __riscv_vwmul_vv_i16m4(rhs_vec_lo_0, lhs_0_8, vl * 2); + const vint16m4_t sumi_lo_1 = __riscv_vwmacc_vv_i16m4(sumi_lo_0, rhs_vec_lo_1, lhs_1_8, vl * 2); + const vint16m4_t sumi_hi_0 = __riscv_vwmacc_vv_i16m4(sumi_lo_1, rhs_vec_hi_0, lhs_2_8, vl * 2); + const vint16m4_t sumi_hi_m = __riscv_vwmacc_vv_i16m4(sumi_hi_0, rhs_vec_hi_1, lhs_3_8, vl * 2); + + sumi_l3 = sumi_hi_m; + } + + { + const vuint32m4_t sumi_i32 = __riscv_vreinterpret_v_i32m4_u32m4(__riscv_vreinterpret_v_i16m4_i32m4(sumi_l3)); + const vuint16m2_t sumi_h2_0 = __riscv_vnsrl_wx_u16m2(sumi_i32, 0, vl); + const vuint16m2_t sumi_h2_1 = __riscv_vnsrl_wx_u16m2(sumi_i32, 16, vl); + const vuint16m2_t sumi_h2 = __riscv_vadd_vv_u16m2(sumi_h2_0, sumi_h2_1, vl); + const vuint32m2_t sumi_h2_i32 = __riscv_vreinterpret_v_u16m2_u32m2(sumi_h2); + const vuint16m1_t sumi_h4_0 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 0, vl / 2); + const vuint16m1_t sumi_h4_1 = __riscv_vnsrl_wx_u16m1(sumi_h2_i32, 16, vl / 2); + const vuint16m1_t sumi_h4 = __riscv_vadd_vv_u16m1(sumi_h4_0, sumi_h4_1, vl / 2); + const vuint32m1_t sumi_h4_i32 = __riscv_vreinterpret_v_u16m1_u32m1(sumi_h4); + const vint16mf2_t sumi_h8_0 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 0, vl / 4)); + const vint16mf2_t sumi_h8_1 = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vnsrl_wx_u16mf2(sumi_h4_i32, 16, vl / 4)); + const vint32m1_t sumi_h8 = __riscv_vwadd_vv_i32m1(sumi_h8_0, sumi_h8_1, vl / 4); + const vfloat32m1_t facc = __riscv_vfcvt_f_x_v_f32m1(sumi_h8, vl / 4); + + const vfloat32m1_t tmp1 = __riscv_vfmul_vf_f32m1(facc, a_scales[3], vl / 4); + sumf3 = __riscv_vfmacc_vv_f32m1(sumf3, tmp1, b_scales_vec, vl / 4); + } + } + __riscv_vse32_v_f32m1(&s[(y * 4 + 0) * bs + x * ncols_interleaved], sumf0, vl / 4); + __riscv_vse32_v_f32m1(&s[(y * 4 + 1) * bs + x * ncols_interleaved], sumf1, vl / 4); + __riscv_vse32_v_f32m1(&s[(y * 4 + 2) * bs + x * ncols_interleaved], sumf2, vl / 4); + __riscv_vse32_v_f32m1(&s[(y * 4 + 3) * bs + x * ncols_interleaved], sumf3, vl / 4); + } + } + + return; + } +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) + float sumf[4][8]; + int sumi; + + for (int y = 0; y < nr / 4; y++) { + const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb); + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0; + } + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4); + const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0); + sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) + + (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4; + } + sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d[m]); + } + } + } + } + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) + s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j]; + } + } + } +} + +void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 4; + const int blocklen = 4; + + assert (n % qk == 0); + assert (nr % 4 == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD) + if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) { + const int8x16_t kvalues = vld1q_s8(kvalues_iq4nl); + + for (int y = 0; y < nr / 4; y++) { + const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_iq4_nlx4 * b_ptr = (const block_iq4_nlx4 *) vx + (x * nb); + + float32x4_t sumf[4]; + for (int m = 0; m < 4; m++) { + sumf[m] = vdupq_n_f32(0); + } + + for (int l = 0; l < nb; l++) { + float32x4_t a_d = vcvt_f32_f16(vld1_f16((const float16_t *)a_ptr[l].d)); + float32x4_t b_d = vcvt_f32_f16(vld1_f16((const float16_t *)b_ptr[l].d)); + + int32x4_t sumi_0 = vdupq_n_s32(0); + int32x4_t sumi_1 = vdupq_n_s32(0); + int32x4_t sumi_2 = vdupq_n_s32(0); + int32x4_t sumi_3 = vdupq_n_s32(0); + + for (int k = 0; k < 4; k++) { + int8x16_t a_0 = vld1q_s8(a_ptr[l].qs + 16 * k + 0); + int8x16_t a_1 = vld1q_s8(a_ptr[l].qs + 16 * k + 64); + + uint8x16_t b = vld1q_u8(b_ptr[l].qs + 16 * k); + int8x16_t b_hi = vqtbl1q_s8(kvalues, b >> 4); + int8x16_t b_lo = vqtbl1q_s8(kvalues, b & 0xF); + + sumi_0 = vdotq_laneq_s32(sumi_0, b_lo, a_0, 0); + sumi_1 = vdotq_laneq_s32(sumi_1, b_lo, a_0, 1); + sumi_2 = vdotq_laneq_s32(sumi_2, b_lo, a_0, 2); + sumi_3 = vdotq_laneq_s32(sumi_3, b_lo, a_0, 3); + sumi_0 = vdotq_laneq_s32(sumi_0, b_hi, a_1, 0); + sumi_1 = vdotq_laneq_s32(sumi_1, b_hi, a_1, 1); + sumi_2 = vdotq_laneq_s32(sumi_2, b_hi, a_1, 2); + sumi_3 = vdotq_laneq_s32(sumi_3, b_hi, a_1, 3); + } + + sumf[0] = vmlaq_f32(sumf[0], vmulq_laneq_f32(b_d, a_d, 0), vcvtq_f32_s32(sumi_0)); + sumf[1] = vmlaq_f32(sumf[1], vmulq_laneq_f32(b_d, a_d, 1), vcvtq_f32_s32(sumi_1)); + sumf[2] = vmlaq_f32(sumf[2], vmulq_laneq_f32(b_d, a_d, 2), vcvtq_f32_s32(sumi_2)); + sumf[3] = vmlaq_f32(sumf[3], vmulq_laneq_f32(b_d, a_d, 3), vcvtq_f32_s32(sumi_3)); + } + + for (int m = 0; m < 4; m++) { + vst1q_f32(s + (y * 4 + m) * bs + x * 4, sumf[m]); + } + } + } + return; + } +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) + { + float sumf[4][4]; + int sumi; + + for (int y = 0; y < nr / 4; y++) { + const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_iq4_nlx4 * b_ptr = (const block_iq4_nlx4 *) vx + (x * nb); + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0; + } + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F]; + const int v1 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4]; + sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) + + (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])); + } + sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d[m]); + } + } + } + } + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) + s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j]; + } + } + } + } +} + +// FIXME: this code is duplicated from ggml-aarch64.c +static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int blck_size_interleave) { + block_q4_0x4 out; + + for (int i = 0; i < 4; i++) { + out.d[i] = in[i].d; + } + + const int end = QK4_0 * 2 / blck_size_interleave; + + if (blck_size_interleave == 8) { + const uint64_t xor_mask = 0x8888888888888888ULL; + for (int i = 0; i < end; ++i) { + int src_id = i % 4; + int src_offset = (i / 4) * blck_size_interleave; + int dst_offset = i * blck_size_interleave; + + uint64_t elems; + // Using memcpy to avoid unaligned memory accesses + memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint64_t)); + elems ^= xor_mask; + memcpy(&out.qs[dst_offset], &elems, sizeof(uint64_t)); + } + } else if (blck_size_interleave == 4) { + const uint32_t xor_mask = 0x88888888; + for (int i = 0; i < end; ++i) { + int src_id = i % 4; + int src_offset = (i / 4) * blck_size_interleave; + int dst_offset = i * blck_size_interleave; + + uint32_t elems; + memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint32_t)); + elems ^= xor_mask; + memcpy(&out.qs[dst_offset], &elems, sizeof(uint32_t)); + } + } else { + GGML_ASSERT(false); + } + + return out; +} + +// interleave 8 block_q4_0s in blocks of blck_size_interleave +// returns an interleaved block_q4_0x8 +// in the interleaved block_q4_0x8, place deltas for 8 block_q4_0 blocks +// first, then interleave quants from 8 block_q4_0s in blocks of blck_size_interleave +static block_q4_0x8 make_block_q4_0x8(block_q4_0 * in, unsigned int blck_size_interleave) { + block_q4_0x8 out; + + for (int i = 0; i < 8; i++) { + out.d[i] = in[i].d; + } + + const int end = QK4_0 * 4 / blck_size_interleave; + const uint64_t xor_mask = 0x8888888888888888ULL; + + for (int i = 0; i < end; ++i) { + int src_id = i % 8; + int src_offset = (i / 8) * blck_size_interleave; + int dst_offset = i * blck_size_interleave; + + uint64_t elems; + memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint64_t)); + elems ^= xor_mask; + memcpy(&out.qs[dst_offset], &elems, sizeof(uint64_t)); + } + + return out; +} + +static int repack_q4_0_to_q4_0_4_bl(struct ggml_tensor * t, int interleave_block, const void * restrict data, size_t data_size) { + GGML_ASSERT(t->type == GGML_TYPE_Q4_0); + GGML_ASSERT(interleave_block == 4 || interleave_block == 8); + + block_q4_0x4 * dst = (block_q4_0x4 *)t->data; + const block_q4_0 * src = (const block_q4_0 *)data; + block_q4_0 dst_tmp[4]; + int nrow = t->ne[1]; // Number of rows + int nrows_interleaved = 4; + int nblocks = t->ne[0] / QK4_0; + + GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q4_0)); + + if (nrow % nrows_interleaved != 0 || t->ne[0] % 8 != 0) { + return -1; + } + + for (int b = 0; b < nrow; b += nrows_interleaved) { + for (int64_t x = 0; x < nblocks; x++) { + for (int i = 0; i < nrows_interleaved; i++) { + dst_tmp[i] = src[x + i * nblocks]; + } + *dst++ = make_block_q4_0x4(dst_tmp, interleave_block); + } + src += nrows_interleaved * nblocks; + } + return 0; + + GGML_UNUSED(data_size); +} + +static int repack_q4_0_to_q4_0_8_bl(struct ggml_tensor *t, int interleave_block, const void * restrict data, size_t data_size) { + GGML_ASSERT(t->type == GGML_TYPE_Q4_0); + GGML_ASSERT(interleave_block == 8); + + block_q4_0x8 * dst = (block_q4_0x8*)t->data; + const block_q4_0 * src = (const block_q4_0*) data; + block_q4_0 dst_tmp[8]; + int nrow = t->ne[1]; // Number of rows + int nrows_interleaved = 8; + int nblocks = t->ne[0] / QK4_0; + + GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q4_0)); + + if (nrow % nrows_interleaved != 0 || t->ne[0] % 8 != 0) { + return -1; + } + + for (int b = 0; b < nrow; b += nrows_interleaved) { + for (int64_t x = 0; x < nblocks; x++) { + for (int i = 0; i < nrows_interleaved; i++ ) { + dst_tmp[i] = src[x + i * nblocks]; + } + *dst++ = make_block_q4_0x8(dst_tmp, interleave_block); + } + src += nrows_interleaved * nblocks; + } + return 0; + + GGML_UNUSED(data_size); +} + +static block_iq4_nlx4 make_block_iq4_nlx4(block_iq4_nl * in, unsigned int blck_size_interleave) { + block_iq4_nlx4 out; + + for (int i = 0; i < 4; i++) { + out.d[i] = in[i].d; + } + + const int end = QK4_NL * 2 / blck_size_interleave; + + if (blck_size_interleave == 8) { + for (int i = 0; i < end; ++i) { + int src_id = i % 4; + int src_offset = (i / 4) * blck_size_interleave; + int dst_offset = i * blck_size_interleave; + + // Using memcpy to avoid unaligned memory accesses + memcpy(&out.qs[dst_offset], &in[src_id].qs[src_offset], sizeof(uint64_t)); + } + } else if (blck_size_interleave == 4) { + for (int i = 0; i < end; ++i) { + int src_id = i % 4; + int src_offset = (i / 4) * blck_size_interleave; + int dst_offset = i * blck_size_interleave; + + memcpy(&out.qs[dst_offset], &in[src_id].qs[src_offset], sizeof(uint32_t)); + } + } else { + GGML_ASSERT(false); + } + + return out; +} + +static int repack_iq4_nl_to_iq4_nl_4_bl(struct ggml_tensor * t, int interleave_block, const void * restrict data, size_t data_size) { + GGML_ASSERT(t->type == GGML_TYPE_IQ4_NL); + GGML_ASSERT(interleave_block == 4 || interleave_block == 8); + + block_iq4_nlx4 * dst = (block_iq4_nlx4 *)t->data; + const block_iq4_nl * src = (const block_iq4_nl *)data; + block_iq4_nl dst_tmp[4]; + int nrow = t->ne[1]; // Number of rows + int nrows_interleaved = 4; + int nblocks = t->ne[0] / QK4_0; + + GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_iq4_nl)); + + if (nrow % nrows_interleaved != 0 || t->ne[0] % 8 != 0) { + return -1; + } + + for (int b = 0; b < nrow; b += nrows_interleaved) { + for (int64_t x = 0; x < nblocks; x++) { + for (int i = 0; i < nrows_interleaved; i++) { + dst_tmp[i] = src[x + i * nblocks]; + } + *dst++ = make_block_iq4_nlx4(dst_tmp, interleave_block); + } + src += nrows_interleaved * nblocks; + } + return 0; + + GGML_UNUSED(data_size); +} + +// Prepare for optimized kernels if applicable +void ggml_aarch64_repack_tensor(struct ggml_tensor * cur, enum ggml_type repack_type, const void * restrict data, size_t data_size) { + if (cur->type == repack_type) { + memcpy(cur->data, data, data_size); + return; + } + + if (cur->type == GGML_TYPE_Q4_0) { + switch (repack_type) { + case GGML_TYPE_Q4_0_8_8: + repack_q4_0_to_q4_0_8_bl(cur, 8, data, data_size); + break; + case GGML_TYPE_Q4_0_4_8: + repack_q4_0_to_q4_0_4_bl(cur, 8, data, data_size); + break; + case GGML_TYPE_Q4_0_4_4: + repack_q4_0_to_q4_0_4_bl(cur, 4, data, data_size); + break; + default: + GGML_ABORT("Unsupported type"); + } + } else if (cur->type == GGML_TYPE_IQ4_NL) { + switch (repack_type) { + case GGML_TYPE_IQ4_NL_4_4: + repack_iq4_nl_to_iq4_nl_4_bl(cur, 4, data, data_size); + break; + default: + GGML_ABORT("Unsupported type"); + } + } else { + GGML_ABORT("Unsupported type"); + } +} + +enum ggml_type ggml_aarch64_get_optimal_repack_type(const struct ggml_tensor * cur) { + if (cur->type == GGML_TYPE_Q4_0) { + // TODO: enable for AVX2 - currently disabled due to bad gemv performance + if (/* ggml_cpu_has_avx2() || */ (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0)) { + return GGML_TYPE_Q4_0_8_8; + } + if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) { + return GGML_TYPE_Q4_0_4_8; + } + if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) { + return GGML_TYPE_Q4_0_4_4; + } + } else if (cur->type == GGML_TYPE_IQ4_NL) { + if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) { + return GGML_TYPE_IQ4_NL_4_4; + } + } + + return cur->type; +} diff --git a/ggml/src/ggml-cpu/ggml-cpu-aarch64.h b/ggml/src/ggml-cpu/ggml-cpu-aarch64.h new file mode 100644 index 000000000..3d9db6a19 --- /dev/null +++ b/ggml/src/ggml-cpu/ggml-cpu-aarch64.h @@ -0,0 +1,32 @@ +#pragma once + +#include "ggml.h" + +// GGML internal header + +#ifdef __cplusplus +extern "C" { +#endif + +// Quantization +void quantize_mat_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nrows, int64_t n_per_row, int64_t blck_size_interleave); + +// GEMV +void ggml_gemv_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemv_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); + +// GEMM +void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); + +void ggml_aarch64_repack_tensor(struct ggml_tensor * cur, enum ggml_type repack_type, const void * data, size_t data_size); +enum ggml_type ggml_aarch64_get_optimal_repack_type(const struct ggml_tensor * cur); + +#ifdef __cplusplus +} +#endif + diff --git a/ggml/src/ggml-cpu-impl.h b/ggml/src/ggml-cpu/ggml-cpu-impl.h similarity index 53% rename from ggml/src/ggml-cpu-impl.h rename to ggml/src/ggml-cpu/ggml-cpu-impl.h index 5b45155b0..d71076ad1 100644 --- a/ggml/src/ggml-cpu-impl.h +++ b/ggml/src/ggml-cpu/ggml-cpu-impl.h @@ -15,6 +15,18 @@ extern "C" { #endif +struct ggml_compute_params { + // ith = thread index, nth = number of threads + int ith, nth; + + // work buffer for all threads + size_t wsize; + void * wdata; + + struct ggml_threadpool * threadpool; +}; + + #if defined(_MSC_VER) #define m512bh(p) p @@ -27,80 +39,6 @@ extern "C" { #endif -/** - * Converts brain16 to float32. - * - * The bfloat16 floating point format has the following structure: - * - * ┌sign - * │ - * │ ┌exponent - * │ │ - * │ │ ┌mantissa - * │ │ │ - * │┌──┴───┐┌─┴───┐ - * 0b0000000000000000 brain16 - * - * Since bf16 has the same number of exponent bits as a 32bit float, - * encoding and decoding numbers becomes relatively straightforward. - * - * ┌sign - * │ - * │ ┌exponent - * │ │ - * │ │ ┌mantissa - * │ │ │ - * │┌──┴───┐┌─┴───────────────────┐ - * 0b00000000000000000000000000000000 IEEE binary32 - * - * For comparison, the standard fp16 format has fewer exponent bits. - * - * ┌sign - * │ - * │ ┌exponent - * │ │ - * │ │ ┌mantissa - * │ │ │ - * │┌─┴─┐┌─┴──────┐ - * 0b0000000000000000 IEEE binary16 - * - * @see IEEE 754-2008 - */ -static inline float ggml_compute_bf16_to_fp32(ggml_bf16_t h) { - union { - float f; - uint32_t i; - } u; - u.i = (uint32_t)h.bits << 16; - return u.f; -} - -/** - * Converts float32 to brain16. - * - * This is binary identical with Google Brain float conversion. - * Floats shall round to nearest even, and NANs shall be quiet. - * Subnormals aren't flushed to zero, except perhaps when used. - * This code should vectorize nicely if using modern compilers. - */ -static inline ggml_bf16_t ggml_compute_fp32_to_bf16(float s) { - ggml_bf16_t h; - union { - float f; - uint32_t i; - } u; - u.f = s; - if ((u.i & 0x7fffffff) > 0x7f800000) { /* nan */ - h.bits = (u.i >> 16) | 64; /* force to quiet */ - return h; - } - h.bits = (u.i + (0x7fff + ((u.i >> 16) & 1))) >> 16; - return h; -} - -#define GGML_FP32_TO_BF16(x) ggml_compute_fp32_to_bf16(x) -#define GGML_BF16_TO_FP32(x) ggml_compute_bf16_to_fp32(x) - // __FMA__ and __F16C__ are not defined in MSVC, however they are implied with AVX2/AVX512 #if defined(_MSC_VER) && (defined(__AVX2__) || defined(__AVX512F__)) #ifndef __FMA__ @@ -388,28 +326,6 @@ inline static int32x4_t ggml_vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b) #endif // defined(__ARM_NEON) -#if defined(__ARM_NEON) && !defined(_MSC_VER) - -#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) -#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) - -#define GGML_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) - -static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) { - ggml_fp16_internal_t tmp; - memcpy(&tmp, &h, sizeof(ggml_fp16_t)); - return (float)tmp; -} - -static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { - ggml_fp16_t res; - ggml_fp16_internal_t tmp = f; - memcpy(&res, &tmp, sizeof(ggml_fp16_t)); - return res; -} - -#else - #ifdef __wasm_simd128__ #include #else @@ -462,152 +378,8 @@ static __m256 __lasx_xvreplfr2vr_s(float val) { } #endif -#ifdef __F16C__ - -#ifdef _MSC_VER -#define GGML_COMPUTE_FP16_TO_FP32(x) _mm_cvtss_f32(_mm_cvtph_ps(_mm_cvtsi32_si128(x))) -#define GGML_COMPUTE_FP32_TO_FP16(x) _mm_extract_epi16(_mm_cvtps_ph(_mm_set_ss(x), 0), 0) -#else -#define GGML_COMPUTE_FP16_TO_FP32(x) _cvtsh_ss(x) -#define GGML_COMPUTE_FP32_TO_FP16(x) _cvtss_sh(x, 0) -#endif - -#elif defined(__POWER9_VECTOR__) - -#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) -#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) -/* the inline asm below is about 12% faster than the lookup method */ -#define GGML_FP16_TO_FP32(x) GGML_COMPUTE_FP16_TO_FP32(x) -#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x) - -static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) { - register float f; - register double d; - __asm__( - "mtfprd %0,%2\n" - "xscvhpdp %0,%0\n" - "frsp %1,%0\n" : - /* temp */ "=d"(d), - /* out */ "=f"(f): - /* in */ "r"(h)); - return f; -} - -static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { - register double d; - register ggml_fp16_t r; - __asm__( /* xscvdphp can work on double or single precision */ - "xscvdphp %0,%2\n" - "mffprd %1,%0\n" : - /* temp */ "=d"(d), - /* out */ "=r"(r): - /* in */ "f"(f)); - return r; -} - -#else - -// FP16 <-> FP32 -// ref: https://github.com/Maratyszcza/FP16 - -static inline float fp32_from_bits(uint32_t w) { - union { - uint32_t as_bits; - float as_value; - } fp32; - fp32.as_bits = w; - return fp32.as_value; -} - -static inline uint32_t fp32_to_bits(float f) { - union { - float as_value; - uint32_t as_bits; - } fp32; - fp32.as_value = f; - return fp32.as_bits; -} - -static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) { - const uint32_t w = (uint32_t) h << 16; - const uint32_t sign = w & UINT32_C(0x80000000); - const uint32_t two_w = w + w; - - const uint32_t exp_offset = UINT32_C(0xE0) << 23; -#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__) - const float exp_scale = 0x1.0p-112f; -#else - const float exp_scale = fp32_from_bits(UINT32_C(0x7800000)); -#endif - const float normalized_value = fp32_from_bits((two_w >> 4) + exp_offset) * exp_scale; - - const uint32_t magic_mask = UINT32_C(126) << 23; - const float magic_bias = 0.5f; - const float denormalized_value = fp32_from_bits((two_w >> 17) | magic_mask) - magic_bias; - - const uint32_t denormalized_cutoff = UINT32_C(1) << 27; - const uint32_t result = sign | - (two_w < denormalized_cutoff ? fp32_to_bits(denormalized_value) : fp32_to_bits(normalized_value)); - return fp32_from_bits(result); -} - -static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { -#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__) - const float scale_to_inf = 0x1.0p+112f; - const float scale_to_zero = 0x1.0p-110f; -#else - const float scale_to_inf = fp32_from_bits(UINT32_C(0x77800000)); - const float scale_to_zero = fp32_from_bits(UINT32_C(0x08800000)); -#endif - float base = (fabsf(f) * scale_to_inf) * scale_to_zero; - - const uint32_t w = fp32_to_bits(f); - const uint32_t shl1_w = w + w; - const uint32_t sign = w & UINT32_C(0x80000000); - uint32_t bias = shl1_w & UINT32_C(0xFF000000); - if (bias < UINT32_C(0x71000000)) { - bias = UINT32_C(0x71000000); - } - - base = fp32_from_bits((bias >> 1) + UINT32_C(0x07800000)) + base; - const uint32_t bits = fp32_to_bits(base); - const uint32_t exp_bits = (bits >> 13) & UINT32_C(0x00007C00); - const uint32_t mantissa_bits = bits & UINT32_C(0x00000FFF); - const uint32_t nonsign = exp_bits + mantissa_bits; - return (sign >> 16) | (shl1_w > UINT32_C(0xFF000000) ? UINT16_C(0x7E00) : nonsign); -} - -#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) -#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) - -#endif // __F16C__ - -#endif // defined(__ARM_NEON) && (!defined(__MSC_VER) - -#ifdef __ARM_FEATURE_SVE -#include -#endif // __ARM_FEATURE_SVE - -// precomputed f32 table for f16 (256 KB) -// defined in ggml.c, initialized in ggml_init() -extern float ggml_table_f32_f16[1 << 16]; - -// On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32, -// so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON. -// This is also true for POWER9. -#if !defined(GGML_FP16_TO_FP32) -inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) { - uint16_t s; - memcpy(&s, &f, sizeof(uint16_t)); - return ggml_table_f32_f16[s]; -} - -#define GGML_FP16_TO_FP32(x) ggml_lookup_fp16_to_fp32(x) -#endif - -#if !defined(GGML_FP32_TO_FP16) -#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x) -#endif +// TODO: move to ggml-threading +void ggml_barrier(struct ggml_threadpool * tp); #ifdef __cplusplus } diff --git a/ggml/src/ggml-cpu/ggml-cpu-quants.c b/ggml/src/ggml-cpu/ggml-cpu-quants.c new file mode 100644 index 000000000..634c5fa11 --- /dev/null +++ b/ggml/src/ggml-cpu/ggml-cpu-quants.c @@ -0,0 +1,10835 @@ +#define GGML_COMMON_IMPL_C +#include "ggml-common.h" + +#include "ggml-quants.h" +#include "ggml-cpu-quants.h" +#include "ggml-impl.h" +#include "ggml-cpu-impl.h" +#include "ggml-cpu.h" + +#include +#include +#include +#include +#include // for qsort +#include // for GGML_ASSERT + +#define GROUP_MAX_EPS 1e-15f +#define GROUP_MAX_EPS_IQ3_XXS 1e-8f +#define GROUP_MAX_EPS_IQ2_S 1e-8f +#define GROUP_MAX_EPS_IQ1_M 1e-7f +#define GROUP_MAX_EPS_IQ1_S 1e-12f + +#if defined(_MSC_VER) +// disable "possible loss of data" to avoid warnings for hundreds of casts +// we should just be careful :) +#pragma warning(disable: 4244 4267) +#endif + +#define UNUSED GGML_UNUSED + +// some compilers don't provide _mm256_set_m128i, e.g. gcc 7 +#define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1) + +#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) +// multiply int8_t, add results pairwise twice +static inline __m128i mul_sum_i8_pairs(const __m128i x, const __m128i y) { + // Get absolute values of x vectors + const __m128i ax = _mm_sign_epi8(x, x); + // Sign the values of the y vectors + const __m128i sy = _mm_sign_epi8(y, x); + // Perform multiplication and create 16-bit values + const __m128i dot = _mm_maddubs_epi16(ax, sy); + const __m128i ones = _mm_set1_epi16(1); + return _mm_madd_epi16(ones, dot); +} + +#if __AVX__ || __AVX2__ || __AVX512F__ +// horizontally add 8 floats +static inline float hsum_float_8(const __m256 x) { + __m128 res = _mm256_extractf128_ps(x, 1); + res = _mm_add_ps(res, _mm256_castps256_ps128(x)); + res = _mm_add_ps(res, _mm_movehl_ps(res, res)); + res = _mm_add_ss(res, _mm_movehdup_ps(res)); + return _mm_cvtss_f32(res); +} + +// horizontally add 8 int32_t +static inline int hsum_i32_8(const __m256i a) { + const __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(a), _mm256_extractf128_si256(a, 1)); + const __m128i hi64 = _mm_unpackhi_epi64(sum128, sum128); + const __m128i sum64 = _mm_add_epi32(hi64, sum128); + const __m128i hi32 = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1)); + return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32)); +} + +// horizontally add 4 int32_t +static inline int hsum_i32_4(const __m128i a) { + const __m128i hi64 = _mm_unpackhi_epi64(a, a); + const __m128i sum64 = _mm_add_epi32(hi64, a); + const __m128i hi32 = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1)); + return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32)); +} + +#if defined(__AVX2__) || defined(__AVX512F__) +// spread 32 bits to 32 bytes { 0x00, 0xFF } +static inline __m256i bytes_from_bits_32(const uint8_t * x) { + uint32_t x32; + memcpy(&x32, x, sizeof(uint32_t)); + const __m256i shuf_mask = _mm256_set_epi64x( + 0x0303030303030303, 0x0202020202020202, + 0x0101010101010101, 0x0000000000000000); + __m256i bytes = _mm256_shuffle_epi8(_mm256_set1_epi32(x32), shuf_mask); + const __m256i bit_mask = _mm256_set1_epi64x(0x7fbfdfeff7fbfdfe); + bytes = _mm256_or_si256(bytes, bit_mask); + return _mm256_cmpeq_epi8(bytes, _mm256_set1_epi64x(-1)); +} + +// Unpack 32 4-bit fields into 32 bytes +// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval +static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) +{ + const __m128i tmp = _mm_loadu_si128((const __m128i *)rsi); + const __m256i bytes = MM256_SET_M128I(_mm_srli_epi16(tmp, 4), tmp); + const __m256i lowMask = _mm256_set1_epi8( 0xF ); + return _mm256_and_si256(lowMask, bytes); +} + +// add int16_t pairwise and return as float vector +static inline __m256 sum_i16_pairs_float(const __m256i x) { + const __m256i ones = _mm256_set1_epi16(1); + const __m256i summed_pairs = _mm256_madd_epi16(ones, x); + return _mm256_cvtepi32_ps(summed_pairs); +} + +static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) { +#if defined(__AVXVNNI__) || (defined(__AVX512VNNI__) && defined(__AVX512VL__)) + const __m256i zero = _mm256_setzero_si256(); + const __m256i summed_pairs = _mm256_dpbusd_epi32(zero, ax, sy); + return _mm256_cvtepi32_ps(summed_pairs); +#else + // Perform multiplication and create 16-bit values + const __m256i dot = _mm256_maddubs_epi16(ax, sy); + return sum_i16_pairs_float(dot); +#endif +} + +// multiply int8_t, add results pairwise twice and return as float vector +static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) { +#if __AVXVNNIINT8__ + const __m256i zero = _mm256_setzero_si256(); + const __m256i summed_pairs = _mm256_dpbssd_epi32(zero, x, y); + return _mm256_cvtepi32_ps(summed_pairs); +#else + // Get absolute values of x vectors + const __m256i ax = _mm256_sign_epi8(x, x); + // Sign the values of the y vectors + const __m256i sy = _mm256_sign_epi8(y, x); + return mul_sum_us8_pairs_float(ax, sy); +#endif +} + +static inline __m128i packNibbles( __m256i bytes ) +{ + // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh +#if __AVX512F__ + const __m256i bytes_srli_4 = _mm256_srli_epi16(bytes, 4); // 0000_0000_abcd_0000 + bytes = _mm256_or_si256(bytes, bytes_srli_4); // 0000_abcd_abcd_efgh + return _mm256_cvtepi16_epi8(bytes); // abcd_efgh +#else + const __m256i lowByte = _mm256_set1_epi16( 0xFF ); + __m256i high = _mm256_andnot_si256( lowByte, bytes ); + __m256i low = _mm256_and_si256( lowByte, bytes ); + high = _mm256_srli_epi16( high, 4 ); + bytes = _mm256_or_si256( low, high ); + + // Compress uint16_t lanes into bytes + __m128i r0 = _mm256_castsi256_si128( bytes ); + __m128i r1 = _mm256_extracti128_si256( bytes, 1 ); + return _mm_packus_epi16( r0, r1 ); +#endif +} +#elif defined(__AVX__) +static inline __m128i packNibbles( __m128i bytes1, __m128i bytes2 ) +{ + // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh + const __m128i lowByte = _mm_set1_epi16( 0xFF ); + __m128i high = _mm_andnot_si128( lowByte, bytes1 ); + __m128i low = _mm_and_si128( lowByte, bytes1 ); + high = _mm_srli_epi16( high, 4 ); + bytes1 = _mm_or_si128( low, high ); + high = _mm_andnot_si128( lowByte, bytes2 ); + low = _mm_and_si128( lowByte, bytes2 ); + high = _mm_srli_epi16( high, 4 ); + bytes2 = _mm_or_si128( low, high ); + + return _mm_packus_epi16( bytes1, bytes2); +} + +static inline __m128i mul_add_epi8_sse(const __m128i x, const __m128i y) { + const __m128i ax = _mm_sign_epi8(x, x); + const __m128i sy = _mm_sign_epi8(y, x); + return _mm_maddubs_epi16(ax, sy); +} + +// spread 32 bits to 32 bytes { 0x00, 0xFF } +static inline __m256i bytes_from_bits_32(const uint8_t * x) { + uint32_t x32; + memcpy(&x32, x, sizeof(uint32_t)); + const __m128i shuf_maskl = _mm_set_epi64x(0x0101010101010101, 0x0000000000000000); + const __m128i shuf_maskh = _mm_set_epi64x(0x0303030303030303, 0x0202020202020202); + __m128i bytesl = _mm_shuffle_epi8(_mm_set1_epi32(x32), shuf_maskl); + __m128i bytesh = _mm_shuffle_epi8(_mm_set1_epi32(x32), shuf_maskh); + const __m128i bit_mask = _mm_set1_epi64x(0x7fbfdfeff7fbfdfe); + bytesl = _mm_or_si128(bytesl, bit_mask); + bytesh = _mm_or_si128(bytesh, bit_mask); + bytesl = _mm_cmpeq_epi8(bytesl, _mm_set1_epi64x(-1)); + bytesh = _mm_cmpeq_epi8(bytesh, _mm_set1_epi64x(-1)); + return MM256_SET_M128I(bytesh, bytesl); +} + +// Unpack 32 4-bit fields into 32 bytes +// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval +static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) +{ + // Load 16 bytes from memory + __m128i tmpl = _mm_loadu_si128((const __m128i *)rsi); + __m128i tmph = _mm_srli_epi16(tmpl, 4); + const __m128i lowMask = _mm_set1_epi8(0xF); + tmpl = _mm_and_si128(lowMask, tmpl); + tmph = _mm_and_si128(lowMask, tmph); + return MM256_SET_M128I(tmph, tmpl); +} + +// add int16_t pairwise and return as float vector +static inline __m256 sum_i16_pairs_float(const __m128i xh, const __m128i xl) { + const __m128i ones = _mm_set1_epi16(1); + const __m128i summed_pairsl = _mm_madd_epi16(ones, xl); + const __m128i summed_pairsh = _mm_madd_epi16(ones, xh); + const __m256i summed_pairs = MM256_SET_M128I(summed_pairsh, summed_pairsl); + return _mm256_cvtepi32_ps(summed_pairs); +} + +static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) { + const __m128i axl = _mm256_castsi256_si128(ax); + const __m128i axh = _mm256_extractf128_si256(ax, 1); + const __m128i syl = _mm256_castsi256_si128(sy); + const __m128i syh = _mm256_extractf128_si256(sy, 1); + // Perform multiplication and create 16-bit values + const __m128i dotl = _mm_maddubs_epi16(axl, syl); + const __m128i doth = _mm_maddubs_epi16(axh, syh); + return sum_i16_pairs_float(doth, dotl); +} + +// multiply int8_t, add results pairwise twice and return as float vector +static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) { + const __m128i xl = _mm256_castsi256_si128(x); + const __m128i xh = _mm256_extractf128_si256(x, 1); + const __m128i yl = _mm256_castsi256_si128(y); + const __m128i yh = _mm256_extractf128_si256(y, 1); + // Get absolute values of x vectors + const __m128i axl = _mm_sign_epi8(xl, xl); + const __m128i axh = _mm_sign_epi8(xh, xh); + // Sign the values of the y vectors + const __m128i syl = _mm_sign_epi8(yl, xl); + const __m128i syh = _mm_sign_epi8(yh, xh); + // Perform multiplication and create 16-bit values + const __m128i dotl = _mm_maddubs_epi16(axl, syl); + const __m128i doth = _mm_maddubs_epi16(axh, syh); + return sum_i16_pairs_float(doth, dotl); +} + +// larger version of mul_sum_i8_pairs_float where x and y are each represented by four 128-bit vectors +static inline __m256 mul_sum_i8_quad_float(const __m128i x_1_0, const __m128i x_1_1, const __m128i x_2_0, const __m128i x_2_1, + const __m128i y_1_0, const __m128i y_1_1, const __m128i y_2_0, const __m128i y_2_1) { + const __m128i mone = _mm_set1_epi16(1); + + const __m128i p16_1_0 = mul_add_epi8_sse(x_1_0, y_1_0); + const __m128i p16_1_1 = mul_add_epi8_sse(x_1_1, y_1_1); + const __m128i p16_2_0 = mul_add_epi8_sse(x_2_0, y_2_0); + const __m128i p16_2_1 = mul_add_epi8_sse(x_2_1, y_2_1); + const __m128i p_1_0 = _mm_madd_epi16(p16_1_0, mone); + const __m128i p_1_1 = _mm_madd_epi16(p16_1_1, mone); + const __m128i p_2_0 = _mm_madd_epi16(p16_2_0, mone); + const __m128i p_2_1 = _mm_madd_epi16(p16_2_1, mone); + const __m128i p_1 = _mm_add_epi32(p_1_0, p_1_1); + const __m128i p_2 = _mm_add_epi32(p_2_0, p_2_1); + return _mm256_cvtepi32_ps(MM256_SET_M128I(p_2, p_1)); +} + +// quad fp16 delta calculation +static inline __m256 quad_fp16_delta_float(const float x0, const float y0, const float x1, const float y1) { + // GGML_FP16_TO_FP32 is faster than Intel F16C + return _mm256_set_m128(_mm_set1_ps(GGML_FP16_TO_FP32(x1) * GGML_FP16_TO_FP32(y1)), + _mm_set1_ps(GGML_FP16_TO_FP32(x0) * GGML_FP16_TO_FP32(y0))); +} +#endif +#elif defined(__SSSE3__) +// horizontally add 4x4 floats +static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128 c, const __m128 d) { + __m128 res_0 =_mm_hadd_ps(a, b); + __m128 res_1 =_mm_hadd_ps(c, d); + __m128 res =_mm_hadd_ps(res_0, res_1); + res =_mm_hadd_ps(res, res); + res =_mm_hadd_ps(res, res); + + return _mm_cvtss_f32(res); +} +#endif // __AVX__ || __AVX2__ || __AVX512F__ +#endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) + +#if defined(__ARM_NEON) || defined(__wasm_simd128__) || defined(__POWER9_VECTOR__) +#define B1(c,s,n) 0x ## n ## c , 0x ## n ## s +#define B2(c,s,n) B1(c,s,n ## c), B1(c,s,n ## s) +#define B3(c,s,n) B2(c,s,n ## c), B2(c,s,n ## s) +#define B4(c,s,n) B3(c,s,n ## c), B3(c,s,n ## s) +#define B5(c,s,n) B4(c,s,n ## c), B4(c,s,n ## s) +#define B6(c,s,n) B5(c,s,n ## c), B5(c,s,n ## s) +#define B7(c,s,n) B6(c,s,n ## c), B6(c,s,n ## s) +#define B8(c,s ) B7(c,s, c), B7(c,s, s) + +// precomputed tables for expanding 8bits to 8 bytes: +static const uint64_t table_b2b_0[1 << 8] = { B8(00, 10) }; // ( b) << 4 +static const uint64_t table_b2b_1[1 << 8] = { B8(10, 00) }; // (!b) << 4 +#endif + +#if defined(__loongarch_asx) + +#ifdef __clang__ +#define VREGS_PREFIX "$vr" +#define XREGS_PREFIX "$xr" +#else // GCC +#define VREGS_PREFIX "$f" +#define XREGS_PREFIX "$f" +#endif +#define __ALL_REGS "0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31" +// Convert __m128i to __m256i +static inline __m256i ____m256i(__m128i in) { + __m256i out = __lasx_xvldi(0); + __asm__ volatile ( + ".irp i," __ALL_REGS "\n\t" + " .ifc %[out], " XREGS_PREFIX"\\i \n\t" + " .irp j," __ALL_REGS "\n\t" + " .ifc %[in], " VREGS_PREFIX "\\j \n\t" + " xvpermi.q $xr\\i, $xr\\j, 0x20 \n\t" + " .endif \n\t" + " .endr \n\t" + " .endif \n\t" + ".endr \n\t" + : [out] "+f" (out) : [in] "f" (in) + ); + return out; +} +// Convert two __m128i to __m256i +static inline __m256i lasx_set_q(__m128i inhi, __m128i inlo) { + __m256i out; + __asm__ volatile ( + ".irp i," __ALL_REGS "\n\t" + " .ifc %[hi], " VREGS_PREFIX "\\i \n\t" + " .irp j," __ALL_REGS "\n\t" + " .ifc %[lo], " VREGS_PREFIX "\\j \n\t" + " xvpermi.q $xr\\i, $xr\\j, 0x20 \n\t" + " .endif \n\t" + " .endr \n\t" + " .endif \n\t" + ".endr \n\t" + ".ifnc %[out], %[hi] \n\t" + ".irp i," __ALL_REGS "\n\t" + " .ifc %[out], " XREGS_PREFIX "\\i \n\t" + " .irp j," __ALL_REGS "\n\t" + " .ifc %[hi], " VREGS_PREFIX "\\j \n\t" + " xvori.b $xr\\i, $xr\\j, 0 \n\t" + " .endif \n\t" + " .endr \n\t" + " .endif \n\t" + ".endr \n\t" + ".endif \n\t" + : [out] "=f" (out), [hi] "+f" (inhi) + : [lo] "f" (inlo) + ); + return out; +} +// Convert __m256i low part to __m128i +static inline __m128i lasx_extracti128_lo(__m256i in) { + __m128i out; + __asm__ volatile ( + ".ifnc %[out], %[in] \n\t" + ".irp i," __ALL_REGS "\n\t" + " .ifc %[out], " VREGS_PREFIX "\\i \n\t" + " .irp j," __ALL_REGS "\n\t" + " .ifc %[in], " XREGS_PREFIX "\\j \n\t" + " vori.b $vr\\i, $vr\\j, 0 \n\t" + " .endif \n\t" + " .endr \n\t" + " .endif \n\t" + ".endr \n\t" + ".endif \n\t" + : [out] "=f" (out) : [in] "f" (in) + ); + return out; +} +// Convert __m256i high part to __m128i +static inline __m128i lasx_extracti128_hi(__m256i in) { + __m128i out; + __asm__ volatile ( + ".irp i," __ALL_REGS "\n\t" + " .ifc %[out], " VREGS_PREFIX "\\i \n\t" + " .irp j," __ALL_REGS "\n\t" + " .ifc %[in], " XREGS_PREFIX "\\j \n\t" + " xvpermi.q $xr\\i, $xr\\j, 0x11 \n\t" + " .endif \n\t" + " .endr \n\t" + " .endif \n\t" + ".endr \n\t" + : [out] "=f" (out) : [in] "f" (in) + ); + return out; +} + +static __m256i lasx_set_w(int e7, int e6, int e5, int e4, int e3, int e2, int e1, int e0) { + v8i32 __ret = {e0, e1, e2, e3, e4, e5, e6, e7}; + return (__m256i)__ret; +} + +static __m128i lsx_set_w(int32_t a, int32_t b, int32_t c, int32_t d) { + v4i32 __ret = {d, c, b, a}; + return (__m128i)__ret; +} + +static __m256i lasx_set_d(int64_t a, int64_t b, int64_t c, int64_t d) { + v4i64 __ret = {d, c, b, a}; + return (__m256i)__ret; +} + +static __m256i lasx_insertf128( __m128i x, __m128i y) { + return lasx_set_q(x, y); +} + +static __m128i lsx_shuffle_b(__m128i a, __m128i b) { + __m128i mask_f, zero, tmp0, tmp2, mask; + int f = 0x8f; + mask_f = __lsx_vreplgr2vr_b(f); + zero = __lsx_vldi(0); + tmp0 = __lsx_vand_v(b, mask_f); // get mask with low 4 bit and sign bits + tmp0 = __lsx_vori_b(tmp0, 0x10); // make each mask or with 0x10 prepare for positive + mask = __lsx_vsle_b(zero, tmp0); // if mask >= 0, set mask + tmp2 = __lsx_vand_v(tmp0, mask); // maskout the in2 < ones + return __lsx_vshuf_b(a, zero, tmp2); +} + +static __m256i lasx_shuffle_b(__m256i a, __m256i b) { + __m256i mask_f, zero, tmp0, tmp2, mask; + int f = 0x8f; + mask_f = __lasx_xvreplgr2vr_b(f); + zero = __lasx_xvldi(0); + tmp0 = __lasx_xvand_v(b, mask_f); // get mask with low 4 bit and sign bits + tmp0 = __lasx_xvori_b(tmp0, 0x10); // make each mask or with 0x10 prepare for positive + mask = __lasx_xvsle_b(zero, tmp0); // if mask >= 0, set mask + tmp2 = __lasx_xvand_v(tmp0, mask); // maskout the in2 < ones + return __lasx_xvshuf_b(a, zero, tmp2); +} + +static __m256i lasx_extu8_16(__m128i a) { + __m128i zero = __lsx_vldi(0); + __m128i vlo = __lsx_vilvl_b(zero, a); + __m128i vhi = __lsx_vilvh_b(zero, a); + return lasx_set_q(vhi, vlo); +} + +static __m256i lasx_ext8_16(__m128i a) { + __m128i sign = __lsx_vslti_b(a, 0); + __m128i vlo = __lsx_vilvl_b(sign, a); + __m128i vhi = __lsx_vilvh_b(sign, a); + return lasx_set_q(vhi, vlo); +} + +static __m256i lasx_ext16_32(__m128i a) { + __m256i tmp1; + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 0), 0); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 1), 1); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 2), 2); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 3), 3); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 4), 4); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 5), 5); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 6), 6); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 7), 7); + return tmp1; +} + +static __m128i lasx_extracti128( __m256i a, int pos) { + __m128i ret; + if( pos == 0) + { + ret = lasx_extracti128_lo(a); + } else { + ret = lasx_extracti128_hi(a); + } + return ret; +} + +static __m128 lasx_extractf128( __m256 a, int pos) { + __m128 ret; + if( pos == 0) + { + ret = (__m128)lasx_extracti128_lo((__m256i)a); + } else { + ret = (__m128)lasx_extracti128_hi((__m256i)a); + } + return ret; +} + +static __m128i lsx_hadd_h(__m128i a, __m128i b) { + __m128i tmp1 = __lsx_vpickev_h(b, a); + __m128i tmp2 = __lsx_vpickod_h(b, a); + return __lsx_vadd_h(tmp1, tmp2); +} + +static __m128i lsx_hadd_w(__m128i a, __m128i b) { + __m128i tmp1 = __lsx_vpickev_w(b, a); + __m128i tmp2 = __lsx_vpickod_w(b, a); + return __lsx_vadd_w(tmp1, tmp2); +} + +static __m128 lsx_hadd_s(__m128 a, __m128 b) { + __m128 tmp1 = (__m128)__lsx_vpickev_w((__m128i)b, (__m128i)a); + __m128 tmp2 = (__m128)__lsx_vpickod_w((__m128i)b, (__m128i)a); + + return __lsx_vfadd_s(tmp1, tmp2); +} + +static __m256i lasx_maddubs_h(__m256i a, __m256i b) { + __m256i tmp1, tmp2; + tmp1 = __lasx_xvmulwev_h_b(a, b); + tmp2 = __lasx_xvmulwod_h_b(a, b); + return __lasx_xvsadd_h(tmp1, tmp2); +} + +static __m256i lasx_madd_h(__m256i a, __m256i b) { + __m256i tmp1, tmp2; + tmp1 = __lasx_xvmulwev_w_h(a, b); + tmp2 = __lasx_xvmulwod_w_h(a, b); + return __lasx_xvadd_w(tmp1, tmp2); +} + +static __m256i lasx_packs_w(__m256i a, __m256i b) { + __m256i tmp, tmp1; + tmp = __lasx_xvsat_w(a, 15); + tmp1 = __lasx_xvsat_w(b, 15); + return __lasx_xvpickev_h(tmp1, tmp); +} + +static __m256i lasx_packs_h(__m256i a, __m256i b) { + __m256i tmp, tmp1; + tmp = __lasx_xvsat_h(a, 7); + tmp1 = __lasx_xvsat_h(b, 7); + return __lasx_xvpickev_b(tmp1, tmp); +} + +static __m128i lsx_packs_w(__m128i a, __m128i b) { + __m128i tmp, tmp1; + tmp = __lsx_vsat_w(a, 15); + tmp1 = __lsx_vsat_w(b, 15); + return __lsx_vpickev_h(tmp1, tmp); +} + +static __m128i lsx_packs_h(__m128i a, __m128i b) { + __m128i tmp, tmp1; + tmp = __lsx_vsat_h(a, 7); + tmp1 = __lsx_vsat_h(b, 7); + return __lsx_vpickev_b(tmp1, tmp); +} + +static __m128i lsx_packus_h(__m128i a, __m128i b) { + __m128i tmp, tmp1; + tmp = __lsx_vsat_hu(a, 7); + tmp1 = __lsx_vsat_hu(b, 7); + return __lsx_vpickev_b(tmp1, tmp); +} + + +static __m128i lsx_maddubs_h(__m128i a, __m128i b) { + __m128i tmp1, tmp2; + tmp1 = __lsx_vmulwev_h_b(a, b); + tmp2 = __lsx_vmulwod_h_b(a, b); + return __lsx_vsadd_h(tmp1, tmp2); +} + +static __m128i lsx_madd_h(__m128i a, __m128i b) { + __m128i tmp1, tmp2; + tmp1 = __lsx_vmulwev_w_h(a, b); + tmp2 = __lsx_vmulwod_w_h(a, b); + return __lsx_vadd_w(tmp1, tmp2); +} + +// multiply int8_t, add results pairwise twice +static inline __m128i mul_sum_i8_pairs(const __m128i x, const __m128i y) { + // Get absolute values of x vectors + const __m128i ax = __lsx_vsigncov_b(x, x); + // Sign the values of the y vectors + const __m128i sy = __lsx_vsigncov_b(x, y); + // Perform multiplication and create 16-bit values + const __m128i dot = lsx_maddubs_h(ax, sy); + const __m128i ones = __lsx_vreplgr2vr_h(1); + return lsx_madd_h(ones, dot); +} + +// horizontally add 8 floats +static inline float hsum_float_8(const __m256 x) { + __m128 res = lasx_extractf128(x, 1); + ft_union tmp; + res = __lsx_vfadd_s(res, lasx_extractf128(x, 0)); + res = __lsx_vfadd_s(res, (__m128)__lsx_vpickod_d((__m128i)res, (__m128i)res)); + res = __lsx_vfadd_s(res, (__m128)__lsx_vinsgr2vr_w(__lsx_vldi(0), __lsx_vpickve2gr_w(res, 1), 0)); + tmp.i = __lsx_vpickve2gr_w(res, 0); + return tmp.f; +} + +// horizontally add 8 int32_t +static inline int hsum_i32_8(const __m256i a) { + + __m256i tmp1 = __lasx_xvpermi_q(a, a, 0x11); + __m256i tmp2 = __lasx_xvpermi_q(a, a, 0x00); + + __m128i tmp1_128 = lasx_extracti128_lo(tmp1); + __m128i tmp2_128 = lasx_extracti128_lo(tmp2); + + __m128i sum128 = __lsx_vadd_w(tmp1_128, tmp2_128); + + __m128i ev = __lsx_vpickev_w(sum128, sum128); + __m128i od = __lsx_vpickod_w(sum128, sum128); + __m128i sum64 = __lsx_vadd_w(ev, od); + + int sum64_1, sum64_2; + sum64_1 = __lsx_vpickve2gr_w(sum64, 0); + sum64_2 = __lsx_vpickve2gr_w(sum64, 1); + + return sum64_1 + sum64_2; +} + +// horizontally add 4 int32_t +static inline int hsum_i32_4(const __m128i a) { + __m128i ev = __lsx_vpickev_w(a, a); + __m128i od = __lsx_vpickod_w(a, a); + __m128i sum64 = __lsx_vadd_w(ev, od); + + int sum64_1, sum64_2; + sum64_1 = __lsx_vpickve2gr_w(sum64, 0); + sum64_2 = __lsx_vpickve2gr_w(sum64, 1); + + return sum64_1 + sum64_2; +} + +// spread 32 bits to 32 bytes { 0x00, 0xFF } +static inline __m256i bytes_from_bits_32(const uint8_t * x) { + + uint32_t x32; + memcpy(&x32, x, sizeof(uint32_t)); + const __m256i shuf_mask = lasx_set_d( + 0x0303030303030303, 0x0202020202020202, + 0x0101010101010101, 0x0000000000000000); + + __m256i bytes = lasx_shuffle_b(__lasx_xvreplgr2vr_w(x32), shuf_mask); + const __m256i bit_mask = __lasx_xvreplgr2vr_d(0x7fbfdfeff7fbfdfe); + bytes = __lasx_xvor_v(bytes, bit_mask); + return __lasx_xvseq_b(bytes, __lasx_xvreplgr2vr_d(-1)); +} + +// Unpack 32 4-bit fields into 32 bytes +// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval +static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) { + const __m128i lo = __lsx_vld((const __m128i *)rsi, 0); + __m128i hi = __lsx_vsrli_h(lo, 4); + return __lasx_xvandi_b(lasx_insertf128(hi, lo), 0xf); +} + +// add int16_t pairwise and return as float vector +static inline __m256 sum_i16_pairs_float(const __m256i x) { + __m256i v = __lasx_xvpackod_h(x, x); + __m256i summed_pairs = __lasx_xvaddwev_w_h(x, v); + return __lasx_xvffint_s_w(summed_pairs); +} + +static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) { + // Perform multiplication and create 16-bit values + const __m256i dot = lasx_maddubs_h(ax, sy); + return sum_i16_pairs_float(dot); +} + +// multiply int8_t, add results pairwise twice and return as float vector +static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) { + + // Get absolute values of x vectors + const __m256i ax = __lasx_xvsigncov_b(x, x); + // Sign the values of the y vectors + const __m256i sy = __lasx_xvsigncov_b(x, y); + + return mul_sum_us8_pairs_float(ax, sy); +} + +static inline __m128i packNibbles( __m256i bytes ) { + // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh + const __m256i lowByte = __lasx_xvreplgr2vr_h(0xFF); + __m256i high = __lasx_xvandn_v(lowByte, bytes); + __m256i low = __lasx_xvand_v(lowByte, bytes); + high = __lasx_xvsrli_h(high, 4); + bytes = __lasx_xvor_v(low, high); + // Compress uint16_t lanes into bytes + __m128i *r0 = (__m128i *)&bytes; + __m256i tmp_h128 = __lasx_xvpermi_q(bytes, bytes, 0x11); + __m128i *r1 = (__m128i *)&tmp_h128; + + __m128i zero = __lsx_vldi(0); + __m128i tmp, tmp2, tmp3; + + tmp = __lsx_vmax_h(zero, *r0); + tmp2 = __lsx_vsat_hu(tmp, 7); + + tmp = __lsx_vmax_h(zero, *r1); + tmp3 = __lsx_vsat_hu(tmp, 7); + return __lsx_vpickev_b(tmp3, tmp2); +} +#endif //__loongarch_asx + +void quantize_row_q4_0(const float * restrict x, void * restrict y, int64_t k) { + quantize_row_q4_0_ref(x, y, k); +} + +void quantize_row_q4_1(const float * restrict x, void * restrict y, int64_t k) { + quantize_row_q4_1_ref(x, y, k); +} + +void quantize_row_q5_0(const float * restrict x, void * restrict y, int64_t k) { + quantize_row_q5_0_ref(x, y, k); +} + +void quantize_row_q5_1(const float * restrict x, void * restrict y, int64_t k) { + quantize_row_q5_1_ref(x, y, k); +} + +void quantize_row_q8_0(const float * restrict x, void * restrict vy, int64_t k) { + assert(QK8_0 == 32); + assert(k % QK8_0 == 0); + const int nb = k / QK8_0; + + block_q8_0 * restrict y = vy; + +#if defined(__ARM_NEON) + for (int i = 0; i < nb; i++) { + float32x4_t srcv [8]; + float32x4_t asrcv[8]; + float32x4_t amaxv[8]; + + for (int j = 0; j < 8; j++) srcv[j] = vld1q_f32(x + i*32 + 4*j); + for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[j]); + + for (int j = 0; j < 4; j++) amaxv[2*j] = vmaxq_f32(asrcv[2*j], asrcv[2*j+1]); + for (int j = 0; j < 2; j++) amaxv[4*j] = vmaxq_f32(amaxv[4*j], amaxv[4*j+2]); + for (int j = 0; j < 1; j++) amaxv[8*j] = vmaxq_f32(amaxv[8*j], amaxv[8*j+4]); + + const float amax = vmaxvq_f32(amaxv[0]); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = GGML_FP32_TO_FP16(d); + + for (int j = 0; j < 8; j++) { + const float32x4_t v = vmulq_n_f32(srcv[j], id); + const int32x4_t vi = vcvtnq_s32_f32(v); + + y[i].qs[4*j + 0] = vgetq_lane_s32(vi, 0); + y[i].qs[4*j + 1] = vgetq_lane_s32(vi, 1); + y[i].qs[4*j + 2] = vgetq_lane_s32(vi, 2); + y[i].qs[4*j + 3] = vgetq_lane_s32(vi, 3); + } + } +#elif defined(__wasm_simd128__) + for (int i = 0; i < nb; i++) { + v128_t srcv [8]; + v128_t asrcv[8]; + v128_t amaxv[8]; + + for (int j = 0; j < 8; j++) srcv[j] = wasm_v128_load(x + i*32 + 4*j); + for (int j = 0; j < 8; j++) asrcv[j] = wasm_f32x4_abs(srcv[j]); + + for (int j = 0; j < 4; j++) amaxv[2*j] = wasm_f32x4_max(asrcv[2*j], asrcv[2*j+1]); + for (int j = 0; j < 2; j++) amaxv[4*j] = wasm_f32x4_max(amaxv[4*j], amaxv[4*j+2]); + for (int j = 0; j < 1; j++) amaxv[8*j] = wasm_f32x4_max(amaxv[8*j], amaxv[8*j+4]); + + const float amax = MAX(MAX(wasm_f32x4_extract_lane(amaxv[0], 0), + wasm_f32x4_extract_lane(amaxv[0], 1)), + MAX(wasm_f32x4_extract_lane(amaxv[0], 2), + wasm_f32x4_extract_lane(amaxv[0], 3))); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = GGML_FP32_TO_FP16(d); + + for (int j = 0; j < 8; j++) { + const v128_t v = wasm_f32x4_mul(srcv[j], wasm_f32x4_splat(id)); + const v128_t vi = wasm_i32x4_trunc_sat_f32x4(v); + + y[i].qs[4*j + 0] = wasm_i32x4_extract_lane(vi, 0); + y[i].qs[4*j + 1] = wasm_i32x4_extract_lane(vi, 1); + y[i].qs[4*j + 2] = wasm_i32x4_extract_lane(vi, 2); + y[i].qs[4*j + 3] = wasm_i32x4_extract_lane(vi, 3); + } + } +#elif defined(__AVX2__) || defined(__AVX__) + for (int i = 0; i < nb; i++) { + // Load elements into 4 AVX vectors + __m256 v0 = _mm256_loadu_ps( x ); + __m256 v1 = _mm256_loadu_ps( x + 8 ); + __m256 v2 = _mm256_loadu_ps( x + 16 ); + __m256 v3 = _mm256_loadu_ps( x + 24 ); + x += 32; + + // Compute max(abs(e)) for the block + const __m256 signBit = _mm256_set1_ps( -0.0f ); + __m256 maxAbs = _mm256_andnot_ps( signBit, v0 ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) ); + + __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) ); + max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) ); + max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) ); + const float maxScalar = _mm_cvtss_f32( max4 ); + + // Quantize these floats + const float d = maxScalar / 127.f; + y[i].d = GGML_FP32_TO_FP16(d); + const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f; + const __m256 mul = _mm256_set1_ps( id ); + + // Apply the multiplier + v0 = _mm256_mul_ps( v0, mul ); + v1 = _mm256_mul_ps( v1, mul ); + v2 = _mm256_mul_ps( v2, mul ); + v3 = _mm256_mul_ps( v3, mul ); + + // Round to nearest integer + v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST ); + v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST ); + v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST ); + v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST ); + + // Convert floats to integers + __m256i i0 = _mm256_cvtps_epi32( v0 ); + __m256i i1 = _mm256_cvtps_epi32( v1 ); + __m256i i2 = _mm256_cvtps_epi32( v2 ); + __m256i i3 = _mm256_cvtps_epi32( v3 ); + +#if defined(__AVX2__) + // Convert int32 to int16 + i0 = _mm256_packs_epi32( i0, i1 ); // 0, 1, 2, 3, 8, 9, 10, 11, 4, 5, 6, 7, 12, 13, 14, 15 + i2 = _mm256_packs_epi32( i2, i3 ); // 16, 17, 18, 19, 24, 25, 26, 27, 20, 21, 22, 23, 28, 29, 30, 31 + // Convert int16 to int8 + i0 = _mm256_packs_epi16( i0, i2 ); // 0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27, 4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31 + + // We got our precious signed bytes, but the order is now wrong + // These AVX2 pack instructions process 16-byte pieces independently + // The following instruction is fixing the order + const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 ); + i0 = _mm256_permutevar8x32_epi32( i0, perm ); + + _mm256_storeu_si256((__m256i *)y[i].qs, i0); +#else + // Since we don't have in AVX some necessary functions, + // we split the registers in half and call AVX2 analogs from SSE + __m128i ni0 = _mm256_castsi256_si128( i0 ); + __m128i ni1 = _mm256_extractf128_si256( i0, 1); + __m128i ni2 = _mm256_castsi256_si128( i1 ); + __m128i ni3 = _mm256_extractf128_si256( i1, 1); + __m128i ni4 = _mm256_castsi256_si128( i2 ); + __m128i ni5 = _mm256_extractf128_si256( i2, 1); + __m128i ni6 = _mm256_castsi256_si128( i3 ); + __m128i ni7 = _mm256_extractf128_si256( i3, 1); + + // Convert int32 to int16 + ni0 = _mm_packs_epi32( ni0, ni1 ); + ni2 = _mm_packs_epi32( ni2, ni3 ); + ni4 = _mm_packs_epi32( ni4, ni5 ); + ni6 = _mm_packs_epi32( ni6, ni7 ); + // Convert int16 to int8 + ni0 = _mm_packs_epi16( ni0, ni2 ); + ni4 = _mm_packs_epi16( ni4, ni6 ); + + _mm_storeu_si128((__m128i *)(y[i].qs + 0), ni0); + _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4); +#endif + } +#elif defined(__riscv_v_intrinsic) + + size_t vl = __riscv_vsetvl_e32m4(QK8_0); + + for (int i = 0; i < nb; i++) { + // load elements + vfloat32m4_t v_x = __riscv_vle32_v_f32m4(x+i*QK8_0, vl); + + vfloat32m4_t vfabs = __riscv_vfabs_v_f32m4(v_x, vl); + vfloat32m1_t tmp = __riscv_vfmv_v_f_f32m1(0.0f, vl); + vfloat32m1_t vmax = __riscv_vfredmax_vs_f32m4_f32m1(vfabs, tmp, vl); + float amax = __riscv_vfmv_f_s_f32m1_f32(vmax); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = GGML_FP32_TO_FP16(d); + + vfloat32m4_t x0 = __riscv_vfmul_vf_f32m4(v_x, id, vl); + + // convert to integer + vint16m2_t vi = __riscv_vfncvt_x_f_w_i16m2(x0, vl); + vint8m1_t vs = __riscv_vncvt_x_x_w_i8m1(vi, vl); + + // store result + __riscv_vse8_v_i8m1(y[i].qs , vs, vl); + } + +#elif defined(__POWER9_VECTOR__) + for (int i = 0; i < nb; i++) { + vector float srcv [8]; + vector float asrcv[8]; + vector float amaxv[8]; + vector signed int vi[8]; + + for (int j = 0; j < 8; j++) srcv[j] = vec_xl(0, x + i*32 + 4*j); + for (int j = 0; j < 8; j++) asrcv[j] = vec_abs(srcv[j]); + + for (int j = 0; j < 4; j++) amaxv[2*j] = vec_max(asrcv[2*j], asrcv[2*j+1]); + for (int j = 0; j < 2; j++) amaxv[4*j] = vec_max(amaxv[4*j], amaxv[4*j+2]); + for (int j = 0; j < 1; j++) amaxv[8*j] = vec_max(amaxv[8*j], amaxv[8*j+4]); + + const float amax = MAX(MAX(vec_extract(amaxv[0], 0), + vec_extract(amaxv[0], 1)), + MAX(vec_extract(amaxv[0], 2), + vec_extract(amaxv[0], 3))); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + const vector float vid = vec_splats(id); + + y[i].d = GGML_FP32_TO_FP16(d); + + for (int j = 0; j < 8; j++) { + const vector float v = vec_round(vec_mul(srcv[j], vid)); + vi[j] = vec_cts(v, 0); + } + vec_xst(vec_pack(vec_pack(vi[0], vi[1]), vec_pack(vi[2], vi[3])), 0, &y[i].qs[0]); + vec_xst(vec_pack(vec_pack(vi[4], vi[5]), vec_pack(vi[6], vi[7])), 16, &y[i].qs[0]); + } + +#elif defined(__loongarch_asx) + for (int i = 0; i < nb; i++) { + ft_union fi; + __m256 v0 = (__m256)__lasx_xvld( x , 0); + __m256 v1 = (__m256)__lasx_xvld( x , 32); + __m256 v2 = (__m256)__lasx_xvld( x , 64); + __m256 v3 = (__m256)__lasx_xvld( x , 96); + x += 32; + + // Compute max(abs(e)) for the block + const __m256 sign_bit = __lasx_xvreplfr2vr_s( -0.0f ); + __m256 max_abs = (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v0 ); + max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v1 ) ); + max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v2 ) ); + max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v3 ) ); + + __m128 max4 = __lsx_vfmax_s( lasx_extractf128( max_abs, 1 ), lasx_extractf128( max_abs , 0) ); + max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vpickod_d((__m128i) max4, (__m128i)max4 ) ); + __m128 tmp = max4; + max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vinsgr2vr_w(tmp, __lsx_vpickve2gr_w( max4, 1 ), 0 )); + fi.i = __lsx_vpickve2gr_w( (__m128i)max4, 0 ); + const float max_scalar = fi.f; + + // Quantize these floats + const float d = max_scalar / 127.f; + y[i].d = GGML_FP32_TO_FP16(d); + const float id = ( max_scalar != 0.0f ) ? 127.f / max_scalar : 0.0f; + const __m256 mul = (__m256)__lasx_xvreplfr2vr_s( id ); + + // Apply the multiplier + v0 = __lasx_xvfmul_s( v0, mul ); + v1 = __lasx_xvfmul_s( v1, mul ); + v2 = __lasx_xvfmul_s( v2, mul ); + v3 = __lasx_xvfmul_s( v3, mul ); + + // Round to nearest integer + __m256i i0 = __lasx_xvftintrne_w_s( v0 ); + __m256i i1 = __lasx_xvftintrne_w_s( v1 ); + __m256i i2 = __lasx_xvftintrne_w_s( v2 ); + __m256i i3 = __lasx_xvftintrne_w_s( v3 ); + + __m128i ni0 = lasx_extracti128( i0, 0 ); + __m128i ni1 = lasx_extracti128( i0, 1); + __m128i ni2 = lasx_extracti128( i1, 0); + __m128i ni3 = lasx_extracti128( i1, 1); + __m128i ni4 = lasx_extracti128( i2, 0); + __m128i ni5 = lasx_extracti128( i2, 1); + __m128i ni6 = lasx_extracti128( i3, 0); + __m128i ni7 = lasx_extracti128( i3, 1); + + // Convert int32 to int16 + ni0 = lsx_packs_w( ni0, ni1 ); + ni2 = lsx_packs_w( ni2, ni3 ); + ni4 = lsx_packs_w( ni4, ni5 ); + ni6 = lsx_packs_w( ni6, ni7 ); + // Convert int16 to int8 + ni0 = lsx_packs_h( ni0, ni2 ); + ni4 = lsx_packs_h( ni4, ni6 ); + + __lsx_vst(ni0, (__m128i *)(y[i].qs + 0), 0); + __lsx_vst(ni4, (__m128i *)(y[i].qs + 16), 0); + + } +#else + GGML_UNUSED(nb); + // scalar + quantize_row_q8_0_ref(x, y, k); +#endif +} + +void quantize_row_q8_1(const float * restrict x, void * restrict vy, int64_t k) { + assert(k % QK8_1 == 0); + const int nb = k / QK8_1; + + block_q8_1 * restrict y = vy; + +#if defined(__ARM_NEON) + for (int i = 0; i < nb; i++) { + float32x4_t srcv [8]; + float32x4_t asrcv[8]; + float32x4_t amaxv[8]; + + for (int j = 0; j < 8; j++) srcv[j] = vld1q_f32(x + i*32 + 4*j); + for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[j]); + + for (int j = 0; j < 4; j++) amaxv[2*j] = vmaxq_f32(asrcv[2*j], asrcv[2*j+1]); + for (int j = 0; j < 2; j++) amaxv[4*j] = vmaxq_f32(amaxv[4*j], amaxv[4*j+2]); + for (int j = 0; j < 1; j++) amaxv[8*j] = vmaxq_f32(amaxv[8*j], amaxv[8*j+4]); + + const float amax = vmaxvq_f32(amaxv[0]); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = GGML_FP32_TO_FP16(d); + + int32x4_t accv = vdupq_n_s32(0); + + for (int j = 0; j < 8; j++) { + const float32x4_t v = vmulq_n_f32(srcv[j], id); + const int32x4_t vi = vcvtnq_s32_f32(v); + + y[i].qs[4*j + 0] = vgetq_lane_s32(vi, 0); + y[i].qs[4*j + 1] = vgetq_lane_s32(vi, 1); + y[i].qs[4*j + 2] = vgetq_lane_s32(vi, 2); + y[i].qs[4*j + 3] = vgetq_lane_s32(vi, 3); + + accv = vaddq_s32(accv, vi); + } + + y[i].s = GGML_FP32_TO_FP16(d * vaddvq_s32(accv)); + } +#elif defined(__wasm_simd128__) + for (int i = 0; i < nb; i++) { + v128_t srcv [8]; + v128_t asrcv[8]; + v128_t amaxv[8]; + + for (int j = 0; j < 8; j++) srcv[j] = wasm_v128_load(x + i*32 + 4*j); + for (int j = 0; j < 8; j++) asrcv[j] = wasm_f32x4_abs(srcv[j]); + + for (int j = 0; j < 4; j++) amaxv[2*j] = wasm_f32x4_max(asrcv[2*j], asrcv[2*j+1]); + for (int j = 0; j < 2; j++) amaxv[4*j] = wasm_f32x4_max(amaxv[4*j], amaxv[4*j+2]); + for (int j = 0; j < 1; j++) amaxv[8*j] = wasm_f32x4_max(amaxv[8*j], amaxv[8*j+4]); + + const float amax = MAX(MAX(wasm_f32x4_extract_lane(amaxv[0], 0), + wasm_f32x4_extract_lane(amaxv[0], 1)), + MAX(wasm_f32x4_extract_lane(amaxv[0], 2), + wasm_f32x4_extract_lane(amaxv[0], 3))); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = GGML_FP32_TO_FP16(d); + + v128_t accv = wasm_i32x4_splat(0); + + for (int j = 0; j < 8; j++) { + const v128_t v = wasm_f32x4_mul(srcv[j], wasm_f32x4_splat(id)); + const v128_t vi = wasm_i32x4_trunc_sat_f32x4(v); + + y[i].qs[4*j + 0] = wasm_i32x4_extract_lane(vi, 0); + y[i].qs[4*j + 1] = wasm_i32x4_extract_lane(vi, 1); + y[i].qs[4*j + 2] = wasm_i32x4_extract_lane(vi, 2); + y[i].qs[4*j + 3] = wasm_i32x4_extract_lane(vi, 3); + + accv = wasm_i32x4_add(accv, vi); + } + + y[i].s = GGML_FP32_TO_FP16( + d * (wasm_i32x4_extract_lane(accv, 0) + + wasm_i32x4_extract_lane(accv, 1) + + wasm_i32x4_extract_lane(accv, 2) + + wasm_i32x4_extract_lane(accv, 3))); + } +#elif defined(__AVX2__) || defined(__AVX__) + for (int i = 0; i < nb; i++) { + // Load elements into 4 AVX vectors + __m256 v0 = _mm256_loadu_ps( x ); + __m256 v1 = _mm256_loadu_ps( x + 8 ); + __m256 v2 = _mm256_loadu_ps( x + 16 ); + __m256 v3 = _mm256_loadu_ps( x + 24 ); + x += 32; + + // Compute max(abs(e)) for the block + const __m256 signBit = _mm256_set1_ps( -0.0f ); + __m256 maxAbs = _mm256_andnot_ps( signBit, v0 ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) ); + maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) ); + + __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) ); + max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) ); + max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) ); + const float max_scalar = _mm_cvtss_f32( max4 ); + + // Quantize these floats + const float d = max_scalar / 127.f; + y[i].d = GGML_FP32_TO_FP16(d); + const float id = ( max_scalar != 0.0f ) ? 127.f / max_scalar : 0.0f; + const __m256 mul = _mm256_set1_ps( id ); + + // Apply the multiplier + v0 = _mm256_mul_ps( v0, mul ); + v1 = _mm256_mul_ps( v1, mul ); + v2 = _mm256_mul_ps( v2, mul ); + v3 = _mm256_mul_ps( v3, mul ); + + // Round to nearest integer + v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST ); + v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST ); + v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST ); + v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST ); + + // Convert floats to integers + __m256i i0 = _mm256_cvtps_epi32( v0 ); + __m256i i1 = _mm256_cvtps_epi32( v1 ); + __m256i i2 = _mm256_cvtps_epi32( v2 ); + __m256i i3 = _mm256_cvtps_epi32( v3 ); + +#if defined(__AVX2__) + // Compute the sum of the quants and set y[i].s + y[i].s = GGML_FP32_TO_FP16(d * hsum_i32_8(_mm256_add_epi32(_mm256_add_epi32(i0, i1), _mm256_add_epi32(i2, i3)))); + + // Convert int32 to int16 + i0 = _mm256_packs_epi32( i0, i1 ); // 0, 1, 2, 3, 8, 9, 10, 11, 4, 5, 6, 7, 12, 13, 14, 15 + i2 = _mm256_packs_epi32( i2, i3 ); // 16, 17, 18, 19, 24, 25, 26, 27, 20, 21, 22, 23, 28, 29, 30, 31 + // Convert int16 to int8 + i0 = _mm256_packs_epi16( i0, i2 ); // 0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27, 4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31 + + // We got our precious signed bytes, but the order is now wrong + // These AVX2 pack instructions process 16-byte pieces independently + // The following instruction is fixing the order + const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 ); + i0 = _mm256_permutevar8x32_epi32( i0, perm ); + + _mm256_storeu_si256((__m256i *)y[i].qs, i0); +#else + // Since we don't have in AVX some necessary functions, + // we split the registers in half and call AVX2 analogs from SSE + __m128i ni0 = _mm256_castsi256_si128( i0 ); + __m128i ni1 = _mm256_extractf128_si256( i0, 1); + __m128i ni2 = _mm256_castsi256_si128( i1 ); + __m128i ni3 = _mm256_extractf128_si256( i1, 1); + __m128i ni4 = _mm256_castsi256_si128( i2 ); + __m128i ni5 = _mm256_extractf128_si256( i2, 1); + __m128i ni6 = _mm256_castsi256_si128( i3 ); + __m128i ni7 = _mm256_extractf128_si256( i3, 1); + + // Compute the sum of the quants and set y[i].s + const __m128i s0 = _mm_add_epi32(_mm_add_epi32(ni0, ni1), _mm_add_epi32(ni2, ni3)); + const __m128i s1 = _mm_add_epi32(_mm_add_epi32(ni4, ni5), _mm_add_epi32(ni6, ni7)); + y[i].s = GGML_FP32_TO_FP16(d * hsum_i32_4(_mm_add_epi32(s0, s1))); + + // Convert int32 to int16 + ni0 = _mm_packs_epi32( ni0, ni1 ); + ni2 = _mm_packs_epi32( ni2, ni3 ); + ni4 = _mm_packs_epi32( ni4, ni5 ); + ni6 = _mm_packs_epi32( ni6, ni7 ); + // Convert int16 to int8 + ni0 = _mm_packs_epi16( ni0, ni2 ); + ni4 = _mm_packs_epi16( ni4, ni6 ); + + _mm_storeu_si128((__m128i *)(y[i].qs + 0), ni0); + _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4); +#endif + } +#elif defined(__riscv_v_intrinsic) + + size_t vl = __riscv_vsetvl_e32m4(QK8_1); + + for (int i = 0; i < nb; i++) { + // load elements + vfloat32m4_t v_x = __riscv_vle32_v_f32m4(x+i*QK8_1, vl); + + vfloat32m4_t vfabs = __riscv_vfabs_v_f32m4(v_x, vl); + vfloat32m1_t tmp = __riscv_vfmv_v_f_f32m1(0.0, vl); + vfloat32m1_t vmax = __riscv_vfredmax_vs_f32m4_f32m1(vfabs, tmp, vl); + float amax = __riscv_vfmv_f_s_f32m1_f32(vmax); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + + y[i].d = GGML_FP32_TO_FP16(d); + + vfloat32m4_t x0 = __riscv_vfmul_vf_f32m4(v_x, id, vl); + + // convert to integer + vint16m2_t vi = __riscv_vfncvt_x_f_w_i16m2(x0, vl); + vint8m1_t vs = __riscv_vncvt_x_x_w_i8m1(vi, vl); + + // store result + __riscv_vse8_v_i8m1(y[i].qs , vs, vl); + + // compute sum for y[i].s + vint16m1_t tmp2 = __riscv_vmv_v_x_i16m1(0, vl); + vint16m1_t vwrs = __riscv_vwredsum_vs_i8m1_i16m1(vs, tmp2, vl); + + // set y[i].s + int sum = __riscv_vmv_x_s_i16m1_i16(vwrs); + y[i].s = GGML_FP32_TO_FP16(sum*d); + } + +#elif defined(__POWER9_VECTOR__) + for (int i = 0; i < nb; i++) { + vector float srcv [8]; + vector float asrcv[8]; + vector float amaxv[8]; + vector signed int vi[8]; + + for (int j = 0; j < 8; j++) srcv[j] = vec_xl(0, x + i*32 + 4*j); + for (int j = 0; j < 8; j++) asrcv[j] = vec_abs(srcv[j]); + + for (int j = 0; j < 4; j++) amaxv[2*j] = vec_max(asrcv[2*j], asrcv[2*j+1]); + for (int j = 0; j < 2; j++) amaxv[4*j] = vec_max(amaxv[4*j], amaxv[4*j+2]); + for (int j = 0; j < 1; j++) amaxv[8*j] = vec_max(amaxv[8*j], amaxv[8*j+4]); + + const float amax = MAX(MAX(vec_extract(amaxv[0], 0), + vec_extract(amaxv[0], 1)), + MAX(vec_extract(amaxv[0], 2), + vec_extract(amaxv[0], 3))); + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + const vector float vid = vec_splats(id); + + y[i].d = GGML_FP32_TO_FP16(d); + + vector int accv = vec_splats(0); + + for (int j = 0; j < 8; j++) { + const vector float v = vec_round(vec_mul(srcv[j], vid)); + vi[j] = vec_cts(v, 0); + + accv = vec_add(accv, vi[j]); + } + vec_xst(vec_pack(vec_pack(vi[0], vi[1]), vec_pack(vi[2], vi[3])), 0, &y[i].qs[0]); + vec_xst(vec_pack(vec_pack(vi[4], vi[5]), vec_pack(vi[6], vi[7])), 16, &y[i].qs[0]); + + accv = vec_add(accv, vec_sld(accv, accv, 4)); + accv = vec_add(accv, vec_sld(accv, accv, 8)); + y[i].s = GGML_FP32_TO_FP16(d * vec_extract(accv, 0)); + } + +#elif defined(__loongarch_asx) + for (int i = 0; i < nb; i++) { + ft_union ft; + __m256 v0 = (__m256)__lasx_xvld( x , 0 ); + __m256 v1 = (__m256)__lasx_xvld( x , 32 ); + __m256 v2 = (__m256)__lasx_xvld( x , 64 ); + __m256 v3 = (__m256)__lasx_xvld( x , 96 ); + x += 32; + + // Compute max(abs(e)) for the block + const __m256 sign_bit = __lasx_xvreplfr2vr_s( -0.0f ); + __m256 max_abs = (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v0 ); + max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v1 ) ); + max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v2 ) ); + max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v3 ) ); + + __m128 max4 = __lsx_vfmax_s( lasx_extractf128( max_abs, 1 ), lasx_extractf128( max_abs, 0) ); + max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vpickod_d((__m128i) max4, (__m128i)max4 ) ); + __m128 tmp = max4; + max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vextrins_w((__m128i)tmp, (__m128i)max4, 0x10 )); + ft.i = __lsx_vpickve2gr_w( (__m128i)max4, 0 ); + const float max_scalar = ft.f; + + // Quantize these floats + const float d = max_scalar / 127.f; + y[i].d = GGML_FP32_TO_FP16(d); + const float id = ( max_scalar != 0.0f ) ? 127.f / max_scalar : 0.0f; + const __m256 mul = __lasx_xvreplfr2vr_s( id ); + + // Apply the multiplier + v0 = __lasx_xvfmul_s( v0, mul ); + v1 = __lasx_xvfmul_s( v1, mul ); + v2 = __lasx_xvfmul_s( v2, mul ); + v3 = __lasx_xvfmul_s( v3, mul ); + + // Round to nearest integer + __m256i i0 = __lasx_xvftintrne_w_s( v0 ); + __m256i i1 = __lasx_xvftintrne_w_s( v1 ); + __m256i i2 = __lasx_xvftintrne_w_s( v2 ); + __m256i i3 = __lasx_xvftintrne_w_s( v3 ); + + __m128i ni0 = lasx_extracti128(i0, 0); + __m128i ni1 = lasx_extracti128( i0, 1); + __m128i ni2 = lasx_extracti128( i1, 0); + __m128i ni3 = lasx_extracti128( i1, 1); + __m128i ni4 = lasx_extracti128( i2, 0 ); + __m128i ni5 = lasx_extracti128( i2, 1); + __m128i ni6 = lasx_extracti128( i3, 0); + __m128i ni7 = lasx_extracti128( i3, 1); + + // Compute the sum of the quants and set y[i].s + const __m128i s0 = __lsx_vadd_w(__lsx_vadd_w(ni0, ni1), __lsx_vadd_w(ni2, ni3)); + const __m128i s1 = __lsx_vadd_w(__lsx_vadd_w(ni4, ni5), __lsx_vadd_w(ni6, ni7)); + y[i].s = GGML_FP32_TO_FP16(d * hsum_i32_4(__lsx_vadd_w(s0, s1))); + + // Convert int32 to int16 + ni0 = lsx_packs_w( ni0, ni1 ); + ni2 = lsx_packs_w( ni2, ni3 ); + ni4 = lsx_packs_w( ni4, ni5 ); + ni6 = lsx_packs_w( ni6, ni7 ); + // Convert int16 to int8 + ni0 = lsx_packs_h( ni0, ni2 ); + ni4 = lsx_packs_h( ni4, ni6 ); + + __lsx_vst(ni0, (__m128i *)(y[i].qs + 0), 0); + __lsx_vst(ni4, (__m128i *)(y[i].qs + 16), 0); + } +#else + GGML_UNUSED(nb); + // scalar + quantize_row_q8_1_ref(x, y, k); +#endif +} + +// +// 2-6 bit quantization in super-blocks +// + +// +// ===================== Helper functions +// +static inline int nearest_int(float fval) { + assert(fabsf(fval) <= 4194303.f); + float val = fval + 12582912.f; + int i; memcpy(&i, &val, sizeof(int)); + return (i & 0x007fffff) - 0x00400000; +} + +static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t * restrict L, int rmse_type, + const float * restrict qw) { + float max = 0; + float amax = 0; + for (int i = 0; i < n; ++i) { + float ax = fabsf(x[i]); + if (ax > amax) { amax = ax; max = x[i]; } + } + if (amax < GROUP_MAX_EPS) { // all zero + for (int i = 0; i < n; ++i) { + L[i] = 0; + } + return 0.f; + } + float iscale = -nmax / max; + if (rmse_type == 0) { + for (int i = 0; i < n; ++i) { + int l = nearest_int(iscale * x[i]); + L[i] = nmax + MAX(-nmax, MIN(nmax-1, l)); + } + return 1/iscale; + } + bool return_early = false; + if (rmse_type < 0) { + rmse_type = -rmse_type; + return_early = true; + } + float sumlx = 0; + float suml2 = 0; +#ifdef HAVE_BUGGY_APPLE_LINKER + // use 'volatile' to prevent unroll and work around a bug in Apple ld64 1015.7 + for (volatile int i = 0; i < n; ++i) { +#else + for (int i = 0; i < n; ++i) { +#endif + int l = nearest_int(iscale * x[i]); + l = MAX(-nmax, MIN(nmax-1, l)); + L[i] = l + nmax; + float w = qw ? qw[i] : rmse_type == 1 ? x[i] * x[i] : rmse_type == 2 ? 1 : rmse_type == 3 ? fabsf(x[i]) : sqrtf(fabsf(x[i])); + sumlx += w*x[i]*l; + suml2 += w*l*l; + } + float scale = suml2 ? sumlx/suml2 : 0.0f; + if (return_early) return suml2 > 0 ? 0.5f*(scale + 1/iscale) : 1/iscale; + float best = scale * sumlx; + for (int is = -9; is <= 9; ++is) { + if (is == 0) { + continue; + } + iscale = -(nmax + 0.1f*is) / max; + sumlx = suml2 = 0; + for (int i = 0; i < n; ++i) { + int l = nearest_int(iscale * x[i]); + l = MAX(-nmax, MIN(nmax-1, l)); + float w = qw ? qw[i] : rmse_type == 1 ? x[i] * x[i] : rmse_type == 2 ? 1 : rmse_type == 3 ? fabsf(x[i]) : sqrtf(fabsf(x[i])); + sumlx += w*x[i]*l; + suml2 += w*l*l; + } + if (suml2 > 0 && sumlx*sumlx > best*suml2) { + for (int i = 0; i < n; ++i) { + int l = nearest_int(iscale * x[i]); + L[i] = nmax + MAX(-nmax, MIN(nmax-1, l)); + } + scale = sumlx/suml2; best = scale*sumlx; + } + } + return scale; +} + +static float make_q3_quants(int n, int nmax, const float * restrict x, int8_t * restrict L, bool do_rmse) { + float max = 0; + float amax = 0; + for (int i = 0; i < n; ++i) { + float ax = fabsf(x[i]); + if (ax > amax) { amax = ax; max = x[i]; } + } + if (amax < GROUP_MAX_EPS) { // all zero + for (int i = 0; i < n; ++i) { L[i] = 0; } + return 0.f; + } + float iscale = -nmax / max; + if (do_rmse) { + float sumlx = 0; + float suml2 = 0; + for (int i = 0; i < n; ++i) { + int l = nearest_int(iscale * x[i]); + l = MAX(-nmax, MIN(nmax-1, l)); + L[i] = l; + float w = x[i]*x[i]; + sumlx += w*x[i]*l; + suml2 += w*l*l; + } + for (int itry = 0; itry < 5; ++itry) { + int n_changed = 0; + for (int i = 0; i < n; ++i) { + float w = x[i]*x[i]; + float slx = sumlx - w*x[i]*L[i]; + if (slx > 0) { + float sl2 = suml2 - w*L[i]*L[i]; + int new_l = nearest_int(x[i] * sl2 / slx); + new_l = MAX(-nmax, MIN(nmax-1, new_l)); + if (new_l != L[i]) { + slx += w*x[i]*new_l; + sl2 += w*new_l*new_l; + if (sl2 > 0 && slx*slx*suml2 > sumlx*sumlx*sl2) { + L[i] = new_l; sumlx = slx; suml2 = sl2; + ++n_changed; + } + } + } + } + if (!n_changed) { + break; + } + } + for (int i = 0; i < n; ++i) { + L[i] += nmax; + } + return sumlx / suml2; + } + for (int i = 0; i < n; ++i) { + int l = nearest_int(iscale * x[i]); + l = MAX(-nmax, MIN(nmax-1, l)); + L[i] = l + nmax; + } + return 1/iscale; +} + +static float make_qkx1_quants(int n, int nmax, const float * restrict x, uint8_t * restrict L, float * restrict the_min, + int ntry, float alpha) { + float min = x[0]; + float max = x[0]; + for (int i = 1; i < n; ++i) { + if (x[i] < min) min = x[i]; + if (x[i] > max) max = x[i]; + } + if (max == min) { + for (int i = 0; i < n; ++i) L[i] = 0; + *the_min = 0; + return 0.f; + } + if (min > 0) min = 0; + float iscale = nmax/(max - min); + float scale = 1/iscale; + for (int itry = 0; itry < ntry; ++itry) { + float sumlx = 0; int suml2 = 0; + bool did_change = false; + for (int i = 0; i < n; ++i) { + int l = nearest_int(iscale*(x[i] - min)); + l = MAX(0, MIN(nmax, l)); + if (l != L[i]) { + L[i] = l; + did_change = true; + } + sumlx += (x[i] - min)*l; + suml2 += l*l; + } + scale = sumlx/suml2; + float sum = 0; + for (int i = 0; i < n; ++i) { + sum += x[i] - scale*L[i]; + } + min = alpha*min + (1 - alpha)*sum/n; + if (min > 0) min = 0; + iscale = 1/scale; + if (!did_change) break; + } + *the_min = -min; + return scale; +} + +static float make_qkx2_quants(int n, int nmax, const float * restrict x, const float * restrict weights, + uint8_t * restrict L, float * restrict the_min, uint8_t * restrict Laux, + float rmin, float rdelta, int nstep, bool use_mad) { + float min = x[0]; + float max = x[0]; + float sum_w = weights[0]; + float sum_x = sum_w * x[0]; +#ifdef HAVE_BUGGY_APPLE_LINKER + // use 'volatile' to prevent unroll and work around a bug in Apple ld64 1015.7 + for (volatile int i = 1; i < n; ++i) { +#else + for (int i = 1; i < n; ++i) { +#endif + if (x[i] < min) min = x[i]; + if (x[i] > max) max = x[i]; + float w = weights[i]; + sum_w += w; + sum_x += w * x[i]; + } + if (min > 0) min = 0; + if (max == min) { + for (int i = 0; i < n; ++i) L[i] = 0; + *the_min = -min; + return 0.f; + } + float iscale = nmax/(max - min); + float scale = 1/iscale; + float best_mad = 0; + for (int i = 0; i < n; ++i) { + int l = nearest_int(iscale*(x[i] - min)); + L[i] = MAX(0, MIN(nmax, l)); + float diff = scale * L[i] + min - x[i]; + diff = use_mad ? fabsf(diff) : diff * diff; + float w = weights[i]; + best_mad += w * diff; + } + if (nstep < 1) { + *the_min = -min; + return scale; + } + for (int is = 0; is <= nstep; ++is) { + iscale = (rmin + rdelta*is + nmax)/(max - min); + float sum_l = 0, sum_l2 = 0, sum_xl = 0; + for (int i = 0; i < n; ++i) { + int l = nearest_int(iscale*(x[i] - min)); + l = MAX(0, MIN(nmax, l)); + Laux[i] = l; + float w = weights[i]; + sum_l += w*l; + sum_l2 += w*l*l; + sum_xl += w*l*x[i]; + } + float D = sum_w * sum_l2 - sum_l * sum_l; + if (D > 0) { + float this_scale = (sum_w * sum_xl - sum_x * sum_l)/D; + float this_min = (sum_l2 * sum_x - sum_l * sum_xl)/D; + if (this_min > 0) { + this_min = 0; + this_scale = sum_xl / sum_l2; + } + float mad = 0; + for (int i = 0; i < n; ++i) { + float diff = this_scale * Laux[i] + this_min - x[i]; + diff = use_mad ? fabsf(diff) : diff * diff; + float w = weights[i]; + mad += w * diff; + } + if (mad < best_mad) { + for (int i = 0; i < n; ++i) { + L[i] = Laux[i]; + } + best_mad = mad; + scale = this_scale; + min = this_min; + } + } + } + *the_min = -min; + return scale; +} + +static inline void get_scale_min_k4(int j, const uint8_t * restrict q, uint8_t * restrict d, uint8_t * restrict m) { + if (j < 4) { + *d = q[j] & 63; *m = q[j + 4] & 63; + } else { + *d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4); + *m = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4); + } +} + +//========================- 2-bit (de)-quantization + +void quantize_row_q2_K(const float * restrict x, void * restrict vy, int64_t k) { + quantize_row_q2_K_ref(x, vy, k); +} + +//========================= 3-bit (de)-quantization + +void quantize_row_q3_K(const float * restrict x, void * restrict vy, int64_t k) { + quantize_row_q3_K_ref(x, vy, k); +} + +// ====================== 4-bit (de)-quantization + +void quantize_row_q4_K(const float * restrict x, void * restrict vy, int64_t k) { + assert(k % QK_K == 0); + block_q4_K * restrict y = vy; + quantize_row_q4_K_ref(x, y, k); +} + +// ====================== 5-bit (de)-quantization + +void quantize_row_q5_K(const float * restrict x, void * restrict vy, int64_t k) { + assert(k % QK_K == 0); + block_q5_K * restrict y = vy; + quantize_row_q5_K_ref(x, y, k); +} + +// ====================== 6-bit (de)-quantization + +void quantize_row_q6_K(const float * restrict x, void * restrict vy, int64_t k) { + assert(k % QK_K == 0); + block_q6_K * restrict y = vy; + quantize_row_q6_K_ref(x, y, k); +} + +// ====================== Ternary (de)-quantization (BitNet b1.58 and TriLMs) + +void quantize_row_tq1_0(const float * restrict x, void * restrict vy, int64_t k) { + assert(k % QK_K == 0); + block_tq1_0 * restrict y = vy; + quantize_row_tq1_0_ref(x, y, k); +} + +void quantize_row_tq2_0(const float * restrict x, void * restrict vy, int64_t k) { + assert(k % QK_K == 0); + block_tq2_0 * restrict y = vy; + quantize_row_tq2_0_ref(x, y, k); +} + +static const int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113}; + +//===================================== Q8_K ============================================== + +void quantize_row_q8_K(const float * restrict x, void * restrict y, int64_t k) { + quantize_row_q8_K_ref(x, y, k); +} + +//===================================== Dot products ================================= + +// +// Helper functions +// +#if __AVX__ || __AVX2__ || __AVX512F__ + +// shuffles to pick the required scales in dot products +static inline __m256i get_scale_shuffle_q3k(int i) { + static const uint8_t k_shuffle[128] = { + 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, + 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, + 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11, + 12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13, 14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15, + }; + return _mm256_loadu_si256((const __m256i*)k_shuffle + i); +} +static inline __m256i get_scale_shuffle_k4(int i) { + static const uint8_t k_shuffle[256] = { + 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, + 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, + 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, + 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, + 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, + 10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11, + 12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13, + 14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15 + }; + return _mm256_loadu_si256((const __m256i*)k_shuffle + i); +} +static inline __m128i get_scale_shuffle(int i) { + static const uint8_t k_shuffle[128] = { + 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, + 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, + 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, + 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, + 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, + 10,10,10,10,10,10,10,10, 11,11,11,11,11,11,11,11, + 12,12,12,12,12,12,12,12, 13,13,13,13,13,13,13,13, + 14,14,14,14,14,14,14,14, 15,15,15,15,15,15,15,15 + }; + return _mm_loadu_si128((const __m128i*)k_shuffle + i); +} +#elif defined(__loongarch_asx) +// shuffles to pick the required scales in dot products +static inline __m256i get_scale_shuffle_q3k(int i) { + static const uint8_t k_shuffle[128] = { + 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, + 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, + 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11, + 12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13, 14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15, + }; + return __lasx_xvld((const __m256i*)k_shuffle + i, 0); +} +static inline __m256i get_scale_shuffle_k4(int i) { + static const uint8_t k_shuffle[256] = { + 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, + 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, + 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, + 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, + 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, + 10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11, + 12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13, + 14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15 + }; + return __lasx_xvld((const __m256i*)k_shuffle + i, 0); +} +static inline __m128i get_scale_shuffle(int i) { + static const uint8_t k_shuffle[128] = { + 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, + 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, + 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, + 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, + 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, + 10,10,10,10,10,10,10,10, 11,11,11,11,11,11,11,11, + 12,12,12,12,12,12,12,12, 13,13,13,13,13,13,13,13, + 14,14,14,14,14,14,14,14, 15,15,15,15,15,15,15,15 + }; + return __lsx_vld((const __m128i*)k_shuffle + i, 0); +} +#endif + +void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + const int qk = QK8_0; + const int nb = n / qk; + + assert(n % qk == 0); +#if defined(__ARM_FEATURE_MATMUL_INT8) + assert((nrc == 2) || (nrc == 1)); +#else + assert(nrc == 1); +#endif + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_q4_0 * restrict x = vx; + const block_q8_0 * restrict y = vy; + +#if defined(__ARM_FEATURE_MATMUL_INT8) + if (nrc == 2) { + const block_q4_0 * restrict vx0 = vx; + const block_q4_0 * restrict vx1 = (const block_q4_0 *) ((const uint8_t*)vx + bx); + const block_q8_0 * restrict vy0 = vy; + const block_q8_0 * restrict vy1 = (const block_q8_0 *) ((const uint8_t*)vy + by); + + float32x4_t sumv0 = vdupq_n_f32(0.0f); + + for (int i = 0; i < nb; i++) { + const block_q4_0 * restrict b_x0 = &vx0[i]; + const block_q4_0 * restrict b_x1 = &vx1[i]; + const block_q8_0 * restrict b_y0 = &vy0[i]; + const block_q8_0 * restrict b_y1 = &vy1[i]; + + const uint8x16_t m4b = vdupq_n_u8(0x0F); + const int8x16_t s8b = vdupq_n_s8(0x8); + + const uint8x16_t v0_0 = vld1q_u8(b_x0->qs); + const uint8x16_t v0_1 = vld1q_u8(b_x1->qs); + + // 4-bit -> 8-bit + const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); + const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); + const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); + const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); + + // sub 8 + const int8x16_t x0_l = vsubq_s8(v0_0l, s8b); + const int8x16_t x0_h = vsubq_s8(v0_0h, s8b); + const int8x16_t x1_l = vsubq_s8(v0_1l, s8b); + const int8x16_t x1_h = vsubq_s8(v0_1h, s8b); + + // load y + const int8x16_t y0_l = vld1q_s8(b_y0->qs); + const int8x16_t y0_h = vld1q_s8(b_y0->qs + 16); + const int8x16_t y1_l = vld1q_s8(b_y1->qs); + const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); + + float32_t _scale[4] = { + GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d) + }; + float32x4_t scale = vld1q_f32(_scale); + + int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); + int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); + + int8x16_t l2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); + int8x16_t l3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); + + int8x16_t r0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); + int8x16_t r1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); + + int8x16_t r2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); + int8x16_t r3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); + + sumv0 = vmlaq_f32(sumv0,(vcvtq_f32_s32(vmmlaq_s32((vmmlaq_s32((vmmlaq_s32((vmmlaq_s32(vdupq_n_s32(0), l0, r0)), + l1, r1)), l2, r2)), l3, r3))), scale); + } + + float32x4_t sumv1 = vextq_f32 (sumv0, sumv0, 2); + float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1); + + vst1_f32(s, vget_low_f32 (sumv2)); + vst1_f32(s + bs, vget_high_f32(sumv2)); + + return; + } +#endif + + int ib = 0; + float sumf = 0; + +#if defined(__ARM_FEATURE_SVE) + svfloat32_t sumv0 = svdup_n_f32(0.0f); + svfloat32_t sumv1 = svdup_n_f32(0.0f); + + const int vector_length = ggml_cpu_get_sve_cnt()*8; + + // VLA Implementation using switch case + switch (vector_length) { + case 128: + { + // predicate for activating higher lanes for 4 float32 elements + const svbool_t ph4 = svptrue_pat_b32(SV_VL4); + + for (; ib + 1 < nb; ib += 2) { + const block_q4_0 * restrict x0 = &x[ib + 0]; + const block_q4_0 * restrict x1 = &x[ib + 1]; + const block_q8_0 * restrict y0 = &y[ib + 0]; + const block_q8_0 * restrict y1 = &y[ib + 1]; + + // load x + const svuint8_t qx0r = svld1rq_u8(svptrue_b8(), x0->qs); + const svuint8_t qx1r = svld1rq_u8(svptrue_b8(), x1->qs); + + // 4-bit -> 8-bit + const svint8_t qx0l = svreinterpret_s8_u8(svand_n_u8_m(svptrue_b8(), qx0r, 0x0F)); + const svint8_t qx0h = svreinterpret_s8_u8(svlsr_n_u8_m(svptrue_b8(), qx0r, 0x04)); + const svint8_t qx1l = svreinterpret_s8_u8(svand_n_u8_m(svptrue_b8(), qx1r, 0x0F)); + const svint8_t qx1h = svreinterpret_s8_u8(svlsr_n_u8_m(svptrue_b8(), qx1r, 0x04)); + + // sub 8 + const svint8_t qx0ls = svsub_n_s8_x(svptrue_b8(), qx0h, 8); + const svint8_t qx0hs = svsub_n_s8_x(svptrue_b8(), qx0l, 8); + const svint8_t qx1ls = svsub_n_s8_x(svptrue_b8(), qx1h, 8); + const svint8_t qx1hs = svsub_n_s8_x(svptrue_b8(), qx1l, 8); + + // load y + const svint8_t qy0h = svld1_s8(svptrue_b8(), y0->qs); + const svint8_t qy0l = svld1_s8(svptrue_b8(), y0->qs + 16); + const svint8_t qy1h = svld1_s8(svptrue_b8(), y1->qs); + const svint8_t qy1l = svld1_s8(svptrue_b8(), y1->qs + 16); + + // dot product + sumv0 = svmla_n_f32_x(ph4, sumv0, svcvt_f32_s32_x(ph4, svadd_x(ph4, + svdot_s32(svdup_n_s32(0), qx0ls, qy0l), + svdot_s32(svdup_n_s32(0), qx0hs, qy0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + sumv1 = svmla_n_f32_x(ph4, sumv1, svcvt_f32_s32_x(ph4, svadd_x(ph4, + svdot_s32(svdup_n_s32(0), qx1ls, qy1l), + svdot_s32(svdup_n_s32(0), qx1hs, qy1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + sumf = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1)); + } break; + case 256: + { + // predicate for activating higher lanes for 16 int8 elements + const svbool_t ph16 = svptrue_pat_b8(SV_VL16); + // predicate for activating lower lanes for 16 int8 elements + const svbool_t pl16 = svnot_b_z(svptrue_b8(), ph16); + + for (; ib + 1 < nb; ib += 2) { + const block_q4_0 * restrict x0 = &x[ib + 0]; + const block_q4_0 * restrict x1 = &x[ib + 1]; + const block_q8_0 * restrict y0 = &y[ib + 0]; + const block_q8_0 * restrict y1 = &y[ib + 1]; + + // load x + const svuint8_t qx0r = svld1rq_u8(svptrue_b8(), x0->qs); + const svuint8_t qx1r = svld1rq_u8(svptrue_b8(), x1->qs); + + // 4-bit -> 8-bit + const svint8_t qx0 = svreinterpret_s8_u8(svlsr_n_u8_m(pl16, svand_n_u8_m(ph16, qx0r, 0x0F), 0x04)); + const svint8_t qx1 = svreinterpret_s8_u8(svlsr_n_u8_m(pl16, svand_n_u8_m(ph16, qx1r, 0x0F), 0x04)); + + // sub 8 + const svint8_t qx0s = svsub_n_s8_x(svptrue_b8(), qx0, 8); + const svint8_t qx1s = svsub_n_s8_x(svptrue_b8(), qx1, 8); + + // load y + const svint8_t qy0 = svld1_s8(svptrue_b8(), y0->qs); + const svint8_t qy1 = svld1_s8(svptrue_b8(), y1->qs); + + // dot product + sumv0 = svmla_n_f32_x(svptrue_b32(), sumv0, svcvt_f32_s32_x(svptrue_b32(), + svdot_s32(svdup_n_s32(0), qx0s, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), + svdot_s32(svdup_n_s32(0), qx1s, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + sumf = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1)); + } break; + case 512: + { + // predicate for activating higher lanes for 32 int8 elements + const svbool_t ph32 = svptrue_pat_b8(SV_VL32); + + // predicate for activating higher lanes for 16 int8 elements + const svbool_t ph16 = svptrue_pat_b8(SV_VL16); + // predicate for activating lower lanes for 16 int8 elements from first 32 int8 activated lanes + const svbool_t pl16 = svnot_b_z(ph32, ph16); + + for (; ib + 1 < nb; ib += 2) { + const block_q4_0 * restrict x0 = &x[ib + 0]; + const block_q4_0 * restrict x1 = &x[ib + 1]; + const block_q8_0 * restrict y0 = &y[ib + 0]; + const block_q8_0 * restrict y1 = &y[ib + 1]; + + // load x + const svuint8_t qx0r = svld1rq_u8(ph32, x0->qs); + const svuint8_t qx1r = svld1rq_u8(ph32, x1->qs); + + // 4-bit -> 8-bit + const svint8_t qx0 = svreinterpret_s8_u8(svlsr_n_u8_m(pl16, svand_n_u8_m(ph16, qx0r, 0x0F), 0x04)); + const svint8_t qx1 = svreinterpret_s8_u8(svlsr_n_u8_m(pl16, svand_n_u8_m(ph16, qx1r, 0x0F), 0x04)); + + // sub 8 + const svint8_t qx0s = svsub_n_s8_x(ph32, qx0, 8); + const svint8_t qx1s = svsub_n_s8_x(ph32, qx1, 8); + + // load y + const svint8_t qy0 = svld1_s8(ph32, y0->qs); + const svint8_t qy1 = svld1_s8(ph32, y1->qs); + + // dot product + sumv0 = svmla_n_f32_x(ph32, sumv0, svcvt_f32_s32_x(ph32, + svdot_s32(svdup_n_s32(0), qx0s, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + sumv1 = svmla_n_f32_x(ph32, sumv1, svcvt_f32_s32_x(ph32, + svdot_s32(svdup_n_s32(0), qx1s, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + sumf = svaddv_f32(ph32, svadd_f32_x(ph32, sumv0, sumv1)); + } break; + default: + assert(false && "Unsupported vector length"); + break; + } + +#elif defined(__ARM_NEON) + float32x4_t sumv0 = vdupq_n_f32(0.0f); + float32x4_t sumv1 = vdupq_n_f32(0.0f); + + for (; ib + 1 < nb; ib += 2) { + const block_q4_0 * restrict x0 = &x[ib + 0]; + const block_q4_0 * restrict x1 = &x[ib + 1]; + const block_q8_0 * restrict y0 = &y[ib + 0]; + const block_q8_0 * restrict y1 = &y[ib + 1]; + + const uint8x16_t m4b = vdupq_n_u8(0x0F); + const int8x16_t s8b = vdupq_n_s8(0x8); + + const uint8x16_t v0_0 = vld1q_u8(x0->qs); + const uint8x16_t v0_1 = vld1q_u8(x1->qs); + + // 4-bit -> 8-bit + const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); + const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); + const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); + const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); + + // sub 8 + const int8x16_t v0_0ls = vsubq_s8(v0_0l, s8b); + const int8x16_t v0_0hs = vsubq_s8(v0_0h, s8b); + const int8x16_t v0_1ls = vsubq_s8(v0_1l, s8b); + const int8x16_t v0_1hs = vsubq_s8(v0_1h, s8b); + + // load y + const int8x16_t v1_0l = vld1q_s8(y0->qs); + const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); + const int8x16_t v1_1l = vld1q_s8(y1->qs); + const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); + + // dot product into int32x4_t + const int32x4_t p_0 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_0ls, v1_0l), v0_0hs, v1_0h); + const int32x4_t p_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_1ls, v1_1l), v0_1hs, v1_1h); + + sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); +#elif defined(__AVX2__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + + // Main loop + for (; ib < nb; ++ib) { + /* Compute combined scale for the block */ + const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) ); + + __m256i qx = bytes_from_nibbles_32(x[ib].qs); + + // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval. + const __m256i off = _mm256_set1_epi8( 8 ); + qx = _mm256_sub_epi8( qx, off ); + + __m256i qy = _mm256_loadu_si256((const __m256i *)y[ib].qs); + + const __m256 q = mul_sum_i8_pairs_float(qx, qy); + + /* Multiply q with scale and accumulate */ + acc = _mm256_fmadd_ps( d, q, acc ); + } + + sumf = hsum_float_8(acc); +#elif defined(__AVX__) + __m256 accum = _mm256_setzero_ps(); + for (; ib + 1 < nb; ib += 2) { + const __m128i q4bits_1 = _mm_loadu_si128((const __m128i *)x[ib + 0].qs); + const __m128i q4bits_2 = _mm_loadu_si128((const __m128i *)x[ib + 1].qs); + const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)y[ib + 0].qs); + const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)y[ib + 0].qs + 1); + const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs); + const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs + 1); + + const __m128i q4b_1_0 = _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), q4bits_1), _mm_set1_epi8(8)); + const __m128i q4b_1_1 = _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), _mm_srli_epi16(q4bits_1, 4)), _mm_set1_epi8(8)); + const __m128i q4b_2_0 = _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), q4bits_2), _mm_set1_epi8(8)); + const __m128i q4b_2_1 = _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), _mm_srli_epi16(q4bits_2, 4)), _mm_set1_epi8(8)); + + const __m128i p16_1_0 = mul_add_epi8_sse(q4b_1_0, q8b_1_0); + const __m128i p16_1_1 = mul_add_epi8_sse(q4b_1_1, q8b_1_1); + const __m128i p16_2_0 = mul_add_epi8_sse(q4b_2_0, q8b_2_0); + const __m128i p16_2_1 = mul_add_epi8_sse(q4b_2_1, q8b_2_1); + const __m128i p_1 = _mm_add_epi16(p16_1_0, p16_1_1); + const __m128i p_2 = _mm_add_epi16(p16_2_0, p16_2_1); + const __m256 p = sum_i16_pairs_float(p_2, p_1); + + const __m256 deltas = quad_fp16_delta_float(x[ib].d, y[ib].d, x[ib + 1].d, y[ib + 1].d); + accum = _mm256_add_ps(_mm256_mul_ps(deltas, p), accum); + } + + sumf = hsum_float_8(accum); +#elif defined(__SSSE3__) + // set constants + const __m128i lowMask = _mm_set1_epi8(0xF); + const __m128i off = _mm_set1_epi8(8); + + // Initialize accumulator with zeros + __m128 acc_0 = _mm_setzero_ps(); + __m128 acc_1 = _mm_setzero_ps(); + __m128 acc_2 = _mm_setzero_ps(); + __m128 acc_3 = _mm_setzero_ps(); + + for (; ib + 1 < nb; ib += 2) { + _mm_prefetch(&x[ib] + sizeof(block_q4_0), _MM_HINT_T0); + _mm_prefetch(&y[ib] + sizeof(block_q8_0), _MM_HINT_T0); + + // Compute combined scale for the block 0 and 1 + const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) ); + + const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[ib].qs); + + __m128i bx_0 = _mm_and_si128(lowMask, tmp_0_1); + __m128i by_0 = _mm_loadu_si128((const __m128i *)y[ib].qs); + bx_0 = _mm_sub_epi8(bx_0, off); + const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0); + + __m128i bx_1 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_0_1, 4)); + __m128i by_1 = _mm_loadu_si128((const __m128i *)(y[ib].qs + 16)); + bx_1 = _mm_sub_epi8(bx_1, off); + const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1); + + _mm_prefetch(&x[ib] + 2 * sizeof(block_q4_0), _MM_HINT_T0); + _mm_prefetch(&y[ib] + 2 * sizeof(block_q8_0), _MM_HINT_T0); + + // Compute combined scale for the block 2 and 3 + const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[ib + 1].d) * GGML_FP16_TO_FP32(y[ib + 1].d) ); + + const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[ib + 1].qs); + + __m128i bx_2 = _mm_and_si128(lowMask, tmp_2_3); + __m128i by_2 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs); + bx_2 = _mm_sub_epi8(bx_2, off); + const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2); + + __m128i bx_3 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_2_3, 4)); + __m128i by_3 = _mm_loadu_si128((const __m128i *)(y[ib + 1].qs + 16)); + bx_3 = _mm_sub_epi8(bx_3, off); + const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3); + + // Convert int32_t to float + __m128 p0 = _mm_cvtepi32_ps(i32_0); + __m128 p1 = _mm_cvtepi32_ps(i32_1); + __m128 p2 = _mm_cvtepi32_ps(i32_2); + __m128 p3 = _mm_cvtepi32_ps(i32_3); + + // Apply the scale + __m128 p0_d = _mm_mul_ps( d_0_1, p0 ); + __m128 p1_d = _mm_mul_ps( d_0_1, p1 ); + __m128 p2_d = _mm_mul_ps( d_2_3, p2 ); + __m128 p3_d = _mm_mul_ps( d_2_3, p3 ); + + // Acummulate + acc_0 = _mm_add_ps(p0_d, acc_0); + acc_1 = _mm_add_ps(p1_d, acc_1); + acc_2 = _mm_add_ps(p2_d, acc_2); + acc_3 = _mm_add_ps(p3_d, acc_3); + } + + sumf = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3); +#elif defined(__riscv_v_intrinsic) + size_t vl = __riscv_vsetvl_e8m1(qk/2); + + for (; ib < nb; ++ib) { + // load elements + vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[ib].qs, vl); + + vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[ib].qs, vl); + vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[ib].qs+16, vl); + + // mask and store lower part of x, and then upper part + vuint8mf2_t x_a = __riscv_vand_vx_u8mf2(tx, 0x0F, vl); + vuint8mf2_t x_l = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl); + + vint8mf2_t x_ai = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a); + vint8mf2_t x_li = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l); + + // subtract offset + vint8mf2_t v0 = __riscv_vsub_vx_i8mf2(x_ai, 8, vl); + vint8mf2_t v1 = __riscv_vsub_vx_i8mf2(x_li, 8, vl); + + vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl); + vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl); + + vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); + + vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl); + vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl); + + int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); + + sumf += sumi*GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d); + } + +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector signed int v0 = vec_splats((int32_t)0); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + const vector signed char v8 = vec_splats((signed char)0x8); + + vector float vsumf0 = vec_splats(0.0f); + +#pragma GCC unroll 8 + for (; ib < nb; ++ib) { + __builtin_prefetch(x[ib].qs, 0, 1); + __builtin_prefetch(y[ib].qs, 0, 1); + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); + vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); + vector float vd = vec_mul(vxd, vyd); + + vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs); + vector signed char q8y0 = vec_xl( 0, y[ib].qs); + vector signed char q8y1 = vec_xl(16, y[ib].qs); + + vector signed char q4x0 = vec_and(qxs, lowMask); + vector signed char q4x1 = vec_sr(qxs, v4); + + q4x0 = vec_sub(q4x0, v8); + q4x1 = vec_sub(q4x1, v8); + + vector signed short qv0 = vec_add(vec_mule(q4x0, q8y0), vec_mulo(q4x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q4x1, q8y1), vec_mulo(q4x1, q8y1)); + + vector signed int vsumi0 = v0; + + vsumi0 = vec_sum4s(qv0, vsumi0); + vsumi0 = vec_sum4s(qv1, vsumi0); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + } + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + sumf = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + // Initialize accumulator with zeros + __m256 acc = (__m256)__lasx_xvldi(0); + + // Main loop + for (; ib < nb; ++ib) { + /* Compute combined scale for the block */ + const __m256 d = __lasx_xvreplfr2vr_s( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) ); + + __m256i qx = bytes_from_nibbles_32(x[ib].qs); + + // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval. + const __m256i off = __lasx_xvreplgr2vr_b( 8 ); + qx = __lasx_xvsub_b( qx, off ); + + __m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0); + + const __m256 q = mul_sum_i8_pairs_float(qx, qy); + + /* Multiply q with scale and accumulate */ + acc = __lasx_xvfmadd_s( d, q, acc ); + } + + sumf = hsum_float_8(acc); +#elif defined(__loongarch_sx) + // set constants + const __m128i low_mask = __lsx_vreplgr2vr_b(0xF); + const __m128i off = __lsx_vreplgr2vr_b(8); + + // Initialize accumulator with zeros + __m128 acc_0 = __lsx_vldi(0); + __m128 acc_1 = __lsx_vldi(0); + __m128 acc_2 = __lsx_vldi(0); + __m128 acc_3 = __lsx_vldi(0); + + for (; ib + 1 < nb; ib += 2) { + + // Compute combined scale for the block 0 and 1 + const __m128 d_0_1 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) ); + + const __m128i tmp_0_1 = __lsx_vld((const __m128i *)x[ib].qs, 0); + + __m128i bx_0 = __lsx_vand_v(low_mask, tmp_0_1); + __m128i by_0 = __lsx_vld((const __m128i *)y[ib].qs, 0); + bx_0 = __lsx_vsub_b(bx_0, off); + const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0); + + __m128i bx_1 = __lsx_vand_v(low_mask, __lsx_vsrli_d(tmp_0_1, 4)); + __m128i by_1 = __lsx_vld((const __m128i *)(y[ib].qs + 16), 0); + bx_1 = __lsx_vsub_b(bx_1, off); + const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1); + + //_mm_prefetch(&x[ib] + 2 * sizeof(block_q4_0), _MM_HINT_T0); + //_mm_prefetch(&y[ib] + 2 * sizeof(block_q8_0), _MM_HINT_T0); + + // Compute combined scale for the block 2 and 3 + const __m128 d_2_3 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[ib + 1].d) * GGML_FP16_TO_FP32(y[ib + 1].d) ); + + const __m128i tmp_2_3 = __lsx_vld((const __m128i *)x[ib + 1].qs, 0); + + __m128i bx_2 = __lsx_vand_v(low_mask, tmp_2_3); + __m128i by_2 = __lsx_vld((const __m128i *)y[ib + 1].qs, 0); + bx_2 = __lsx_vsub_b(bx_2, off); + const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2); + + __m128i bx_3 = __lsx_vand_v(low_mask, __lsx_vsrli_d(tmp_2_3, 4)); + __m128i by_3 = __lsx_vld((const __m128i *)(y[ib + 1].qs + 16), 0); + bx_3 = __lsx_vsub_b(bx_3, off); + const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3); + + // Convert int32_t to float + __m128 p0 = __lsx_vffint_s_w(i32_0); + __m128 p1 = __lsx_vffint_s_w(i32_1); + __m128 p2 = __lsx_vffint_s_w(i32_2); + __m128 p3 = __lsx_vffint_s_w(i32_3); + + // Apply the scale + __m128 p0_d = __lsx_vfmul_s( d_0_1, p0 ); + __m128 p1_d = __lsx_vfmul_s( d_0_1, p1 ); + __m128 p2_d = __lsx_vfmul_s( d_2_3, p2 ); + __m128 p3_d = __lsx_vfmul_s( d_2_3, p3 ); + + // Acummulate + acc_0 = __lsx_vfadd_s(p0_d, acc_0); + acc_1 = __lsx_vfadd_s(p1_d, acc_1); + acc_2 = __lsx_vfadd_s(p2_d, acc_2); + acc_3 = __lsx_vfadd_s(p3_d, acc_3); + } + + sumf = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3); +#endif + for (; ib < nb; ++ib) { + int sumi0 = 0; + int sumi1 = 0; + + for (int j = 0; j < qk/2; ++j) { + const int v0 = (x[ib].qs[j] & 0x0F) - 8; + const int v1 = (x[ib].qs[j] >> 4) - 8; + + sumi0 += (v0 * y[ib].qs[j]); + sumi1 += (v1 * y[ib].qs[j + qk/2]); + } + + int sumi = sumi0 + sumi1; + sumf += sumi*GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d); + } + + *s = sumf; +} + +void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + const int qk = QK8_1; + const int nb = n / qk; + + assert(n % qk == 0); +#if defined(__ARM_FEATURE_MATMUL_INT8) + assert((nrc == 2) || (nrc == 1)); +#else + assert(nrc == 1); +#endif + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_q4_1 * restrict x = vx; + const block_q8_1 * restrict y = vy; + +#if defined(__ARM_FEATURE_MATMUL_INT8) + if (nrc == 2) { + const block_q4_1 * restrict vx0 = vx; + const block_q4_1 * restrict vx1 = (const block_q4_1 *) ((const uint8_t*)vx + bx); + const block_q8_1 * restrict vy0 = vy; + const block_q8_1 * restrict vy1 = (const block_q8_1 *) ((const uint8_t*)vy + by); + + float32x4_t sumv0 = vdupq_n_f32(0.0f); + float32x4_t summs0 = vdupq_n_f32(0.0f); + + for (int i = 0; i < nb; i++) { + const block_q4_1 * restrict b_x0 = &vx0[i]; + const block_q4_1 * restrict b_x1 = &vx1[i]; + const block_q8_1 * restrict b_y0 = &vy0[i]; + const block_q8_1 * restrict b_y1 = &vy1[i]; + + float32_t summs_t[4] = { + GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y0->s), + GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y0->s), + GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y1->s), + GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y1->s) + }; + summs0 = vaddq_f32(summs0, vld1q_f32(summs_t)); + + const uint8x16_t m4b = vdupq_n_u8(0x0F); + + const uint8x16_t v0_0 = vld1q_u8(b_x0->qs); + const uint8x16_t v0_1 = vld1q_u8(b_x1->qs); + + // 4-bit -> 8-bit + const int8x16_t x0_l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); + const int8x16_t x0_h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); + const int8x16_t x1_l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); + const int8x16_t x1_h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); + + // load y + const int8x16_t y0_l = vld1q_s8(b_y0->qs); + const int8x16_t y0_h = vld1q_s8(b_y0->qs + 16); + const int8x16_t y1_l = vld1q_s8(b_y1->qs); + const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); + + // mmla into int32x4_t + float32_t _scale[4] = { + GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d) + }; + float32x4_t scale = vld1q_f32(_scale); + + int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); + int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); + + int8x16_t l2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); + int8x16_t l3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); + + int8x16_t r0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); + int8x16_t r1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); + + int8x16_t r2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); + int8x16_t r3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); + sumv0 = vmlaq_f32(sumv0,(vcvtq_f32_s32(vmmlaq_s32((vmmlaq_s32((vmmlaq_s32((vmmlaq_s32(vdupq_n_s32(0), l0, r0)), + l1, r1)), l2, r2)), l3, r3))), scale); + } + + float32x4_t sumv1 = vextq_f32 (sumv0, sumv0, 2); + float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1); + + sumv2 = vaddq_f32(sumv2, summs0); + + vst1_f32(s, vget_low_f32 (sumv2)); + vst1_f32(s + bs, vget_high_f32(sumv2)); + + return; + } +#endif + + int ib = 0; + float sumf = 0; + + // TODO: add WASM SIMD +#if defined(__ARM_NEON) + float32x4_t sumv0 = vdupq_n_f32(0.0f); + float32x4_t sumv1 = vdupq_n_f32(0.0f); + + float summs = 0; + + for (; ib + 1 < nb; ib += 2) { + const block_q4_1 * restrict x0 = &x[ib + 0]; + const block_q4_1 * restrict x1 = &x[ib + 1]; + const block_q8_1 * restrict y0 = &y[ib + 0]; + const block_q8_1 * restrict y1 = &y[ib + 1]; + + summs += GGML_FP16_TO_FP32(x0->m) * GGML_FP16_TO_FP32(y0->s) + GGML_FP16_TO_FP32(x1->m) * GGML_FP16_TO_FP32(y1->s); + + const uint8x16_t m4b = vdupq_n_u8(0x0F); + + const uint8x16_t v0_0 = vld1q_u8(x0->qs); + const uint8x16_t v0_1 = vld1q_u8(x1->qs); + + // 4-bit -> 8-bit + const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); + const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); + const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); + const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); + + // load y + const int8x16_t v1_0l = vld1q_s8(y0->qs); + const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); + const int8x16_t v1_1l = vld1q_s8(y1->qs); + const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); + + // dot product into int32x4_t + const int32x4_t p_0 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0l), v0_0h, v1_0h); + const int32x4_t p_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1l), v0_1h, v1_1h); + + sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs; +#elif defined(__AVX2__) || defined(__AVX__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + + float summs = 0; + + // Main loop + for (; ib < nb; ++ib) { + const float d0 = GGML_FP16_TO_FP32(x[ib].d); + const float d1 = GGML_FP16_TO_FP32(y[ib].d); + + summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s); + + const __m256 d0v = _mm256_set1_ps( d0 ); + const __m256 d1v = _mm256_set1_ps( d1 ); + + // Compute combined scales + const __m256 d0d1 = _mm256_mul_ps( d0v, d1v ); + + // Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes + const __m256i qx = bytes_from_nibbles_32(x[ib].qs); + const __m256i qy = _mm256_loadu_si256( (const __m256i *)y[ib].qs ); + + const __m256 xy = mul_sum_us8_pairs_float(qx, qy); + + // Accumulate d0*d1*x*y +#if defined(__AVX2__) + acc = _mm256_fmadd_ps( d0d1, xy, acc ); +#else + acc = _mm256_add_ps( _mm256_mul_ps( d0d1, xy ), acc ); +#endif + } + + sumf = hsum_float_8(acc) + summs; +#elif defined(__riscv_v_intrinsic) + size_t vl = __riscv_vsetvl_e8m1(qk/2); + + for (; ib < nb; ++ib) { + // load elements + vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[ib].qs, vl); + + vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[ib].qs, vl); + vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[ib].qs+16, vl); + + // mask and store lower part of x, and then upper part + vuint8mf2_t x_a = __riscv_vand_vx_u8mf2(tx, 0x0F, vl); + vuint8mf2_t x_l = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl); + + vint8mf2_t v0 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a); + vint8mf2_t v1 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l); + + vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl); + vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl); + + vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); + + vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl); + vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl); + + int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); + + sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s); + } + +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector signed int v0 = vec_splats((int32_t)0); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + +#pragma GCC unroll 4 + for (; ib < nb; ++ib) { + __builtin_prefetch(x[ib].qs, 0, 1); + __builtin_prefetch(y[ib].qs, 0, 1); + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); + vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); + vector float vd = vec_mul(vxd, vyd); + + vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[ib].m)); + vector float vys = {GGML_FP16_TO_FP32(y[ib].s), 0.0f, 0.0f, 0.0f}; + vsumf0 = vec_madd(vxmin, vys, vsumf0); + + vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs); + vector signed char q8y0 = vec_xl( 0, y[ib].qs); + vector signed char q8y1 = vec_xl(16, y[ib].qs); + + vector unsigned char q4x0 = (vector unsigned char)vec_and(qxs, lowMask); + vector unsigned char q4x1 = (vector unsigned char)vec_sr(qxs, v4); + + vector signed int vsumi0 = v0; + + vsumi0 = vec_msum(q8y0, q4x0, vsumi0); + vsumi0 = vec_msum(q8y1, q4x1, vsumi0); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + } + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + sumf = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + // Initialize accumulator with zeros + __m256 acc = (__m256)__lasx_xvldi(0); + + float summs = 0; + + // Main loop + for (; ib < nb; ++ib) { + const float d0 = GGML_FP16_TO_FP32(x[ib].d); + const float d1 = GGML_FP16_TO_FP32(y[ib].d); + + summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s); + + const __m256 d0v = __lasx_xvreplfr2vr_s( d0 ); + const __m256 d1v = __lasx_xvreplfr2vr_s( d1 ); + + // Compute combined scales + const __m256 d0d1 = __lasx_xvfmul_s( d0v, d1v ); + + // Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes + const __m256i qx = bytes_from_nibbles_32(x[ib].qs); + const __m256i qy = __lasx_xvld( (const __m256i *)y[ib].qs, 0); + + const __m256 xy = mul_sum_us8_pairs_float(qx, qy); + + // Accumulate d0*d1*x*y + acc = __lasx_xvfmadd_s( d0d1, xy, acc ); + } + + sumf = hsum_float_8(acc) + summs; +#endif + for (; ib < nb; ++ib) { + int sumi0 = 0; + int sumi1 = 0; + + for (int j = 0; j < qk/2; ++j) { + const int v0 = (x[ib].qs[j] & 0x0F); + const int v1 = (x[ib].qs[j] >> 4); + + sumi0 += (v0 * y[ib].qs[j]); + sumi1 += (v1 * y[ib].qs[j + qk/2]); + } + + int sumi = sumi0 + sumi1; + sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s); + } + + *s = sumf; +} + +void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + const int qk = QK8_0; + const int nb = n / qk; + + int ib = 0; + float sumf = 0; + + assert(n % qk == 0); + assert(qk == QK5_0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_q5_0 * restrict x = vx; + const block_q8_0 * restrict y = vy; + +#if defined(__ARM_NEON) + float32x4_t sumv0 = vdupq_n_f32(0.0f); + float32x4_t sumv1 = vdupq_n_f32(0.0f); + + uint32_t qh0; + uint32_t qh1; + + uint64_t tmp0[4]; + uint64_t tmp1[4]; + + for (; ib + 1 < nb; ib += 2) { + const block_q5_0 * restrict x0 = &x[ib]; + const block_q5_0 * restrict x1 = &x[ib + 1]; + const block_q8_0 * restrict y0 = &y[ib]; + const block_q8_0 * restrict y1 = &y[ib + 1]; + + const uint8x16_t m4b = vdupq_n_u8(0x0F); + + // extract the 5th bit via lookup table ((!b) << 4) + memcpy(&qh0, x0->qh, sizeof(qh0)); + memcpy(&qh1, x1->qh, sizeof(qh1)); + + tmp0[0] = table_b2b_1[(qh0 >> 0) & 0xFF]; + tmp0[1] = table_b2b_1[(qh0 >> 8) & 0xFF]; + tmp0[2] = table_b2b_1[(qh0 >> 16) & 0xFF]; + tmp0[3] = table_b2b_1[(qh0 >> 24) ]; + + tmp1[0] = table_b2b_1[(qh1 >> 0) & 0xFF]; + tmp1[1] = table_b2b_1[(qh1 >> 8) & 0xFF]; + tmp1[2] = table_b2b_1[(qh1 >> 16) & 0xFF]; + tmp1[3] = table_b2b_1[(qh1 >> 24) ]; + + const int8x16_t qhl0 = vld1q_s8((const int8_t *)(tmp0 + 0)); + const int8x16_t qhh0 = vld1q_s8((const int8_t *)(tmp0 + 2)); + const int8x16_t qhl1 = vld1q_s8((const int8_t *)(tmp1 + 0)); + const int8x16_t qhh1 = vld1q_s8((const int8_t *)(tmp1 + 2)); + + const uint8x16_t v0_0 = vld1q_u8(x0->qs); + const uint8x16_t v0_1 = vld1q_u8(x1->qs); + + // 4-bit -> 8-bit + int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); + int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); + int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); + int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); + + // add high bit and sub 16 (equivalent to sub 0x10 when bit is zero) + const int8x16_t v0_0lf = vsubq_s8(v0_0l, qhl0); + const int8x16_t v0_0hf = vsubq_s8(v0_0h, qhh0); + const int8x16_t v0_1lf = vsubq_s8(v0_1l, qhl1); + const int8x16_t v0_1hf = vsubq_s8(v0_1h, qhh1); + + // load y + const int8x16_t v1_0l = vld1q_s8(y0->qs); + const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); + const int8x16_t v1_1l = vld1q_s8(y1->qs); + const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); + + sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( + ggml_vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l), + ggml_vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( + ggml_vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l), + ggml_vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); +#elif defined(__wasm_simd128__) + v128_t sumv = wasm_f32x4_splat(0.0f); + + uint32_t qh; + uint64_t tmp[4]; + + // TODO: check if unrolling this is better + for (; ib < nb; ++ib) { + const block_q5_0 * restrict x0 = &x[ib]; + const block_q8_0 * restrict y0 = &y[ib]; + + const v128_t m4b = wasm_i8x16_splat(0x0F); + + // extract the 5th bit + memcpy(&qh, x0->qh, sizeof(qh)); + + tmp[0] = table_b2b_1[(qh >> 0) & 0xFF]; + tmp[1] = table_b2b_1[(qh >> 8) & 0xFF]; + tmp[2] = table_b2b_1[(qh >> 16) & 0xFF]; + tmp[3] = table_b2b_1[(qh >> 24) ]; + + const v128_t qhl = wasm_v128_load(tmp + 0); + const v128_t qhh = wasm_v128_load(tmp + 2); + + const v128_t v0 = wasm_v128_load(x0->qs); + + // 4-bit -> 8-bit + const v128_t v0l = wasm_v128_and (v0, m4b); + const v128_t v0h = wasm_u8x16_shr(v0, 4); + + // add high bit and sub 16 (equivalent to sub 0x10 when bit is zero) + const v128_t v0lf = wasm_i8x16_sub(v0l, qhl); + const v128_t v0hf = wasm_i8x16_sub(v0h, qhh); + + // load y + const v128_t v1l = wasm_v128_load(y0->qs); + const v128_t v1h = wasm_v128_load(y0->qs + 16); + + // int8x16 -> int16x8 + const v128_t v0lfl = wasm_i16x8_extend_low_i8x16 (v0lf); + const v128_t v0lfh = wasm_i16x8_extend_high_i8x16(v0lf); + const v128_t v0hfl = wasm_i16x8_extend_low_i8x16 (v0hf); + const v128_t v0hfh = wasm_i16x8_extend_high_i8x16(v0hf); + + const v128_t v1ll = wasm_i16x8_extend_low_i8x16 (v1l); + const v128_t v1lh = wasm_i16x8_extend_high_i8x16(v1l); + const v128_t v1hl = wasm_i16x8_extend_low_i8x16 (v1h); + const v128_t v1hh = wasm_i16x8_extend_high_i8x16(v1h); + + // dot product + sumv = wasm_f32x4_add(sumv, wasm_f32x4_mul(wasm_f32x4_convert_i32x4( + wasm_i32x4_add( + wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll), + wasm_i32x4_dot_i16x8(v0lfh, v1lh)), + wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl), + wasm_i32x4_dot_i16x8(v0hfh, v1hh)))), + wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d)))); + } + + sumf = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) + + wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3); +#elif defined(__AVX2__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + + // Main loop + for (; ib < nb; ++ib) { + /* Compute combined scale for the block */ + const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d)); + + __m256i qx = bytes_from_nibbles_32(x[ib].qs); + __m256i bxhi = bytes_from_bits_32(x[ib].qh); + bxhi = _mm256_andnot_si256(bxhi, _mm256_set1_epi8((char)0xF0)); + qx = _mm256_or_si256(qx, bxhi); + + __m256i qy = _mm256_loadu_si256((const __m256i *)y[ib].qs); + + const __m256 q = mul_sum_i8_pairs_float(qx, qy); + + /* Multiply q with scale and accumulate */ + acc = _mm256_fmadd_ps(d, q, acc); + } + + sumf = hsum_float_8(acc); +#elif defined(__AVX__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + __m128i mask = _mm_set1_epi8((char)0xF0); + + // Main loop + for (; ib < nb; ++ib) { + /* Compute combined scale for the block */ + const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d)); + + __m256i bx_0 = bytes_from_nibbles_32(x[ib].qs); + const __m256i bxhi = bytes_from_bits_32(x[ib].qh); + __m128i bxhil = _mm256_castsi256_si128(bxhi); + __m128i bxhih = _mm256_extractf128_si256(bxhi, 1); + bxhil = _mm_andnot_si128(bxhil, mask); + bxhih = _mm_andnot_si128(bxhih, mask); + __m128i bxl = _mm256_castsi256_si128(bx_0); + __m128i bxh = _mm256_extractf128_si256(bx_0, 1); + bxl = _mm_or_si128(bxl, bxhil); + bxh = _mm_or_si128(bxh, bxhih); + bx_0 = MM256_SET_M128I(bxh, bxl); + + const __m256i by_0 = _mm256_loadu_si256((const __m256i *)y[ib].qs); + + const __m256 q = mul_sum_i8_pairs_float(bx_0, by_0); + + /* Multiply q with scale and accumulate */ + acc = _mm256_add_ps(_mm256_mul_ps(d, q), acc); + } + + sumf = hsum_float_8(acc); +#elif defined(__riscv_v_intrinsic) + uint32_t qh; + + size_t vl = __riscv_vsetvl_e8m1(qk/2); + + // These temporary registers are for masking and shift operations + vuint32m2_t vt_1 = __riscv_vid_v_u32m2(vl); + vuint32m2_t vt_2 = __riscv_vsll_vv_u32m2(__riscv_vmv_v_x_u32m2(1, vl), vt_1, vl); + + vuint32m2_t vt_3 = __riscv_vsll_vx_u32m2(vt_2, 16, vl); + vuint32m2_t vt_4 = __riscv_vadd_vx_u32m2(vt_1, 12, vl); + + for (; ib < nb; ++ib) { + memcpy(&qh, x[ib].qh, sizeof(uint32_t)); + + // ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4; + vuint32m2_t xha_0 = __riscv_vand_vx_u32m2(vt_2, qh, vl); + vuint32m2_t xhr_0 = __riscv_vsrl_vv_u32m2(xha_0, vt_1, vl); + vuint32m2_t xhl_0 = __riscv_vsll_vx_u32m2(xhr_0, 4, vl); + + // ((qh & (1u << (j + 16))) >> (j + 12)); + vuint32m2_t xha_1 = __riscv_vand_vx_u32m2(vt_3, qh, vl); + vuint32m2_t xhl_1 = __riscv_vsrl_vv_u32m2(xha_1, vt_4, vl); + + // narrowing + vuint16m1_t xhc_0 = __riscv_vncvt_x_x_w_u16m1(xhl_0, vl); + vuint8mf2_t xh_0 = __riscv_vncvt_x_x_w_u8mf2(xhc_0, vl); + + vuint16m1_t xhc_1 = __riscv_vncvt_x_x_w_u16m1(xhl_1, vl); + vuint8mf2_t xh_1 = __riscv_vncvt_x_x_w_u8mf2(xhc_1, vl); + + // load + vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[ib].qs, vl); + + vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[ib].qs, vl); + vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[ib].qs+16, vl); + + vuint8mf2_t x_at = __riscv_vand_vx_u8mf2(tx, 0x0F, vl); + vuint8mf2_t x_lt = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl); + + vuint8mf2_t x_a = __riscv_vor_vv_u8mf2(x_at, xh_0, vl); + vuint8mf2_t x_l = __riscv_vor_vv_u8mf2(x_lt, xh_1, vl); + + vint8mf2_t x_ai = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a); + vint8mf2_t x_li = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l); + + vint8mf2_t v0 = __riscv_vsub_vx_i8mf2(x_ai, 16, vl); + vint8mf2_t v1 = __riscv_vsub_vx_i8mf2(x_li, 16, vl); + + vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl); + vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl); + + vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); + + vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl); + vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl); + + int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); + + sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d)) * sumi; + } + +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector unsigned char v4 = vec_splats((unsigned char)4); + + vector float vsumf0 = vec_splats(0.0f); + +#pragma GCC unroll 4 + for (; ib < nb; ++ib) { + __builtin_prefetch(x[ib].qs, 0, 1); + __builtin_prefetch(y[ib].qs, 0, 1); + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); + vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); + vector float vd = vec_mul(vxd, vyd); + + vector signed long long aux64x2_0 = {(uint64_t)(table_b2b_1[x[ib].qh[0]]), (uint64_t)(table_b2b_1[x[ib].qh[1]])}; + vector signed long long aux64x2_1 = {(uint64_t)(table_b2b_1[x[ib].qh[2]]), (uint64_t)(table_b2b_1[x[ib].qh[3]])}; + + vector signed char qh0 = (vector signed char)aux64x2_0; + vector signed char qh1 = (vector signed char)aux64x2_1; + + vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs); + + vector signed char q5x0 = vec_sub(vec_and (qxs, lowMask), qh0); + vector signed char q5x1 = vec_sub(vec_sr(qxs, v4), qh1); + + vector signed char q8y0 = vec_xl( 0, y[ib].qs); + vector signed char q8y1 = vec_xl( 16, y[ib].qs); + + vector signed short qv0 = vec_add(vec_mule(q5x0, q8y0), vec_mulo(q5x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q5x1, q8y1), vec_mulo(q5x1, q8y1)); + + qv0 = vec_add(qv0, qv1); + + vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackl(qv0)); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + } + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + sumf = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + // Initialize accumulator with zeros + __m256 acc = (__m256)__lasx_xvldi(0); + + // Main loop + for (; ib < nb; ++ib) { + /* Compute combined scale for the block */ + const __m256 d = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d)); //FIXME + + __m256i qx = bytes_from_nibbles_32(x[ib].qs); + __m256i bxhi = bytes_from_bits_32(x[ib].qh); + bxhi = __lasx_xvandn_v(bxhi, __lasx_xvreplgr2vr_b((char)0xF0)); + qx = __lasx_xvor_v(qx, bxhi); + + __m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0); + + const __m256 q = mul_sum_i8_pairs_float(qx, qy); + + /* Multiply q with scale and accumulate */ + acc = __lasx_xvfmadd_s(d, q, acc); + } + + sumf = hsum_float_8(acc); +#endif + for (; ib < nb; ++ib) { + uint32_t qh; + memcpy(&qh, x[ib].qh, sizeof(qh)); + + int sumi0 = 0; + int sumi1 = 0; + + for (int j = 0; j < qk/2; ++j) { + const uint8_t xh_0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4; + const uint8_t xh_1 = ((qh & (1u << (j + 16))) >> (j + 12)); + + const int32_t x0 = (int8_t)(((x[ib].qs[j] & 0x0F) | xh_0) - 16); + const int32_t x1 = (int8_t)(((x[ib].qs[j] >> 4) | xh_1) - 16); + + sumi0 += (x0 * y[ib].qs[j]); + sumi1 += (x1 * y[ib].qs[j + qk/2]); + } + + int sumi = sumi0 + sumi1; + sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d)) * sumi; + } + + *s = sumf; +} + +void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + const int qk = QK8_1; + const int nb = n / qk; + + int ib = 0; + float sumf = 0; + + assert(n % qk == 0); + assert(qk == QK5_1); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_q5_1 * restrict x = vx; + const block_q8_1 * restrict y = vy; + +#if defined(__ARM_NEON) + float32x4_t sumv0 = vdupq_n_f32(0.0f); + float32x4_t sumv1 = vdupq_n_f32(0.0f); + + float summs0 = 0.0f; + float summs1 = 0.0f; + + uint32_t qh0; + uint32_t qh1; + + uint64_t tmp0[4]; + uint64_t tmp1[4]; + + for (; ib + 1 < nb; ib += 2) { + const block_q5_1 * restrict x0 = &x[ib]; + const block_q5_1 * restrict x1 = &x[ib + 1]; + const block_q8_1 * restrict y0 = &y[ib]; + const block_q8_1 * restrict y1 = &y[ib + 1]; + + const uint8x16_t m4b = vdupq_n_u8(0x0F); + + summs0 += GGML_FP16_TO_FP32(x0->m) * GGML_FP16_TO_FP32(y0->s); + summs1 += GGML_FP16_TO_FP32(x1->m) * GGML_FP16_TO_FP32(y1->s); + + // extract the 5th bit via lookup table ((b) << 4) + memcpy(&qh0, x0->qh, sizeof(qh0)); + memcpy(&qh1, x1->qh, sizeof(qh1)); + + tmp0[0] = table_b2b_0[(qh0 >> 0) & 0xFF]; + tmp0[1] = table_b2b_0[(qh0 >> 8) & 0xFF]; + tmp0[2] = table_b2b_0[(qh0 >> 16) & 0xFF]; + tmp0[3] = table_b2b_0[(qh0 >> 24) ]; + + tmp1[0] = table_b2b_0[(qh1 >> 0) & 0xFF]; + tmp1[1] = table_b2b_0[(qh1 >> 8) & 0xFF]; + tmp1[2] = table_b2b_0[(qh1 >> 16) & 0xFF]; + tmp1[3] = table_b2b_0[(qh1 >> 24) ]; + + const int8x16_t qhl0 = vld1q_s8((const int8_t *)(tmp0 + 0)); + const int8x16_t qhh0 = vld1q_s8((const int8_t *)(tmp0 + 2)); + const int8x16_t qhl1 = vld1q_s8((const int8_t *)(tmp1 + 0)); + const int8x16_t qhh1 = vld1q_s8((const int8_t *)(tmp1 + 2)); + + const uint8x16_t v0_0 = vld1q_u8(x0->qs); + const uint8x16_t v0_1 = vld1q_u8(x1->qs); + + // 4-bit -> 8-bit + const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); + const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); + const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); + const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); + + // add high bit + const int8x16_t v0_0lf = vorrq_s8(v0_0l, qhl0); + const int8x16_t v0_0hf = vorrq_s8(v0_0h, qhh0); + const int8x16_t v0_1lf = vorrq_s8(v0_1l, qhl1); + const int8x16_t v0_1hf = vorrq_s8(v0_1h, qhh1); + + // load y + const int8x16_t v1_0l = vld1q_s8(y0->qs); + const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); + const int8x16_t v1_1l = vld1q_s8(y1->qs); + const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); + + sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( + ggml_vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l), + ggml_vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( + ggml_vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l), + ggml_vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs0 + summs1; +#elif defined(__wasm_simd128__) + v128_t sumv = wasm_f32x4_splat(0.0f); + + float summs = 0.0f; + + uint32_t qh; + uint64_t tmp[4]; + + // TODO: check if unrolling this is better + for (; ib < nb; ++ib) { + const block_q5_1 * restrict x0 = &x[ib]; + const block_q8_1 * restrict y0 = &y[ib]; + + summs += GGML_FP16_TO_FP32(x0->m) * GGML_FP16_TO_FP32(y0->s); + + const v128_t m4b = wasm_i8x16_splat(0x0F); + + // extract the 5th bit + memcpy(&qh, x0->qh, sizeof(qh)); + + tmp[0] = table_b2b_0[(qh >> 0) & 0xFF]; + tmp[1] = table_b2b_0[(qh >> 8) & 0xFF]; + tmp[2] = table_b2b_0[(qh >> 16) & 0xFF]; + tmp[3] = table_b2b_0[(qh >> 24) ]; + + const v128_t qhl = wasm_v128_load(tmp + 0); + const v128_t qhh = wasm_v128_load(tmp + 2); + + const v128_t v0 = wasm_v128_load(x0->qs); + + // 4-bit -> 8-bit + const v128_t v0l = wasm_v128_and (v0, m4b); + const v128_t v0h = wasm_u8x16_shr(v0, 4); + + // add high bit + const v128_t v0lf = wasm_v128_or(v0l, qhl); + const v128_t v0hf = wasm_v128_or(v0h, qhh); + + // load y + const v128_t v1l = wasm_v128_load(y0->qs); + const v128_t v1h = wasm_v128_load(y0->qs + 16); + + // int8x16 -> int16x8 + const v128_t v0lfl = wasm_i16x8_extend_low_i8x16 (v0lf); + const v128_t v0lfh = wasm_i16x8_extend_high_i8x16(v0lf); + const v128_t v0hfl = wasm_i16x8_extend_low_i8x16 (v0hf); + const v128_t v0hfh = wasm_i16x8_extend_high_i8x16(v0hf); + + const v128_t v1ll = wasm_i16x8_extend_low_i8x16 (v1l); + const v128_t v1lh = wasm_i16x8_extend_high_i8x16(v1l); + const v128_t v1hl = wasm_i16x8_extend_low_i8x16 (v1h); + const v128_t v1hh = wasm_i16x8_extend_high_i8x16(v1h); + + // dot product + sumv = wasm_f32x4_add(sumv, + wasm_f32x4_mul(wasm_f32x4_convert_i32x4(wasm_i32x4_add( + wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll), + wasm_i32x4_dot_i16x8(v0lfh, v1lh)), + wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl), + wasm_i32x4_dot_i16x8(v0hfh, v1hh)))), + wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d)))); + } + + sumf = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) + + wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3) + summs; +#elif defined(__AVX2__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + + float summs = 0.0f; + + // Main loop + for (; ib < nb; ++ib) { + const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d)); + + summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s); + + __m256i qx = bytes_from_nibbles_32(x[ib].qs); + __m256i bxhi = bytes_from_bits_32(x[ib].qh); + bxhi = _mm256_and_si256(bxhi, _mm256_set1_epi8(0x10)); + qx = _mm256_or_si256(qx, bxhi); + + const __m256 dy = _mm256_set1_ps(GGML_FP16_TO_FP32(y[ib].d)); + const __m256i qy = _mm256_loadu_si256((const __m256i *)y[ib].qs); + + const __m256 q = mul_sum_us8_pairs_float(qx, qy); + + acc = _mm256_fmadd_ps(q, _mm256_mul_ps(dx, dy), acc); + } + + sumf = hsum_float_8(acc) + summs; +#elif defined(__AVX__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + __m128i mask = _mm_set1_epi8(0x10); + + float summs = 0.0f; + + // Main loop + for (; ib < nb; ++ib) { + const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d)); + + summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s); + + __m256i bx_0 = bytes_from_nibbles_32(x[ib].qs); + const __m256i bxhi = bytes_from_bits_32(x[ib].qh); + __m128i bxhil = _mm256_castsi256_si128(bxhi); + __m128i bxhih = _mm256_extractf128_si256(bxhi, 1); + bxhil = _mm_and_si128(bxhil, mask); + bxhih = _mm_and_si128(bxhih, mask); + __m128i bxl = _mm256_castsi256_si128(bx_0); + __m128i bxh = _mm256_extractf128_si256(bx_0, 1); + bxl = _mm_or_si128(bxl, bxhil); + bxh = _mm_or_si128(bxh, bxhih); + bx_0 = MM256_SET_M128I(bxh, bxl); + + const __m256 dy = _mm256_set1_ps(GGML_FP16_TO_FP32(y[ib].d)); + const __m256i by_0 = _mm256_loadu_si256((const __m256i *)y[ib].qs); + + const __m256 q = mul_sum_us8_pairs_float(bx_0, by_0); + + acc = _mm256_add_ps(_mm256_mul_ps(q, _mm256_mul_ps(dx, dy)), acc); + } + + sumf = hsum_float_8(acc) + summs; +#elif defined(__riscv_v_intrinsic) + uint32_t qh; + + size_t vl = __riscv_vsetvl_e8m1(qk/2); + + // temporary registers for shift operations + vuint32m2_t vt_1 = __riscv_vid_v_u32m2(vl); + vuint32m2_t vt_2 = __riscv_vadd_vx_u32m2(vt_1, 12, vl); + + for (; ib < nb; ++ib) { + memcpy(&qh, x[ib].qh, sizeof(uint32_t)); + + // load qh + vuint32m2_t vqh = __riscv_vmv_v_x_u32m2(qh, vl); + + // ((qh >> (j + 0)) << 4) & 0x10; + vuint32m2_t xhr_0 = __riscv_vsrl_vv_u32m2(vqh, vt_1, vl); + vuint32m2_t xhl_0 = __riscv_vsll_vx_u32m2(xhr_0, 4, vl); + vuint32m2_t xha_0 = __riscv_vand_vx_u32m2(xhl_0, 0x10, vl); + + // ((qh >> (j + 12)) ) & 0x10; + vuint32m2_t xhr_1 = __riscv_vsrl_vv_u32m2(vqh, vt_2, vl); + vuint32m2_t xha_1 = __riscv_vand_vx_u32m2(xhr_1, 0x10, vl); + + // narrowing + vuint16m1_t xhc_0 = __riscv_vncvt_x_x_w_u16m1(xha_0, vl); + vuint8mf2_t xh_0 = __riscv_vncvt_x_x_w_u8mf2(xhc_0, vl); + + vuint16m1_t xhc_1 = __riscv_vncvt_x_x_w_u16m1(xha_1, vl); + vuint8mf2_t xh_1 = __riscv_vncvt_x_x_w_u8mf2(xhc_1, vl); + + // load + vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[ib].qs, vl); + + vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[ib].qs, vl); + vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[ib].qs+16, vl); + + vuint8mf2_t x_at = __riscv_vand_vx_u8mf2(tx, 0x0F, vl); + vuint8mf2_t x_lt = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl); + + vuint8mf2_t x_a = __riscv_vor_vv_u8mf2(x_at, xh_0, vl); + vuint8mf2_t x_l = __riscv_vor_vv_u8mf2(x_lt, xh_1, vl); + + vint8mf2_t v0 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a); + vint8mf2_t v1 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l); + + vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl); + vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl); + + vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); + + vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl); + vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl); + + int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); + + sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s); + } + +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector signed int v0 = vec_splats((int32_t)0); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + +#pragma GCC unroll 4 + for (; ib < nb; ++ib) { + __builtin_prefetch(x[ib].qs, 0, 1); + __builtin_prefetch(y[ib].qs, 0, 1); + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); + vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); + vector float vd = vec_mul(vxd, vyd); + + vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[ib].m)); + vector float vys = {GGML_FP16_TO_FP32(y[ib].s), 0.f, 0.f, 0.f}; + vsumf0 = vec_madd(vxmin, vys, vsumf0); + + vector unsigned long long aux64x2_0 = {(uint64_t)(table_b2b_0[x[ib].qh[0]]), (uint64_t)(table_b2b_0[x[ib].qh[1]])}; + vector unsigned long long aux64x2_1 = {(uint64_t)(table_b2b_0[x[ib].qh[2]]), (uint64_t)(table_b2b_0[x[ib].qh[3]])}; + + vector signed char qh0 = (vector signed char)aux64x2_0; + vector signed char qh1 = (vector signed char)aux64x2_1; + + vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs); + + vector unsigned char q5x0 = (vector unsigned char)vec_or(vec_and(qxs, lowMask), qh0); + vector unsigned char q5x1 = (vector unsigned char)vec_or(vec_sr(qxs, v4), qh1); + + vector signed char q8y0 = vec_xl( 0, y[ib].qs); + vector signed char q8y1 = vec_xl( 16, y[ib].qs); + + vector signed int vsumi0 = v0; + + vsumi0 = vec_msum(q8y0, q5x0, vsumi0); + vsumi0 = vec_msum(q8y1, q5x1, vsumi0); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + } + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + sumf = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + // Initialize accumulator with zeros + __m256 acc = (__m256)__lasx_xvldi(0); + + float summs = 0.0f; + + // Main loop + for (; ib < nb; ++ib) { + const __m256 dx = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ib].d)); + + summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s); + + __m256i qx = bytes_from_nibbles_32(x[ib].qs); + __m256i bxhi = bytes_from_bits_32(x[ib].qh); + bxhi = __lasx_xvand_v(bxhi, __lasx_xvreplgr2vr_b(0x10)); + qx = __lasx_xvor_v(qx, bxhi); + + const __m256 dy = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[ib].d)); + const __m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0); + + const __m256 q = mul_sum_us8_pairs_float(qx, qy); + + acc = __lasx_xvfmadd_s(q, __lasx_xvfmul_s(dx, dy), acc); + } + + sumf = hsum_float_8(acc) + summs; +#endif + for (; ib < nb; ++ib) { + uint32_t qh; + memcpy(&qh, x[ib].qh, sizeof(qh)); + + int sumi0 = 0; + int sumi1 = 0; + + for (int j = 0; j < qk/2; ++j) { + const uint8_t xh_0 = ((qh >> (j + 0)) << 4) & 0x10; + const uint8_t xh_1 = ((qh >> (j + 12)) ) & 0x10; + + const int32_t x0 = (x[ib].qs[j] & 0xF) | xh_0; + const int32_t x1 = (x[ib].qs[j] >> 4) | xh_1; + + sumi0 += (x0 * y[ib].qs[j]); + sumi1 += (x1 * y[ib].qs[j + qk/2]); + } + + int sumi = sumi0 + sumi1; + sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s); + } + + *s = sumf; +} + +void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + const int qk = QK8_0; + const int nb = n / qk; + + assert(n % qk == 0); +#if defined(__ARM_FEATURE_MATMUL_INT8) + assert((nrc == 2) || (nrc == 1)); +#else + assert(nrc == 1); +#endif + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_q8_0 * restrict x = vx; + const block_q8_0 * restrict y = vy; + +#if defined(__ARM_FEATURE_MATMUL_INT8) + if (nrc == 2) { + const block_q8_0 * restrict vx0 = vx; + const block_q8_0 * restrict vx1 = (const block_q8_0 *) ((const uint8_t*)vx + bx); + const block_q8_0 * restrict vy0 = vy; + const block_q8_0 * restrict vy1 = (const block_q8_0 *) ((const uint8_t*)vy + by); + + float32x4_t sumv0 = vdupq_n_f32(0.0f); + + for (int i = 0; i < nb; i++) { + const block_q8_0 * restrict b_x0 = &vx0[i]; + const block_q8_0 * restrict b_y0 = &vy0[i]; + + const block_q8_0 * restrict b_x1 = &vx1[i]; + const block_q8_0 * restrict b_y1 = &vy1[i]; + + const int8x16_t x0_l = vld1q_s8(b_x0->qs); + const int8x16_t x0_h = vld1q_s8(b_x0->qs + 16); + const int8x16_t x1_l = vld1q_s8(b_x1->qs); + const int8x16_t x1_h = vld1q_s8(b_x1->qs + 16); + + // load y + const int8x16_t y0_l = vld1q_s8(b_y0->qs); + const int8x16_t y0_h = vld1q_s8(b_y0->qs + 16); + const int8x16_t y1_l = vld1q_s8(b_y1->qs); + const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); + + float32_t _scale[4] = { + GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d) + }; + float32x4_t scale = vld1q_f32(_scale); + + int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); + int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); + + int8x16_t l2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); + int8x16_t l3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); + + int8x16_t r0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); + int8x16_t r1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); + + int8x16_t r2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); + int8x16_t r3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); + + sumv0 = vmlaq_f32(sumv0,(vcvtq_f32_s32(vmmlaq_s32((vmmlaq_s32((vmmlaq_s32((vmmlaq_s32(vdupq_n_s32(0), l0, r0)), + l1, r1)), l2, r2)), l3, r3))), scale); + } + + float32x4_t sumv1 = vextq_f32 (sumv0, sumv0, 2); + float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1); + + vst1_f32(s, vget_low_f32 (sumv2)); + vst1_f32(s + bs, vget_high_f32(sumv2)); + + return; + } +#endif + + int ib = 0; + float sumf = 0; + +#if defined(__ARM_FEATURE_SVE) + svfloat32_t sumv0 = svdup_n_f32(0.0f); + svfloat32_t sumv1 = svdup_n_f32(0.0f); + + const int vector_length = ggml_cpu_get_sve_cnt()*8; + + //VLA Implemenation for SVE + switch (vector_length) { + case 128: + { + // predicate for activating lanes for 16 Int8 elements + const svbool_t ph16 = svptrue_pat_b8 (SV_VL16); + const svbool_t pl16 = svptrue_pat_b32(SV_VL4); + + for (; ib + 1 < nb; ib += 2) { + const block_q8_0 * restrict x0 = &x[ib + 0]; + const block_q8_0 * restrict x1 = &x[ib + 1]; + const block_q8_0 * restrict y0 = &y[ib + 0]; + const block_q8_0 * restrict y1 = &y[ib + 1]; + + // load x + const svint8_t qx0_0 = svld1_s8(ph16, x0->qs); + const svint8_t qx0_1 = svld1_s8(ph16, x0->qs+16); + const svint8_t qx1_0 = svld1_s8(ph16, x1->qs); + const svint8_t qx1_1 = svld1_s8(ph16, x1->qs+16); + + // load y + const svint8_t qy0_0 = svld1_s8(ph16, y0->qs); + const svint8_t qy0_1 = svld1_s8(ph16, y0->qs+16); + const svint8_t qy1_0 = svld1_s8(ph16, y1->qs); + const svint8_t qy1_1 = svld1_s8(ph16, y1->qs+16); + + sumv0 = svmla_n_f32_x(pl16, sumv0, svcvt_f32_s32_x(pl16, svadd_x(pl16, + svdot_s32(svdup_n_s32(0), qx0_0, qy0_0), + svdot_s32(svdup_n_s32(0), qx0_1, qy0_1))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + sumv1 = svmla_n_f32_x(pl16, sumv1, svcvt_f32_s32_x(pl16, svadd_x(pl16, + svdot_s32(svdup_n_s32(0), qx1_0, qy1_0), + svdot_s32(svdup_n_s32(0), qx1_1, qy1_1))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + sumf = svaddv_f32(pl16, svadd_f32_x(pl16, sumv0, sumv1)); + } break; + case 256: + { + //printf("sve256"); + for (; ib + 1 < nb; ib += 2) { + const block_q8_0 * restrict x0 = &x[ib + 0]; + const block_q8_0 * restrict x1 = &x[ib + 1]; + const block_q8_0 * restrict y0 = &y[ib + 0]; + const block_q8_0 * restrict y1 = &y[ib + 1]; + + // load x + const svint8_t qx0 = svld1_s8(svptrue_b8(), x0->qs); + const svint8_t qx1 = svld1_s8(svptrue_b8(), x1->qs); + + // load y + const svint8_t qy0 = svld1_s8(svptrue_b8(), y0->qs); + const svint8_t qy1 = svld1_s8(svptrue_b8(), y1->qs); + + sumv0 = svmla_n_f32_x(svptrue_b32(), sumv0, svcvt_f32_s32_x(svptrue_b32(), + svdot_s32(svdup_n_s32(0), qx0, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), + svdot_s32(svdup_n_s32(0), qx1, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + sumf = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1)); + } break; + case 512: + { + // predicate for activating high 256 bit + const svbool_t ph32 = svptrue_pat_b8(SV_VL32); + // predicate for activating low 256 bit + const svbool_t pl32 = svnot_b_z(svptrue_b8(), ph32); + + // predicate for activating high lanes for 8 float32 elements + const svbool_t ph8 = svptrue_pat_b32(SV_VL8); + // predicate for activating low lanes for 8 float32 elements + const svbool_t pl8 = svnot_b_z(svptrue_b32(), ph8); + + svfloat32_t sumv00 = svdup_n_f32(0.0f); + + for (; ib + 1 < nb; ib += 2) { + const block_q8_0 * restrict x0 = &x[ib + 0]; + const block_q8_0 * restrict x1 = &x[ib + 1]; + const block_q8_0 * restrict y0 = &y[ib + 0]; + const block_q8_0 * restrict y1 = &y[ib + 1]; + + //load 32 int8_t in first half of vector and put another 32 int8_t in second vector lower bits + // and add them to make one 64 element vector + // load x + const svint8_t qx_32 = svld1_s8(ph32, x0->qs); + svint8_t qx_64 = svld1_s8(pl32, x0->qs + 2); + + qx_64 = svadd_s8_x(svptrue_b8(), qx_32, qx_64); + + // load y + const svint8_t qy_32 = svld1_s8(ph32, y0->qs); + svint8_t qy_64 = svld1_s8(pl32, y0->qs + 2); + + qy_64 = svadd_s8_x(svptrue_b8(), qy_32, qy_64); + + // scale creation + const float32_t deq1 = GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d); + const float32_t deq2 = GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d); + + // duplicate deq1 in first half of vector and deq2 in second half of vector + const svfloat32_t temp = svdup_f32_m(svdup_f32_z(ph8, deq1), pl8, deq2); + + const svfloat32_t sumvt = svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx_64, qy_64)); + + sumv00 = svmla_f32_m(svptrue_b32(), sumv00, sumvt, temp); + } + + sumf = svaddv_f32(svptrue_b32(), sumv00); + break; + } + default: + assert(false && "Unsupported vector length"); + break; + } +#elif defined(__ARM_NEON) + float32x4_t sumv0 = vdupq_n_f32(0.0f); + float32x4_t sumv1 = vdupq_n_f32(0.0f); + + for (; ib + 1 < nb; ib += 2) { + const block_q8_0 * restrict x0 = &x[ib + 0]; + const block_q8_0 * restrict x1 = &x[ib + 1]; + const block_q8_0 * restrict y0 = &y[ib + 0]; + const block_q8_0 * restrict y1 = &y[ib + 1]; + + const int8x16_t x0_0 = vld1q_s8(x0->qs); + const int8x16_t x0_1 = vld1q_s8(x0->qs + 16); + const int8x16_t x1_0 = vld1q_s8(x1->qs); + const int8x16_t x1_1 = vld1q_s8(x1->qs + 16); + + // load y + const int8x16_t y0_0 = vld1q_s8(y0->qs); + const int8x16_t y0_1 = vld1q_s8(y0->qs + 16); + const int8x16_t y1_0 = vld1q_s8(y1->qs); + const int8x16_t y1_1 = vld1q_s8(y1->qs + 16); + + sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( + ggml_vdotq_s32(vdupq_n_s32(0), x0_0, y0_0), + ggml_vdotq_s32(vdupq_n_s32(0), x0_1, y0_1))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + + sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( + ggml_vdotq_s32(vdupq_n_s32(0), x1_0, y1_0), + ggml_vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); +#elif defined(__AVX2__) + // Initialize accumulator with zeros + __m256 acc = _mm256_setzero_ps(); + + // Main loop + for (; ib < nb; ++ib) { + // Compute combined scale for the block + const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d)); + __m256i qx = _mm256_loadu_si256((const __m256i *)x[ib].qs); + __m256i qy = _mm256_loadu_si256((const __m256i *)y[ib].qs); + + const __m256 q = mul_sum_i8_pairs_float(qx, qy); + + // Multiply q with scale and accumulate + acc = _mm256_fmadd_ps( d, q, acc ); + } + + sumf = hsum_float_8(acc); +#elif defined(__AVX__) + __m256 accum = _mm256_setzero_ps(); + + for (; ib + 1 < nb; ib += 2) { + const __m128i qx_1_0 = _mm_loadu_si128((const __m128i *)x[ib].qs); + const __m128i qx_1_1 = _mm_loadu_si128((const __m128i *)x[ib].qs + 1); + const __m128i qx_2_0 = _mm_loadu_si128((const __m128i *)x[ib + 1].qs); + const __m128i qx_2_1 = _mm_loadu_si128((const __m128i *)x[ib + 1].qs + 1); + const __m128i qy_1_0 = _mm_loadu_si128((const __m128i *)y[ib].qs); + const __m128i qy_1_1 = _mm_loadu_si128((const __m128i *)y[ib].qs + 1); + const __m128i qy_2_0 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs); + const __m128i qy_2_1 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs + 1); + + const __m256 p = mul_sum_i8_quad_float(qx_1_0, qx_1_1, qx_2_0, qx_2_1, qy_1_0, qy_1_1, qy_2_0, qy_2_1); + const __m256 deltas = quad_fp16_delta_float(x[ib].d, y[ib].d, x[ib + 1].d, y[ib + 1].d); + accum = _mm256_add_ps(_mm256_mul_ps(deltas, p), accum); + } + + sumf = hsum_float_8(accum); +#elif defined(__riscv_v_intrinsic) + size_t vl = __riscv_vsetvl_e8m1(qk); + + for (; ib < nb; ++ib) { + // load elements + vint8m1_t bx_0 = __riscv_vle8_v_i8m1(x[ib].qs, vl); + vint8m1_t by_0 = __riscv_vle8_v_i8m1(y[ib].qs, vl); + + vint16m2_t vw_mul = __riscv_vwmul_vv_i16m2(bx_0, by_0, vl); + + vint32m1_t v_zero = __riscv_vmv_v_x_i32m1(0, vl); + vint32m1_t v_sum = __riscv_vwredsum_vs_i16m2_i32m1(vw_mul, v_zero, vl); + + int sumi = __riscv_vmv_x_s_i32m1_i32(v_sum); + + sumf += sumi*(GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d)); + } +#elif defined(__POWER9_VECTOR__) + const vector signed int v0 = vec_splats((int32_t)0); + vector float vsumf0 = vec_splats(0.0f); + +#pragma GCC unroll 8 + for (; ib < nb; ++ib) { + __builtin_prefetch(x[ib].qs, 0, 1); + __builtin_prefetch(y[ib].qs, 0, 1); + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); + vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); + vector float vd = vec_mul(vxd, vyd); + + vector signed char q8x0 = vec_xl( 0, x[ib].qs); + vector signed char q8x1 = vec_xl(16, x[ib].qs); + vector signed char q8y0 = vec_xl( 0, y[ib].qs); + vector signed char q8y1 = vec_xl(16, y[ib].qs); + + vector signed short qv0 = vec_mule(q8x0, q8y0); + vector signed short qv1 = vec_mulo(q8x0, q8y0); + vector signed short qv2 = vec_mule(q8x1, q8y1); + vector signed short qv3 = vec_mulo(q8x1, q8y1); + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + + vsumi0 = vec_sum4s(qv0, vsumi0); + vsumi1 = vec_sum4s(qv1, vsumi1); + vsumi0 = vec_sum4s(qv2, vsumi0); + vsumi1 = vec_sum4s(qv3, vsumi1); + + vsumi0 = vec_add(vsumi0, vsumi1); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + } + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + sumf = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + // Initialize accumulator with zeros + __m256 acc = (__m256)__lasx_xvldi(0); + + // Main loop + for (; ib < nb; ++ib) { + // Compute combined scale for the block + const __m256 d = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d)); + __m256i qx = __lasx_xvld((const __m256i *)x[ib].qs, 0); + __m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0); + + const __m256 q = mul_sum_i8_pairs_float(qx, qy); + + // Multiply q with scale and accumulate + acc = __lasx_xvfmadd_s( d, q, acc ); + } + + sumf = hsum_float_8(acc); +#endif + for (; ib < nb; ++ib) { + int sumi = 0; + + for (int j = 0; j < qk; j++) { + sumi += x[ib].qs[j]*y[ib].qs[j]; + } + + sumf += sumi*(GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d)); + } + + *s = sumf; +} + +void ggml_vec_dot_tq1_0_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_tq1_0 * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined(__ARM_NEON) + float sumf = 0.0f; + + uint8_t k_shift[16] = {1, 1, 1, 1, 3, 3, 3, 3, 9, 9, 9, 9, 27, 27, 27, 27}; + + const uint8x16_t shift = vld1q_u8(k_shift); + + for (int i = 0; i < nb; ++i) { +#if defined(__ARM_FEATURE_DOTPROD) + int32x4_t sumi0 = vdupq_n_s32(0); + int32x4_t sumi1 = vdupq_n_s32(0); +#else + int16x8_t sumi0 = vdupq_n_s16(0); + int16x8_t sumi1 = vdupq_n_s16(0); +#endif + + // first 32 bytes of 5 elements + { + uint8x16_t qx0 = vld1q_u8(x[i].qs + 0); + uint8x16_t qx1 = vld1q_u8(x[i].qs + 16); + uint8x16_t qx2 = vmulq_u8(qx0, vdupq_n_u8(3)); + uint8x16_t qx3 = vmulq_u8(qx1, vdupq_n_u8(3)); + uint8x16_t qx4 = vmulq_u8(qx0, vdupq_n_u8(9)); + uint8x16_t qx5 = vmulq_u8(qx1, vdupq_n_u8(9)); + uint8x16_t qx6 = vmulq_u8(qx0, vdupq_n_u8(27)); + uint8x16_t qx7 = vmulq_u8(qx1, vdupq_n_u8(27)); + uint8x16_t qx8 = vmulq_u8(qx0, vdupq_n_u8(81)); + uint8x16_t qx9 = vmulq_u8(qx1, vdupq_n_u8(81)); + + // multiply by 3 and keep the 2 bits above 8 bits + int8x16_t sqx0 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx0, vshrq_n_u8(qx0, 1)), 6)); + int8x16_t sqx1 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx1, vshrq_n_u8(qx1, 1)), 6)); + int8x16_t sqx2 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx2, vshrq_n_u8(qx2, 1)), 6)); + int8x16_t sqx3 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx3, vshrq_n_u8(qx3, 1)), 6)); + int8x16_t sqx4 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx4, vshrq_n_u8(qx4, 1)), 6)); + int8x16_t sqx5 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx5, vshrq_n_u8(qx5, 1)), 6)); + int8x16_t sqx6 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx6, vshrq_n_u8(qx6, 1)), 6)); + int8x16_t sqx7 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx7, vshrq_n_u8(qx7, 1)), 6)); + int8x16_t sqx8 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx8, vshrq_n_u8(qx8, 1)), 6)); + int8x16_t sqx9 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx9, vshrq_n_u8(qx9, 1)), 6)); + + const int8x16_t qy0 = vld1q_s8(y[i].qs + 0); + const int8x16_t qy1 = vld1q_s8(y[i].qs + 16); + const int8x16_t qy2 = vld1q_s8(y[i].qs + 32); + const int8x16_t qy3 = vld1q_s8(y[i].qs + 48); + const int8x16_t qy4 = vld1q_s8(y[i].qs + 64); + const int8x16_t qy5 = vld1q_s8(y[i].qs + 80); + const int8x16_t qy6 = vld1q_s8(y[i].qs + 96); + const int8x16_t qy7 = vld1q_s8(y[i].qs + 112); + const int8x16_t qy8 = vld1q_s8(y[i].qs + 128); + const int8x16_t qy9 = vld1q_s8(y[i].qs + 144); + +#if defined(__ARM_FEATURE_DOTPROD) + sumi0 = vdotq_s32(sumi0, sqx0, qy0); + sumi1 = vdotq_s32(sumi1, sqx1, qy1); + sumi0 = vdotq_s32(sumi0, sqx2, qy2); + sumi1 = vdotq_s32(sumi1, sqx3, qy3); + sumi0 = vdotq_s32(sumi0, sqx4, qy4); + sumi1 = vdotq_s32(sumi1, sqx5, qy5); + sumi0 = vdotq_s32(sumi0, sqx6, qy6); + sumi1 = vdotq_s32(sumi1, sqx7, qy7); + sumi0 = vdotq_s32(sumi0, sqx8, qy8); + sumi1 = vdotq_s32(sumi1, sqx9, qy9); +#else + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx0), vget_low_s8(qy0)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx0), vget_high_s8(qy0)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx1), vget_low_s8(qy1)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx1), vget_high_s8(qy1)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx2), vget_low_s8(qy2)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx2), vget_high_s8(qy2)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx3), vget_low_s8(qy3)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx3), vget_high_s8(qy3)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx4), vget_low_s8(qy4)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx4), vget_high_s8(qy4)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx5), vget_low_s8(qy5)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx5), vget_high_s8(qy5)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx6), vget_low_s8(qy6)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx6), vget_high_s8(qy6)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx7), vget_low_s8(qy7)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx7), vget_high_s8(qy7)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx8), vget_low_s8(qy8)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx8), vget_high_s8(qy8)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx9), vget_low_s8(qy9)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx9), vget_high_s8(qy9)); +#endif + } + + // last 16 bytes of 5-element, along with the 4 bytes of 4 elements + { + uint8x16_t qx0 = vld1q_u8(x[i].qs + 32); + uint8x16_t qx1 = vmulq_u8(qx0, vdupq_n_u8(3)); + uint8x16_t qx2 = vmulq_u8(qx0, vdupq_n_u8(9)); + uint8x16_t qx3 = vmulq_u8(qx0, vdupq_n_u8(27)); + uint8x16_t qx4 = vmulq_u8(qx0, vdupq_n_u8(81)); + uint32_t qh; + memcpy(&qh, x[i].qh, sizeof(qh)); // potentially unaligned + uint8x16_t qx5 = vreinterpretq_u8_u32(vdupq_n_u32(qh)); + qx5 = vmulq_u8(qx5, shift); + + // multiply by 3 and keep the 2 bits above 8 bits + int8x16_t sqx0 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx0, vshrq_n_u8(qx0, 1)), 6)); + int8x16_t sqx1 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx1, vshrq_n_u8(qx1, 1)), 6)); + int8x16_t sqx2 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx2, vshrq_n_u8(qx2, 1)), 6)); + int8x16_t sqx3 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx3, vshrq_n_u8(qx3, 1)), 6)); + int8x16_t sqx4 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx4, vshrq_n_u8(qx4, 1)), 6)); + int8x16_t sqx5 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx5, vshrq_n_u8(qx5, 1)), 6)); + + const int8x16_t qy0 = vld1q_s8(y[i].qs + 160); + const int8x16_t qy1 = vld1q_s8(y[i].qs + 176); + const int8x16_t qy2 = vld1q_s8(y[i].qs + 192); + const int8x16_t qy3 = vld1q_s8(y[i].qs + 208); + const int8x16_t qy4 = vld1q_s8(y[i].qs + 224); + const int8x16_t qy5 = vld1q_s8(y[i].qs + 240); + +#if defined(__ARM_FEATURE_DOTPROD) + sumi0 = vdotq_s32(sumi0, sqx0, qy0); + sumi1 = vdotq_s32(sumi1, sqx1, qy1); + sumi0 = vdotq_s32(sumi0, sqx2, qy2); + sumi1 = vdotq_s32(sumi1, sqx3, qy3); + sumi0 = vdotq_s32(sumi0, sqx4, qy4); + sumi1 = vdotq_s32(sumi1, sqx5, qy5); +#else + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx0), vget_low_s8(qy0)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx0), vget_high_s8(qy0)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx1), vget_low_s8(qy1)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx1), vget_high_s8(qy1)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx2), vget_low_s8(qy2)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx2), vget_high_s8(qy2)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx3), vget_low_s8(qy3)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx3), vget_high_s8(qy3)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx4), vget_low_s8(qy4)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx4), vget_high_s8(qy4)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx5), vget_low_s8(qy5)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx5), vget_high_s8(qy5)); +#endif + } + + const int16x8_t ysum0 = vld1q_s16(y[i].bsums); + const int16x8_t ysum1 = vld1q_s16(y[i].bsums + 8); + + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + +#if defined(__ARM_FEATURE_DOTPROD) + sumi0 = vaddq_s32(sumi0, sumi1); + sumi0 = vsubq_s32(sumi0, vpaddlq_s16(vaddq_s16(ysum0, ysum1))); + + sumf += d * (float) vaddvq_s32(sumi0); +#else + sumi0 = vaddq_s16(sumi0, sumi1); + sumi0 = vsubq_s16(sumi0, vaddq_s16(ysum0, ysum1)); + + sumf += d * (float) vaddlvq_s16(sumi0); +#endif + } + + *s = sumf; + +#elif defined(__AVX2__) + __m256 sumf = _mm256_setzero_ps(); + + for (int i = 0; i < nb; ++i) { + // 16-bit sums + __m256i sumi0 = _mm256_setzero_si256(); + __m256i sumi1 = _mm256_setzero_si256(); + __m256i sumi2 = _mm256_setzero_si256(); + + // first 32 bytes of 5 elements + { + __m256i qx0 = _mm256_loadu_si256((const __m256i *) (x[i].qs)); + // 8-bit multiplies with shifts, masks and adds + __m256i qx1 = _mm256_add_epi8(qx0, _mm256_add_epi8(qx0, qx0)); // 1 * 3 + __m256i qx2 = _mm256_add_epi8(_mm256_and_si256(_mm256_slli_epi16(qx0, 3), _mm256_set1_epi8(-8)), qx0); // 1 * 9 + __m256i qx3 = _mm256_add_epi8(_mm256_and_si256(_mm256_slli_epi16(qx1, 3), _mm256_set1_epi8(-8)), qx1); // 3 * 9 + __m256i qx4 = _mm256_add_epi8(_mm256_and_si256(_mm256_slli_epi16(qx2, 3), _mm256_set1_epi8(-8)), qx2); // 9 * 9 + + // TODO: can _mm256_mulhi_epu16 be faster even if 16-bits? + + // Cancel the +1 from avg so that it behaves like a halving add + qx0 = _mm256_subs_epu8(qx0, _mm256_set1_epi8(1)); + qx1 = _mm256_subs_epu8(qx1, _mm256_set1_epi8(1)); + qx2 = _mm256_subs_epu8(qx2, _mm256_set1_epi8(1)); + qx3 = _mm256_subs_epu8(qx3, _mm256_set1_epi8(1)); + qx4 = _mm256_subs_epu8(qx4, _mm256_set1_epi8(1)); + // Multiply by 3 and get the top 2 bits + qx0 = _mm256_avg_epu8(qx0, _mm256_avg_epu8(qx0, _mm256_setzero_si256())); + qx1 = _mm256_avg_epu8(qx1, _mm256_avg_epu8(qx1, _mm256_setzero_si256())); + qx2 = _mm256_avg_epu8(qx2, _mm256_avg_epu8(qx2, _mm256_setzero_si256())); + qx3 = _mm256_avg_epu8(qx3, _mm256_avg_epu8(qx3, _mm256_setzero_si256())); + qx4 = _mm256_avg_epu8(qx4, _mm256_avg_epu8(qx4, _mm256_setzero_si256())); + qx0 = _mm256_and_si256(_mm256_srli_epi16(qx0, 6), _mm256_set1_epi8(3)); + qx1 = _mm256_and_si256(_mm256_srli_epi16(qx1, 6), _mm256_set1_epi8(3)); + qx2 = _mm256_and_si256(_mm256_srli_epi16(qx2, 6), _mm256_set1_epi8(3)); + qx3 = _mm256_and_si256(_mm256_srli_epi16(qx3, 6), _mm256_set1_epi8(3)); + qx4 = _mm256_and_si256(_mm256_srli_epi16(qx4, 6), _mm256_set1_epi8(3)); + + const __m256i qy0 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 0)); + const __m256i qy1 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 32)); + const __m256i qy2 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 64)); + const __m256i qy3 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 96)); + const __m256i qy4 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 128)); + + qx0 = _mm256_maddubs_epi16(qx0, qy0); + qx1 = _mm256_maddubs_epi16(qx1, qy1); + qx2 = _mm256_maddubs_epi16(qx2, qy2); + qx3 = _mm256_maddubs_epi16(qx3, qy3); + qx4 = _mm256_maddubs_epi16(qx4, qy4); + + sumi0 = _mm256_add_epi16(sumi0, _mm256_add_epi16(qx0, qx1)); + sumi1 = _mm256_add_epi16(sumi1, _mm256_add_epi16(qx2, qx3)); + sumi2 = _mm256_add_epi16(sumi2, qx4); + } + + // last 16 bytes of 5-element, along with the 4 bytes of 4 elements + { + __m128i qx0 = _mm_loadu_si128((const __m128i *) (x[i].qs + 32)); + uint32_t qh; + memcpy(&qh, x[i].qh, sizeof(qh)); // potentially unaligned + __m256i qx5_l = _mm256_cvtepu8_epi16(_mm_set1_epi32(qh)); + __m128i qx1 = _mm_add_epi8(qx0, _mm_add_epi8(qx0, qx0)); // 1 * 3 + __m128i qx2 = _mm_add_epi8(_mm_and_si128(_mm_slli_epi16(qx0, 3), _mm_set1_epi8(-8)), qx0); // 1 * 9 + __m128i qx3 = _mm_add_epi8(_mm_and_si128(_mm_slli_epi16(qx1, 3), _mm_set1_epi8(-8)), qx1); // 3 * 9 + __m128i qx4 = _mm_add_epi8(_mm_and_si128(_mm_slli_epi16(qx2, 3), _mm_set1_epi8(-8)), qx2); // 9 * 9 + __m256i qx01 = MM256_SET_M128I(qx1, qx0); + __m256i qx23 = MM256_SET_M128I(qx3, qx2); + + // avx2 does not have 8-bit multiplies, so 16-bit it is. + qx5_l = _mm256_mullo_epi16(qx5_l, _mm256_set_epi16(27, 27, 27, 27, 9, 9, 9, 9, 3, 3, 3, 3, 1, 1, 1, 1)); + qx5_l = _mm256_and_si256(qx5_l, _mm256_set1_epi16(0xFF)); + __m128i qx5 = _mm_packus_epi16(_mm256_castsi256_si128(qx5_l), _mm256_extracti128_si256(qx5_l, 1)); + + __m256i qx45 = MM256_SET_M128I(qx5, qx4); + + // Cancel the +1 from avg so that it behaves like a halving add + qx01 = _mm256_subs_epu8(qx01, _mm256_set1_epi8(1)); + qx23 = _mm256_subs_epu8(qx23, _mm256_set1_epi8(1)); + qx45 = _mm256_subs_epu8(qx45, _mm256_set1_epi8(1)); + // Multiply by 3 and get the top 2 bits + qx01 = _mm256_avg_epu8(qx01, _mm256_avg_epu8(qx01, _mm256_setzero_si256())); + qx23 = _mm256_avg_epu8(qx23, _mm256_avg_epu8(qx23, _mm256_setzero_si256())); + qx45 = _mm256_avg_epu8(qx45, _mm256_avg_epu8(qx45, _mm256_setzero_si256())); + qx01 = _mm256_and_si256(_mm256_srli_epi16(qx01, 6), _mm256_set1_epi8(3)); + qx23 = _mm256_and_si256(_mm256_srli_epi16(qx23, 6), _mm256_set1_epi8(3)); + qx45 = _mm256_and_si256(_mm256_srli_epi16(qx45, 6), _mm256_set1_epi8(3)); + + const __m256i qy01 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 160)); + const __m256i qy23 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 192)); + const __m256i qy45 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 224)); + + qx01 = _mm256_maddubs_epi16(qx01, qy01); + qx23 = _mm256_maddubs_epi16(qx23, qy23); + qx45 = _mm256_maddubs_epi16(qx45, qy45); + + sumi0 = _mm256_add_epi16(sumi0, qx01); + sumi1 = _mm256_add_epi16(sumi1, qx23); + sumi2 = _mm256_add_epi16(sumi2, qx45); + } + + const __m256i ysum = _mm256_loadu_si256((const __m256i *) y[i].bsums); + const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(x[i].d)); + + sumi0 = _mm256_sub_epi16(sumi0, ysum); + sumi0 = _mm256_add_epi16(sumi0, _mm256_add_epi16(sumi1, sumi2)); + sumi0 = _mm256_madd_epi16(sumi0, _mm256_set1_epi16(1)); + + sumf = _mm256_add_ps(_mm256_mul_ps(_mm256_cvtepi32_ps(sumi0), d), sumf); + } + + *s = hsum_float_8(sumf); + +#else + const uint8_t pow3[6] = {1, 3, 9, 27, 81, 243}; + + float sumf = 0.0f; + + for (int i = 0; i < nb; ++i) { + int sum = 0; + + for (size_t j = 0; j < sizeof(x->qs) - sizeof(x->qs) % 32; j += 32) { + for (size_t l = 0; l < 5; ++l) { + for (size_t m = 0; m < 32; ++m) { + uint8_t q = x[i].qs[j + m] * pow3[l]; + uint16_t xi = ((uint16_t) q * 3) >> 8; + sum += (xi - 1) * y[i].qs[j*5 + l*32 + m]; + } + } + } + for (size_t j = sizeof(x->qs) - sizeof(x->qs) % 32; j < sizeof(x->qs); j += 16) { + for (size_t l = 0; l < 5; ++l) { + for (size_t m = 0; m < 16; ++m) { + uint8_t q = x[i].qs[j + m] * pow3[l]; + uint16_t xi = ((uint16_t) q * 3) >> 8; + sum += (xi - 1) * y[i].qs[j*5 + l*16 + m]; + } + } + } + + for (size_t l = 0; l < 4; ++l) { + for (size_t j = 0; j < sizeof(x->qh); ++j) { + uint8_t q = x[i].qh[j] * pow3[l]; + uint16_t xi = ((uint16_t) q * 3) >> 8; + sum += (xi - 1) * y[i].qs[sizeof(x->qs)*5 + l*sizeof(x->qh) + j]; + } + } + + sumf += (float) sum * (GGML_FP16_TO_FP32(x[i].d) * y[i].d); + } + + *s = sumf; +#endif +} + +void ggml_vec_dot_tq2_0_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_tq2_0 * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined(__ARM_NEON) + float sumf = 0.0f; + + const uint8x16_t m3 = vdupq_n_u8(3); + + for (int i = 0; i < nb; ++i) { +#if defined(__ARM_FEATURE_DOTPROD) + int32x4_t sumi0 = vdupq_n_s32(0); + int32x4_t sumi1 = vdupq_n_s32(0); +#else + int16x8_t sumi0 = vdupq_n_s16(0); + int16x8_t sumi1 = vdupq_n_s16(0); +#endif + + for (size_t j = 0; j < sizeof(x->qs); j += 32) { + uint8x16_t qx0 = vld1q_u8(x[i].qs + j); + uint8x16_t qx1 = vld1q_u8(x[i].qs + j + 16); + uint8x16_t qx2 = vshrq_n_u8(qx0, 2); + uint8x16_t qx3 = vshrq_n_u8(qx1, 2); + uint8x16_t qx4 = vshrq_n_u8(qx0, 4); + uint8x16_t qx5 = vshrq_n_u8(qx1, 4); + uint8x16_t qx6 = vshrq_n_u8(qx0, 6); + uint8x16_t qx7 = vshrq_n_u8(qx1, 6); + + int8x16_t sqx0 = vreinterpretq_s8_u8(vandq_u8(qx0, m3)); + int8x16_t sqx1 = vreinterpretq_s8_u8(vandq_u8(qx1, m3)); + int8x16_t sqx2 = vreinterpretq_s8_u8(vandq_u8(qx2, m3)); + int8x16_t sqx3 = vreinterpretq_s8_u8(vandq_u8(qx3, m3)); + int8x16_t sqx4 = vreinterpretq_s8_u8(vandq_u8(qx4, m3)); + int8x16_t sqx5 = vreinterpretq_s8_u8(vandq_u8(qx5, m3)); + int8x16_t sqx6 = vreinterpretq_s8_u8(vandq_u8(qx6, m3)); + int8x16_t sqx7 = vreinterpretq_s8_u8(vandq_u8(qx7, m3)); + + const int8x16_t qy0 = vld1q_s8(y[i].qs + j*4 + 0); + const int8x16_t qy1 = vld1q_s8(y[i].qs + j*4 + 16); + const int8x16_t qy2 = vld1q_s8(y[i].qs + j*4 + 32); + const int8x16_t qy3 = vld1q_s8(y[i].qs + j*4 + 48); + const int8x16_t qy4 = vld1q_s8(y[i].qs + j*4 + 64); + const int8x16_t qy5 = vld1q_s8(y[i].qs + j*4 + 80); + const int8x16_t qy6 = vld1q_s8(y[i].qs + j*4 + 96); + const int8x16_t qy7 = vld1q_s8(y[i].qs + j*4 + 112); + +#if defined(__ARM_FEATURE_DOTPROD) + sumi0 = vdotq_s32(sumi0, sqx0, qy0); + sumi1 = vdotq_s32(sumi1, sqx1, qy1); + sumi0 = vdotq_s32(sumi0, sqx2, qy2); + sumi1 = vdotq_s32(sumi1, sqx3, qy3); + sumi0 = vdotq_s32(sumi0, sqx4, qy4); + sumi1 = vdotq_s32(sumi1, sqx5, qy5); + sumi0 = vdotq_s32(sumi0, sqx6, qy6); + sumi1 = vdotq_s32(sumi1, sqx7, qy7); +#else + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx0), vget_low_s8(qy0)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx0), vget_high_s8(qy0)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx1), vget_low_s8(qy1)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx1), vget_high_s8(qy1)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx2), vget_low_s8(qy2)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx2), vget_high_s8(qy2)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx3), vget_low_s8(qy3)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx3), vget_high_s8(qy3)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx4), vget_low_s8(qy4)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx4), vget_high_s8(qy4)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx5), vget_low_s8(qy5)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx5), vget_high_s8(qy5)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx6), vget_low_s8(qy6)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx6), vget_high_s8(qy6)); + sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx7), vget_low_s8(qy7)); + sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx7), vget_high_s8(qy7)); +#endif + } + + const int16x8_t ysum0 = vld1q_s16(y[i].bsums); + const int16x8_t ysum1 = vld1q_s16(y[i].bsums + 8); + + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + +#if defined(__ARM_FEATURE_DOTPROD) + sumi0 = vaddq_s32(sumi0, sumi1); + sumi0 = vsubq_s32(sumi0, vpaddlq_s16(vaddq_s16(ysum0, ysum1))); + + sumf += d * (float) vaddvq_s32(sumi0); +#else + sumi0 = vaddq_s16(sumi0, sumi1); + sumi0 = vsubq_s16(sumi0, vaddq_s16(ysum0, ysum1)); + + sumf += d * (float) vaddlvq_s16(sumi0); +#endif + } + + *s = sumf; + +#elif defined(__AVX2__) + __m256 sumf = _mm256_setzero_ps(); + + for (int i = 0; i < nb; ++i) { + // 16-bit sums, because 256*127 still fits + __m256i sumi0 = _mm256_setzero_si256(); + __m256i sumi1 = _mm256_setzero_si256(); + + for (size_t j = 0; j < sizeof(x->qs); j += 32) { + __m256i qx0 = _mm256_loadu_si256((const __m256i *) (x[i].qs + j)); + __m256i qx1 = _mm256_srli_epi16(qx0, 2); + __m256i qx2 = _mm256_srli_epi16(qx0, 4); + __m256i qx3 = _mm256_srli_epi16(qx0, 6); + + // 0, 1, 2 (should not be 3) + qx0 = _mm256_and_si256(qx0, _mm256_set1_epi8(3)); + qx1 = _mm256_and_si256(qx1, _mm256_set1_epi8(3)); + qx2 = _mm256_and_si256(qx2, _mm256_set1_epi8(3)); + qx3 = _mm256_and_si256(qx3, _mm256_set1_epi8(3)); + + const __m256i qy0 = _mm256_loadu_si256((const __m256i *) (y[i].qs + j*4 + 0)); + const __m256i qy1 = _mm256_loadu_si256((const __m256i *) (y[i].qs + j*4 + 32)); + const __m256i qy2 = _mm256_loadu_si256((const __m256i *) (y[i].qs + j*4 + 64)); + const __m256i qy3 = _mm256_loadu_si256((const __m256i *) (y[i].qs + j*4 + 96)); + + qx0 = _mm256_maddubs_epi16(qx0, qy0); + qx1 = _mm256_maddubs_epi16(qx1, qy1); + qx2 = _mm256_maddubs_epi16(qx2, qy2); + qx3 = _mm256_maddubs_epi16(qx3, qy3); + + sumi0 = _mm256_add_epi16(sumi0, _mm256_add_epi16(qx0, qx1)); + sumi1 = _mm256_add_epi16(sumi1, _mm256_add_epi16(qx2, qx3)); + } + + const __m256i ysum = _mm256_loadu_si256((const __m256i *) y[i].bsums); + const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(x[i].d)); + + sumi0 = _mm256_add_epi16(sumi0, sumi1); + sumi0 = _mm256_sub_epi16(sumi0, ysum); + sumi0 = _mm256_madd_epi16(sumi0, _mm256_set1_epi16(1)); + + sumf = _mm256_add_ps(_mm256_mul_ps(_mm256_cvtepi32_ps(sumi0), d), sumf); + } + + *s = hsum_float_8(sumf); + +#else + float sumf = 0.0f; + + for (int i = 0; i < nb; ++i) { + int32_t sumi = 0; + + for (size_t j = 0; j < sizeof(x->qs); j += 32) { + for (size_t l = 0; l < 4; ++l) { + for (size_t k = 0; k < 32; ++k) { + sumi += y[i].qs[j*4 + l*32 + k] * (((x[i].qs[j + k] >> (l*2)) & 3) - 1); + } + } + } + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + + sumf += (float) sumi * d; + } + + *s = sumf; +#endif +} + +void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_q2_K * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#ifdef __ARM_NEON + const uint8x16_t m3 = vdupq_n_u8(0x3); + const uint8x16_t m4 = vdupq_n_u8(0xF); + + const int32x4_t vzero = vdupq_n_s32(0); + + ggml_int8x16x2_t q2bytes; + uint8_t aux[16]; + + float sum = 0; + + for (int i = 0; i < nb; ++i) { + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + const uint8_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + const uint8_t * restrict sc = x[i].scales; + + const uint8x16_t mins_and_scales = vld1q_u8(sc); + const uint8x16_t scales = vandq_u8(mins_and_scales, m4); + vst1q_u8(aux, scales); + + const uint8x16_t mins = vshrq_n_u8(mins_and_scales, 4); + const ggml_int16x8x2_t q8sums = ggml_vld1q_s16_x2(y[i].bsums); + const ggml_int16x8x2_t mins16 = {{vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(mins))), vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(mins)))}}; + const int32x4_t s0 = vaddq_s32(vmull_s16(vget_low_s16 (mins16.val[0]), vget_low_s16 (q8sums.val[0])), + vmull_s16(vget_high_s16(mins16.val[0]), vget_high_s16(q8sums.val[0]))); + const int32x4_t s1 = vaddq_s32(vmull_s16(vget_low_s16 (mins16.val[1]), vget_low_s16 (q8sums.val[1])), + vmull_s16(vget_high_s16(mins16.val[1]), vget_high_s16(q8sums.val[1]))); + sum += dmin * vaddvq_s32(vaddq_s32(s0, s1)); + + int isum = 0; + int is = 0; + +// We use this macro instead of a function call because for some reason +// the code runs 2-3% slower, even if the function is declared inline +#define MULTIPLY_ACCUM_WITH_SCALE(index)\ + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * aux[is+(index)];\ + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * aux[is+1+(index)]; + +#define SHIFT_MULTIPLY_ACCUM_WITH_SCALE(shift, index)\ + q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32;\ + q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits.val[0], (shift)), m3));\ + q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits.val[1], (shift)), m3));\ + MULTIPLY_ACCUM_WITH_SCALE((index)); + + for (int j = 0; j < QK_K/128; ++j) { + const ggml_uint8x16x2_t q2bits = ggml_vld1q_u8_x2(q2); q2 += 32; + + ggml_int8x16x2_t q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; + q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(q2bits.val[0], m3)); + q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(q2bits.val[1], m3)); + + MULTIPLY_ACCUM_WITH_SCALE(0); + + SHIFT_MULTIPLY_ACCUM_WITH_SCALE(2, 2); + SHIFT_MULTIPLY_ACCUM_WITH_SCALE(4, 4); + SHIFT_MULTIPLY_ACCUM_WITH_SCALE(6, 6); + + is += 8; + } + + sum += d * isum; + } + + *s = sum; + +#elif defined __AVX2__ + + const __m256i m3 = _mm256_set1_epi8(3); + const __m128i m4 = _mm_set1_epi8(0xF); + + __m256 acc = _mm256_setzero_ps(); + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + const uint8_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + const __m128i mins_and_scales = _mm_loadu_si128((const __m128i*)x[i].scales); + const __m128i scales8 = _mm_and_si128(mins_and_scales, m4); + const __m128i mins8 = _mm_and_si128(_mm_srli_epi16(mins_and_scales, 4), m4); + const __m256i mins = _mm256_cvtepi8_epi16(mins8); + const __m256i prod = _mm256_madd_epi16(mins, _mm256_loadu_si256((const __m256i*)y[i].bsums)); + + acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&dmin), _mm256_cvtepi32_ps(prod), acc); + + const __m256i all_scales = _mm256_cvtepi8_epi16(scales8); + const __m128i l_scales = _mm256_extracti128_si256(all_scales, 0); + const __m128i h_scales = _mm256_extracti128_si256(all_scales, 1); + const __m256i scales[2] = {MM256_SET_M128I(l_scales, l_scales), MM256_SET_M128I(h_scales, h_scales)}; + + __m256i sumi = _mm256_setzero_si256(); + + for (int j = 0; j < QK_K/128; ++j) { + + const __m256i q2bits = _mm256_loadu_si256((const __m256i*)q2); q2 += 32; + + const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q8_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q8_3 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + + const __m256i q2_0 = _mm256_and_si256(q2bits, m3); + const __m256i q2_1 = _mm256_and_si256(_mm256_srli_epi16(q2bits, 2), m3); + const __m256i q2_2 = _mm256_and_si256(_mm256_srli_epi16(q2bits, 4), m3); + const __m256i q2_3 = _mm256_and_si256(_mm256_srli_epi16(q2bits, 6), m3); + + __m256i p0 = _mm256_maddubs_epi16(q2_0, q8_0); + __m256i p1 = _mm256_maddubs_epi16(q2_1, q8_1); + __m256i p2 = _mm256_maddubs_epi16(q2_2, q8_2); + __m256i p3 = _mm256_maddubs_epi16(q2_3, q8_3); + + p0 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(0)), p0); + p1 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(1)), p1); + p2 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(2)), p2); + p3 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(3)), p3); + + p0 = _mm256_add_epi32(p0, p1); + p2 = _mm256_add_epi32(p2, p3); + + sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p0, p2)); + } + + acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi), acc); + + } + + *s = hsum_float_8(acc); + +#elif defined __AVX__ + + const __m128i m3 = _mm_set1_epi8(0x3); + const __m128i m4 = _mm_set1_epi8(0xF); + const __m128i m2 = _mm_set1_epi8(0x2); + + __m256 acc = _mm256_setzero_ps(); + + for (int i = 0; i < nb; ++i) { + + const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + const uint8_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + // load mins and scales from block_q2_K.scales[QK_K/16] + const __m128i mins_and_scales = _mm_loadu_si128((const __m128i*)x[i].scales); + const __m128i scales16 = _mm_and_si128(mins_and_scales, m4); + const __m128i mins16 = _mm_and_si128(_mm_srli_epi16(mins_and_scales, 4), m4); + const __m128i mins_0 = _mm_cvtepi8_epi16(mins16); + const __m128i mins_1 = _mm_cvtepi8_epi16(_mm_unpackhi_epi64(mins16, mins16)); + + // summs = y[i].bsums * (x[i].scales >> 4) in 16bits*8*2 to 32bits*4*2 + const __m128i summs_0 = _mm_madd_epi16(mins_0, _mm_loadu_si128((const __m128i*)&y[i].bsums[0])); + const __m128i summs_1 = _mm_madd_epi16(mins_1, _mm_loadu_si128((const __m128i*)&y[i].bsums[8])); + + // sumf += -dmin * summs in 32bits*8 + acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&dmin), _mm256_cvtepi32_ps(MM256_SET_M128I(summs_1, summs_0))), acc); + + const __m128i scales_0 = _mm_cvtepi8_epi16(scales16); + const __m128i scales_1 = _mm_cvtepi8_epi16(_mm_unpackhi_epi64(scales16, scales16)); + const __m128i scales[2] = { scales_0, scales_1 }; + + __m128i sumi_0 = _mm_setzero_si128(); + __m128i sumi_1 = _mm_setzero_si128(); + + for (int j = 0; j < QK_K/128; ++j) { + + // load Q8 quants int8*16*8 from block_q8_K.qs[QK_K] + const __m128i q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_2 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_3 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_4 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_5 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_6 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_7 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + + // load 2bits*16*8 from block_q2_K.qs[QK_K/4] + __m128i q2bits = _mm_loadu_si128((const __m128i*)q2); q2 += 16; + const __m128i q2_0 = _mm_and_si128(q2bits, m3); + const __m128i q2_2 = _mm_and_si128(_mm_srli_epi16(q2bits, 2), m3); + const __m128i q2_4 = _mm_and_si128(_mm_srli_epi16(q2bits, 4), m3); + const __m128i q2_6 = _mm_and_si128(_mm_srli_epi16(q2bits, 6), m3); + q2bits = _mm_loadu_si128((const __m128i*)q2); q2 += 16; + const __m128i q2_1 = _mm_and_si128(q2bits, m3); + const __m128i q2_3 = _mm_and_si128(_mm_srli_epi16(q2bits, 2), m3); + const __m128i q2_5 = _mm_and_si128(_mm_srli_epi16(q2bits, 4), m3); + const __m128i q2_7 = _mm_and_si128(_mm_srli_epi16(q2bits, 6), m3); + + // isuml = q8[l] * ((q2[l] >> shift) & 3) in 8bits*16*8 to 16bits*8*8 + __m128i p0 = _mm_maddubs_epi16(q2_0, q8_0); + __m128i p1 = _mm_maddubs_epi16(q2_1, q8_1); + __m128i p2 = _mm_maddubs_epi16(q2_2, q8_2); + __m128i p3 = _mm_maddubs_epi16(q2_3, q8_3); + __m128i p4 = _mm_maddubs_epi16(q2_4, q8_4); + __m128i p5 = _mm_maddubs_epi16(q2_5, q8_5); + __m128i p6 = _mm_maddubs_epi16(q2_6, q8_6); + __m128i p7 = _mm_maddubs_epi16(q2_7, q8_7); + + // isum += (x[i].scales[is++] & 0xF) * isuml in 16bits*8*8 to 32bits*4*8 + __m128i shuffle = _mm_set1_epi16(0x0100); + p0 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p0); + shuffle = _mm_add_epi16(shuffle, m2); + p1 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p1); + shuffle = _mm_add_epi16(shuffle, m2); + p2 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p2); + shuffle = _mm_add_epi16(shuffle, m2); + p3 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p3); + shuffle = _mm_add_epi16(shuffle, m2); + p4 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p4); + shuffle = _mm_add_epi16(shuffle, m2); + p5 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p5); + shuffle = _mm_add_epi16(shuffle, m2); + p6 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p6); + shuffle = _mm_add_epi16(shuffle, m2); + p7 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p7); + + p0 = _mm_add_epi32(p0, p1); + p2 = _mm_add_epi32(p2, p3); + p4 = _mm_add_epi32(p4, p5); + p6 = _mm_add_epi32(p6, p7); + + // isum in 32bits*4*2 + sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p0, p2)); + sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p4, p6)); + } + + // sumf += dall * isum - dmin * summs in 32bits + __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0); + acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&dall), _mm256_cvtepi32_ps(sumi)), acc); + } + + *s = hsum_float_8(acc); + +#elif defined __riscv_v_intrinsic + + float sumf = 0; + uint8_t temp_01[32] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}; + + for (int i = 0; i < nb; ++i) { + + const uint8_t * q2 = x[i].qs; + const int8_t * q8 = y[i].qs; + const uint8_t * sc = x[i].scales; + + const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + size_t vl = 16; + + vuint8m1_t scales = __riscv_vle8_v_u8m1(sc, vl); + vuint8m1_t aux = __riscv_vand_vx_u8m1(scales, 0x0F, vl); + + vint16m1_t q8sums = __riscv_vle16_v_i16m1(y[i].bsums, vl); + + vuint8mf2_t scales_2 = __riscv_vle8_v_u8mf2(sc, vl); + vuint8mf2_t mins8 = __riscv_vsrl_vx_u8mf2(scales_2, 0x4, vl); + vint16m1_t mins = __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vzext_vf2_u16m1(mins8, vl)); + vint32m2_t prod = __riscv_vwmul_vv_i32m2(q8sums, mins, vl); + vint32m1_t vsums = __riscv_vredsum_vs_i32m2_i32m1(prod, __riscv_vmv_v_x_i32m1(0, 1), vl); + + sumf += dmin * __riscv_vmv_x_s_i32m1_i32(vsums); + + vl = 32; + + vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); + vuint8m1_t v_b = __riscv_vle8_v_u8m1(temp_01, vl); + + uint8_t is=0; + int isum=0; + + for (int j = 0; j < QK_K/128; ++j) { + // load Q2 + vuint8m1_t q2_x = __riscv_vle8_v_u8m1(q2, vl); + + vuint8m1_t q2_0 = __riscv_vand_vx_u8m1(q2_x, 0x03, vl); + vuint8m1_t q2_1 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q2_x, 0x2, vl), 0x03 , vl); + vuint8m1_t q2_2 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q2_x, 0x4, vl), 0x03 , vl); + vuint8m1_t q2_3 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q2_x, 0x6, vl), 0x03 , vl); + + // duplicate scale elements for product + vuint8m1_t sc0 = __riscv_vrgather_vv_u8m1(aux, __riscv_vadd_vx_u8m1(v_b, 0+is, vl), vl); + vuint8m1_t sc1 = __riscv_vrgather_vv_u8m1(aux, __riscv_vadd_vx_u8m1(v_b, 2+is, vl), vl); + vuint8m1_t sc2 = __riscv_vrgather_vv_u8m1(aux, __riscv_vadd_vx_u8m1(v_b, 4+is, vl), vl); + vuint8m1_t sc3 = __riscv_vrgather_vv_u8m1(aux, __riscv_vadd_vx_u8m1(v_b, 6+is, vl), vl); + + vint16m2_t p0 = __riscv_vreinterpret_v_u16m2_i16m2(__riscv_vwmulu_vv_u16m2(q2_0, sc0, vl)); + vint16m2_t p1 = __riscv_vreinterpret_v_u16m2_i16m2(__riscv_vwmulu_vv_u16m2(q2_1, sc1, vl)); + vint16m2_t p2 = __riscv_vreinterpret_v_u16m2_i16m2(__riscv_vwmulu_vv_u16m2(q2_2, sc2, vl)); + vint16m2_t p3 = __riscv_vreinterpret_v_u16m2_i16m2(__riscv_vwmulu_vv_u16m2(q2_3, sc3, vl)); + + // load Q8 + vint8m1_t q8_0 = __riscv_vle8_v_i8m1(q8, vl); + vint8m1_t q8_1 = __riscv_vle8_v_i8m1(q8+32, vl); + vint8m1_t q8_2 = __riscv_vle8_v_i8m1(q8+64, vl); + vint8m1_t q8_3 = __riscv_vle8_v_i8m1(q8+96, vl); + + vint32m4_t s0 = __riscv_vwmul_vv_i32m4(p0, __riscv_vwcvt_x_x_v_i16m2(q8_0, vl), vl); + vint32m4_t s1 = __riscv_vwmul_vv_i32m4(p1, __riscv_vwcvt_x_x_v_i16m2(q8_1, vl), vl); + vint32m4_t s2 = __riscv_vwmul_vv_i32m4(p2, __riscv_vwcvt_x_x_v_i16m2(q8_2, vl), vl); + vint32m4_t s3 = __riscv_vwmul_vv_i32m4(p3, __riscv_vwcvt_x_x_v_i16m2(q8_3, vl), vl); + + vint32m1_t isum0 = __riscv_vredsum_vs_i32m4_i32m1(__riscv_vadd_vv_i32m4(s0, s1, vl), vzero, vl); + vint32m1_t isum1 = __riscv_vredsum_vs_i32m4_i32m1(__riscv_vadd_vv_i32m4(s2, s3, vl), isum0, vl); + + isum += __riscv_vmv_x_s_i32m1_i32(isum1); + + q2+=32; q8+=128; is=8; + + } + + sumf += dall * isum; + + } + + *s = sumf; + +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0x3); + const vector signed char lowScaleMask = vec_splats((signed char)0xF); + const vector int v0 = vec_splats((int32_t)0); + const vector unsigned char v2 = vec_splats((unsigned char)0x2); + const vector unsigned char v6 = vec_splats((unsigned char)0x6); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].dmin)); + vector float vdmin = vec_mul(vxmin, vyd); + + vector signed short q8ysums0 = vec_xl( 0, y[i].bsums); + vector signed short q8ysums1 = vec_xl(16, y[i].bsums); + + vector signed char q2xmins = (vector signed char)vec_xl( 0, x[i].scales); + vector signed char vscales = vec_and(q2xmins, lowScaleMask); + + q2xmins = vec_sr(q2xmins, v4); + vector signed short q2xmins0 = vec_unpackh(q2xmins); + vector signed short q2xmins1 = vec_unpackl(q2xmins); + + vector signed int prod0 = vec_mule(q2xmins0, q8ysums0); + vector signed int prod1 = vec_mulo(q2xmins0, q8ysums0); + vector signed int prod2 = vec_mule(q2xmins1, q8ysums1); + vector signed int prod3 = vec_mulo(q2xmins1, q8ysums1); + + vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vdmin, vsumf0); + vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vdmin, vsumf1); + vsumf2 = vec_nmsub(vec_ctf(prod2, 0), vdmin, vsumf2); + vsumf3 = vec_nmsub(vec_ctf(prod3, 0), vdmin, vsumf3); + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; + vector signed int vsumi4 = v0; + vector signed int vsumi5 = v0; + vector signed int vsumi6 = v0; + vector signed int vsumi7 = v0; + + const uint8_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/128; ++j) { + __builtin_prefetch(q2, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, q2); + vector signed char qxs1 = (vector signed char)vec_xl(16, q2); + q2 += 32; + + vector unsigned char q2x00 = (vector unsigned char)vec_and(qxs0, lowMask); + vector unsigned char q2x01 = (vector unsigned char)vec_and(vec_sr(qxs0, v2), lowMask); + vector unsigned char q2x02 = (vector unsigned char)vec_and(vec_sr(qxs0, v4), lowMask); + vector unsigned char q2x03 = (vector unsigned char)vec_and(vec_sr(qxs0, v6), lowMask); + vector unsigned char q2x10 = (vector unsigned char)vec_and(qxs1, lowMask); + vector unsigned char q2x11 = (vector unsigned char)vec_and(vec_sr(qxs1, v2), lowMask); + vector unsigned char q2x12 = (vector unsigned char)vec_and(vec_sr(qxs1, v4), lowMask); + vector unsigned char q2x13 = (vector unsigned char)vec_and(vec_sr(qxs1, v6), lowMask); + + vector signed char q8y00 = vec_xl( 0, q8); + vector signed char q8y10 = vec_xl( 16, q8); + vector signed char q8y01 = vec_xl( 32, q8); + vector signed char q8y11 = vec_xl( 48, q8); + vector signed char q8y02 = vec_xl( 64, q8); + vector signed char q8y12 = vec_xl( 80, q8); + vector signed char q8y03 = vec_xl( 96, q8); + vector signed char q8y13 = vec_xl(112, q8); + q8 += 128; + + vector signed int qv0 = vec_msum(q8y00, q2x00, v0); + vector signed int qv1 = vec_msum(q8y01, q2x01, v0); + vector signed int qv2 = vec_msum(q8y02, q2x02, v0); + vector signed int qv3 = vec_msum(q8y03, q2x03, v0); + vector signed int qv4 = vec_msum(q8y10, q2x10, v0); + vector signed int qv5 = vec_msum(q8y11, q2x11, v0); + vector signed int qv6 = vec_msum(q8y12, q2x12, v0); + vector signed int qv7 = vec_msum(q8y13, q2x13, v0); + + vector signed short vscales_07 = vec_unpackh(vscales); + vector signed int vscales_03 = vec_unpackh(vscales_07); + vector signed int vscales_47 = vec_unpackl(vscales_07); + vector signed int vs0 = vec_splat(vscales_03, 0); + vector signed int vs1 = vec_splat(vscales_03, 1); + vector signed int vs2 = vec_splat(vscales_03, 2); + vector signed int vs3 = vec_splat(vscales_03, 3); + vector signed int vs4 = vec_splat(vscales_47, 0); + vector signed int vs5 = vec_splat(vscales_47, 1); + vector signed int vs6 = vec_splat(vscales_47, 2); + vector signed int vs7 = vec_splat(vscales_47, 3); + vscales = vec_sld(vscales, vscales, 8); + + vsumi0 = vec_add(vec_mul(qv0, vs0), vsumi0); + vsumi1 = vec_add(vec_mul(qv1, vs2), vsumi1); + vsumi2 = vec_add(vec_mul(qv2, vs4), vsumi2); + vsumi3 = vec_add(vec_mul(qv3, vs6), vsumi3); + vsumi4 = vec_add(vec_mul(qv4, vs1), vsumi4); + vsumi5 = vec_add(vec_mul(qv5, vs3), vsumi5); + vsumi6 = vec_add(vec_mul(qv6, vs5), vsumi6); + vsumi7 = vec_add(vec_mul(qv7, vs7), vsumi7); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined __loongarch_asx + + const __m256i m3 = __lasx_xvreplgr2vr_b(3); + const __m128i m4 = __lsx_vreplgr2vr_b(0xF); + + __m256 acc = (__m256)__lasx_xvldi(0); + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + const uint8_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + const __m128i mins_and_scales = __lsx_vld((const __m128i*)x[i].scales, 0); + const __m128i scales8 = __lsx_vand_v(mins_and_scales, m4); + const __m128i mins8 = __lsx_vand_v(__lsx_vsrli_h(mins_and_scales, 4), m4); + const __m256i mins = lasx_ext8_16(mins8); + const __m256i prod = lasx_madd_h(mins, __lasx_xvld((const __m256i*)y[i].bsums, 0)); + + acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(dmin), __lasx_xvffint_s_w(prod), acc); + + const __m256i all_scales = lasx_ext8_16(scales8); + const __m128i l_scales = lasx_extracti128(all_scales, 0); + const __m128i h_scales = lasx_extracti128(all_scales, 1); + const __m256i scales[2] = {lasx_insertf128(l_scales, l_scales), lasx_insertf128(h_scales, h_scales)}; + + __m256i sumi = __lasx_xvldi(0); + + for (int j = 0; j < QK_K/128; ++j) { + + const __m256i q2bits = __lasx_xvld((const __m256i*)q2, 0); q2 += 32; + + const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_3 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + + const __m256i q2_0 = __lasx_xvand_v(q2bits, m3); + const __m256i q2_1 = __lasx_xvand_v(__lasx_xvsrli_h(q2bits, 2), m3); + const __m256i q2_2 = __lasx_xvand_v(__lasx_xvsrli_h(q2bits, 4), m3); + const __m256i q2_3 = __lasx_xvand_v(__lasx_xvsrli_h(q2bits, 6), m3); + + __m256i p0 = lasx_maddubs_h(q2_0, q8_0); + __m256i p1 = lasx_maddubs_h(q2_1, q8_1); + __m256i p2 = lasx_maddubs_h(q2_2, q8_2); + __m256i p3 = lasx_maddubs_h(q2_3, q8_3); + + p0 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(0)), p0); + p1 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(1)), p1); + p2 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(2)), p2); + p3 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(3)), p3); + + p0 = __lasx_xvadd_w(p0, p1); + p2 = __lasx_xvadd_w(p2, p3); + + sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p0, p2)); + } + + acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), acc); + + } + + *s = hsum_float_8(acc); + +#else + + float sumf = 0; + + for (int i = 0; i < nb; ++i) { + + const uint8_t * q2 = x[i].qs; + const int8_t * q8 = y[i].qs; + const uint8_t * sc = x[i].scales; + + int summs = 0; + for (int j = 0; j < 16; ++j) { + summs += y[i].bsums[j] * (sc[j] >> 4); + } + + const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + int isum = 0; + int is = 0; + int d; + for (int k = 0; k < QK_K/128; ++k) { + int shift = 0; + for (int j = 0; j < 4; ++j) { + d = sc[is++] & 0xF; + int isuml = 0; + for (int l = 0; l < 16; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3); + isum += d * isuml; + d = sc[is++] & 0xF; + isuml = 0; + for (int l = 16; l < 32; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3); + isum += d * isuml; + shift += 2; + q8 += 32; + } + q2 += 32; + } + sumf += dall * isum - dmin * summs; + } + *s = sumf; +#endif +} + +void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const uint32_t kmask1 = 0x03030303; + const uint32_t kmask2 = 0x0f0f0f0f; + + const block_q3_K * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#ifdef __ARM_NEON + + uint32_t aux[3]; + uint32_t utmp[4]; + + const uint8x16_t m3b = vdupq_n_u8(0x3); + const int32x4_t vzero = vdupq_n_s32(0); + + const uint8x16_t m0 = vdupq_n_u8(1); + const uint8x16_t m1 = vshlq_n_u8(m0, 1); + const uint8x16_t m2 = vshlq_n_u8(m0, 2); + const uint8x16_t m3 = vshlq_n_u8(m0, 3); + const int8_t m32 = 32; + + ggml_int8x16x4_t q3bytes; + + float sum = 0; + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict qh = x[i].hmask; + const int8_t * restrict q8 = y[i].qs; + + ggml_uint8x16x2_t qhbits = ggml_vld1q_u8_x2(qh); + + ggml_uint8x16x4_t q3h; + + int32_t isum = 0; + + // Set up scales + memcpy(aux, x[i].scales, 12); + utmp[3] = ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4); + utmp[2] = ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4); + utmp[1] = (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4); + utmp[0] = (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4); + + int8_t * scale = (int8_t *)utmp; + for (int j = 0; j < 16; ++j) scale[j] -= m32; + + for (int j = 0; j < QK_K/128; ++j) { + + const ggml_uint8x16x2_t q3bits = ggml_vld1q_u8_x2(q3); q3 += 32; + const ggml_int8x16x4_t q8bytes_1 = ggml_vld1q_s8_x4(q8); q8 += 64; + const ggml_int8x16x4_t q8bytes_2 = ggml_vld1q_s8_x4(q8); q8 += 64; + + q3h.val[0] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[0]), 2); + q3h.val[1] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[1]), 2); + q3h.val[2] = vshlq_n_u8(vbicq_u8(m1, qhbits.val[0]), 1); + q3h.val[3] = vshlq_n_u8(vbicq_u8(m1, qhbits.val[1]), 1); + + q3bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q3bits.val[0], m3b)), vreinterpretq_s8_u8(q3h.val[0])); + q3bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q3bits.val[1], m3b)), vreinterpretq_s8_u8(q3h.val[1])); + q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 2), m3b)), vreinterpretq_s8_u8(q3h.val[2])); + q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 2), m3b)), vreinterpretq_s8_u8(q3h.val[3])); + + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes_1.val[0])) * scale[0]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes_1.val[1])) * scale[1]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes_1.val[2])) * scale[2]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes_1.val[3])) * scale[3]; + + scale += 4; + + q3h.val[0] = vbicq_u8(m2, qhbits.val[0]); + q3h.val[1] = vbicq_u8(m2, qhbits.val[1]); + q3h.val[2] = vshrq_n_u8(vbicq_u8(m3, qhbits.val[0]), 1); + q3h.val[3] = vshrq_n_u8(vbicq_u8(m3, qhbits.val[1]), 1); + + q3bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 4), m3b)), vreinterpretq_s8_u8(q3h.val[0])); + q3bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 4), m3b)), vreinterpretq_s8_u8(q3h.val[1])); + q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 6), m3b)), vreinterpretq_s8_u8(q3h.val[2])); + q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 6), m3b)), vreinterpretq_s8_u8(q3h.val[3])); + + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes_2.val[0])) * scale[0]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes_2.val[1])) * scale[1]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes_2.val[2])) * scale[2]; + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes_2.val[3])) * scale[3]; + + scale += 4; + + if (j == 0) { + qhbits.val[0] = vshrq_n_u8(qhbits.val[0], 4); + qhbits.val[1] = vshrq_n_u8(qhbits.val[1], 4); + } + + } + sum += d * isum; + + } + + *s = sum; + +#elif defined __AVX2__ + + const __m256i m3 = _mm256_set1_epi8(3); + const __m256i mone = _mm256_set1_epi8(1); + const __m128i m32 = _mm_set1_epi8(32); + + __m256 acc = _mm256_setzero_ps(); + + uint32_t aux[3]; + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + + const uint8_t * restrict q3 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + // Set up scales + memcpy(aux, x[i].scales, 12); + __m128i scales128 = _mm_set_epi32( + ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4), + ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4), + (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4), + (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4)); + scales128 = _mm_sub_epi8(scales128, m32); + const __m256i all_scales = _mm256_cvtepi8_epi16(scales128); + const __m128i l_scales = _mm256_extracti128_si256(all_scales, 0); + const __m128i h_scales = _mm256_extracti128_si256(all_scales, 1); + const __m256i scales[2] = {MM256_SET_M128I(l_scales, l_scales), MM256_SET_M128I(h_scales, h_scales)}; + + // high bit + const __m256i hbits = _mm256_loadu_si256((const __m256i*)x[i].hmask); + + // integer accumulator + __m256i sumi = _mm256_setzero_si256(); + + int bit = 0; + int is = 0; + + for (int j = 0; j < QK_K/128; ++j) { + // load low 2 bits + const __m256i q3bits = _mm256_loadu_si256((const __m256i*)q3); q3 += 32; + + // prepare low and high bits + const __m256i q3l_0 = _mm256_and_si256(q3bits, m3); + const __m256i q3h_0 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, bit)), bit), 2); + ++bit; + + const __m256i q3l_1 = _mm256_and_si256(_mm256_srli_epi16(q3bits, 2), m3); + const __m256i q3h_1 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, bit)), bit), 2); + ++bit; + + const __m256i q3l_2 = _mm256_and_si256(_mm256_srli_epi16(q3bits, 4), m3); + const __m256i q3h_2 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, bit)), bit), 2); + ++bit; + + const __m256i q3l_3 = _mm256_and_si256(_mm256_srli_epi16(q3bits, 6), m3); + const __m256i q3h_3 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, bit)), bit), 2); + ++bit; + + // load Q8 quants + const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q8_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q8_3 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + + // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use _mm256_maddubs_epi16, + // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set, + // and 2 if the high bit was set) + __m256i q8s_0 = _mm256_maddubs_epi16(q3h_0, q8_0); + __m256i q8s_1 = _mm256_maddubs_epi16(q3h_1, q8_1); + __m256i q8s_2 = _mm256_maddubs_epi16(q3h_2, q8_2); + __m256i q8s_3 = _mm256_maddubs_epi16(q3h_3, q8_3); + + __m256i p16_0 = _mm256_maddubs_epi16(q3l_0, q8_0); + __m256i p16_1 = _mm256_maddubs_epi16(q3l_1, q8_1); + __m256i p16_2 = _mm256_maddubs_epi16(q3l_2, q8_2); + __m256i p16_3 = _mm256_maddubs_epi16(q3l_3, q8_3); + + p16_0 = _mm256_sub_epi16(p16_0, q8s_0); + p16_1 = _mm256_sub_epi16(p16_1, q8s_1); + p16_2 = _mm256_sub_epi16(p16_2, q8s_2); + p16_3 = _mm256_sub_epi16(p16_3, q8s_3); + + // multiply with scales + p16_0 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(is + 0)), p16_0); + p16_1 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(is + 1)), p16_1); + p16_2 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(is + 2)), p16_2); + p16_3 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(is + 3)), p16_3); + + // accumulate + p16_0 = _mm256_add_epi32(p16_0, p16_1); + p16_2 = _mm256_add_epi32(p16_2, p16_3); + sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_0, p16_2)); + + } + + // multiply with block scale and accumulate + acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi), acc); + + } + + *s = hsum_float_8(acc); + +#elif defined __AVX__ + + const __m128i m3 = _mm_set1_epi8(3); + const __m128i mone = _mm_set1_epi8(1); + const __m128i m32 = _mm_set1_epi8(32); + const __m128i m2 = _mm_set1_epi8(2); + + __m256 acc = _mm256_setzero_ps(); + + const uint32_t *aux; + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + + const uint8_t * restrict q3 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + // Set up scales + aux = (const uint32_t *)x[i].scales; + __m128i scales128 = _mm_set_epi32( + ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4), + ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4), + (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4), + (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4)); + scales128 = _mm_sub_epi8(scales128, m32); + const __m128i scales_0 = _mm_cvtepi8_epi16(scales128); + const __m128i scales_1 = _mm_cvtepi8_epi16(_mm_unpackhi_epi64(scales128, scales128)); + const __m128i scales[2] = { scales_0, scales_1 }; + + // high bit *128*2 from block_q3_K.hmask[QK_K/8] + const __m128i hbits_0 = _mm_loadu_si128((const __m128i*)&x[i].hmask[0]); + const __m128i hbits_1 = _mm_loadu_si128((const __m128i*)&x[i].hmask[16]); + + // integer accumulator + __m128i sumi_0 = _mm_setzero_si128(); + __m128i sumi_1 = _mm_setzero_si128(); + + for (int j = 0; j < QK_K/128; ++j) { + // load low 2 bits *64*2 from block_q3_K.qs[QK_K/4] + const __m128i q3bits_0 = _mm_loadu_si128((const __m128i*)q3); q3 += 16; + const __m128i q3bits_1 = _mm_loadu_si128((const __m128i*)q3); q3 += 16; + + // prepare low and high bits + const int bit = j << 2; + + const __m128i q3l_0 = _mm_and_si128(q3bits_0, m3); + const __m128i q3l_1 = _mm_and_si128(q3bits_1, m3); + const __m128i q3h_0 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_0, _mm_slli_epi16(mone, bit)), bit), 2); + const __m128i q3h_1 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_1, _mm_slli_epi16(mone, bit)), bit), 2); + + const __m128i q3l_2 = _mm_and_si128(_mm_srli_epi16(q3bits_0, 2), m3); + const __m128i q3l_3 = _mm_and_si128(_mm_srli_epi16(q3bits_1, 2), m3); + const __m128i q3h_2 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_0, _mm_slli_epi16(mone, bit+1)), bit+1), 2); + const __m128i q3h_3 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_1, _mm_slli_epi16(mone, bit+1)), bit+1), 2); + + const __m128i q3l_4 = _mm_and_si128(_mm_srli_epi16(q3bits_0, 4), m3); + const __m128i q3l_5 = _mm_and_si128(_mm_srli_epi16(q3bits_1, 4), m3); + const __m128i q3h_4 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_0, _mm_slli_epi16(mone, bit+2)), bit+2), 2); + const __m128i q3h_5 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_1, _mm_slli_epi16(mone, bit+2)), bit+2), 2); + + const __m128i q3l_6 = _mm_and_si128(_mm_srli_epi16(q3bits_0, 6), m3); + const __m128i q3l_7 = _mm_and_si128(_mm_srli_epi16(q3bits_1, 6), m3); + const __m128i q3h_6 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_0, _mm_slli_epi16(mone, bit+3)), bit+3), 2); + const __m128i q3h_7 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_1, _mm_slli_epi16(mone, bit+3)), bit+3), 2); + + // load Q8 quants from block_q8_K.qs[QK_K] + const __m128i q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_2 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_3 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_4 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_5 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_6 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_7 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + + // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use _mm256_maddubs_epi16, + // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set, + // and 2 if the high bit was set) + __m128i q8s_0 = _mm_maddubs_epi16(q3h_0, q8_0); + __m128i q8s_1 = _mm_maddubs_epi16(q3h_1, q8_1); + __m128i q8s_2 = _mm_maddubs_epi16(q3h_2, q8_2); + __m128i q8s_3 = _mm_maddubs_epi16(q3h_3, q8_3); + __m128i q8s_4 = _mm_maddubs_epi16(q3h_4, q8_4); + __m128i q8s_5 = _mm_maddubs_epi16(q3h_5, q8_5); + __m128i q8s_6 = _mm_maddubs_epi16(q3h_6, q8_6); + __m128i q8s_7 = _mm_maddubs_epi16(q3h_7, q8_7); + + __m128i p16_0 = _mm_maddubs_epi16(q3l_0, q8_0); + __m128i p16_1 = _mm_maddubs_epi16(q3l_1, q8_1); + __m128i p16_2 = _mm_maddubs_epi16(q3l_2, q8_2); + __m128i p16_3 = _mm_maddubs_epi16(q3l_3, q8_3); + __m128i p16_4 = _mm_maddubs_epi16(q3l_4, q8_4); + __m128i p16_5 = _mm_maddubs_epi16(q3l_5, q8_5); + __m128i p16_6 = _mm_maddubs_epi16(q3l_6, q8_6); + __m128i p16_7 = _mm_maddubs_epi16(q3l_7, q8_7); + + p16_0 = _mm_sub_epi16(p16_0, q8s_0); + p16_1 = _mm_sub_epi16(p16_1, q8s_1); + p16_2 = _mm_sub_epi16(p16_2, q8s_2); + p16_3 = _mm_sub_epi16(p16_3, q8s_3); + p16_4 = _mm_sub_epi16(p16_4, q8s_4); + p16_5 = _mm_sub_epi16(p16_5, q8s_5); + p16_6 = _mm_sub_epi16(p16_6, q8s_6); + p16_7 = _mm_sub_epi16(p16_7, q8s_7); + + // multiply with scales + __m128i shuffle = _mm_set1_epi16(0x0100); + p16_0 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_0); + shuffle = _mm_add_epi16(shuffle, m2); + p16_1 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_1); + shuffle = _mm_add_epi16(shuffle, m2); + p16_2 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_2); + shuffle = _mm_add_epi16(shuffle, m2); + p16_3 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_3); + shuffle = _mm_add_epi16(shuffle, m2); + p16_4 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_4); + shuffle = _mm_add_epi16(shuffle, m2); + p16_5 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_5); + shuffle = _mm_add_epi16(shuffle, m2); + p16_6 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_6); + shuffle = _mm_add_epi16(shuffle, m2); + p16_7 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_7); + + // accumulate + p16_0 = _mm_add_epi32(p16_0, p16_1); + p16_2 = _mm_add_epi32(p16_2, p16_3); + p16_4 = _mm_add_epi32(p16_4, p16_5); + p16_6 = _mm_add_epi32(p16_6, p16_7); + sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2)); + sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_4, p16_6)); + + } + + // multiply with block scale and accumulate + __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0); + acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi)), acc); + + } + + *s = hsum_float_8(acc); + +#elif defined __riscv_v_intrinsic + + uint32_t aux[3]; + uint32_t utmp[4]; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict qh = x[i].hmask; + const int8_t * restrict q8 = y[i].qs; + + memcpy(aux, x[i].scales, 12); + utmp[3] = ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4); + utmp[2] = ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4); + utmp[1] = (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4); + utmp[0] = (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4); + + int8_t * scale = (int8_t *)utmp; + for (int j = 0; j < 16; ++j) scale[j] -= 32; + + + size_t vl = 32; + uint8_t m = 1; + + vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); + vuint8m1_t vqh = __riscv_vle8_v_u8m1(qh, vl); + + int sum_t = 0; + + for (int j = 0; j < QK_K; j += 128) { + + vl = 32; + + // load Q3 + vuint8m1_t q3_x = __riscv_vle8_v_u8m1(q3, vl); + + vint8m1_t q3_0 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(q3_x, 0x03, vl)); + vint8m1_t q3_1 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q3_x, 0x2, vl), 0x03 , vl)); + vint8m1_t q3_2 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q3_x, 0x4, vl), 0x03 , vl)); + vint8m1_t q3_3 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q3_x, 0x6, vl), 0x03 , vl)); + + // compute mask for subtraction + vuint8m1_t qh_m0 = __riscv_vand_vx_u8m1(vqh, m, vl); + vbool8_t vmask_0 = __riscv_vmseq_vx_u8m1_b8(qh_m0, 0, vl); + vint8m1_t q3_m0 = __riscv_vsub_vx_i8m1_mu(vmask_0, q3_0, q3_0, 0x4, vl); + m <<= 1; + + vuint8m1_t qh_m1 = __riscv_vand_vx_u8m1(vqh, m, vl); + vbool8_t vmask_1 = __riscv_vmseq_vx_u8m1_b8(qh_m1, 0, vl); + vint8m1_t q3_m1 = __riscv_vsub_vx_i8m1_mu(vmask_1, q3_1, q3_1, 0x4, vl); + m <<= 1; + + vuint8m1_t qh_m2 = __riscv_vand_vx_u8m1(vqh, m, vl); + vbool8_t vmask_2 = __riscv_vmseq_vx_u8m1_b8(qh_m2, 0, vl); + vint8m1_t q3_m2 = __riscv_vsub_vx_i8m1_mu(vmask_2, q3_2, q3_2, 0x4, vl); + m <<= 1; + + vuint8m1_t qh_m3 = __riscv_vand_vx_u8m1(vqh, m, vl); + vbool8_t vmask_3 = __riscv_vmseq_vx_u8m1_b8(qh_m3, 0, vl); + vint8m1_t q3_m3 = __riscv_vsub_vx_i8m1_mu(vmask_3, q3_3, q3_3, 0x4, vl); + m <<= 1; + + // load Q8 and take product with Q3 + vint16m2_t a0 = __riscv_vwmul_vv_i16m2(q3_m0, __riscv_vle8_v_i8m1(q8, vl), vl); + vint16m2_t a1 = __riscv_vwmul_vv_i16m2(q3_m1, __riscv_vle8_v_i8m1(q8+32, vl), vl); + vint16m2_t a2 = __riscv_vwmul_vv_i16m2(q3_m2, __riscv_vle8_v_i8m1(q8+64, vl), vl); + vint16m2_t a3 = __riscv_vwmul_vv_i16m2(q3_m3, __riscv_vle8_v_i8m1(q8+96, vl), vl); + + vl = 16; + + // retrieve lane to multiply with scale + vint32m2_t aux0_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a0, 0), (scale[0]), vl); + vint32m2_t aux0_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a0, 1), (scale[1]), vl); + vint32m2_t aux1_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a1, 0), (scale[2]), vl); + vint32m2_t aux1_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a1, 1), (scale[3]), vl); + vint32m2_t aux2_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a2, 0), (scale[4]), vl); + vint32m2_t aux2_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a2, 1), (scale[5]), vl); + vint32m2_t aux3_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a3, 0), (scale[6]), vl); + vint32m2_t aux3_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a3, 1), (scale[7]), vl); + + vint32m1_t isum0 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux0_0, aux0_1, vl), vzero, vl); + vint32m1_t isum1 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux1_0, aux1_1, vl), isum0, vl); + vint32m1_t isum2 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux2_0, aux2_1, vl), isum1, vl); + vint32m1_t isum3 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux3_0, aux3_1, vl), isum2, vl); + + sum_t += __riscv_vmv_x_s_i32m1_i32(isum3); + + q3 += 32; q8 += 128; scale += 8; + + } + + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + + sumf += d*sum_t; + + } + + *s = sumf; + +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0x3); + const vector signed char lowMask1 = vec_splats((int8_t)0xf); + const vector signed char lowMask2 = vec_splats((int8_t)0x30); + const vector int v0 = vec_splats((int32_t)0); + const vector signed char v1 = vec_splats((signed char)0x1); + const vector unsigned char v2 = vec_splats((unsigned char)0x2); + const vector unsigned char v3 = vec_splats((unsigned char)0x3); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + const vector unsigned char v6 = vec_splats((unsigned char)0x6); + const vector signed char off = vec_splats((signed char)0x20); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + UNUSED(kmask1); + UNUSED(kmask2); + + vector signed char u0 = (vector signed char)vec_xl_len(x[i].scales, 8); + vector signed char u1 = vec_and(u0, lowMask1); + vector signed char u2 = (vector signed char)vec_xl_len(x[i].scales + 8, 4); + vector signed char u3 = (vector signed char)vec_mergeh((vector signed int)u2, (vector signed int)vec_sr(u2, v2)); + vector signed char u30 = vec_sl(vec_and(u3, lowMask), v4); + vector signed char u31 = vec_and(u3, lowMask2); + + u1 = vec_or(u1, u30); + u2 = vec_or(vec_sr(u0, v4), u31); + + vector signed char vscales = (vector signed char)vec_mergeh((vector signed long long)u1, (vector signed long long)u2); + vector signed char qxhs0 = (vector signed char)vec_xl( 0, x[i].hmask); + vector signed char qxhs1 = (vector signed char)vec_xl(16, x[i].hmask); + + vscales = vec_sub(vscales, off); + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; + vector signed int vsumi4 = v0; + vector signed int vsumi5 = v0; + vector signed int vsumi6 = v0; + vector signed int vsumi7 = v0; + + const uint8_t * restrict q3 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/128; ++j) { + __builtin_prefetch(q3, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, q3); + vector signed char qxs1 = (vector signed char)vec_xl(16, q3); + q3 += 32; + + //the low 2 bits + vector signed char qxs00 = vec_and(qxs0, lowMask); + vector signed char qxs01 = vec_and(vec_sr(qxs0, v2), lowMask); + vector signed char qxs02 = vec_and(vec_sr(qxs0, v4), lowMask); + vector signed char qxs03 = vec_and(vec_sr(qxs0, v6), lowMask); + vector signed char qxs10 = vec_and(qxs1, lowMask); + vector signed char qxs11 = vec_and(vec_sr(qxs1, v2), lowMask); + vector signed char qxs12 = vec_and(vec_sr(qxs1, v4), lowMask); + vector signed char qxs13 = vec_and(vec_sr(qxs1, v6), lowMask); + + //the 3rd bit + vector signed char qxh00 = vec_sl(vec_andc(v1, qxhs0), v2); + vector signed char qxh01 = vec_sl(vec_andc(v1, vec_sr(qxhs0, (vector unsigned char)v1)), v2); + vector signed char qxh02 = vec_sl(vec_andc(v1, vec_sr(qxhs0, v2)), v2); + vector signed char qxh03 = vec_sl(vec_andc(v1, vec_sr(qxhs0, v3)), v2); + vector signed char qxh10 = vec_sl(vec_andc(v1, qxhs1), v2); + vector signed char qxh11 = vec_sl(vec_andc(v1, vec_sr(qxhs1, (vector unsigned char)v1)), v2); + vector signed char qxh12 = vec_sl(vec_andc(v1, vec_sr(qxhs1, v2)), v2); + vector signed char qxh13 = vec_sl(vec_andc(v1, vec_sr(qxhs1, v3)), v2); + qxhs0 = vec_sr(qxhs0, v4); + qxhs1 = vec_sr(qxhs1, v4); + + vector signed char q3x00 = vec_sub(qxs00, qxh00); + vector signed char q3x01 = vec_sub(qxs01, qxh01); + vector signed char q3x02 = vec_sub(qxs02, qxh02); + vector signed char q3x03 = vec_sub(qxs03, qxh03); + vector signed char q3x10 = vec_sub(qxs10, qxh10); + vector signed char q3x11 = vec_sub(qxs11, qxh11); + vector signed char q3x12 = vec_sub(qxs12, qxh12); + vector signed char q3x13 = vec_sub(qxs13, qxh13); + + vector signed char q8y00 = vec_xl( 0, q8); + vector signed char q8y10 = vec_xl( 16, q8); + vector signed char q8y01 = vec_xl( 32, q8); + vector signed char q8y11 = vec_xl( 48, q8); + vector signed char q8y02 = vec_xl( 64, q8); + vector signed char q8y12 = vec_xl( 80, q8); + vector signed char q8y03 = vec_xl( 96, q8); + vector signed char q8y13 = vec_xl(112, q8); + q8 += 128; + + vector signed short vscales_h = vec_unpackh(vscales); + vector signed short vs0 = vec_splat(vscales_h, 0); + vector signed short vs1 = vec_splat(vscales_h, 1); + vector signed short vs2 = vec_splat(vscales_h, 2); + vector signed short vs3 = vec_splat(vscales_h, 3); + vector signed short vs4 = vec_splat(vscales_h, 4); + vector signed short vs5 = vec_splat(vscales_h, 5); + vector signed short vs6 = vec_splat(vscales_h, 6); + vector signed short vs7 = vec_splat(vscales_h, 7); + vscales = vec_sld(vscales, vscales, 8); + + vector signed short qv00 = vec_add(vec_mule(q3x00, q8y00), vec_mulo(q3x00, q8y00)); + vector signed short qv01 = vec_add(vec_mule(q3x01, q8y01), vec_mulo(q3x01, q8y01)); + vector signed short qv02 = vec_add(vec_mule(q3x02, q8y02), vec_mulo(q3x02, q8y02)); + vector signed short qv03 = vec_add(vec_mule(q3x03, q8y03), vec_mulo(q3x03, q8y03)); + vector signed short qv10 = vec_add(vec_mule(q3x10, q8y10), vec_mulo(q3x10, q8y10)); + vector signed short qv11 = vec_add(vec_mule(q3x11, q8y11), vec_mulo(q3x11, q8y11)); + vector signed short qv12 = vec_add(vec_mule(q3x12, q8y12), vec_mulo(q3x12, q8y12)); + vector signed short qv13 = vec_add(vec_mule(q3x13, q8y13), vec_mulo(q3x13, q8y13)); + + vsumi0 = vec_msum(qv00, vs0, vsumi0); + vsumi1 = vec_msum(qv01, vs2, vsumi1); + vsumi2 = vec_msum(qv02, vs4, vsumi2); + vsumi3 = vec_msum(qv03, vs6, vsumi3); + vsumi4 = vec_msum(qv10, vs1, vsumi4); + vsumi5 = vec_msum(qv11, vs3, vsumi5); + vsumi6 = vec_msum(qv12, vs5, vsumi6); + vsumi7 = vec_msum(qv13, vs7, vsumi7); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined __loongarch_asx + + const __m256i m3 = __lasx_xvreplgr2vr_b(3); + const __m256i mone = __lasx_xvreplgr2vr_b(1); + const __m128i m32 = __lsx_vreplgr2vr_b(32); + + __m256 acc = (__m256)__lasx_xvldi(0); + + uint32_t aux[3]; + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const uint8_t * restrict q3 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + // Set up scales + memcpy(aux, x[i].scales, 12); + __m128i scales128 = lsx_set_w( + ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4), + ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4), + (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4), + (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4)); + scales128 = __lsx_vsub_b(scales128, m32); + const __m256i all_scales = lasx_ext8_16(scales128); + const __m128i l_scales = lasx_extracti128(all_scales, 0); + const __m128i h_scales = lasx_extracti128(all_scales, 1); + const __m256i scales[2] = {lasx_insertf128(l_scales, l_scales), lasx_insertf128(h_scales, h_scales)}; + + // high bit + const __m256i hbits = __lasx_xvld((const __m256i*)x[i].hmask, 0); + + // integer accumulator + __m256i sumi = __lasx_xvldi(0); + + int bit = 0; + int is = 0; + __m256i xvbit; + + + for (int j = 0; j < QK_K/128; ++j) { + // load low 2 bits + const __m256i q3bits = __lasx_xvld((const __m256i*)q3, 0); q3 += 32; + + xvbit = __lasx_xvreplgr2vr_h(bit); + // prepare low and high bits + const __m256i q3l_0 = __lasx_xvand_v(q3bits, m3); + const __m256i q3h_0 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); + ++bit; + + xvbit = __lasx_xvreplgr2vr_h(bit); + const __m256i q3l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 2), m3); + const __m256i q3h_1 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); + ++bit; + + xvbit = __lasx_xvreplgr2vr_h(bit); + const __m256i q3l_2 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 4), m3); + const __m256i q3h_2 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); + ++bit; + + xvbit = __lasx_xvreplgr2vr_h(bit); + const __m256i q3l_3 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 6), m3); + const __m256i q3h_3 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); + ++bit; + + // load Q8 quants + const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_3 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + + // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use lasx_maddubs_h, + // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set, + // and 2 if the high bit was set) + __m256i q8s_0 = lasx_maddubs_h(q3h_0, q8_0); + __m256i q8s_1 = lasx_maddubs_h(q3h_1, q8_1); + __m256i q8s_2 = lasx_maddubs_h(q3h_2, q8_2); + __m256i q8s_3 = lasx_maddubs_h(q3h_3, q8_3); + + __m256i p16_0 = lasx_maddubs_h(q3l_0, q8_0); + __m256i p16_1 = lasx_maddubs_h(q3l_1, q8_1); + __m256i p16_2 = lasx_maddubs_h(q3l_2, q8_2); + __m256i p16_3 = lasx_maddubs_h(q3l_3, q8_3); + + p16_0 = __lasx_xvsub_h(p16_0, q8s_0); + p16_1 = __lasx_xvsub_h(p16_1, q8s_1); + p16_2 = __lasx_xvsub_h(p16_2, q8s_2); + p16_3 = __lasx_xvsub_h(p16_3, q8s_3); + + // multiply with scales + p16_0 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 0)), p16_0); + p16_1 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 1)), p16_1); + p16_2 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 2)), p16_2); + p16_3 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 3)), p16_3); + + // accumulate + p16_0 = __lasx_xvadd_w(p16_0, p16_1); + p16_2 = __lasx_xvadd_w(p16_2, p16_3); + sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_2)); + } + // multiply with block scale and accumulate + acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), acc);//FIXME + } + + *s = hsum_float_8(acc); + +#else + // scalar version + // This function is written like this so the compiler can manage to vectorize most of it + // Using -Ofast, GCC and clang manage to produce code that is within a factor of 2 or so from the + // manually vectorized version above. Every other version I tried would run at least 4 times slower. + // The ideal situation would be if we could just write the code once, and the compiler would + // automatically produce the best possible set of machine instructions, instead of us having to manually + // write vectorized versions for AVX, ARM_NEON, etc. + + int8_t aux8[QK_K]; + int16_t aux16[8]; + float sums [8]; + int32_t aux32[8]; + memset(sums, 0, 8*sizeof(float)); + + uint32_t auxs[4]; + const int8_t * scales = (const int8_t*)auxs; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict hm = x[i].hmask; + const int8_t * restrict q8 = y[i].qs; + memset(aux32, 0, 8*sizeof(int32_t)); + int8_t * restrict a = aux8; + uint8_t m = 1; + for (int j = 0; j < QK_K; j += 128) { + for (int l = 0; l < 32; ++l) a[l] = q3[l] & 3; + for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4); + a += 32; m <<= 1; + for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 2) & 3; + for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4); + a += 32; m <<= 1; + for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 4) & 3; + for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4); + a += 32; m <<= 1; + for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 6) & 3; + for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4); + a += 32; m <<= 1; + q3 += 32; + } + a = aux8; + + memcpy(auxs, x[i].scales, 12); + uint32_t tmp = auxs[2]; + auxs[2] = ((auxs[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4); + auxs[3] = ((auxs[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4); + auxs[0] = (auxs[0] & kmask2) | (((tmp >> 0) & kmask1) << 4); + auxs[1] = (auxs[1] & kmask2) | (((tmp >> 2) & kmask1) << 4); + for (int j = 0; j < QK_K/16; ++j) { + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l]; + q8 += 8; a += 8; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l]; + q8 += 8; a += 8; + } + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; + } + for (int l = 0; l < 8; ++l) sumf += sums[l]; + *s = sumf; + +#endif + +} + +void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_q4_K * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + + static const uint32_t kmask1 = 0x3f3f3f3f; + static const uint32_t kmask2 = 0x0f0f0f0f; + static const uint32_t kmask3 = 0x03030303; + + uint32_t utmp[4]; + +#ifdef __ARM_NEON + const uint8x16_t m4b = vdupq_n_u8(0xf); + const int32x4_t mzero = vdupq_n_s32(0); + + ggml_int8x16x2_t q4bytes; + ggml_int8x16x2_t q8bytes; + + float sumf = 0; + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + const int16x8_t q8sums = vpaddq_s16(vld1q_s16(y[i].bsums), vld1q_s16(y[i].bsums + 8)); + + memcpy(utmp, x[i].scales, 12); + + uint32x2_t mins8 = { 0 }; + mins8 = vset_lane_u32(utmp[1] & kmask1, mins8, 0); + mins8 = vset_lane_u32(((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4), mins8, 1); + + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[0] &= kmask1; + + const int16x8_t mins = vreinterpretq_s16_u16(vmovl_u8(vreinterpret_u8_u32(mins8))); + const int32x4_t prod = vaddq_s32(vmull_s16(vget_low_s16 (q8sums), vget_low_s16 (mins)), + vmull_s16(vget_high_s16(q8sums), vget_high_s16(mins))); + sumf -= dmin * vaddvq_s32(prod); + + const uint8_t * scales = (const uint8_t *)utmp; + + const uint8_t * restrict q4 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + int32_t sumi1 = 0; + int32_t sumi2 = 0; + + for (int j = 0; j < QK_K/64; ++j) { + const ggml_uint8x16x2_t q4bits = ggml_vld1q_u8_x2(q4); q4 += 32; + + q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; + q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b)); + q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b)); + + const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); + sumi1 += vaddvq_s32(p1) * scales[2*j+0]; + + q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; + q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4)); + q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4)); + + const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); + + sumi2 += vaddvq_s32(p2) * scales[2*j+1]; + } + + sumf += d * (sumi1 + sumi2); + + } + + *s = sumf; + +#elif defined __AVX2__ + + const __m256i m4 = _mm256_set1_epi8(0xF); + + __m256 acc = _mm256_setzero_ps(); + __m128 acc_m = _mm_setzero_ps(); + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + const uint8_t * restrict q4 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + const __m256i mins_and_scales = _mm256_cvtepu8_epi16(_mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0])); + + const __m256i q8sums = _mm256_loadu_si256((const __m256i*)y[i].bsums); + const __m128i q8s = _mm_hadd_epi16(_mm256_extracti128_si256(q8sums, 0), _mm256_extracti128_si256(q8sums, 1)); + const __m128i prod = _mm_madd_epi16(_mm256_extracti128_si256(mins_and_scales, 1), q8s); + acc_m = _mm_fmadd_ps(_mm_set1_ps(dmin), _mm_cvtepi32_ps(prod), acc_m); + + const __m128i sc128 = _mm256_extracti128_si256(mins_and_scales, 0); + const __m256i scales = MM256_SET_M128I(sc128, sc128); + + __m256i sumi = _mm256_setzero_si256(); + + for (int j = 0; j < QK_K/64; ++j) { + + const __m256i scale_l = _mm256_shuffle_epi8(scales, get_scale_shuffle_k4(2*j+0)); + const __m256i scale_h = _mm256_shuffle_epi8(scales, get_scale_shuffle_k4(2*j+1)); + + const __m256i q4bits = _mm256_loadu_si256((const __m256i*)q4); q4 += 32; + const __m256i q4l = _mm256_and_si256(q4bits, m4); + const __m256i q4h = _mm256_and_si256(_mm256_srli_epi16(q4bits, 4), m4); + + const __m256i q8l = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + __m256i p16l = _mm256_maddubs_epi16(q4l, q8l); + p16l = _mm256_madd_epi16(scale_l, p16l); + + const __m256i q8h = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + __m256i p16h = _mm256_maddubs_epi16(q4h, q8h); + p16h = _mm256_madd_epi16(scale_h, p16h); + const __m256i sumj = _mm256_add_epi32(p16l, p16h); + + sumi = _mm256_add_epi32(sumi, sumj); + } + + __m256 vd = _mm256_set1_ps(d); + acc = _mm256_fmadd_ps(vd, _mm256_cvtepi32_ps(sumi), acc); + + } + + acc_m = _mm_add_ps(acc_m, _mm_movehl_ps(acc_m, acc_m)); + acc_m = _mm_add_ss(acc_m, _mm_movehdup_ps(acc_m)); + + *s = hsum_float_8(acc) + _mm_cvtss_f32(acc_m); + +#elif defined __AVX__ + + const __m128i m4 = _mm_set1_epi8(0xF); + const __m128i m2 = _mm_set1_epi8(0x2); + + __m256 acc = _mm256_setzero_ps(); + __m128 acc_m = _mm_setzero_ps(); + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + const uint8_t * restrict q4 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + const __m128i utmps = _mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0]); + const __m128i scales = _mm_cvtepu8_epi16(utmps); + const __m128i mins = _mm_cvtepu8_epi16(_mm_unpackhi_epi64(utmps, utmps)); + + const __m128i q8sums_0 = _mm_loadu_si128((const __m128i*)&y[i].bsums[0]); + const __m128i q8sums_1 = _mm_loadu_si128((const __m128i*)&y[i].bsums[8]); + const __m128i q8s = _mm_hadd_epi16(q8sums_0, q8sums_1); + const __m128i prod = _mm_madd_epi16(mins, q8s); + acc_m = _mm_add_ps(_mm_mul_ps(_mm_set1_ps(dmin), _mm_cvtepi32_ps(prod)), acc_m); + + __m128i sumi_0 = _mm_setzero_si128(); + __m128i sumi_1 = _mm_setzero_si128(); + + __m128i shuffle = _mm_set1_epi16(0x0100); + for (int j = 0; j < QK_K/64; ++j) { + + const __m128i scale_l = _mm_shuffle_epi8(scales, shuffle); + shuffle = _mm_add_epi16(shuffle, m2); + const __m128i scale_h = _mm_shuffle_epi8(scales, shuffle); + shuffle = _mm_add_epi16(shuffle, m2); + + __m128i q4bits = _mm_loadu_si128((const __m128i*)q4); q4 += 16; + const __m128i q4l_0 = _mm_and_si128(q4bits, m4); + const __m128i q4h_0 = _mm_and_si128(_mm_srli_epi16(q4bits, 4), m4); + q4bits = _mm_loadu_si128((const __m128i*)q4); q4 += 16; + const __m128i q4l_1 = _mm_and_si128(q4bits, m4); + const __m128i q4h_1 = _mm_and_si128(_mm_srli_epi16(q4bits, 4), m4); + + const __m128i q8l_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + __m128i p16l = _mm_maddubs_epi16(q4l_0, q8l_0); + p16l = _mm_madd_epi16(scale_l, p16l); + sumi_0 = _mm_add_epi32(sumi_0, p16l); + const __m128i q8l_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + p16l = _mm_maddubs_epi16(q4l_1, q8l_1); + p16l = _mm_madd_epi16(scale_l, p16l); + sumi_1 = _mm_add_epi32(sumi_1, p16l); + + const __m128i q8h_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + __m128i p16h = _mm_maddubs_epi16(q4h_0, q8h_0); + p16h = _mm_madd_epi16(scale_h, p16h); + sumi_0 = _mm_add_epi32(sumi_0, p16h); + const __m128i q8h_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + p16h = _mm_maddubs_epi16(q4h_1, q8h_1); + p16h = _mm_madd_epi16(scale_h, p16h); + sumi_1 = _mm_add_epi32(sumi_1, p16h); + + } + + __m256 vd = _mm256_set1_ps(d); + __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0); + acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(sumi)), acc); + + } + + acc_m = _mm_add_ps(acc_m, _mm_movehl_ps(acc_m, acc_m)); + acc_m = _mm_add_ss(acc_m, _mm_movehdup_ps(acc_m)); + + *s = hsum_float_8(acc) + _mm_cvtss_f32(acc_m); + +#elif defined __riscv_v_intrinsic + + const uint8_t * scales = (const uint8_t*)&utmp[0]; + const uint8_t * mins = (const uint8_t*)&utmp[2]; + + float sumf = 0; + + for (int i = 0; i < nb; ++i) { + + size_t vl = 8; + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + vint16mf2_t q8sums_0 = __riscv_vlse16_v_i16mf2(y[i].bsums, 4, vl); + vint16mf2_t q8sums_1 = __riscv_vlse16_v_i16mf2(y[i].bsums+1, 4, vl); + vint16mf2_t q8sums = __riscv_vadd_vv_i16mf2(q8sums_0, q8sums_1, vl); + + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + vuint8mf4_t mins8 = __riscv_vle8_v_u8mf4(mins, vl); + vint16mf2_t v_mins = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vzext_vf2_u16mf2(mins8, vl)); + vint32m1_t prod = __riscv_vwmul_vv_i32m1(q8sums, v_mins, vl); + + vint32m1_t sumi = __riscv_vredsum_vs_i32m1_i32m1(prod, __riscv_vmv_v_x_i32m1(0, 1), vl); + sumf -= dmin * __riscv_vmv_x_s_i32m1_i32(sumi); + + const uint8_t * restrict q4 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + vl = 32; + + int32_t sum_1 = 0; + int32_t sum_2 = 0; + + vint16m1_t vzero = __riscv_vmv_v_x_i16m1(0, 1); + + for (int j = 0; j < QK_K/64; ++j) { + // load Q4 + vuint8m1_t q4_x = __riscv_vle8_v_u8m1(q4, vl); + + // load Q8 and multiply it with lower Q4 nibble + vint8m1_t q8_0 = __riscv_vle8_v_i8m1(q8, vl); + vint8m1_t q4_0 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(q4_x, 0x0F, vl)); + vint16m2_t qv_0 = __riscv_vwmul_vv_i16m2(q4_0, q8_0, vl); + vint16m1_t vs_0 = __riscv_vredsum_vs_i16m2_i16m1(qv_0, vzero, vl); + + sum_1 += __riscv_vmv_x_s_i16m1_i16(vs_0) * scales[2*j+0]; + + // load Q8 and multiply it with upper Q4 nibble + vint8m1_t q8_1 = __riscv_vle8_v_i8m1(q8+32, vl); + vint8m1_t q4_1 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vsrl_vx_u8m1(q4_x, 0x04, vl)); + vint16m2_t qv_1 = __riscv_vwmul_vv_i16m2(q4_1, q8_1, vl); + vint16m1_t vs_1 = __riscv_vredsum_vs_i16m2_i16m1(qv_1, vzero, vl); + + sum_2 += __riscv_vmv_x_s_i16m1_i16(vs_1) * scales[2*j+1]; + + q4 += 32; q8 += 64; + + } + + sumf += d*(sum_1 + sum_2); + + } + + *s = sumf; + +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector signed char lowMask1 = vec_splats((int8_t)0x3f); + const vector signed char lowMask2 = vec_splats((int8_t)0x30); + const vector int v0 = vec_splats((int32_t)0); + const vector unsigned char v2 = vec_splats((uint8_t)2); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].dmin)); + vector float vdmin = vec_mul(vxmin, vyd); + + vector signed short q8ysums0 = vec_xl( 0, y[i].bsums); + vector signed short q8ysums1 = vec_xl(16, y[i].bsums); + + UNUSED(kmask1); + UNUSED(kmask2); + UNUSED(kmask3); + UNUSED(utmp); + + vector signed char u0 = (vector signed char)vec_xl_len(x[i].scales, 8); + vector signed char u1 = vec_and(vec_sr(u0, v2), lowMask2); + vector signed char u2 = (vector signed char)vec_xl_len(x[i].scales + 8, 4); + vector signed char u3 = vec_sr(u2, v4); + + vector signed char u30 = u1; + vector signed char u31 = (vector signed char)vec_mergeh((vector signed int)vec_and(u2, lowMask), (vector signed int)u3); + + u1 = vec_and(u0, lowMask1); + u2 = vec_or(u30, u31); + + vector signed char utmps = (vector signed char)vec_mergeh((vector signed int)u1, (vector signed int)u2); + + vector signed short vscales = vec_unpackh(utmps); + vector signed short q4xmins = vec_unpackl(utmps); + vector signed short q4xmins0 = vec_mergeh(q4xmins, q4xmins); + vector signed short q4xmins1 = vec_mergel(q4xmins, q4xmins); + + vector signed int prod0 = vec_mule(q4xmins0, q8ysums0); + vector signed int prod1 = vec_mule(q4xmins1, q8ysums1); + vector signed int prod2 = vec_mulo(q4xmins0, q8ysums0); + vector signed int prod3 = vec_mulo(q4xmins1, q8ysums1); + + vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vdmin, vsumf0); + vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vdmin, vsumf1); + vsumf2 = vec_nmsub(vec_ctf(prod2, 0), vdmin, vsumf2); + vsumf3 = vec_nmsub(vec_ctf(prod3, 0), vdmin, vsumf3); + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; + + const uint8_t * restrict q4 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/64; j+=2) { + __builtin_prefetch(q4, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, q4); + vector signed char qxs1 = (vector signed char)vec_xl(16, q4); + vector signed char qxs2 = (vector signed char)vec_xl(32, q4); + vector signed char qxs3 = (vector signed char)vec_xl(48, q4); + q4 += 64; + + vector unsigned char q4x00 = (vector unsigned char)vec_and(qxs0, lowMask); + vector unsigned char q4x01 = (vector unsigned char)vec_sr(qxs0, v4); + vector unsigned char q4x10 = (vector unsigned char)vec_and(qxs1, lowMask); + vector unsigned char q4x11 = (vector unsigned char)vec_sr(qxs1, v4); + vector unsigned char q4x20 = (vector unsigned char)vec_and(qxs2, lowMask); + vector unsigned char q4x21 = (vector unsigned char)vec_sr(qxs2, v4); + vector unsigned char q4x30 = (vector unsigned char)vec_and(qxs3, lowMask); + vector unsigned char q4x31 = (vector unsigned char)vec_sr(qxs3, v4); + + vector signed char q8y00 = vec_xl( 0, q8); + vector signed char q8y10 = vec_xl( 16, q8); + vector signed char q8y01 = vec_xl( 32, q8); + vector signed char q8y11 = vec_xl( 48, q8); + vector signed char q8y20 = vec_xl( 64, q8); + vector signed char q8y30 = vec_xl( 80, q8); + vector signed char q8y21 = vec_xl( 96, q8); + vector signed char q8y31 = vec_xl(112, q8); + q8 += 128; + + vector signed int qv00 = vec_msum(q8y00, q4x00, v0); + vector signed int qv01 = vec_msum(q8y01, q4x01, v0); + vector signed int qv10 = vec_msum(q8y10, q4x10, v0); + vector signed int qv11 = vec_msum(q8y11, q4x11, v0); + vector signed int qv20 = vec_msum(q8y20, q4x20, v0); + vector signed int qv21 = vec_msum(q8y21, q4x21, v0); + vector signed int qv30 = vec_msum(q8y30, q4x30, v0); + vector signed int qv31 = vec_msum(q8y31, q4x31, v0); + + vector signed int vscales_h = vec_unpackh(vscales); + vector signed int vs0 = vec_splat(vscales_h, 0); + vector signed int vs1 = vec_splat(vscales_h, 1); + vector signed int vs2 = vec_splat(vscales_h, 2); + vector signed int vs3 = vec_splat(vscales_h, 3); + vscales = vec_sld(vscales, vscales, 8); + + vsumi0 = vec_add(vec_mul(qv00, vs0), vsumi0); + vsumi1 = vec_add(vec_mul(qv01, vs1), vsumi1); + vsumi2 = vec_add(vec_mul(qv20, vs2), vsumi2); + vsumi3 = vec_add(vec_mul(qv21, vs3), vsumi3); + + vsumi0 = vec_add(vec_mul(qv10, vs0), vsumi0); + vsumi1 = vec_add(vec_mul(qv11, vs1), vsumi1); + vsumi2 = vec_add(vec_mul(qv30, vs2), vsumi2); + vsumi3 = vec_add(vec_mul(qv31, vs3), vsumi3); + } + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined __loongarch_asx + GGML_UNUSED(kmask1); + GGML_UNUSED(kmask2); + GGML_UNUSED(kmask3); + + const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); + + __m256 acc = (__m256)__lasx_xvldi(0); + __m128 acc_m = (__m128)__lsx_vldi(0); + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + const uint8_t * restrict q4 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + const __m256i mins_and_scales = lasx_extu8_16(lsx_set_w(utmp[3], utmp[2], utmp[1], utmp[0])); + + const __m256i q8sums = __lasx_xvld((const __m256i*)y[i].bsums, 0); + const __m128i q8s = lsx_hadd_h(lasx_extracti128(q8sums, 0), lasx_extracti128(q8sums, 1)); + const __m128i prod = lsx_madd_h(lasx_extracti128(mins_and_scales, 1), q8s); + acc_m = __lsx_vfmadd_s(__lsx_vreplfr2vr_s(dmin), __lsx_vffint_s_w(prod), acc_m); + + const __m128i sc128 = lasx_extracti128(mins_and_scales, 0); + const __m256i scales = lasx_insertf128(sc128, sc128); + + __m256i sumi = __lasx_xvldi(0); + + for (int j = 0; j < QK_K/64; ++j) { + + const __m256i scale_l = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+0)); + const __m256i scale_h = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+1)); + + const __m256i q4bits = __lasx_xvld((const __m256i*)q4, 0); q4 += 32; + const __m256i q4l = __lasx_xvand_v(q4bits, m4); + const __m256i q4h = __lasx_xvand_v(__lasx_xvsrli_h(q4bits, 4), m4); + + const __m256i q8l = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + __m256i p16l = lasx_maddubs_h(q4l, q8l); + p16l = lasx_madd_h(scale_l, p16l); + + const __m256i q8h = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + __m256i p16h = lasx_maddubs_h(q4h, q8h); + p16h = lasx_madd_h(scale_h, p16h); + const __m256i sumj = __lasx_xvadd_w(p16l, p16h); + + sumi = __lasx_xvadd_w(sumi, sumj); + } + + __m256 vd = __lasx_xvreplfr2vr_s(d); + acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(sumi), acc); + + } + + acc_m = __lsx_vfadd_s(acc_m, (__m128)__lsx_vpermi_w((__m128i)acc_m, (__m128i)acc_m, 0xee)); + __m128i tmp1 = __lsx_vinsgr2vr_w(__lsx_vldi(0), __lsx_vpickve2gr_w((__m128i)acc_m, 1), 0); + acc_m = __lsx_vfadd_s(acc_m, (__m128)tmp1); + + + ft_union fi; + fi.i = __lsx_vpickve2gr_w(acc_m, 0); + *s = hsum_float_8(acc) + fi.f ; +#else + + const uint8_t * scales = (const uint8_t*)&utmp[0]; + const uint8_t * mins = (const uint8_t*)&utmp[2]; + + int8_t aux8[QK_K]; + int16_t aux16[8]; + float sums [8]; + int32_t aux32[8]; + memset(sums, 0, 8*sizeof(float)); + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + const uint8_t * restrict q4 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + memset(aux32, 0, 8*sizeof(int32_t)); + int8_t * restrict a = aux8; + for (int j = 0; j < QK_K/64; ++j) { + for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF); + a += 32; + for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] >> 4); + a += 32; q4 += 32; + } + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + int sumi = 0; + for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2]; + a = aux8; + int is = 0; + for (int j = 0; j < QK_K/32; ++j) { + int32_t scale = scales[is++]; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + } + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; + const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d; + sumf -= dmin * sumi; + } + for (int l = 0; l < 8; ++l) sumf += sums[l]; + *s = sumf; +#endif +} + +void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_q5_K * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + + static const uint32_t kmask1 = 0x3f3f3f3f; + static const uint32_t kmask2 = 0x0f0f0f0f; + static const uint32_t kmask3 = 0x03030303; + + uint32_t utmp[4]; + +#ifdef __ARM_NEON + const uint8x16_t m4b = vdupq_n_u8(0xf); + const uint8x16_t mone = vdupq_n_u8(1); + const uint8x16_t mtwo = vdupq_n_u8(2); + const int32x4_t mzero = vdupq_n_s32(0); + + ggml_int8x16x4_t q5bytes; + + float sumf = 0; + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + const int16x8_t q8sums = vpaddq_s16(vld1q_s16(y[i].bsums), vld1q_s16(y[i].bsums + 8)); + + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + const uint8x8_t mins8 = vld1_u8((const uint8_t*)utmp + 8); + const int16x8_t mins = vreinterpretq_s16_u16(vmovl_u8(mins8)); + const int32x4_t prod = vaddq_s32(vmull_s16(vget_low_s16 (q8sums), vget_low_s16 (mins)), + vmull_s16(vget_high_s16(q8sums), vget_high_s16(mins))); + int32_t sumi_mins = vaddvq_s32(prod); + + const uint8_t * scales = (const uint8_t *)utmp; + + const uint8_t * restrict q5 = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + + ggml_uint8x16x2_t qhbits = ggml_vld1q_u8_x2(qh); + + ggml_uint8x16x4_t q5h; + + int32_t sumi = 0; + + for (int j = 0; j < QK_K/64; ++j) { + + const ggml_uint8x16x2_t q5bits = ggml_vld1q_u8_x2(q5); q5 += 32; + const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); q8 += 64; + + q5h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits.val[0]), 4); + q5h.val[1] = vshlq_n_u8(vandq_u8(mone, qhbits.val[1]), 4); + q5h.val[2] = vshlq_n_u8(vandq_u8(mtwo, qhbits.val[0]), 3); + q5h.val[3] = vshlq_n_u8(vandq_u8(mtwo, qhbits.val[1]), 3); + qhbits.val[0] = vshrq_n_u8(qhbits.val[0], 2); + qhbits.val[1] = vshrq_n_u8(qhbits.val[1], 2); + + q5bytes.val[0] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q5bits.val[0], m4b), q5h.val[0])); + q5bytes.val[1] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q5bits.val[1], m4b), q5h.val[1])); + q5bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5bits.val[0], 4), q5h.val[2])); + q5bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5bits.val[1], 4), q5h.val[3])); + + sumi += vaddvq_s32(ggml_vdotq_s32(ggml_vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0]), q5bytes.val[1], q8bytes.val[1])) * *scales++; + sumi += vaddvq_s32(ggml_vdotq_s32(ggml_vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2]), q5bytes.val[3], q8bytes.val[3])) * *scales++; + } + + sumf += d * sumi - dmin * sumi_mins; + } + + *s = sumf; + +#elif defined __AVX2__ + + const __m256i m4 = _mm256_set1_epi8(0xF); + const __m128i mzero = _mm_setzero_si128(); + const __m256i mone = _mm256_set1_epi8(1); + + __m256 acc = _mm256_setzero_ps(); + + float summs = 0.f; + + for (int i = 0; i < nb; ++i) { + const uint8_t * restrict q5 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + const __m256i mins_and_scales = _mm256_cvtepu8_epi16(_mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0])); + + const __m256i q8sums = _mm256_loadu_si256((const __m256i*)y[i].bsums); + const __m128i q8s = _mm_hadd_epi16(_mm256_extracti128_si256(q8sums, 0), _mm256_extracti128_si256(q8sums, 1)); + const __m128i prod = _mm_madd_epi16(_mm256_extracti128_si256(mins_and_scales, 1), q8s); + const __m128i hsum = _mm_hadd_epi32(_mm_hadd_epi32(prod, mzero), mzero); + summs += dmin * _mm_extract_epi32(hsum, 0); + + const __m128i sc128 = _mm256_extracti128_si256(mins_and_scales, 0); + const __m256i scales = MM256_SET_M128I(sc128, sc128); + + const __m256i hbits = _mm256_loadu_si256((const __m256i*)x[i].qh); + __m256i hmask = mone; + + __m256i sumi = _mm256_setzero_si256(); + + int bit = 0; + + for (int j = 0; j < QK_K/64; ++j) { + + const __m256i scale_0 = _mm256_shuffle_epi8(scales, get_scale_shuffle_k4(2*j+0)); + const __m256i scale_1 = _mm256_shuffle_epi8(scales, get_scale_shuffle_k4(2*j+1)); + + const __m256i q5bits = _mm256_loadu_si256((const __m256i*)q5); q5 += 32; + + const __m256i q5l_0 = _mm256_and_si256(q5bits, m4); + const __m256i q5h_0 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_and_si256(hbits, hmask), bit++), 4); + const __m256i q5_0 = _mm256_add_epi8(q5l_0, q5h_0); + hmask = _mm256_slli_epi16(hmask, 1); + + const __m256i q5l_1 = _mm256_and_si256(_mm256_srli_epi16(q5bits, 4), m4); + const __m256i q5h_1 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_and_si256(hbits, hmask), bit++), 4); + const __m256i q5_1 = _mm256_add_epi8(q5l_1, q5h_1); + hmask = _mm256_slli_epi16(hmask, 1); + + const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + + __m256i p16_0 = _mm256_maddubs_epi16(q5_0, q8_0); + __m256i p16_1 = _mm256_maddubs_epi16(q5_1, q8_1); + + p16_0 = _mm256_madd_epi16(scale_0, p16_0); + p16_1 = _mm256_madd_epi16(scale_1, p16_1); + + sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_0, p16_1)); + + } + + __m256 vd = _mm256_set1_ps(d); + acc = _mm256_fmadd_ps(vd, _mm256_cvtepi32_ps(sumi), acc); + + } + + *s = hsum_float_8(acc) + summs; + +#elif defined __AVX__ + + const __m128i m4 = _mm_set1_epi8(0xF); + const __m128i mzero = _mm_setzero_si128(); + const __m128i mone = _mm_set1_epi8(1); + const __m128i m2 = _mm_set1_epi8(2); + + __m256 acc = _mm256_setzero_ps(); + + float summs = 0.f; + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + const uint8_t * restrict q5 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + const __m128i utmps = _mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0]); + const __m128i scales = _mm_cvtepu8_epi16(utmps); + const __m128i mins = _mm_cvtepu8_epi16(_mm_unpackhi_epi64(utmps, utmps)); + + const __m128i q8sums_0 = _mm_loadu_si128((const __m128i*)&y[i].bsums[0]); + const __m128i q8sums_1 = _mm_loadu_si128((const __m128i*)&y[i].bsums[8]); + const __m128i q8s = _mm_hadd_epi16(q8sums_0, q8sums_1); + const __m128i prod = _mm_madd_epi16(mins, q8s); + const __m128i hsum = _mm_hadd_epi32(_mm_hadd_epi32(prod, mzero), mzero); + summs += dmin * _mm_extract_epi32(hsum, 0); + + const __m128i hbits_0 = _mm_loadu_si128((const __m128i*)&x[i].qh[0]); + const __m128i hbits_1 = _mm_loadu_si128((const __m128i*)&x[i].qh[16]); + __m128i hmask = mone; + + __m128i sumi_0 = _mm_setzero_si128(); + __m128i sumi_1 = _mm_setzero_si128(); + + int bit = 0; + + __m128i shuffle = _mm_set1_epi16(0x0100); + for (int j = 0; j < QK_K/64; ++j) { + + const __m128i scale_0 = _mm_shuffle_epi8(scales, shuffle); + shuffle = _mm_add_epi16(shuffle, m2); + const __m128i scale_1 = _mm_shuffle_epi8(scales, shuffle); + shuffle = _mm_add_epi16(shuffle, m2); + + const __m128i q5bits_0 = _mm_loadu_si128((const __m128i*)q5); q5 += 16; + const __m128i q5bits_1 = _mm_loadu_si128((const __m128i*)q5); q5 += 16; + + __m128i q5l_0 = _mm_and_si128(q5bits_0, m4); + __m128i q5l_1 = _mm_and_si128(q5bits_1, m4); + __m128i q5h_0 = _mm_slli_epi16(_mm_srli_epi16(_mm_and_si128(hbits_0, hmask), bit), 4); + __m128i q5h_1 = _mm_slli_epi16(_mm_srli_epi16(_mm_and_si128(hbits_1, hmask), bit++), 4); + __m128i q5_0 = _mm_add_epi8(q5l_0, q5h_0); + __m128i q5_1 = _mm_add_epi8(q5l_1, q5h_1); + hmask = _mm_slli_epi16(hmask, 1); + + __m128i q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + __m128i q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + __m128i p16_0 = _mm_maddubs_epi16(q5_0, q8_0); + __m128i p16_1 = _mm_maddubs_epi16(q5_1, q8_1); + p16_0 = _mm_madd_epi16(scale_0, p16_0); + p16_1 = _mm_madd_epi16(scale_0, p16_1); + + q5l_0 = _mm_and_si128(_mm_srli_epi16(q5bits_0, 4), m4); + q5l_1 = _mm_and_si128(_mm_srli_epi16(q5bits_1, 4), m4); + q5h_0 = _mm_slli_epi16(_mm_srli_epi16(_mm_and_si128(hbits_0, hmask), bit), 4); + q5h_1 = _mm_slli_epi16(_mm_srli_epi16(_mm_and_si128(hbits_1, hmask), bit++), 4); + q5_0 = _mm_add_epi8(q5l_0, q5h_0); + q5_1 = _mm_add_epi8(q5l_1, q5h_1); + hmask = _mm_slli_epi16(hmask, 1); + + q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + __m128i p16_2 = _mm_maddubs_epi16(q5_0, q8_0); + __m128i p16_3 = _mm_maddubs_epi16(q5_1, q8_1); + p16_2 = _mm_madd_epi16(scale_1, p16_2); + p16_3 = _mm_madd_epi16(scale_1, p16_3); + + sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2)); + sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_1, p16_3)); + + } + + __m256 vd = _mm256_set1_ps(d); + __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0); + acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(sumi)), acc); + + } + + *s = hsum_float_8(acc) + summs; + +#elif defined __riscv_v_intrinsic + + const uint8_t * scales = (const uint8_t*)&utmp[0]; + const uint8_t * mins = (const uint8_t*)&utmp[2]; + + float sumf = 0; + float sums = 0.0; + + size_t vl; + + for (int i = 0; i < nb; ++i) { + + vl = 8; + + const uint8_t * restrict q5 = x[i].qs; + const uint8_t * restrict hm = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d; + + vint16mf2_t q8sums_0 = __riscv_vlse16_v_i16mf2(y[i].bsums, 4, vl); + vint16mf2_t q8sums_1 = __riscv_vlse16_v_i16mf2(y[i].bsums+1, 4, vl); + vint16mf2_t q8sums = __riscv_vadd_vv_i16mf2(q8sums_0, q8sums_1, vl); + + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + vuint8mf4_t mins8 = __riscv_vle8_v_u8mf4(mins, vl); + vint16mf2_t v_mins = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vzext_vf2_u16mf2(mins8, vl)); + vint32m1_t prod = __riscv_vwmul_vv_i32m1(q8sums, v_mins, vl); + + vint32m1_t sumi = __riscv_vredsum_vs_i32m1_i32m1(prod, __riscv_vmv_v_x_i32m1(0, 1), vl); + sumf -= dmin * __riscv_vmv_x_s_i32m1_i32(sumi); + + vl = 32; + int32_t aux32 = 0; + int is = 0; + + uint8_t m = 1; + vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); + vuint8m1_t vqh = __riscv_vle8_v_u8m1(hm, vl); + + for (int j = 0; j < QK_K/64; ++j) { + // load Q5 and Q8 + vuint8m1_t q5_x = __riscv_vle8_v_u8m1(q5, vl); + vint8m1_t q8_y1 = __riscv_vle8_v_i8m1(q8, vl); + vint8m1_t q8_y2 = __riscv_vle8_v_i8m1(q8+32, vl); + + // compute mask for addition + vint8m1_t q5_a = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(q5_x, 0x0F, vl)); + vuint8m1_t qh_m1 = __riscv_vand_vx_u8m1(vqh, m, vl); + vbool8_t vmask_1 = __riscv_vmsne_vx_u8m1_b8(qh_m1, 0, vl); + vint8m1_t q5_m1 = __riscv_vadd_vx_i8m1_mu(vmask_1, q5_a, q5_a, 16, vl); + m <<= 1; + + vint8m1_t q5_l = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vsrl_vx_u8m1(q5_x, 0x04, vl)); + vuint8m1_t qh_m2 = __riscv_vand_vx_u8m1(vqh, m, vl); + vbool8_t vmask_2 = __riscv_vmsne_vx_u8m1_b8(qh_m2, 0, vl); + vint8m1_t q5_m2 = __riscv_vadd_vx_i8m1_mu(vmask_2, q5_l, q5_l, 16, vl); + m <<= 1; + + vint16m2_t v0 = __riscv_vwmul_vv_i16m2(q5_m1, q8_y1, vl); + vint16m2_t v1 = __riscv_vwmul_vv_i16m2(q5_m2, q8_y2, vl); + + vint32m4_t vs1 = __riscv_vwmul_vx_i32m4(v0, scales[is++], vl); + vint32m4_t vs2 = __riscv_vwmul_vx_i32m4(v1, scales[is++], vl); + + vint32m1_t vacc1 = __riscv_vredsum_vs_i32m4_i32m1(vs1, vzero, vl); + vint32m1_t vacc2 = __riscv_vredsum_vs_i32m4_i32m1(vs2, vzero, vl); + + aux32 += __riscv_vmv_x_s_i32m1_i32(vacc1) + __riscv_vmv_x_s_i32m1_i32(vacc2); + q5 += 32; q8 += 64; + + } + + vfloat32m1_t vaux = __riscv_vfmul_vf_f32m1(__riscv_vfmv_v_f_f32m1(aux32, 1), d, 1); + sums += __riscv_vfmv_f_s_f32m1_f32(vaux); + + } + + *s = sumf+sums; + +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector signed char lowMask1 = vec_splats((int8_t)0x3f); + const vector signed char lowMask2 = vec_splats((int8_t)0x30); + const vector int v0 = vec_splats((int32_t)0); + const vector unsigned char v1 = vec_splats((unsigned char)0x1); + const vector unsigned char v2 = vec_splats((unsigned char)0x2); + const vector unsigned char v3 = vec_splats((unsigned char)0x3); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].dmin)); + vector float vdmin = vec_mul(vxmin, vyd); + + UNUSED(kmask1); + UNUSED(kmask2); + UNUSED(kmask3); + UNUSED(utmp); + + vector signed char u0 = (vector signed char)vec_xl_len(x[i].scales, 8); + vector signed char u1 = vec_and(vec_sr(u0, v2), lowMask2); + vector signed char u2 = (vector signed char)vec_xl_len(x[i].scales + 8, 4); + vector signed char u3 = vec_sr(u2, v4); + + vector signed char u30 = u1; + vector signed char u31 = (vector signed char)vec_mergeh((vector signed int)vec_and(u2, lowMask), (vector signed int)u3); + + u1 = vec_and(u0, lowMask1); + u2 = vec_or(u30, u31); + + vector signed char utmps = (vector signed char)vec_mergeh((vector signed int)u1, (vector signed int)u2); + + vector signed short q8ysums0 = vec_xl( 0, y[i].bsums); + vector signed short q8ysums1 = vec_xl(16, y[i].bsums); + + vector signed short vscales = vec_unpackh(utmps); + + vector signed short q5xmins = vec_unpackl(utmps); + vector signed short q5xmins0 = vec_mergeh(q5xmins, q5xmins); + vector signed short q5xmins1 = vec_mergel(q5xmins, q5xmins); + + vector signed int prod0 = vec_mule(q5xmins0, q8ysums0); + vector signed int prod1 = vec_mule(q5xmins1, q8ysums1); + vector signed int prod2 = vec_mulo(q5xmins0, q8ysums0); + vector signed int prod3 = vec_mulo(q5xmins1, q8ysums1); + + vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vdmin, vsumf0); + vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vdmin, vsumf1); + vsumf2 = vec_nmsub(vec_ctf(prod2, 0), vdmin, vsumf2); + vsumf3 = vec_nmsub(vec_ctf(prod3, 0), vdmin, vsumf3); + + vector signed char qxhs0 = (vector signed char)vec_xl( 0, x[i].qh); + vector signed char qxhs1 = (vector signed char)vec_xl(16, x[i].qh); + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; + + const uint8_t * restrict q5 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/64; ++j) { + __builtin_prefetch(q5, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, q5); + vector signed char qxs1 = (vector signed char)vec_xl(16, q5); + q5 += 32; + + vector signed char qxs00 = vec_and(qxs0, lowMask); + vector signed char qxs01 = vec_sr(qxs0, v4); + vector signed char qxs10 = vec_and(qxs1, lowMask); + vector signed char qxs11 = vec_sr(qxs1, v4); + + vector signed char q5h00 = vec_sl(vec_and((vector signed char)v1, qxhs0), v4); + vector signed char q5h01 = vec_sl(vec_and((vector signed char)v2, qxhs0), v3); + vector signed char q5h10 = vec_sl(vec_and((vector signed char)v1, qxhs1), v4); + vector signed char q5h11 = vec_sl(vec_and((vector signed char)v2, qxhs1), v3); + qxhs0 = vec_sr(qxhs0, v2); + qxhs1 = vec_sr(qxhs1, v2); + + vector unsigned char q5x00 = (vector unsigned char)vec_or(q5h00, qxs00); + vector unsigned char q5x01 = (vector unsigned char)vec_or(q5h01, qxs01); + vector unsigned char q5x10 = (vector unsigned char)vec_or(q5h10, qxs10); + vector unsigned char q5x11 = (vector unsigned char)vec_or(q5h11, qxs11); + + vector signed char q8y00 = vec_xl( 0, q8); + vector signed char q8y10 = vec_xl(16, q8); + vector signed char q8y01 = vec_xl(32, q8); + vector signed char q8y11 = vec_xl(48, q8); + q8 += 64; + + vector signed int qv00 = vec_msum(q8y00, q5x00, v0); + vector signed int qv01 = vec_msum(q8y01, q5x01, v0); + vector signed int qv10 = vec_msum(q8y10, q5x10, v0); + vector signed int qv11 = vec_msum(q8y11, q5x11, v0); + + vector signed int vscales_h = vec_unpackh(vscales); + vector signed int vs0 = vec_splat(vscales_h, 0); + vector signed int vs1 = vec_splat(vscales_h, 1); + vscales = vec_sld(vscales, vscales, 12); + + vsumi0 = vec_add(vec_mul(qv00, vs0), vsumi0); + vsumi1 = vec_add(vec_mul(qv10, vs0), vsumi1); + vsumi2 = vec_add(vec_mul(qv01, vs1), vsumi2); + vsumi3 = vec_add(vec_mul(qv11, vs1), vsumi3); + } + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined __loongarch_asx + GGML_UNUSED(kmask1); + GGML_UNUSED(kmask2); + GGML_UNUSED(kmask3); + + const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); + const __m128i mzero = __lsx_vldi(0); + const __m256i mone = __lasx_xvreplgr2vr_b(1); + + __m256 acc = (__m256)__lasx_xvldi(0); + + float summs = 0.f; + + for (int i = 0; i < nb; ++i) { + + const uint8_t * restrict q5 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + const __m256i mins_and_scales = lasx_extu8_16(lsx_set_w(utmp[3], utmp[2], utmp[1], utmp[0])); + + const __m256i q8sums = __lasx_xvld((const __m256i*)y[i].bsums, 0); + const __m128i q8s = lsx_hadd_h(lasx_extracti128(q8sums, 0), lasx_extracti128(q8sums, 1)); + const __m128i prod = lsx_madd_h(lasx_extracti128(mins_and_scales, 1), q8s); + const __m128i hsum = lsx_hadd_w(lsx_hadd_w(prod, mzero), mzero); + summs += dmin * __lsx_vpickve2gr_w(hsum, 0); //TODO check + + const __m128i sc128 = lasx_extracti128(mins_and_scales, 0); + const __m256i scales = lasx_insertf128(sc128, sc128); + + const __m256i hbits = __lasx_xvld((const __m256i*)x[i].qh, 0); + __m256i hmask = mone; + + __m256i sumi = __lasx_xvldi(0); + + int bit = 0; + __m256i xvbit; + + for (int j = 0; j < QK_K/64; ++j) { + + const __m256i scale_0 = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+0)); + const __m256i scale_1 = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+1)); + + const __m256i q5bits = __lasx_xvld((const __m256i*)q5, 0); q5 += 32; + + xvbit = __lasx_xvreplgr2vr_h(bit++); + const __m256i q5l_0 = __lasx_xvand_v(q5bits, m4); + const __m256i q5h_0 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvand_v(hbits, hmask), xvbit), 4); + const __m256i q5_0 = __lasx_xvadd_b(q5l_0, q5h_0); + hmask = __lasx_xvslli_h(hmask, 1); + + xvbit = __lasx_xvreplgr2vr_h(bit++); + const __m256i q5l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q5bits, 4), m4); + const __m256i q5h_1 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvand_v(hbits, hmask), xvbit), 4); + const __m256i q5_1 = __lasx_xvadd_b(q5l_1, q5h_1); + hmask = __lasx_xvslli_h(hmask, 1); + + const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + + __m256i p16_0 = lasx_maddubs_h(q5_0, q8_0); + __m256i p16_1 = lasx_maddubs_h(q5_1, q8_1); + + p16_0 = lasx_madd_h(scale_0, p16_0); + p16_1 = lasx_madd_h(scale_1, p16_1); + + sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_1)); + + } + + __m256 vd = __lasx_xvreplfr2vr_s(d); + acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(sumi), acc); + + } + + *s = hsum_float_8(acc) + summs; + +#else + + const uint8_t * scales = (const uint8_t*)&utmp[0]; + const uint8_t * mins = (const uint8_t*)&utmp[2]; + + int8_t aux8[QK_K]; + int16_t aux16[8]; + float sums [8]; + int32_t aux32[8]; + memset(sums, 0, 8*sizeof(float)); + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + const uint8_t * restrict q4 = x[i].qs; + const uint8_t * restrict hm = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + memset(aux32, 0, 8*sizeof(int32_t)); + int8_t * restrict a = aux8; + uint8_t m = 1; + for (int j = 0; j < QK_K/64; ++j) { + for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF); + for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0); + a += 32; m <<= 1; + for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] >> 4); + for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0); + a += 32; m <<= 1; + q4 += 32; + } + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + int sumi = 0; + for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2]; + a = aux8; + int is = 0; + for (int j = 0; j < QK_K/32; ++j) { + int32_t scale = scales[is++]; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + } + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; + const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d; + sumf -= dmin * sumi; + } + for (int l = 0; l < 8; ++l) sumf += sums[l]; + *s = sumf; +#endif +} + +void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_q6_K * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#ifdef __ARM_NEON + float sum = 0; + + const uint8x16_t m4b = vdupq_n_u8(0xF); + const int32x4_t vzero = vdupq_n_s32(0); + //const int8x16_t m32s = vdupq_n_s8(32); + + const uint8x16_t mone = vdupq_n_u8(3); + + ggml_int8x16x4_t q6bytes; + ggml_uint8x16x4_t q6h; + + for (int i = 0; i < nb; ++i) { + + const float d_all = GGML_FP16_TO_FP32(x[i].d); + + const uint8_t * restrict q6 = x[i].ql; + const uint8_t * restrict qh = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + + const int8_t * restrict scale = x[i].scales; + + const ggml_int16x8x2_t q8sums = ggml_vld1q_s16_x2(y[i].bsums); + const int8x16_t scales = vld1q_s8(scale); + const ggml_int16x8x2_t q6scales = {{vmovl_s8(vget_low_s8(scales)), vmovl_s8(vget_high_s8(scales))}}; + + const int32x4_t prod = vaddq_s32(vaddq_s32(vmull_s16(vget_low_s16 (q8sums.val[0]), vget_low_s16 (q6scales.val[0])), + vmull_s16(vget_high_s16(q8sums.val[0]), vget_high_s16(q6scales.val[0]))), + vaddq_s32(vmull_s16(vget_low_s16 (q8sums.val[1]), vget_low_s16 (q6scales.val[1])), + vmull_s16(vget_high_s16(q8sums.val[1]), vget_high_s16(q6scales.val[1])))); + int32_t isum_mins = vaddvq_s32(prod); + + int32_t isum = 0; + + for (int j = 0; j < QK_K/128; ++j) { + + ggml_uint8x16x2_t qhbits = ggml_vld1q_u8_x2(qh); qh += 32; + ggml_uint8x16x4_t q6bits = ggml_vld1q_u8_x4(q6); q6 += 64; + ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); q8 += 64; + + q6h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits.val[0]), 4); + q6h.val[1] = vshlq_n_u8(vandq_u8(mone, qhbits.val[1]), 4); + uint8x16_t shifted = vshrq_n_u8(qhbits.val[0], 2); + q6h.val[2] = vshlq_n_u8(vandq_u8(mone, shifted), 4); + shifted = vshrq_n_u8(qhbits.val[1], 2); + q6h.val[3] = vshlq_n_u8(vandq_u8(mone, shifted), 4); + + //q6bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[0], m4b), q6h.val[0])), m32s); + //q6bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[1], m4b), q6h.val[1])), m32s); + //q6bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[2], m4b), q6h.val[2])), m32s); + //q6bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[3], m4b), q6h.val[3])), m32s); + q6bytes.val[0] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[0], m4b), q6h.val[0])); + q6bytes.val[1] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[1], m4b), q6h.val[1])); + q6bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[2], m4b), q6h.val[2])); + q6bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[3], m4b), q6h.val[3])); + + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; + + scale += 4; + + q8bytes = ggml_vld1q_s8_x4(q8); q8 += 64; + + shifted = vshrq_n_u8(qhbits.val[0], 4); + q6h.val[0] = vshlq_n_u8(vandq_u8(mone, shifted), 4); + shifted = vshrq_n_u8(qhbits.val[1], 4); + q6h.val[1] = vshlq_n_u8(vandq_u8(mone, shifted), 4); + shifted = vshrq_n_u8(qhbits.val[0], 6); + q6h.val[2] = vshlq_n_u8(vandq_u8(mone, shifted), 4); + shifted = vshrq_n_u8(qhbits.val[1], 6); + q6h.val[3] = vshlq_n_u8(vandq_u8(mone, shifted), 4); + + //q6bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[0], 4), q6h.val[0])), m32s); + //q6bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[1], 4), q6h.val[1])), m32s); + //q6bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[2], 4), q6h.val[2])), m32s); + //q6bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[3], 4), q6h.val[3])), m32s); + q6bytes.val[0] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[0], 4), q6h.val[0])); + q6bytes.val[1] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[1], 4), q6h.val[1])); + q6bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[2], 4), q6h.val[2])); + q6bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[3], 4), q6h.val[3])); + + isum += vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + + vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; + scale += 4; + } + //sum += isum * d_all * y[i].d; + sum += d_all * y[i].d * (isum - 32 * isum_mins); + + } + *s = sum; + +#elif defined __AVX2__ + + const __m256i m4 = _mm256_set1_epi8(0xF); + const __m256i m2 = _mm256_set1_epi8(3); + const __m256i m32s = _mm256_set1_epi8(32); + + __m256 acc = _mm256_setzero_ps(); + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + + const uint8_t * restrict q4 = x[i].ql; + const uint8_t * restrict qh = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + + const __m128i scales = _mm_loadu_si128((const __m128i*)x[i].scales); + + __m256i sumi = _mm256_setzero_si256(); + + int is = 0; + + for (int j = 0; j < QK_K/128; ++j) { + + const __m128i scale_0 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 0)); + const __m128i scale_1 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 1)); + const __m128i scale_2 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 2)); + const __m128i scale_3 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 3)); + is += 4; + + const __m256i q4bits1 = _mm256_loadu_si256((const __m256i*)q4); q4 += 32; + const __m256i q4bits2 = _mm256_loadu_si256((const __m256i*)q4); q4 += 32; + const __m256i q4bitsH = _mm256_loadu_si256((const __m256i*)qh); qh += 32; + + const __m256i q4h_0 = _mm256_slli_epi16(_mm256_and_si256(q4bitsH, m2), 4); + const __m256i q4h_1 = _mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q4bitsH, 2), m2), 4); + const __m256i q4h_2 = _mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q4bitsH, 4), m2), 4); + const __m256i q4h_3 = _mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q4bitsH, 6), m2), 4); + + const __m256i q4_0 = _mm256_or_si256(_mm256_and_si256(q4bits1, m4), q4h_0); + const __m256i q4_1 = _mm256_or_si256(_mm256_and_si256(q4bits2, m4), q4h_1); + const __m256i q4_2 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits1, 4), m4), q4h_2); + const __m256i q4_3 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits2, 4), m4), q4h_3); + + const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q8_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q8_3 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + + __m256i q8s_0 = _mm256_maddubs_epi16(m32s, q8_0); + __m256i q8s_1 = _mm256_maddubs_epi16(m32s, q8_1); + __m256i q8s_2 = _mm256_maddubs_epi16(m32s, q8_2); + __m256i q8s_3 = _mm256_maddubs_epi16(m32s, q8_3); + + __m256i p16_0 = _mm256_maddubs_epi16(q4_0, q8_0); + __m256i p16_1 = _mm256_maddubs_epi16(q4_1, q8_1); + __m256i p16_2 = _mm256_maddubs_epi16(q4_2, q8_2); + __m256i p16_3 = _mm256_maddubs_epi16(q4_3, q8_3); + + p16_0 = _mm256_sub_epi16(p16_0, q8s_0); + p16_1 = _mm256_sub_epi16(p16_1, q8s_1); + p16_2 = _mm256_sub_epi16(p16_2, q8s_2); + p16_3 = _mm256_sub_epi16(p16_3, q8s_3); + + p16_0 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_0), p16_0); + p16_1 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_1), p16_1); + p16_2 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_2), p16_2); + p16_3 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_3), p16_3); + + sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_0, p16_1)); + sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_2, p16_3)); + + } + + acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi), acc); + } + + *s = hsum_float_8(acc); + +#elif defined __AVX__ + + const __m128i m3 = _mm_set1_epi8(3); + const __m128i m15 = _mm_set1_epi8(15); + + __m256 acc = _mm256_setzero_ps(); + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + + const uint8_t * restrict q4 = x[i].ql; + const uint8_t * restrict qh = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + + // handle the q6_k -32 offset separately using bsums + const __m128i q8sums_0 = _mm_loadu_si128((const __m128i*)y[i].bsums); + const __m128i q8sums_1 = _mm_loadu_si128((const __m128i*)y[i].bsums + 1); + const __m128i scales = _mm_loadu_si128((const __m128i*)x[i].scales); + const __m128i scales_16_0 = _mm_cvtepi8_epi16(scales); + const __m128i scales_16_1 = _mm_cvtepi8_epi16(_mm_bsrli_si128(scales, 8)); + const __m128i q8sclsub_0 = _mm_slli_epi32(_mm_madd_epi16(q8sums_0, scales_16_0), 5); + const __m128i q8sclsub_1 = _mm_slli_epi32(_mm_madd_epi16(q8sums_1, scales_16_1), 5); + + __m128i sumi_0 = _mm_setzero_si128(); + __m128i sumi_1 = _mm_setzero_si128(); + + int is = 0; + + for (int j = 0; j < QK_K/128; ++j) { + + const __m128i q4bitsH_0 = _mm_loadu_si128((const __m128i*)qh); qh += 16; + const __m128i q4bitsH_1 = _mm_loadu_si128((const __m128i*)qh); qh += 16; + + const __m128i q4h_0 = _mm_slli_epi16(_mm_and_si128(q4bitsH_0, m3), 4); + const __m128i q4h_1 = _mm_slli_epi16(_mm_and_si128(q4bitsH_1, m3), 4); + const __m128i q4h_2 = _mm_slli_epi16(_mm_and_si128(q4bitsH_0, _mm_set1_epi8(12)), 2); + const __m128i q4h_3 = _mm_slli_epi16(_mm_and_si128(q4bitsH_1, _mm_set1_epi8(12)), 2); + const __m128i q4h_4 = _mm_and_si128(q4bitsH_0, _mm_set1_epi8(48)); + const __m128i q4h_5 = _mm_and_si128(q4bitsH_1, _mm_set1_epi8(48)); + const __m128i q4h_6 = _mm_srli_epi16(_mm_and_si128(q4bitsH_0, _mm_set1_epi8(-64)), 2); + const __m128i q4h_7 = _mm_srli_epi16(_mm_and_si128(q4bitsH_1, _mm_set1_epi8(-64)), 2); + + const __m128i q4bits1_0 = _mm_loadu_si128((const __m128i*)q4); q4 += 16; + const __m128i q4bits1_1 = _mm_loadu_si128((const __m128i*)q4); q4 += 16; + const __m128i q4bits2_0 = _mm_loadu_si128((const __m128i*)q4); q4 += 16; + const __m128i q4bits2_1 = _mm_loadu_si128((const __m128i*)q4); q4 += 16; + + const __m128i q4_0 = _mm_or_si128(_mm_and_si128(q4bits1_0, m15), q4h_0); + const __m128i q4_1 = _mm_or_si128(_mm_and_si128(q4bits1_1, m15), q4h_1); + const __m128i q4_2 = _mm_or_si128(_mm_and_si128(q4bits2_0, m15), q4h_2); + const __m128i q4_3 = _mm_or_si128(_mm_and_si128(q4bits2_1, m15), q4h_3); + const __m128i q4_4 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(q4bits1_0, 4), m15), q4h_4); + const __m128i q4_5 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(q4bits1_1, 4), m15), q4h_5); + const __m128i q4_6 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(q4bits2_0, 4), m15), q4h_6); + const __m128i q4_7 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(q4bits2_1, 4), m15), q4h_7); + + const __m128i q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_2 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_3 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_4 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_5 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_6 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_7 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + + __m128i p16_0 = _mm_maddubs_epi16(q4_0, q8_0); + __m128i p16_1 = _mm_maddubs_epi16(q4_1, q8_1); + __m128i p16_2 = _mm_maddubs_epi16(q4_2, q8_2); + __m128i p16_3 = _mm_maddubs_epi16(q4_3, q8_3); + __m128i p16_4 = _mm_maddubs_epi16(q4_4, q8_4); + __m128i p16_5 = _mm_maddubs_epi16(q4_5, q8_5); + __m128i p16_6 = _mm_maddubs_epi16(q4_6, q8_6); + __m128i p16_7 = _mm_maddubs_epi16(q4_7, q8_7); + + const __m128i scale_0 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 0)); + const __m128i scale_1 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 1)); + const __m128i scale_2 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 2)); + const __m128i scale_3 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 3)); + is += 4; + + p16_0 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_0), p16_0); + p16_1 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_bsrli_si128(scale_0, 8)), p16_1); + p16_2 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_1), p16_2); + p16_3 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_bsrli_si128(scale_1, 8)), p16_3); + p16_4 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_2), p16_4); + p16_5 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_bsrli_si128(scale_2, 8)), p16_5); + p16_6 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_3), p16_6); + p16_7 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_bsrli_si128(scale_3, 8)), p16_7); + + sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2)); + sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_1, p16_3)); + sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_4, p16_6)); + sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_5, p16_7)); + + } + + sumi_0 = _mm_sub_epi32(sumi_0, q8sclsub_0); + sumi_1 = _mm_sub_epi32(sumi_1, q8sclsub_1); + const __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0); + acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(sumi)), acc); + } + + *s = hsum_float_8(acc); + +#elif defined __riscv_v_intrinsic + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + + const uint8_t * restrict q6 = x[i].ql; + const uint8_t * restrict qh = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + + const int8_t * restrict scale = x[i].scales; + + size_t vl; + + vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); + + int sum_t = 0; + int is = 0; + + for (int j = 0; j < QK_K/128; ++j) { + + vl = 32; + + // load qh + vuint8m1_t qh_x = __riscv_vle8_v_u8m1(qh, vl); + + // load Q6 + vuint8m1_t q6_0 = __riscv_vle8_v_u8m1(q6, vl); + vuint8m1_t q6_1 = __riscv_vle8_v_u8m1(q6+32, vl); + + vuint8m1_t q6a_0 = __riscv_vand_vx_u8m1(q6_0, 0x0F, vl); + vuint8m1_t q6a_1 = __riscv_vand_vx_u8m1(q6_1, 0x0F, vl); + vuint8m1_t q6s_0 = __riscv_vsrl_vx_u8m1(q6_0, 0x04, vl); + vuint8m1_t q6s_1 = __riscv_vsrl_vx_u8m1(q6_1, 0x04, vl); + + vuint8m1_t qh_0 = __riscv_vand_vx_u8m1(qh_x, 0x03, vl); + vuint8m1_t qh_1 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(qh_x, 0x2, vl), 0x03 , vl); + vuint8m1_t qh_2 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(qh_x, 0x4, vl), 0x03 , vl); + vuint8m1_t qh_3 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(qh_x, 0x6, vl), 0x03 , vl); + + vuint8m1_t qhi_0 = __riscv_vor_vv_u8m1(q6a_0, __riscv_vsll_vx_u8m1(qh_0, 0x04, vl), vl); + vuint8m1_t qhi_1 = __riscv_vor_vv_u8m1(q6a_1, __riscv_vsll_vx_u8m1(qh_1, 0x04, vl), vl); + vuint8m1_t qhi_2 = __riscv_vor_vv_u8m1(q6s_0, __riscv_vsll_vx_u8m1(qh_2, 0x04, vl), vl); + vuint8m1_t qhi_3 = __riscv_vor_vv_u8m1(q6s_1, __riscv_vsll_vx_u8m1(qh_3, 0x04, vl), vl); + + vint8m1_t a_0 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(qhi_0), 32, vl); + vint8m1_t a_1 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(qhi_1), 32, vl); + vint8m1_t a_2 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(qhi_2), 32, vl); + vint8m1_t a_3 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(qhi_3), 32, vl); + + // load Q8 and take product + vint16m2_t va_q_0 = __riscv_vwmul_vv_i16m2(a_0, __riscv_vle8_v_i8m1(q8, vl), vl); + vint16m2_t va_q_1 = __riscv_vwmul_vv_i16m2(a_1, __riscv_vle8_v_i8m1(q8+32, vl), vl); + vint16m2_t va_q_2 = __riscv_vwmul_vv_i16m2(a_2, __riscv_vle8_v_i8m1(q8+64, vl), vl); + vint16m2_t va_q_3 = __riscv_vwmul_vv_i16m2(a_3, __riscv_vle8_v_i8m1(q8+96, vl), vl); + + vl = 16; + + vint32m2_t vaux_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_0, 0), scale[is+0], vl); + vint32m2_t vaux_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_0, 1), scale[is+1], vl); + vint32m2_t vaux_2 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_1, 0), scale[is+2], vl); + vint32m2_t vaux_3 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_1, 1), scale[is+3], vl); + vint32m2_t vaux_4 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_2, 0), scale[is+4], vl); + vint32m2_t vaux_5 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_2, 1), scale[is+5], vl); + vint32m2_t vaux_6 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_3, 0), scale[is+6], vl); + vint32m2_t vaux_7 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_3, 1), scale[is+7], vl); + + vint32m1_t isum0 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(vaux_0, vaux_1, vl), vzero, vl); + vint32m1_t isum1 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(vaux_2, vaux_3, vl), isum0, vl); + vint32m1_t isum2 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(vaux_4, vaux_5, vl), isum1, vl); + vint32m1_t isum3 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(vaux_6, vaux_7, vl), isum2, vl); + + sum_t += __riscv_vmv_x_s_i32m1_i32(isum3); + + q6 += 64; qh += 32; q8 += 128; is=8; + + } + + sumf += d * sum_t; + + } + + *s = sumf; + +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector int v0 = vec_splats((int32_t)0); + const vector unsigned char v2 = vec_splats((unsigned char)0x2); + const vector unsigned char v3 = vec_splats((unsigned char)0x3); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + const vector unsigned char v6 = vec_splats((unsigned char)0x6); + const vector signed char off = vec_splats((signed char)0x20); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; + vector signed int vsumi4 = v0; + vector signed int vsumi5 = v0; + vector signed int vsumi6 = v0; + vector signed int vsumi7 = v0; + + const uint8_t * restrict q6 = x[i].ql; + const uint8_t * restrict qh = x[i].qh; + const int8_t * restrict qs = x[i].scales; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/128; ++j) { + __builtin_prefetch(q6, 0, 0); + __builtin_prefetch(qh, 0, 0); + __builtin_prefetch(q8, 0, 0); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, q6); + vector signed char qxs1 = (vector signed char)vec_xl(16, q6); + vector signed char qxs2 = (vector signed char)vec_xl(32, q6); + vector signed char qxs3 = (vector signed char)vec_xl(48, q6); + q6 += 64; + + vector signed char qxs00 = vec_and(qxs0, lowMask); + vector signed char qxs01 = vec_sr(qxs0, v4); + vector signed char qxs10 = vec_and(qxs1, lowMask); + vector signed char qxs11 = vec_sr(qxs1, v4); + vector signed char qxs20 = vec_and(qxs2, lowMask); + vector signed char qxs21 = vec_sr(qxs2, v4); + vector signed char qxs30 = vec_and(qxs3, lowMask); + vector signed char qxs31 = vec_sr(qxs3, v4); + + vector signed char qxhs0 = (vector signed char)vec_xl( 0, qh); + vector signed char qxhs1 = (vector signed char)vec_xl(16, qh); + qh += 32; + + vector signed char qxh00 = vec_sl(vec_and((vector signed char)v3, qxhs0), v4); + vector signed char qxh01 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v4)), v4); + vector signed char qxh10 = vec_sl(vec_and((vector signed char)v3, qxhs1), v4); + vector signed char qxh11 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs1, v4)), v4); + vector signed char qxh20 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v2)), v4); + vector signed char qxh21 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v6)), v4); + vector signed char qxh30 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs1, v2)), v4); + vector signed char qxh31 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs1, v6)), v4); + + vector signed char q6x00 = vec_sub(vec_or(qxh00, qxs00), off); + vector signed char q6x01 = vec_sub(vec_or(qxh01, qxs01), off); + vector signed char q6x10 = vec_sub(vec_or(qxh10, qxs10), off); + vector signed char q6x11 = vec_sub(vec_or(qxh11, qxs11), off); + vector signed char q6x20 = vec_sub(vec_or(qxh20, qxs20), off); + vector signed char q6x21 = vec_sub(vec_or(qxh21, qxs21), off); + vector signed char q6x30 = vec_sub(vec_or(qxh30, qxs30), off); + vector signed char q6x31 = vec_sub(vec_or(qxh31, qxs31), off); + + vector signed char q8y00 = vec_xl( 0, q8); + vector signed char q8y10 = vec_xl( 16, q8); + vector signed char q8y20 = vec_xl( 32, q8); + vector signed char q8y30 = vec_xl( 48, q8); + vector signed char q8y01 = vec_xl( 64, q8); + vector signed char q8y11 = vec_xl( 80, q8); + vector signed char q8y21 = vec_xl( 96, q8); + vector signed char q8y31 = vec_xl(112, q8); + q8 += 128; + + vector signed short qv00 = vec_add(vec_mule(q6x00, q8y00), vec_mulo(q6x00, q8y00)); + vector signed short qv10 = vec_add(vec_mule(q6x10, q8y10), vec_mulo(q6x10, q8y10)); + vector signed short qv20 = vec_add(vec_mule(q6x20, q8y20), vec_mulo(q6x20, q8y20)); + vector signed short qv30 = vec_add(vec_mule(q6x30, q8y30), vec_mulo(q6x30, q8y30)); + vector signed short qv01 = vec_add(vec_mule(q6x01, q8y01), vec_mulo(q6x01, q8y01)); + vector signed short qv11 = vec_add(vec_mule(q6x11, q8y11), vec_mulo(q6x11, q8y11)); + vector signed short qv21 = vec_add(vec_mule(q6x21, q8y21), vec_mulo(q6x21, q8y21)); + vector signed short qv31 = vec_add(vec_mule(q6x31, q8y31), vec_mulo(q6x31, q8y31)); + + vector signed short vscales = vec_unpackh(vec_xl_len(qs, 8)); + qs += 8; + + vector signed short vs0 = vec_splat(vscales, 0); + vector signed short vs1 = vec_splat(vscales, 1); + vector signed short vs2 = vec_splat(vscales, 2); + vector signed short vs3 = vec_splat(vscales, 3); + vector signed short vs4 = vec_splat(vscales, 4); + vector signed short vs5 = vec_splat(vscales, 5); + vector signed short vs6 = vec_splat(vscales, 6); + vector signed short vs7 = vec_splat(vscales, 7); + + vsumi0 = vec_msum(qv00, vs0, vsumi0); + vsumi1 = vec_msum(qv01, vs4, vsumi1); + vsumi2 = vec_msum(qv10, vs1, vsumi2); + vsumi3 = vec_msum(qv11, vs5, vsumi3); + vsumi4 = vec_msum(qv20, vs2, vsumi4); + vsumi5 = vec_msum(qv21, vs6, vsumi5); + vsumi6 = vec_msum(qv30, vs3, vsumi6); + vsumi7 = vec_msum(qv31, vs7, vsumi7); + } + + vsumi0 = vec_add(vsumi0, vsumi4); + vsumi1 = vec_add(vsumi1, vsumi5); + vsumi2 = vec_add(vsumi2, vsumi6); + vsumi3 = vec_add(vsumi3, vsumi7); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined __loongarch_asx + + const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); + const __m256i m2 = __lasx_xvreplgr2vr_b(3); + const __m256i m32s = __lasx_xvreplgr2vr_b(32); + + __m256 acc = (__m256)__lasx_xvldi(0); + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + + const uint8_t * restrict q4 = x[i].ql; + const uint8_t * restrict qh = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + + const __m128i scales = __lsx_vld((const __m128i*)x[i].scales, 0); + + __m256i sumi = __lasx_xvldi(0); + + int is = 0; + + for (int j = 0; j < QK_K/128; ++j) { + + const __m128i scale_0 = lsx_shuffle_b(scales, get_scale_shuffle(is + 0)); + const __m128i scale_1 = lsx_shuffle_b(scales, get_scale_shuffle(is + 1)); + const __m128i scale_2 = lsx_shuffle_b(scales, get_scale_shuffle(is + 2)); + const __m128i scale_3 = lsx_shuffle_b(scales, get_scale_shuffle(is + 3)); + is += 4; + + const __m256i q4bits1 = __lasx_xvld((const __m256i*)q4, 0); q4 += 32; + const __m256i q4bits2 = __lasx_xvld((const __m256i*)q4, 0); q4 += 32; + const __m256i q4bitsH = __lasx_xvld((const __m256i*)qh, 0); qh += 32; + + const __m256i q4h_0 = __lasx_xvslli_h(__lasx_xvand_v(q4bitsH, m2), 4); + const __m256i q4h_1 = __lasx_xvslli_h(__lasx_xvand_v(__lasx_xvsrli_h(q4bitsH, 2), m2), 4); + const __m256i q4h_2 = __lasx_xvslli_h(__lasx_xvand_v(__lasx_xvsrli_h(q4bitsH, 4), m2), 4); + const __m256i q4h_3 = __lasx_xvslli_h(__lasx_xvand_v(__lasx_xvsrli_h(q4bitsH, 6), m2), 4); + + const __m256i q4_0 = __lasx_xvor_v(__lasx_xvand_v(q4bits1, m4), q4h_0); + const __m256i q4_1 = __lasx_xvor_v(__lasx_xvand_v(q4bits2, m4), q4h_1); + const __m256i q4_2 = __lasx_xvor_v(__lasx_xvand_v(__lasx_xvsrli_h(q4bits1, 4), m4), q4h_2); + const __m256i q4_3 = __lasx_xvor_v(__lasx_xvand_v(__lasx_xvsrli_h(q4bits2, 4), m4), q4h_3); + + const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_3 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + + __m256i q8s_0 = lasx_maddubs_h(m32s, q8_0); + __m256i q8s_1 = lasx_maddubs_h(m32s, q8_1); + __m256i q8s_2 = lasx_maddubs_h(m32s, q8_2); + __m256i q8s_3 = lasx_maddubs_h(m32s, q8_3); + + __m256i p16_0 = lasx_maddubs_h(q4_0, q8_0); + __m256i p16_1 = lasx_maddubs_h(q4_1, q8_1); + __m256i p16_2 = lasx_maddubs_h(q4_2, q8_2); + __m256i p16_3 = lasx_maddubs_h(q4_3, q8_3); + + p16_0 = __lasx_xvsub_h(p16_0, q8s_0); + p16_1 = __lasx_xvsub_h(p16_1, q8s_1); + p16_2 = __lasx_xvsub_h(p16_2, q8s_2); + p16_3 = __lasx_xvsub_h(p16_3, q8s_3); + + p16_0 = lasx_madd_h(lasx_ext8_16(scale_0), p16_0); + p16_1 = lasx_madd_h(lasx_ext8_16(scale_1), p16_1); + p16_2 = lasx_madd_h(lasx_ext8_16(scale_2), p16_2); + p16_3 = lasx_madd_h(lasx_ext8_16(scale_3), p16_3); + + sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_1)); + sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_2, p16_3)); + } + + acc = __lasx_xvfmadd_s((__m256)__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), acc); + } + + *s = hsum_float_8(acc); + +#else + + int8_t aux8[QK_K]; + int16_t aux16[8]; + float sums [8]; + int32_t aux32[8]; + memset(sums, 0, 8*sizeof(float)); + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + const uint8_t * restrict q4 = x[i].ql; + const uint8_t * restrict qh = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + memset(aux32, 0, 8*sizeof(int32_t)); + int8_t * restrict a = aux8; + for (int j = 0; j < QK_K; j += 128) { + for (int l = 0; l < 32; ++l) { + a[l + 0] = (int8_t)((q4[l + 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32; + a[l + 32] = (int8_t)((q4[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32; + a[l + 64] = (int8_t)((q4[l + 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32; + a[l + 96] = (int8_t)((q4[l + 32] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32; + } + a += 128; + q4 += 64; + qh += 32; + } + a = aux8; + int is = 0; + for (int j = 0; j < QK_K/16; ++j) { + int scale = x[i].scales[is++]; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + } + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; + } + for (int l = 0; l < 8; ++l) sumf += sums[l]; + *s = sumf; +#endif +} + +#if defined (__AVX__) || defined (__AVX2__) || defined (__ARM_NEON) || defined (__POWER9_VECTOR__) || defined(__loongarch_asx) +static const int8_t keven_signs_q2xs[1024] = { + 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, 1, + 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, 1, 1, 1, 1, -1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, -1, + 1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, -1, 1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, 1, -1, + 1, 1, -1, -1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 1, 1, -1, 1, -1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, 1, + 1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, 1, -1, 1, 1, 1, 1, -1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, -1, 1, 1, -1, + 1, 1, -1, 1, -1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, 1, + 1, 1, 1, -1, -1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, -1, 1, -1, 1, -1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, 1, + 1, 1, -1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, 1, 1, -1, -1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, -1, + 1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 1, 1, 1, -1, 1, 1, 1, -1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, 1, -1, 1, -1, + 1, 1, -1, 1, 1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, -1, 1, -1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, 1, 1, + 1, 1, 1, -1, 1, -1, 1, 1, -1, 1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, + 1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, 1, 1, -1, -1, -1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, -1, + 1, 1, 1, 1, -1, -1, 1, 1, -1, 1, 1, 1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, -1, -1, 1, 1, + 1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, -1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, -1, + 1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, 1, 1, 1, -1, 1, -1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, -1, + 1, 1, -1, -1, -1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, -1, 1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, 1, + 1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, -1, 1, 1, -1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 1, 1, 1, -1, -1, + 1, 1, -1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, 1, + 1, 1, 1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, 1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, 1, + 1, 1, -1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, -1, 1, 1, -1, -1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, -1, + 1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, 1, -1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, 1, -1, 1, + 1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1, 1, 1, -1, -1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, -1, + 1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, + 1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, 1, + 1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, 1, 1, -1, -1, -1, 1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, -1, 1, + 1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, -1, -1, -1, + 1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, 1, -1, -1, 1, 1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, -1, -1, -1, + 1, 1, -1, -1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, -1, 1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, 1, + 1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, -1, -1, 1, 1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, -1, + 1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1, -1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, 1, + 1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, -1, -1, -1, -1, 1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, 1, + 1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, 1, 1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, -1, +}; +#endif + +void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_iq2_xxs * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined(__ARM_NEON) + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[4]; + const uint8_t * aux8 = (const uint8_t *)aux32; + + ggml_int8x16x4_t q2u; + ggml_int8x16x4_t q2s; + ggml_int8x16x4_t q8b; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + float sumf1 = 0, sumf2 = 0; + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; + memcpy(aux32, q2, 4*sizeof(uint32_t)); q2 += 8; + q2u.val[0] = vcombine_s8(vld1_s8((const void *)(iq2xxs_grid + aux8[ 0])), vld1_s8((const void *)(iq2xxs_grid + aux8[ 1]))); + q2u.val[1] = vcombine_s8(vld1_s8((const void *)(iq2xxs_grid + aux8[ 2])), vld1_s8((const void *)(iq2xxs_grid + aux8[ 3]))); + q2u.val[2] = vcombine_s8(vld1_s8((const void *)(iq2xxs_grid + aux8[ 8])), vld1_s8((const void *)(iq2xxs_grid + aux8[ 9]))); + q2u.val[3] = vcombine_s8(vld1_s8((const void *)(iq2xxs_grid + aux8[10])), vld1_s8((const void *)(iq2xxs_grid + aux8[11]))); + q2s.val[0] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 7) & 127)))); + q2s.val[1] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 21) & 127)))); + q2s.val[2] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[3] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[3] >> 7) & 127)))); + q2s.val[3] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[3] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[3] >> 21) & 127)))); + q2u.val[0] = vmulq_s8(q2u.val[0], q2s.val[0]); + q2u.val[1] = vmulq_s8(q2u.val[1], q2s.val[1]); + q2u.val[2] = vmulq_s8(q2u.val[2], q2s.val[2]); + q2u.val[3] = vmulq_s8(q2u.val[3], q2s.val[3]); + const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[0], q8b.val[0]), q2u.val[1], q8b.val[1]); + const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[2], q8b.val[2]), q2u.val[3], q8b.val[3]); + sumf1 += vaddvq_s32(p1) * (0.5f + (aux32[1] >> 28)); + sumf2 += vaddvq_s32(p2) * (0.5f + (aux32[3] >> 28)); + } + sumf += d*(sumf1 + sumf2); + } + *s = 0.25f * sumf; + +#elif defined(__AVX2__) + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[4]; + const uint8_t * aux8 = (const uint8_t *)aux32; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + __m256i sumi1 = _mm256_setzero_si256(); + __m256i sumi2 = _mm256_setzero_si256(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + memcpy(aux32, q2, 4*sizeof(uint32_t)); q2 += 8; + const __m256i q2_1 = _mm256_set_epi64x(iq2xxs_grid[aux8[ 3]], iq2xxs_grid[aux8[ 2]], iq2xxs_grid[aux8[1]], iq2xxs_grid[aux8[0]]); + const __m256i q2_2 = _mm256_set_epi64x(iq2xxs_grid[aux8[11]], iq2xxs_grid[aux8[10]], iq2xxs_grid[aux8[9]], iq2xxs_grid[aux8[8]]); + const __m256i s2_1 = _mm256_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], + signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); + const __m256i s2_2 = _mm256_set_epi64x(signs64[(aux32[3] >> 21) & 127], signs64[(aux32[3] >> 14) & 127], + signs64[(aux32[3] >> 7) & 127], signs64[(aux32[3] >> 0) & 127]); + const __m256i q8s_1 = _mm256_sign_epi8(q8_1, s2_1); + const __m256i q8s_2 = _mm256_sign_epi8(q8_2, s2_2); + const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); + const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); + const uint16_t ls1 = aux32[1] >> 28; + const uint16_t ls2 = aux32[3] >> 28; + const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1)); + const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1)); + sumi1 = _mm256_add_epi32(sumi1, p1); + sumi2 = _mm256_add_epi32(sumi2, p2); + } + + accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); + + } + + *s = 0.125f * hsum_float_8(accumf); + +#elif defined(__AVX__) + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[4]; + const uint8_t * aux8 = (const uint8_t *)aux32; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + __m128i sumi1_0 = _mm_setzero_si128(); + __m128i sumi1_1 = _mm_setzero_si128(); + __m128i sumi2_0 = _mm_setzero_si128(); + __m128i sumi2_1 = _mm_setzero_si128(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m128i q8_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + memcpy(aux32, q2, 4*sizeof(uint32_t)); q2 += 8; + const __m128i q2_1_0 = _mm_set_epi64x(iq2xxs_grid[aux8[1]], iq2xxs_grid[aux8[0]]); + const __m128i q2_1_1 = _mm_set_epi64x(iq2xxs_grid[aux8[3]], iq2xxs_grid[aux8[2]]); + const __m128i q2_2_0 = _mm_set_epi64x(iq2xxs_grid[aux8[9]], iq2xxs_grid[aux8[8]]); + const __m128i q2_2_1 = _mm_set_epi64x(iq2xxs_grid[aux8[11]], iq2xxs_grid[aux8[10]]); + const __m128i s2_1_0 = _mm_set_epi64x(signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); + const __m128i s2_1_1 = _mm_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127]); + const __m128i s2_2_0 = _mm_set_epi64x(signs64[(aux32[3] >> 7) & 127], signs64[(aux32[3] >> 0) & 127]); + const __m128i s2_2_1 = _mm_set_epi64x(signs64[(aux32[3] >> 21) & 127], signs64[(aux32[3] >> 14) & 127]); + const __m128i q8s_1_0 = _mm_sign_epi8(q8_1_0, s2_1_0); + const __m128i q8s_1_1 = _mm_sign_epi8(q8_1_1, s2_1_1); + const __m128i q8s_2_0 = _mm_sign_epi8(q8_2_0, s2_2_0); + const __m128i q8s_2_1 = _mm_sign_epi8(q8_2_1, s2_2_1); + const __m128i dot1_0 = _mm_maddubs_epi16(q2_1_0, q8s_1_0); + const __m128i dot1_1 = _mm_maddubs_epi16(q2_1_1, q8s_1_1); + const __m128i dot2_0 = _mm_maddubs_epi16(q2_2_0, q8s_2_0); + const __m128i dot2_1 = _mm_maddubs_epi16(q2_2_1, q8s_2_1); + const uint16_t ls1 = aux32[1] >> 28; + const uint16_t ls2 = aux32[3] >> 28; + const __m128i p1_0 = _mm_madd_epi16(dot1_0, _mm_set1_epi16(2*ls1+1)); + const __m128i p1_1 = _mm_madd_epi16(dot1_1, _mm_set1_epi16(2*ls1+1)); + const __m128i p2_0 = _mm_madd_epi16(dot2_0, _mm_set1_epi16(2*ls2+1)); + const __m128i p2_1 = _mm_madd_epi16(dot2_1, _mm_set1_epi16(2*ls2+1)); + sumi1_0 = _mm_add_epi32(sumi1_0, p1_0); + sumi1_1 = _mm_add_epi32(sumi1_1, p1_1); + sumi2_0 = _mm_add_epi32(sumi2_0, p2_0); + sumi2_1 = _mm_add_epi32(sumi2_1, p2_1); + } + + accumf = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_add_epi32(sumi1_1, sumi2_1), _mm_add_epi32(sumi1_0, sumi2_0)))), accumf); + + } + + *s = 0.125f * hsum_float_8(accumf); + +#elif defined(__POWER9_VECTOR__) + const vector int v0 = vec_splats((int32_t)0); + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; + + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/32; j += 2) { + __builtin_prefetch(q2, 0, 1); + __builtin_prefetch(q8, 0, 1); + + uint32_t aux32[4]; + const uint8_t * aux8 = (const uint8_t *)aux32; + + memcpy(aux32, q2, 4*sizeof(uint32_t)); + q2 += 8; + + vector signed long long aux64x2_0 = {*(const int64_t *)(iq2xxs_grid + aux8[ 0]), *(const int64_t *)(iq2xxs_grid + aux8[ 1])}; + vector signed long long aux64x2_1 = {*(const int64_t *)(iq2xxs_grid + aux8[ 2]), *(const int64_t *)(iq2xxs_grid + aux8[ 3])}; + vector signed long long aux64x2_2 = {*(const int64_t *)(iq2xxs_grid + aux8[ 8]), *(const int64_t *)(iq2xxs_grid + aux8[ 9])}; + vector signed long long aux64x2_3 = {*(const int64_t *)(iq2xxs_grid + aux8[10]), *(const int64_t *)(iq2xxs_grid + aux8[11])}; + + vector signed long long vsigns0 = {*(const int64_t *)(signs64 + ((aux32[1] >> 0) & 127)), *(const int64_t *)(signs64 + ((aux32[1] >> 7) & 127))}; + vector signed long long vsigns1 = {*(const int64_t *)(signs64 + ((aux32[1] >> 14) & 127)), *(const int64_t *)(signs64 + ((aux32[1] >> 21) & 127))}; + vector signed long long vsigns2 = {*(const int64_t *)(signs64 + ((aux32[3] >> 0) & 127)), *(const int64_t *)(signs64 + ((aux32[3] >> 7) & 127))}; + vector signed long long vsigns3 = {*(const int64_t *)(signs64 + ((aux32[3] >> 14) & 127)), *(const int64_t *)(signs64 + ((aux32[3] >> 21) & 127))}; + + vector signed char q2x0 = (vector signed char)vec_mul((vector signed char)vsigns0, (vector signed char)aux64x2_0); + vector signed char q2x1 = (vector signed char)vec_mul((vector signed char)vsigns1, (vector signed char)aux64x2_1); + vector signed char q2x2 = (vector signed char)vec_mul((vector signed char)vsigns2, (vector signed char)aux64x2_2); + vector signed char q2x3 = (vector signed char)vec_mul((vector signed char)vsigns3, (vector signed char)aux64x2_3); + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q2x0, q8y0), vec_mulo(q2x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q2x1, q8y1), vec_mulo(q2x1, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q2x2, q8y2), vec_mulo(q2x2, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q2x3, q8y3), vec_mulo(q2x3, q8y3)); + + const uint16_t ls0 = aux32[1] >> 28; + const uint16_t ls1 = aux32[3] >> 28; + + vector signed short vscales01 = vec_splats((int16_t)(2*ls0+1)); + vector signed short vscales23 = vec_splats((int16_t)(2*ls1+1)); + + vsumi0 = vec_msum(qv0, vscales01, vsumi0); + vsumi1 = vec_msum(qv1, vscales01, vsumi1); + vsumi2 = vec_msum(qv2, vscales23, vsumi2); + vsumi3 = vec_msum(qv3, vscales23, vsumi3); + } + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = 0.125f * vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[4]; + const uint8_t * aux8 = (const uint8_t *)aux32; + + __m256 accumf = (__m256)__lasx_xvldi(0); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + __m256i sumi1 = __lasx_xvldi(0); + __m256i sumi2 = __lasx_xvldi(0); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + memcpy(aux32, q2, 4*sizeof(uint32_t)); q2 += 8; + + const __m256i q2_1 = lasx_set_d(iq2xxs_grid[aux8[ 3]], iq2xxs_grid[aux8[ 2]], iq2xxs_grid[aux8[1]], iq2xxs_grid[aux8[0]]); + const __m256i q2_2 = lasx_set_d(iq2xxs_grid[aux8[11]], iq2xxs_grid[aux8[10]], iq2xxs_grid[aux8[9]], iq2xxs_grid[aux8[8]]); + const __m256i s2_1 = lasx_set_d(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], + signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); + const __m256i s2_2 = lasx_set_d(signs64[(aux32[3] >> 21) & 127], signs64[(aux32[3] >> 14) & 127], + signs64[(aux32[3] >> 7) & 127], signs64[(aux32[3] >> 0) & 127]); + const __m256i q8s_1 = __lasx_xvsigncov_b(s2_1, q8_1); + const __m256i q8s_2 = __lasx_xvsigncov_b(s2_2, q8_2); + const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); + const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); + const uint16_t ls1 = aux32[1] >> 28; + const uint16_t ls2 = aux32[3] >> 28; + const __m256i p1 = lasx_madd_h(dot1, __lasx_xvreplgr2vr_h(2*ls1+1)); + const __m256i p2 = lasx_madd_h(dot2, __lasx_xvreplgr2vr_h(2*ls2+1)); + sumi1 = __lasx_xvadd_w(sumi1, p1); + sumi2 = __lasx_xvadd_w(sumi2, p2); + } + + accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); + } + + *s = 0.125f * hsum_float_8(accumf); + +#else + + uint32_t aux32[2]; + const uint8_t * aux8 = (const uint8_t *)aux32; + + float sumf = 0.f; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + int32_t bsum = 0; + for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { + memcpy(aux32, q2, 2*sizeof(uint32_t)); + q2 += 4; + const uint32_t ls = 2*(aux32[1] >> 28) + 1; + int32_t sumi = 0; + for (int l = 0; l < 4; ++l) { + const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]); + const uint8_t signs = ksigns_iq2xs[(aux32[1] >> 7*l) & 127]; + for (int j = 0; j < 8; ++j) { + sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1); + } + q8 += 8; + } + bsum += sumi * ls; + } + sumf += d * bsum; + } + *s = 0.125f * sumf; +#endif +} + +void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_iq2_xs * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined(__ARM_NEON) + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + ggml_int8x16x4_t q2u; + ggml_int8x16x4_t q2s; + ggml_int8x16x4_t q8b; + + int32x4x4_t scales32; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + const uint8x8_t scales8 = vld1_u8(x[i].scales); + const uint8x8_t scales_l = vand_u8(scales8, vdup_n_u8(0xf)); + const uint8x8_t scales_h = vshr_n_u8(scales8, 4); + uint8x16_t scales = vcombine_u8(vzip1_u8(scales_l, scales_h), vzip2_u8(scales_l, scales_h)); + scales = vaddq_u8(vshlq_n_u8(scales, 1), vdupq_n_u8(1)); + const uint16x8_t scales1 = vmovl_u8(vget_low_u8(scales)); + const uint16x8_t scales2 = vmovl_u8(vget_high_u8(scales)); + scales32.val[0] = vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(scales1))); + scales32.val[1] = vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(scales1))); + scales32.val[2] = vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(scales2))); + scales32.val[3] = vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(scales2))); + int32x4_t sumi = vdupq_n_s32(0); + for (int ib64 = 0; ib64 < QK_K/64; ++ib64) { + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; + q2u.val[0] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[0] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[1] & 511)))); + q2u.val[1] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[2] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[3] & 511)))); + q2u.val[2] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[4] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[5] & 511)))); + q2u.val[3] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[6] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[7] & 511)))); + q2s.val[0] = vcombine_s8(vld1_s8((const void *)(signs64 + (q2[0] >> 9))), vld1_s8((const void *)(signs64 + (q2[1] >> 9)))); + q2s.val[1] = vcombine_s8(vld1_s8((const void *)(signs64 + (q2[2] >> 9))), vld1_s8((const void *)(signs64 + (q2[3] >> 9)))); + q2s.val[2] = vcombine_s8(vld1_s8((const void *)(signs64 + (q2[4] >> 9))), vld1_s8((const void *)(signs64 + (q2[5] >> 9)))); + q2s.val[3] = vcombine_s8(vld1_s8((const void *)(signs64 + (q2[6] >> 9))), vld1_s8((const void *)(signs64 + (q2[7] >> 9)))); + q2u.val[0] = vmulq_s8(q2u.val[0], q2s.val[0]); + q2u.val[1] = vmulq_s8(q2u.val[1], q2s.val[1]); + q2u.val[2] = vmulq_s8(q2u.val[2], q2s.val[2]); + q2u.val[3] = vmulq_s8(q2u.val[3], q2s.val[3]); + const int32x4_t p1 = ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[0], q8b.val[0]); + const int32x4_t p2 = ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[1], q8b.val[1]); + const int32x4_t p3 = ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[2], q8b.val[2]); + const int32x4_t p4 = ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[3], q8b.val[3]); + const int32x4_t p = vpaddq_s32(vpaddq_s32(p1, p2), vpaddq_s32(p3, p4)); + sumi = vmlaq_s32(sumi, p, scales32.val[ib64]); + q2 += 8; + } + sumf += d*vaddvq_s32(sumi); + } + *s = 0.125f * sumf; + +#elif defined(__AVX2__) + + const __m256i mone = _mm256_set1_epi8(1); + static const char block_sign_shuffle_mask_1[32] = { + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, + 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, + }; + static const char block_sign_shuffle_mask_2[32] = { + 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, + 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, + }; + static const uint8_t bit_selector_mask_bytes[32] = { + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + }; + + const __m256i bit_selector_mask = _mm256_loadu_si256((const __m256i*)bit_selector_mask_bytes); + const __m256i block_sign_shuffle_1 = _mm256_loadu_si256((const __m256i*)block_sign_shuffle_mask_1); + const __m256i block_sign_shuffle_2 = _mm256_loadu_si256((const __m256i*)block_sign_shuffle_mask_2); + + static const uint8_t k_bit_helper[32] = { + 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, + 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, + }; + const __m256i bit_helper = _mm256_loadu_si256((const __m256i*)k_bit_helper); + const __m256i m511 = _mm256_set1_epi16(511); + const __m128i m4 = _mm_set1_epi8(0xf); + const __m128i m1 = _mm_set1_epi8(1); + + uint64_t aux64; + + // somewhat hacky, but gives a significant boost in performance + __m256i aux_gindex; + const uint16_t * gindex = (const uint16_t *)&aux_gindex; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + memcpy(&aux64, x[i].scales, 8); + __m128i stmp = _mm_set1_epi64x(aux64); + stmp = _mm_unpacklo_epi8(_mm_and_si128(stmp, m4), _mm_and_si128(_mm_srli_epi16(stmp, 4), m4)); + const __m128i scales = _mm_add_epi8(_mm_slli_epi16(stmp, 1), m1); + + __m256i sumi1 = _mm256_setzero_si256(); + __m256i sumi2 = _mm256_setzero_si256(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 4) { + + const __m256i q2_data = _mm256_loadu_si256((const __m256i*)q2); q2 += 16; + aux_gindex = _mm256_and_si256(q2_data, m511); + + const __m256i partial_sign_bits = _mm256_srli_epi16(q2_data, 9); + const __m256i partial_sign_bits_upper = _mm256_srli_epi16(q2_data, 13); + const __m256i partial_sign_bits_for_counting = _mm256_xor_si256(partial_sign_bits, partial_sign_bits_upper); + + const __m256i odd_bits = _mm256_shuffle_epi8(bit_helper, partial_sign_bits_for_counting); + const __m256i full_sign_bits = _mm256_or_si256(partial_sign_bits, odd_bits); + + const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8_3 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8_4 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + + const __m256i q2_1 = _mm256_set_epi64x(iq2xs_grid[gindex[ 3]], iq2xs_grid[gindex[ 2]], + iq2xs_grid[gindex[ 1]], iq2xs_grid[gindex[ 0]]); + const __m256i q2_2 = _mm256_set_epi64x(iq2xs_grid[gindex[ 7]], iq2xs_grid[gindex[ 6]], + iq2xs_grid[gindex[ 5]], iq2xs_grid[gindex[ 4]]); + const __m256i q2_3 = _mm256_set_epi64x(iq2xs_grid[gindex[11]], iq2xs_grid[gindex[10]], + iq2xs_grid[gindex[ 9]], iq2xs_grid[gindex[ 8]]); + const __m256i q2_4 = _mm256_set_epi64x(iq2xs_grid[gindex[15]], iq2xs_grid[gindex[14]], + iq2xs_grid[gindex[13]], iq2xs_grid[gindex[12]]); + + const __m128i full_signs_l = _mm256_castsi256_si128(full_sign_bits); + const __m128i full_signs_h = _mm256_extractf128_si256(full_sign_bits, 1); + const __m256i full_signs_1 = MM256_SET_M128I(full_signs_l, full_signs_l); + const __m256i full_signs_2 = MM256_SET_M128I(full_signs_h, full_signs_h); + + __m256i signs; + signs = _mm256_shuffle_epi8(full_signs_1, block_sign_shuffle_1); + signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_1 = _mm256_sign_epi8(q8_1, _mm256_or_si256(signs, mone)); + + signs = _mm256_shuffle_epi8(full_signs_1, block_sign_shuffle_2); + signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_2 = _mm256_sign_epi8(q8_2, _mm256_or_si256(signs, mone)); + + signs = _mm256_shuffle_epi8(full_signs_2, block_sign_shuffle_1); + signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_3 = _mm256_sign_epi8(q8_3, _mm256_or_si256(signs, mone)); + + signs = _mm256_shuffle_epi8(full_signs_2, block_sign_shuffle_2); + signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_4 = _mm256_sign_epi8(q8_4, _mm256_or_si256(signs, mone)); + + const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); + const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); + const __m256i dot3 = _mm256_maddubs_epi16(q2_3, q8s_3); + const __m256i dot4 = _mm256_maddubs_epi16(q2_4, q8s_4); + + const __m256i sc1 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+0))); + const __m256i sc2 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+1))); + const __m256i sc3 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+2))); + const __m256i sc4 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+3))); + + sumi1 = _mm256_add_epi32(sumi1, _mm256_madd_epi16(dot1, sc1)); + sumi2 = _mm256_add_epi32(sumi2, _mm256_madd_epi16(dot2, sc2)); + sumi1 = _mm256_add_epi32(sumi1, _mm256_madd_epi16(dot3, sc3)); + sumi2 = _mm256_add_epi32(sumi2, _mm256_madd_epi16(dot4, sc4)); + } + + accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); + + } + + *s = 0.125f * hsum_float_8(accumf); + +#elif defined(__AVX__) + const __m128i mone = _mm_set1_epi8(1); + static const char block_sign_shuffle_mask_1[32] = { + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, + 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, + }; + static const char block_sign_shuffle_mask_2[32] = { + 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, + 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, + }; + static const uint8_t bit_selector_mask_bytes[32] = { + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + }; + + const __m128i bit_selector_mask_0 = _mm_loadu_si128((const __m128i*)bit_selector_mask_bytes); + const __m128i bit_selector_mask_1 = _mm_loadu_si128((const __m128i*)bit_selector_mask_bytes + 1); + const __m128i block_sign_shuffle_1_0 = _mm_loadu_si128((const __m128i*)block_sign_shuffle_mask_1); + const __m128i block_sign_shuffle_1_1 = _mm_loadu_si128((const __m128i*)block_sign_shuffle_mask_1 + 1); + const __m128i block_sign_shuffle_2_0 = _mm_loadu_si128((const __m128i*)block_sign_shuffle_mask_2); + const __m128i block_sign_shuffle_2_1 = _mm_loadu_si128((const __m128i*)block_sign_shuffle_mask_2 + 1); + + static const uint8_t k_bit_helper[32] = { + 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, + 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, + }; + const __m128i bit_helper_0 = _mm_loadu_si128((const __m128i*)k_bit_helper); + const __m128i bit_helper_1 = _mm_loadu_si128((const __m128i*)k_bit_helper + 1); + const __m128i m511 = _mm_set1_epi16(511); + const __m128i m4 = _mm_set1_epi8(0xf); + const __m128i m1 = _mm_set1_epi8(1); + + uint64_t aux64; + + // somewhat hacky, but gives a significant boost in performance + __m256i aux_gindex; + const uint16_t * gindex = (const uint16_t *)&aux_gindex; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + memcpy(&aux64, x[i].scales, 8); + __m128i stmp = _mm_set1_epi64x(aux64); + stmp = _mm_unpacklo_epi8(_mm_and_si128(stmp, m4), _mm_and_si128(_mm_srli_epi16(stmp, 4), m4)); + const __m128i scales = _mm_add_epi8(_mm_slli_epi16(stmp, 1), m1); + + __m128i sumi1_0 = _mm_setzero_si128(); + __m128i sumi1_1 = _mm_setzero_si128(); + __m128i sumi2_0 = _mm_setzero_si128(); + __m128i sumi2_1 = _mm_setzero_si128(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 4) { + + const __m128i q2_data_0 = _mm_loadu_si128((const __m128i*)q2); + const __m128i q2_data_1 = _mm_loadu_si128((const __m128i*)q2 + 1); q2 += 16; + aux_gindex = MM256_SET_M128I(_mm_and_si128(q2_data_1, m511), _mm_and_si128(q2_data_0, m511)); + + const __m128i partial_sign_bits_0 = _mm_srli_epi16(q2_data_0, 9); + const __m128i partial_sign_bits_1 = _mm_srli_epi16(q2_data_1, 9); + const __m128i partial_sign_bits_upper_0 = _mm_srli_epi16(q2_data_0, 13); + const __m128i partial_sign_bits_upper_1 = _mm_srli_epi16(q2_data_1, 13); + const __m128i partial_sign_bits_for_counting_0 = _mm_xor_si128(partial_sign_bits_0, partial_sign_bits_upper_0); + const __m128i partial_sign_bits_for_counting_1 = _mm_xor_si128(partial_sign_bits_1, partial_sign_bits_upper_1); + + const __m128i odd_bits_0 = _mm_shuffle_epi8(bit_helper_0, partial_sign_bits_for_counting_0); + const __m128i odd_bits_1 = _mm_shuffle_epi8(bit_helper_1, partial_sign_bits_for_counting_1); + const __m128i full_sign_bits_0 = _mm_or_si128(partial_sign_bits_0, odd_bits_0); + const __m128i full_sign_bits_1 = _mm_or_si128(partial_sign_bits_1, odd_bits_1); + + const __m128i q8_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_3_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_3_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_4_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_4_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + + const __m128i q2_1_0 = _mm_set_epi64x(iq2xs_grid[gindex[1]], iq2xs_grid[gindex[0]]); + const __m128i q2_1_1 = _mm_set_epi64x(iq2xs_grid[gindex[3]], iq2xs_grid[gindex[2]]); + const __m128i q2_2_0 = _mm_set_epi64x(iq2xs_grid[gindex[5]], iq2xs_grid[gindex[4]]); + const __m128i q2_2_1 = _mm_set_epi64x(iq2xs_grid[gindex[7]], iq2xs_grid[gindex[6]]); + const __m128i q2_3_0 = _mm_set_epi64x(iq2xs_grid[gindex[9]], iq2xs_grid[gindex[8]]); + const __m128i q2_3_1 = _mm_set_epi64x(iq2xs_grid[gindex[11]], iq2xs_grid[gindex[10]]); + const __m128i q2_4_0 = _mm_set_epi64x(iq2xs_grid[gindex[13]], iq2xs_grid[gindex[12]]); + const __m128i q2_4_1 = _mm_set_epi64x(iq2xs_grid[gindex[15]], iq2xs_grid[gindex[14]]); + + // AVX2 full_signs_1 is full_sign_bits_0 here + // AVX2 full_signs_2 is full_sign_bits_1 here + __m128i signs_0, signs_1; + signs_0 = _mm_shuffle_epi8(full_sign_bits_0, block_sign_shuffle_1_0); + signs_1 = _mm_shuffle_epi8(full_sign_bits_0, block_sign_shuffle_1_1); + signs_0 = _mm_cmpeq_epi8(_mm_and_si128(signs_0, bit_selector_mask_0), bit_selector_mask_0); + signs_1 = _mm_cmpeq_epi8(_mm_and_si128(signs_1, bit_selector_mask_1), bit_selector_mask_1); + const __m128i q8s_1_0 = _mm_sign_epi8(q8_1_0, _mm_or_si128(signs_0, mone)); + const __m128i q8s_1_1 = _mm_sign_epi8(q8_1_1, _mm_or_si128(signs_1, mone)); + + signs_0 = _mm_shuffle_epi8(full_sign_bits_0, block_sign_shuffle_2_0); + signs_1 = _mm_shuffle_epi8(full_sign_bits_0, block_sign_shuffle_2_1); + signs_0 = _mm_cmpeq_epi8(_mm_and_si128(signs_0, bit_selector_mask_0), bit_selector_mask_0); + signs_1 = _mm_cmpeq_epi8(_mm_and_si128(signs_1, bit_selector_mask_1), bit_selector_mask_1); + const __m128i q8s_2_0 = _mm_sign_epi8(q8_2_0, _mm_or_si128(signs_0, mone)); + const __m128i q8s_2_1 = _mm_sign_epi8(q8_2_1, _mm_or_si128(signs_1, mone)); + + signs_0 = _mm_shuffle_epi8(full_sign_bits_1, block_sign_shuffle_1_0); + signs_1 = _mm_shuffle_epi8(full_sign_bits_1, block_sign_shuffle_1_1); + signs_0 = _mm_cmpeq_epi8(_mm_and_si128(signs_0, bit_selector_mask_0), bit_selector_mask_0); + signs_1 = _mm_cmpeq_epi8(_mm_and_si128(signs_1, bit_selector_mask_1), bit_selector_mask_1); + const __m128i q8s_3_0 = _mm_sign_epi8(q8_3_0, _mm_or_si128(signs_0, mone)); + const __m128i q8s_3_1 = _mm_sign_epi8(q8_3_1, _mm_or_si128(signs_1, mone)); + + signs_0 = _mm_shuffle_epi8(full_sign_bits_1, block_sign_shuffle_2_0); + signs_1 = _mm_shuffle_epi8(full_sign_bits_1, block_sign_shuffle_2_1); + signs_0 = _mm_cmpeq_epi8(_mm_and_si128(signs_0, bit_selector_mask_0), bit_selector_mask_0); + signs_1 = _mm_cmpeq_epi8(_mm_and_si128(signs_1, bit_selector_mask_1), bit_selector_mask_1); + const __m128i q8s_4_0 = _mm_sign_epi8(q8_4_0, _mm_or_si128(signs_0, mone)); + const __m128i q8s_4_1 = _mm_sign_epi8(q8_4_1, _mm_or_si128(signs_1, mone)); + + const __m128i dot1_0 = _mm_maddubs_epi16(q2_1_0, q8s_1_0); + const __m128i dot1_1 = _mm_maddubs_epi16(q2_1_1, q8s_1_1); + const __m128i dot2_0 = _mm_maddubs_epi16(q2_2_0, q8s_2_0); + const __m128i dot2_1 = _mm_maddubs_epi16(q2_2_1, q8s_2_1); + const __m128i dot3_0 = _mm_maddubs_epi16(q2_3_0, q8s_3_0); + const __m128i dot3_1 = _mm_maddubs_epi16(q2_3_1, q8s_3_1); + const __m128i dot4_0 = _mm_maddubs_epi16(q2_4_0, q8s_4_0); + const __m128i dot4_1 = _mm_maddubs_epi16(q2_4_1, q8s_4_1); + + __m128i sc_tmp = _mm_shuffle_epi8(scales, get_scale_shuffle(ib32+0)); + const __m128i sc1_0 = _mm_cvtepi8_epi16(sc_tmp); + const __m128i sc1_1 = _mm_cvtepi8_epi16(_mm_srli_si128(sc_tmp, 8)); + sc_tmp = _mm_shuffle_epi8(scales, get_scale_shuffle(ib32+1)); + const __m128i sc2_0 = _mm_cvtepi8_epi16(sc_tmp); + const __m128i sc2_1 = _mm_cvtepi8_epi16(_mm_srli_si128(sc_tmp, 8)); + sc_tmp = _mm_shuffle_epi8(scales, get_scale_shuffle(ib32+2)); + const __m128i sc3_0 = _mm_cvtepi8_epi16(sc_tmp); + const __m128i sc3_1 = _mm_cvtepi8_epi16(_mm_srli_si128(sc_tmp, 8)); + sc_tmp = _mm_shuffle_epi8(scales, get_scale_shuffle(ib32+3)); + const __m128i sc4_0 = _mm_cvtepi8_epi16(sc_tmp); + const __m128i sc4_1 = _mm_cvtepi8_epi16(_mm_srli_si128(sc_tmp, 8)); + + sumi1_0 = _mm_add_epi32(sumi1_0, _mm_madd_epi16(dot1_0, sc1_0)); + sumi1_1 = _mm_add_epi32(sumi1_1, _mm_madd_epi16(dot1_1, sc1_1)); + sumi2_0 = _mm_add_epi32(sumi2_0, _mm_madd_epi16(dot2_0, sc2_0)); + sumi2_1 = _mm_add_epi32(sumi2_1, _mm_madd_epi16(dot2_1, sc2_1)); + sumi1_0 = _mm_add_epi32(sumi1_0, _mm_madd_epi16(dot3_0, sc3_0)); + sumi1_1 = _mm_add_epi32(sumi1_1, _mm_madd_epi16(dot3_1, sc3_1)); + sumi2_0 = _mm_add_epi32(sumi2_0, _mm_madd_epi16(dot4_0, sc4_0)); + sumi2_1 = _mm_add_epi32(sumi2_1, _mm_madd_epi16(dot4_1, sc4_1)); + } + + accumf = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_add_epi32(sumi1_1, sumi2_1), _mm_add_epi32(sumi1_0, sumi2_0)))), accumf); + + } + + *s = 0.125f * hsum_float_8(accumf); + +#elif defined(__loongarch_asx) + + const __m256i mone = __lasx_xvreplgr2vr_b(1); + static const char block_sign_shuffle_mask_1[32] = { + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, + 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, + }; + static const char block_sign_shuffle_mask_2[32] = { + 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, + 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, + }; + static const uint8_t bit_selector_mask_bytes[32] = { + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + }; + + const __m256i bit_selector_mask = __lasx_xvld((const __m256i*)bit_selector_mask_bytes, 0); + const __m256i block_sign_shuffle_1 = __lasx_xvld((const __m256i*)block_sign_shuffle_mask_1, 0); + const __m256i block_sign_shuffle_2 = __lasx_xvld((const __m256i*)block_sign_shuffle_mask_2, 0); + + static const uint8_t k_bit_helper[32] = { + 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, + 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, + }; + const __m256i bit_helper = __lasx_xvld((const __m256i*)k_bit_helper, 0); + const __m256i m511 = __lasx_xvreplgr2vr_h(511); + const __m128i m4 = __lsx_vreplgr2vr_b(0xf); + const __m128i m1 = __lsx_vreplgr2vr_b(1); + + uint64_t aux64; + + // somewhat hacky, but gives a significant boost in performance + __m256i aux_gindex; + const uint16_t * gindex = (const uint16_t *)&aux_gindex; + + __m256 accumf = (__m256)__lasx_xvldi(0); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + memcpy(&aux64, x[i].scales, 8); + __m128i stmp = __lsx_vreplgr2vr_d(aux64); + stmp = __lsx_vilvl_b( __lsx_vand_v(__lsx_vsrli_h(stmp, 4), m4), __lsx_vand_v(stmp, m4)); + const __m128i scales = __lsx_vadd_b(__lsx_vslli_h(stmp, 1), m1); + + __m256i sumi1 = __lasx_xvldi(0); + __m256i sumi2 = __lasx_xvldi(0); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 4) { + + const __m256i q2_data = __lasx_xvld((const __m256i*)q2, 0); q2 += 16; + aux_gindex = __lasx_xvand_v(q2_data, m511); + + const __m256i partial_sign_bits = __lasx_xvsrli_h(q2_data, 9); + const __m256i partial_sign_bits_upper = __lasx_xvsrli_h(q2_data, 13); + const __m256i partial_sign_bits_for_counting = __lasx_xvxor_v(partial_sign_bits, partial_sign_bits_upper); + + const __m256i odd_bits = lasx_shuffle_b(bit_helper, partial_sign_bits_for_counting); + const __m256i full_sign_bits = __lasx_xvor_v(partial_sign_bits, odd_bits); + + const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_3 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_4 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + + const __m256i q2_1 = lasx_set_d(iq2xs_grid[gindex[ 3]], iq2xs_grid[gindex[ 2]], + iq2xs_grid[gindex[ 1]], iq2xs_grid[gindex[ 0]]); + const __m256i q2_2 = lasx_set_d(iq2xs_grid[gindex[ 7]], iq2xs_grid[gindex[ 6]], + iq2xs_grid[gindex[ 5]], iq2xs_grid[gindex[ 4]]); + const __m256i q2_3 = lasx_set_d(iq2xs_grid[gindex[11]], iq2xs_grid[gindex[10]], + iq2xs_grid[gindex[ 9]], iq2xs_grid[gindex[ 8]]); + const __m256i q2_4 = lasx_set_d(iq2xs_grid[gindex[15]], iq2xs_grid[gindex[14]], + iq2xs_grid[gindex[13]], iq2xs_grid[gindex[12]]); + + const __m128i full_signs_l = lasx_extracti128(full_sign_bits, 0); + const __m128i full_signs_h = lasx_extracti128(full_sign_bits, 1); + const __m256i full_signs_1 = lasx_insertf128(full_signs_l, full_signs_l); + const __m256i full_signs_2 = lasx_insertf128(full_signs_h, full_signs_h); + + __m256i signs; + signs = lasx_shuffle_b(full_signs_1, block_sign_shuffle_1); + signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_1 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_1); + + signs = lasx_shuffle_b(full_signs_1, block_sign_shuffle_2); + signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_2 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_2); + + signs = lasx_shuffle_b(full_signs_2, block_sign_shuffle_1); + signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_3 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_3); + + signs = lasx_shuffle_b(full_signs_2, block_sign_shuffle_2); + signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_4 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_4); + + const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); + const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); + const __m256i dot3 = lasx_maddubs_h(q2_3, q8s_3); + const __m256i dot4 = lasx_maddubs_h(q2_4, q8s_4); + + const __m256i sc1 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+0))); + const __m256i sc2 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+1))); + const __m256i sc3 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+2))); + const __m256i sc4 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+3))); + + sumi1 = __lasx_xvadd_w(sumi1, lasx_madd_h(dot1, sc1)); + sumi2 = __lasx_xvadd_w(sumi2, lasx_madd_h(dot2, sc2)); + sumi1 = __lasx_xvadd_w(sumi1, lasx_madd_h(dot3, sc3)); + sumi2 = __lasx_xvadd_w(sumi2, lasx_madd_h(dot4, sc4)); + } + + accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); + + } + + *s = 0.125f * hsum_float_8(accumf); +#elif defined(__POWER9_VECTOR__) + const vector int v0 = vec_splats((int32_t)0); + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; + + const uint16_t * restrict q2 = x[i].qs; + const uint8_t * restrict sc = x[i].scales; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/64; ++j) { + __builtin_prefetch(q2, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed long long aux64x2_0 = {*(const int64_t *)(iq2xs_grid + (q2[0] & 511)), *(const int64_t *)(iq2xs_grid + (q2[1] & 511))}; + vector signed long long aux64x2_1 = {*(const int64_t *)(iq2xs_grid + (q2[2] & 511)), *(const int64_t *)(iq2xs_grid + (q2[3] & 511))}; + vector signed long long aux64x2_2 = {*(const int64_t *)(iq2xs_grid + (q2[4] & 511)), *(const int64_t *)(iq2xs_grid + (q2[5] & 511))}; + vector signed long long aux64x2_3 = {*(const int64_t *)(iq2xs_grid + (q2[6] & 511)), *(const int64_t *)(iq2xs_grid + (q2[7] & 511))}; + + vector signed long long vsigns0 = {*(const int64_t *)(signs64 + ((q2[0] >> 9))), *(const int64_t *)(signs64 + ((q2[1] >> 9)))}; + vector signed long long vsigns1 = {*(const int64_t *)(signs64 + ((q2[2] >> 9))), *(const int64_t *)(signs64 + ((q2[3] >> 9)))}; + vector signed long long vsigns2 = {*(const int64_t *)(signs64 + ((q2[4] >> 9))), *(const int64_t *)(signs64 + ((q2[5] >> 9)))}; + vector signed long long vsigns3 = {*(const int64_t *)(signs64 + ((q2[6] >> 9))), *(const int64_t *)(signs64 + ((q2[7] >> 9)))}; + q2 += 8; + + vector signed char q2x0 = (vector signed char)vec_mul((vector signed char)vsigns0, (vector signed char)aux64x2_0); + vector signed char q2x1 = (vector signed char)vec_mul((vector signed char)vsigns1, (vector signed char)aux64x2_1); + vector signed char q2x2 = (vector signed char)vec_mul((vector signed char)vsigns2, (vector signed char)aux64x2_2); + vector signed char q2x3 = (vector signed char)vec_mul((vector signed char)vsigns3, (vector signed char)aux64x2_3); + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q2x0, q8y0), vec_mulo(q2x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q2x1, q8y1), vec_mulo(q2x1, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q2x2, q8y2), vec_mulo(q2x2, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q2x3, q8y3), vec_mulo(q2x3, q8y3)); + + const uint16_t ls0 = (uint16_t)(sc[0] & 0xf); + const uint16_t ls1 = (uint16_t)(sc[0] >> 4); + const uint16_t ls2 = (uint16_t)(sc[1] & 0xf); + const uint16_t ls3 = (uint16_t)(sc[1] >> 4); + sc += 2; + + vector signed short vscales0 = vec_splats((int16_t)(2*ls0+1)); + vector signed short vscales1 = vec_splats((int16_t)(2*ls1+1)); + vector signed short vscales2 = vec_splats((int16_t)(2*ls2+1)); + vector signed short vscales3 = vec_splats((int16_t)(2*ls3+1)); + + vsumi0 = vec_msum(qv0, vscales0, vsumi0); + vsumi1 = vec_msum(qv1, vscales1, vsumi1); + vsumi2 = vec_msum(qv2, vscales2, vsumi2); + vsumi3 = vec_msum(qv3, vscales3, vsumi3); + } + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = 0.125f * vec_extract(vsumf0, 0); +#else + + float sumf = 0.f; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const uint8_t * restrict sc = x[i].scales; + const int8_t * restrict q8 = y[i].qs; + int32_t bsum = 0; + for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { + const uint16_t ls1 = 2*(sc[ib32] & 0xf) + 1; + const uint16_t ls2 = 2*(sc[ib32] >> 4) + 1; + int32_t sumi = 0; + for (int l = 0; l < 2; ++l) { + const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511)); + const uint8_t signs = ksigns_iq2xs[q2[l] >> 9]; + for (int j = 0; j < 8; ++j) { + sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1); + } + q8 += 8; + } + bsum += sumi * ls1; + sumi = 0; + for (int l = 2; l < 4; ++l) { + const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511)); + const uint8_t signs = ksigns_iq2xs[q2[l] >> 9]; + for (int j = 0; j < 8; ++j) { + sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1); + } + q8 += 8; + } + bsum += sumi * ls2; + q2 += 4; + } + sumf += d * bsum; + } + *s = 0.125f * sumf; +#endif +} + +void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_iq2_s * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined(__ARM_NEON) + + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; + + const ggml_uint8x16x2_t mask1 = ggml_vld1q_u8_x2(k_mask1); + const uint8x16_t mask2 = vld1q_u8(k_mask2); + const uint8x16_t m1 = vdupq_n_u8(1); + const int32x4_t vzero = vdupq_n_s32(0); + + uint8x16x2_t vs; + ggml_int8x16x4_t q2s; + ggml_int8x16x4_t q8b; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); + const int8_t * restrict q8 = y[i].qs; + + int sumi1 = 0, sumi2 = 0; + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; + q2s.val[0] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[0] | ((qh[ib32+0] << 8) & 0x300)))), + vld1_s8((const int8_t *)(iq2s_grid + (qs[1] | ((qh[ib32+0] << 6) & 0x300))))); + q2s.val[1] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[2] | ((qh[ib32+0] << 4) & 0x300)))), + vld1_s8((const int8_t *)(iq2s_grid + (qs[3] | ((qh[ib32+0] << 2) & 0x300))))); + q2s.val[2] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[4] | ((qh[ib32+1] << 8) & 0x300)))), + vld1_s8((const int8_t *)(iq2s_grid + (qs[5] | ((qh[ib32+1] << 6) & 0x300))))); + q2s.val[3] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[6] | ((qh[ib32+1] << 4) & 0x300)))), + vld1_s8((const int8_t *)(iq2s_grid + (qs[7] | ((qh[ib32+1] << 2) & 0x300))))); + qs += 8; + + vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | ((uint32_t) signs[1] << 16))); + vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); + vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); + vs.val[0] = vceqq_u8(vs.val[0], mask2); + vs.val[1] = vceqq_u8(vs.val[1], mask2); + + q2s.val[0] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[0], m1)), q2s.val[0]); + q2s.val[1] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[1], m1)), q2s.val[1]); + + vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | ((uint32_t) signs[3] << 16))); + vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); + vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); + vs.val[0] = vceqq_u8(vs.val[0], mask2); + vs.val[1] = vceqq_u8(vs.val[1], mask2); + + signs += 4; + + q2s.val[2] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[0], m1)), q2s.val[2]); + q2s.val[3] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[1], m1)), q2s.val[3]); + + const int32x4_t p1 = ggml_vdotq_s32(vzero, q2s.val[0], q8b.val[0]); + const int32x4_t p2 = ggml_vdotq_s32(vzero, q2s.val[1], q8b.val[1]); + const int32x4_t p3 = ggml_vdotq_s32(vzero, q2s.val[2], q8b.val[2]); + const int32x4_t p4 = ggml_vdotq_s32(vzero, q2s.val[3], q8b.val[3]); + + sumi1 += vaddvq_s32(p1) * (1 + 2*(x[i].scales[ib32+0] & 0xf)); + sumi2 += vaddvq_s32(p2) * (1 + 2*(x[i].scales[ib32+0] >> 4)); + sumi1 += vaddvq_s32(p3) * (1 + 2*(x[i].scales[ib32+1] & 0xf)); + sumi2 += vaddvq_s32(p4) * (1 + 2*(x[i].scales[ib32+1] >> 4)); + } + sumf += d*(sumi1 + sumi2); + } + + *s = 0.125f * sumf; + +#elif defined(__AVX2__) + + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + }; + + const __m128i m4 = _mm_set1_epi8(0xf); + const __m128i m1 = _mm_set1_epi8(1); + + const __m256i mask1 = _mm256_loadu_si256((const __m256i*)k_mask1); + const __m256i mask2 = _mm256_loadu_si256((const __m256i*)k_mask2); + + uint64_t aux64; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); + const int8_t * restrict q8 = y[i].qs; + + memcpy(&aux64, x[i].scales, 8); + const __m128i scales8 = _mm_add_epi8(_mm_slli_epi16(_mm_and_si128(_mm_set_epi64x(aux64 >> 4, aux64), m4), 1), m1); + const __m256i scales16 = _mm256_cvtepi8_epi16(scales8); // 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 15 + + __m256i sumi1 = _mm256_setzero_si256(); + __m256i sumi2 = _mm256_setzero_si256(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q2_1 = _mm256_set_epi64x(iq2s_grid[qs[3] | ((qh[ib32+0] << 2) & 0x300)], + iq2s_grid[qs[2] | ((qh[ib32+0] << 4) & 0x300)], + iq2s_grid[qs[1] | ((qh[ib32+0] << 6) & 0x300)], + iq2s_grid[qs[0] | ((qh[ib32+0] << 8) & 0x300)]); + const __m256i q2_2 = _mm256_set_epi64x(iq2s_grid[qs[7] | ((qh[ib32+1] << 2) & 0x300)], + iq2s_grid[qs[6] | ((qh[ib32+1] << 4) & 0x300)], + iq2s_grid[qs[5] | ((qh[ib32+1] << 6) & 0x300)], + iq2s_grid[qs[4] | ((qh[ib32+1] << 8) & 0x300)]); + qs += 8; + + __m256i aux256 = _mm256_set1_epi32(signs[0] | ((uint32_t) signs[1] << 16)); + aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); + const __m256i s2_1 = _mm256_cmpeq_epi8(aux256, mask2); + const __m256i q8s_1 = _mm256_sub_epi8(_mm256_xor_si256(s2_1, q8_1), s2_1); + + aux256 = _mm256_set1_epi32(signs[2] | ((uint32_t) signs[3] << 16)); + aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); + const __m256i s2_2 = _mm256_cmpeq_epi8(aux256, mask2); + const __m256i q8s_2 = _mm256_sub_epi8(_mm256_xor_si256(s2_2, q8_2), s2_2); + + signs += 4; + + const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); // blocks 2*ib32+0, 2*ib32+1 + const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); // blocks 2*ib32+2, 2*ib32+3 + + const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_shuffle_epi8(scales16, get_scale_shuffle_k4(ib32+0))); + const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_shuffle_epi8(scales16, get_scale_shuffle_k4(ib32+1))); + sumi1 = _mm256_add_epi32(sumi1, p1); + sumi2 = _mm256_add_epi32(sumi2, p2); + } + + accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); + + } + + *s = 0.125f * hsum_float_8(accumf); + +#elif defined(__AVX__) + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + }; + + const __m128i m4 = _mm_set1_epi8(0xf); + const __m128i m1 = _mm_set1_epi8(1); + + const __m128i mask1_0 = _mm_loadu_si128((const __m128i*)k_mask1); + const __m128i mask1_1 = _mm_loadu_si128((const __m128i*)k_mask1 + 1); + const __m128i mask2_0 = _mm_loadu_si128((const __m128i*)k_mask2); + const __m128i mask2_1 = _mm_loadu_si128((const __m128i*)k_mask2 + 1); + + uint64_t aux64; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); + const int8_t * restrict q8 = y[i].qs; + + memcpy(&aux64, x[i].scales, 8); + const __m128i scales8 = _mm_add_epi8(_mm_slli_epi16(_mm_and_si128(_mm_set_epi64x(aux64 >> 4, aux64), m4), 1), m1); + const __m128i scales16_0 = _mm_cvtepi8_epi16(scales8); + const __m128i scales16_1 = _mm_cvtepi8_epi16(_mm_srli_si128(scales8, 8)); + + __m128i sumi1_0 = _mm_setzero_si128(); + __m128i sumi1_1 = _mm_setzero_si128(); + __m128i sumi2_0 = _mm_setzero_si128(); + __m128i sumi2_1 = _mm_setzero_si128(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m128i q8_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q2_1_0 = _mm_set_epi64x(iq2s_grid[qs[1] | ((qh[ib32+0] << 6) & 0x300)], + iq2s_grid[qs[0] | ((qh[ib32+0] << 8) & 0x300)]); + const __m128i q2_1_1 = _mm_set_epi64x(iq2s_grid[qs[3] | ((qh[ib32+0] << 2) & 0x300)], + iq2s_grid[qs[2] | ((qh[ib32+0] << 4) & 0x300)]); + const __m128i q2_2_0 = _mm_set_epi64x(iq2s_grid[qs[5] | ((qh[ib32+1] << 6) & 0x300)], + iq2s_grid[qs[4] | ((qh[ib32+1] << 8) & 0x300)]); + const __m128i q2_2_1 = _mm_set_epi64x(iq2s_grid[qs[7] | ((qh[ib32+1] << 2) & 0x300)], + iq2s_grid[qs[6] | ((qh[ib32+1] << 4) & 0x300)]); + qs += 8; + + __m128i aux128_0 = _mm_set1_epi32(signs[0] | ((uint32_t) signs[1] << 16)); + __m128i aux128_1 = aux128_0; + aux128_0 = _mm_and_si128(_mm_shuffle_epi8(aux128_0,mask1_0), mask2_0); + aux128_1 = _mm_and_si128(_mm_shuffle_epi8(aux128_1,mask1_1), mask2_1); + const __m128i s2_1_0 = _mm_cmpeq_epi8(aux128_0, mask2_0); + const __m128i s2_1_1 = _mm_cmpeq_epi8(aux128_1, mask2_1); + const __m128i q8s_1_0 = _mm_sub_epi8(_mm_xor_si128(s2_1_0, q8_1_0), s2_1_0); + const __m128i q8s_1_1 = _mm_sub_epi8(_mm_xor_si128(s2_1_1, q8_1_1), s2_1_1); + + aux128_0 = _mm_set1_epi32(signs[2] | ((uint32_t) signs[3] << 16)); + aux128_1 = aux128_0; + aux128_0 = _mm_and_si128(_mm_shuffle_epi8(aux128_0,mask1_0), mask2_0); + aux128_1 = _mm_and_si128(_mm_shuffle_epi8(aux128_1,mask1_1), mask2_1); + const __m128i s2_2_0 = _mm_cmpeq_epi8(aux128_0, mask2_0); + const __m128i s2_2_1 = _mm_cmpeq_epi8(aux128_1, mask2_1); + const __m128i q8s_2_0 = _mm_sub_epi8(_mm_xor_si128(s2_2_0, q8_2_0), s2_2_0); + const __m128i q8s_2_1 = _mm_sub_epi8(_mm_xor_si128(s2_2_1, q8_2_1), s2_2_1); + + signs += 4; + + const __m128i dot1_0 = _mm_maddubs_epi16(q2_1_0, q8s_1_0); + const __m128i dot1_1 = _mm_maddubs_epi16(q2_1_1, q8s_1_1); + const __m128i dot2_0 = _mm_maddubs_epi16(q2_2_0, q8s_2_0); + const __m128i dot2_1 = _mm_maddubs_epi16(q2_2_1, q8s_2_1); + + const __m128i p1_0 = _mm_madd_epi16(dot1_0, _mm_shuffle_epi8(scales16_0, _mm256_extractf128_si256(get_scale_shuffle_k4(ib32+0), 0))); + const __m128i p1_1 = _mm_madd_epi16(dot1_1, _mm_shuffle_epi8(scales16_1, _mm256_extractf128_si256(get_scale_shuffle_k4(ib32+0), 1))); + const __m128i p2_0 = _mm_madd_epi16(dot2_0, _mm_shuffle_epi8(scales16_0, _mm256_extractf128_si256(get_scale_shuffle_k4(ib32+1), 0))); + const __m128i p2_1 = _mm_madd_epi16(dot2_1, _mm_shuffle_epi8(scales16_1, _mm256_extractf128_si256(get_scale_shuffle_k4(ib32+1), 1))); + sumi1_0 = _mm_add_epi32(sumi1_0, p1_0); + sumi1_1 = _mm_add_epi32(sumi1_1, p1_1); + sumi2_0 = _mm_add_epi32(sumi2_0, p2_0); + sumi2_1 = _mm_add_epi32(sumi2_1, p2_1); + } + + accumf = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_add_epi32(sumi1_1, sumi2_1), _mm_add_epi32(sumi1_0, sumi2_0)))), accumf); + + } + + *s = 0.125f * hsum_float_8(accumf); + +#elif defined(__POWER9_VECTOR__) + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; + + const vector int v0 = vec_splats((int32_t)0); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + const vector unsigned char mask0 = vec_xl( 0, k_mask1); + const vector unsigned char mask1 = vec_xl(16, k_mask1); + const vector signed char mask2 = (vector signed char)vec_xl( 0, k_mask2); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; + + const uint8_t * restrict q2 = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); + const uint8_t * restrict sc = x[i].scales; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/32; j += 2) { + __builtin_prefetch(q2, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed long long aux64x2_0 = {*(const int64_t *)(iq2s_grid + (q2[0] | ((qh[0] << 8) & 0x300))), *(const int64_t *)(iq2s_grid + (q2[1] | ((qh[0] << 6) & 0x300)))}; + vector signed long long aux64x2_1 = {*(const int64_t *)(iq2s_grid + (q2[2] | ((qh[0] << 4) & 0x300))), *(const int64_t *)(iq2s_grid + (q2[3] | ((qh[0] << 2) & 0x300)))}; + vector signed long long aux64x2_2 = {*(const int64_t *)(iq2s_grid + (q2[4] | ((qh[1] << 8) & 0x300))), *(const int64_t *)(iq2s_grid + (q2[5] | ((qh[1] << 6) & 0x300)))}; + vector signed long long aux64x2_3 = {*(const int64_t *)(iq2s_grid + (q2[6] | ((qh[1] << 4) & 0x300))), *(const int64_t *)(iq2s_grid + (q2[7] | ((qh[1] << 2) & 0x300)))}; + q2 += 8; + qh += 2; + + vector signed char vsigns01 = (vector signed char)vec_splats(*(const uint32_t *)&signs[0]); + vector signed char vsigns23 = (vector signed char)vec_splats(*(const uint32_t *)&signs[2]); + signs += 4; + + vector signed char vsigns0 = vec_perm(vsigns01, vsigns01, mask0); + vector signed char vsigns1 = vec_perm(vsigns01, vsigns01, mask1); + vector signed char vsigns2 = vec_perm(vsigns23, vsigns23, mask0); + vector signed char vsigns3 = vec_perm(vsigns23, vsigns23, mask1); + + vsigns0 = (vector signed char)vec_cmpeq(vec_and(vsigns0, mask2), mask2); + vsigns1 = (vector signed char)vec_cmpeq(vec_and(vsigns1, mask2), mask2); + vsigns2 = (vector signed char)vec_cmpeq(vec_and(vsigns2, mask2), mask2); + vsigns3 = (vector signed char)vec_cmpeq(vec_and(vsigns3, mask2), mask2); + + vector signed char q2x0 = vec_sub(vec_xor(vsigns0, (vector signed char)aux64x2_0), vsigns0); + vector signed char q2x1 = vec_sub(vec_xor(vsigns1, (vector signed char)aux64x2_1), vsigns1); + vector signed char q2x2 = vec_sub(vec_xor(vsigns2, (vector signed char)aux64x2_2), vsigns2); + vector signed char q2x3 = vec_sub(vec_xor(vsigns3, (vector signed char)aux64x2_3), vsigns3); + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q2x0, q8y0), vec_mulo(q2x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q2x1, q8y1), vec_mulo(q2x1, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q2x2, q8y2), vec_mulo(q2x2, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q2x3, q8y3), vec_mulo(q2x3, q8y3)); + + const uint16_t ls0 = (uint16_t)(sc[0] & 0xf); + const uint16_t ls1 = (uint16_t)(sc[0] >> 4); + const uint16_t ls2 = (uint16_t)(sc[1] & 0xf); + const uint16_t ls3 = (uint16_t)(sc[1] >> 4); + sc += 2; + + vector signed short vscales0 = vec_splats((int16_t)(2*ls0+1)); + vector signed short vscales1 = vec_splats((int16_t)(2*ls1+1)); + vector signed short vscales2 = vec_splats((int16_t)(2*ls2+1)); + vector signed short vscales3 = vec_splats((int16_t)(2*ls3+1)); + + vsumi0 = vec_msum(qv0, vscales0, vsumi0); + vsumi1 = vec_msum(qv1, vscales1, vsumi1); + vsumi2 = vec_msum(qv2, vscales2, vsumi2); + vsumi3 = vec_msum(qv3, vscales3, vsumi3); + } + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = 0.125f * vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + }; + + + const __m128i m4 = __lsx_vreplgr2vr_b(0xf); + const __m128i m1 = __lsx_vreplgr2vr_b(1); + + const __m256i mask1 = __lasx_xvld((const __m256i*)k_mask1, 0); + const __m256i mask2 = __lasx_xvld((const __m256i*)k_mask2, 0); + uint64_t aux64; + + __m256 accumf = (__m256)__lasx_xvldi(0); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); + const int8_t * restrict q8 = y[i].qs; + + __m128i tmp1; + memcpy(&aux64, x[i].scales, 8); + tmp1 = __lsx_vinsgr2vr_d(tmp1, aux64, 0); + tmp1 = __lsx_vinsgr2vr_d(tmp1, aux64 >> 4, 1); + const __m128i scales8 = __lsx_vadd_b(__lsx_vslli_h(__lsx_vand_v(tmp1, m4), 1), m1); + const __m256i scales16 = lasx_ext8_16(scales8); // 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 15 + + __m256i sumi1 = __lasx_xvldi(0); + __m256i sumi2 = __lasx_xvldi(0); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q2_1 = lasx_set_d(iq2s_grid[qs[3] | ((qh[ib32+0] << 2) & 0x300)], + iq2s_grid[qs[2] | ((qh[ib32+0] << 4) & 0x300)], + iq2s_grid[qs[1] | ((qh[ib32+0] << 6) & 0x300)], + iq2s_grid[qs[0] | ((qh[ib32+0] << 8) & 0x300)]); + const __m256i q2_2 = lasx_set_d(iq2s_grid[qs[7] | ((qh[ib32+1] << 2) & 0x300)], + iq2s_grid[qs[6] | ((qh[ib32+1] << 4) & 0x300)], + iq2s_grid[qs[5] | ((qh[ib32+1] << 6) & 0x300)], + iq2s_grid[qs[4] | ((qh[ib32+1] << 8) & 0x300)]); + qs += 8; + + __m256i aux256 = __lasx_xvreplgr2vr_w(signs[0] | ((uint32_t) signs[1] << 16)); + aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2); + const __m256i s2_1 = __lasx_xvseq_b(aux256, mask2); + const __m256i q8s_1 = __lasx_xvsub_b(__lasx_xvxor_v(s2_1, q8_1), s2_1); + + aux256 = __lasx_xvreplgr2vr_w(signs[2] | ((uint32_t) signs[3] << 16)); + aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2); + const __m256i s2_2 = __lasx_xvseq_b(aux256, mask2); + const __m256i q8s_2 = __lasx_xvsub_b(__lasx_xvxor_v(s2_2, q8_2), s2_2); + + signs += 4; + + const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); // blocks 2*ib32+0, 2*ib32+1 + const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); // blocks 2*ib32+2, 2*ib32+3 + + const __m256i p1 = lasx_madd_h(dot1, lasx_shuffle_b(scales16, get_scale_shuffle_k4(ib32+0))); + const __m256i p2 = lasx_madd_h(dot2, lasx_shuffle_b(scales16, get_scale_shuffle_k4(ib32+1))); + sumi1 = __lasx_xvadd_w(sumi1, p1); + sumi2 = __lasx_xvadd_w(sumi2, p2); + } + + accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); + } + + *s = 0.125f * hsum_float_8(accumf); + +#else + + float sumf = 0; + for (int i = 0; i < nb; i++) { + + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint8_t * qh = x[i].qh; + const uint8_t * signs = qs + QK_K/8; + + int bsum = 0; + for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { + int ls1 = 1 + 2*(x[i].scales[ib32] & 0xf); + int ls2 = 1 + 2*(x[i].scales[ib32] >> 4); + int sumi1 = 0, sumi2 = 0; + for (int l = 0; l < 2; ++l) { + const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300))); + for (int j = 0; j < 8; ++j) { + sumi1 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1); + } + q8 += 8; + } + for (int l = 2; l < 4; ++l) { + const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300))); + for (int j = 0; j < 8; ++j) { + sumi2 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1); + } + q8 += 8; + } + bsum += ls1 * sumi1 + ls2 * sumi2; + qs += 4; + signs += 4; + } + + sumf += d * bsum; + } + + *s = 0.125f * sumf; + +#endif + +} + +void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_iq3_xxs * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined(__ARM_NEON) + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[2]; + + ggml_int8x16x4_t q3s; + ggml_int8x16x4_t q8b; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict gas = x[i].qs + QK_K/4; + const int8_t * restrict q8 = y[i].qs; + float sumf1 = 0, sumf2 = 0; + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; + memcpy(aux32, gas, 2*sizeof(uint32_t)); gas += 2*sizeof(uint32_t); + const uint32x4_t aux32x4_0 = ggml_vld1q_u32(iq3xxs_grid[q3[ 0]], iq3xxs_grid[q3[ 1]], iq3xxs_grid[q3[ 2]], iq3xxs_grid[q3[ 3]]); + const uint32x4_t aux32x4_1 = ggml_vld1q_u32(iq3xxs_grid[q3[ 4]], iq3xxs_grid[q3[ 5]], iq3xxs_grid[q3[ 6]], iq3xxs_grid[q3[ 7]]); + const uint32x4_t aux32x4_2 = ggml_vld1q_u32(iq3xxs_grid[q3[ 8]], iq3xxs_grid[q3[ 9]], iq3xxs_grid[q3[10]], iq3xxs_grid[q3[11]]); + const uint32x4_t aux32x4_3 = ggml_vld1q_u32(iq3xxs_grid[q3[12]], iq3xxs_grid[q3[13]], iq3xxs_grid[q3[14]], iq3xxs_grid[q3[15]]); + q3 += 16; + q3s.val[0] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[0] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[0] >> 7) & 127)))); + q3s.val[1] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[0] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[0] >> 21) & 127)))); + q3s.val[2] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 7) & 127)))); + q3s.val[3] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 21) & 127)))); + q3s.val[0] = vmulq_s8(q3s.val[0], vreinterpretq_s8_u32(aux32x4_0)); + q3s.val[1] = vmulq_s8(q3s.val[1], vreinterpretq_s8_u32(aux32x4_1)); + q3s.val[2] = vmulq_s8(q3s.val[2], vreinterpretq_s8_u32(aux32x4_2)); + q3s.val[3] = vmulq_s8(q3s.val[3], vreinterpretq_s8_u32(aux32x4_3)); + const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[0], q8b.val[0]), q3s.val[1], q8b.val[1]); + const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[2], q8b.val[2]), q3s.val[3], q8b.val[3]); + sumf1 += vaddvq_s32(p1) * (0.5f + (aux32[0] >> 28)); + sumf2 += vaddvq_s32(p2) * (0.5f + (aux32[1] >> 28)); + } + sumf += d*(sumf1 + sumf2); + } + *s = 0.5f * sumf; + +#elif defined(__AVX2__) + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[2]; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict gas = x[i].qs + QK_K/4; + const int8_t * restrict q8 = y[i].qs; + __m256i sumi1 = _mm256_setzero_si256(); + __m256i sumi2 = _mm256_setzero_si256(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q2_1 = _mm256_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]], + iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); + q3 += 8; + const __m256i q2_2 = _mm256_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]], + iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); + q3 += 8; + memcpy(aux32, gas, 8); gas += 8; + const __m256i s2_1 = _mm256_set_epi64x(signs64[(aux32[0] >> 21) & 127], signs64[(aux32[0] >> 14) & 127], + signs64[(aux32[0] >> 7) & 127], signs64[(aux32[0] >> 0) & 127]); + const __m256i s2_2 = _mm256_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], + signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); + const __m256i q8s_1 = _mm256_sign_epi8(q8_1, s2_1); + const __m256i q8s_2 = _mm256_sign_epi8(q8_2, s2_2); + const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); + const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); + const uint16_t ls1 = aux32[0] >> 28; + const uint16_t ls2 = aux32[1] >> 28; + const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1)); + const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1)); + sumi1 = _mm256_add_epi32(sumi1, p1); + sumi2 = _mm256_add_epi32(sumi2, p2); + } + + accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); + + } + + *s = 0.25f * hsum_float_8(accumf); + +#elif defined(__AVX__) + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[2]; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict gas = x[i].qs + QK_K/4; + const int8_t * restrict q8 = y[i].qs; + __m128i sumi1_0 = _mm_setzero_si128(); + __m128i sumi1_1 = _mm_setzero_si128(); + __m128i sumi2_0 = _mm_setzero_si128(); + __m128i sumi2_1 = _mm_setzero_si128(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m128i q8_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q2_1_0 = _mm_set_epi32(iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); + const __m128i q2_1_1 = _mm_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]]); + q3 += 8; + const __m128i q2_2_0 = _mm_set_epi32(iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); + const __m128i q2_2_1 = _mm_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]]); + q3 += 8; + memcpy(aux32, gas, 8); gas += 8; + const __m128i s2_1_0 = _mm_set_epi64x(signs64[(aux32[0] >> 7) & 127], signs64[(aux32[0] >> 0) & 127]); + const __m128i s2_1_1 = _mm_set_epi64x(signs64[(aux32[0] >> 21) & 127], signs64[(aux32[0] >> 14) & 127]); + const __m128i s2_2_0 = _mm_set_epi64x(signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); + const __m128i s2_2_1 = _mm_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127]); + const __m128i q8s_1_0 = _mm_sign_epi8(q8_1_0, s2_1_0); + const __m128i q8s_1_1 = _mm_sign_epi8(q8_1_1, s2_1_1); + const __m128i q8s_2_0 = _mm_sign_epi8(q8_2_0, s2_2_0); + const __m128i q8s_2_1 = _mm_sign_epi8(q8_2_1, s2_2_1); + const __m128i dot1_0 = _mm_maddubs_epi16(q2_1_0, q8s_1_0); + const __m128i dot1_1 = _mm_maddubs_epi16(q2_1_1, q8s_1_1); + const __m128i dot2_0 = _mm_maddubs_epi16(q2_2_0, q8s_2_0); + const __m128i dot2_1 = _mm_maddubs_epi16(q2_2_1, q8s_2_1); + const uint16_t ls1 = aux32[0] >> 28; + const uint16_t ls2 = aux32[1] >> 28; + const __m128i p1_0 = _mm_madd_epi16(dot1_0, _mm_set1_epi16(2*ls1+1)); + const __m128i p1_1 = _mm_madd_epi16(dot1_1, _mm_set1_epi16(2*ls1+1)); + const __m128i p2_0 = _mm_madd_epi16(dot2_0, _mm_set1_epi16(2*ls2+1)); + const __m128i p2_1 = _mm_madd_epi16(dot2_1, _mm_set1_epi16(2*ls2+1)); + sumi1_0 = _mm_add_epi32(sumi1_0, p1_0); + sumi1_1 = _mm_add_epi32(sumi1_1, p1_1); + sumi2_0 = _mm_add_epi32(sumi2_0, p2_0); + sumi2_1 = _mm_add_epi32(sumi2_1, p2_1); + } + + accumf = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_add_epi32(sumi1_1, sumi2_1), _mm_add_epi32(sumi1_0, sumi2_0)))), accumf); + + } + + *s = 0.25f * hsum_float_8(accumf); + +#elif defined(__POWER9_VECTOR__) + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + const vector int v0 = vec_splats((int32_t)0); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; + + const uint8_t * restrict q3 = x[i].qs; + const uint32_t * restrict signs = (const uint32_t *)(x[i].qs + QK_K/4); + const int8_t * restrict q8 = y[i].qs; + +#pragma GCC unroll 1 + for (int j = 0; j < QK_K/32; j += 2) { + __builtin_prefetch(q3, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector unsigned int aux32x4_0 = {iq3xxs_grid[q3[ 0]], iq3xxs_grid[q3[ 1]], iq3xxs_grid[q3[ 2]], iq3xxs_grid[q3[ 3]]}; + vector unsigned int aux32x4_1 = {iq3xxs_grid[q3[ 4]], iq3xxs_grid[q3[ 5]], iq3xxs_grid[q3[ 6]], iq3xxs_grid[q3[ 7]]}; + vector unsigned int aux32x4_2 = {iq3xxs_grid[q3[ 8]], iq3xxs_grid[q3[ 9]], iq3xxs_grid[q3[10]], iq3xxs_grid[q3[11]]}; + vector unsigned int aux32x4_3 = {iq3xxs_grid[q3[12]], iq3xxs_grid[q3[13]], iq3xxs_grid[q3[14]], iq3xxs_grid[q3[15]]}; + q3 += 16; + + vector unsigned long long aux64x2_0 = {(uint64_t)(signs64[(signs[0] >> 0) & 127]), (uint64_t)(signs64[(signs[0] >> 7) & 127])}; + vector unsigned long long aux64x2_1 = {(uint64_t)(signs64[(signs[0] >> 14) & 127]), (uint64_t)(signs64[(signs[0] >> 21) & 127])}; + vector unsigned long long aux64x2_2 = {(uint64_t)(signs64[(signs[1] >> 0) & 127]), (uint64_t)(signs64[(signs[1] >> 7) & 127])}; + vector unsigned long long aux64x2_3 = {(uint64_t)(signs64[(signs[1] >> 14) & 127]), (uint64_t)(signs64[(signs[1] >> 21) & 127])}; + + vector signed char q3x0 = vec_mul((vector signed char)aux64x2_0, (vector signed char)aux32x4_0); + vector signed char q3x1 = vec_mul((vector signed char)aux64x2_1, (vector signed char)aux32x4_1); + vector signed char q3x2 = vec_mul((vector signed char)aux64x2_2, (vector signed char)aux32x4_2); + vector signed char q3x3 = vec_mul((vector signed char)aux64x2_3, (vector signed char)aux32x4_3); + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q3x0, q8y0), vec_mulo(q3x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q3x1, q8y1), vec_mulo(q3x1, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q3x2, q8y2), vec_mulo(q3x2, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q3x3, q8y3), vec_mulo(q3x3, q8y3)); + + const uint16_t ls0 = (uint16_t)(signs[0] >> 28); + const uint16_t ls1 = (uint16_t)(signs[1] >> 28); + signs += 2; + + vector signed short vscales01 = (vector signed short)vec_splats((uint16_t)(2*ls0+1)); + vector signed short vscales23 = (vector signed short)vec_splats((uint16_t)(2*ls1+1)); + + vsumi0 = vec_msum(qv0, vscales01, vsumi0); + vsumi1 = vec_msum(qv1, vscales01, vsumi1); + vsumi2 = vec_msum(qv2, vscales23, vsumi2); + vsumi3 = vec_msum(qv3, vscales23, vsumi3); + } + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = 0.25f * vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[2]; + + __m256 accumf = (__m256)__lasx_xvldi(0); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict gas = x[i].qs + QK_K/4; + const int8_t * restrict q8 = y[i].qs; + __m256i sumi1 = __lasx_xvldi(0); + __m256i sumi2 = __lasx_xvldi(0); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q2_1 = lasx_set_w(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]], + iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); + q3 += 8; + const __m256i q2_2 = lasx_set_w(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]], + iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); + q3 += 8; + memcpy(aux32, gas, 8); gas += 8; + + const __m256i s2_1 = lasx_set_d(signs64[(aux32[0] >> 21) & 127], signs64[(aux32[0] >> 14) & 127], + signs64[(aux32[0] >> 7) & 127], signs64[(aux32[0] >> 0) & 127]); + const __m256i s2_2 = lasx_set_d(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], + signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); + const __m256i q8s_1 = __lasx_xvsigncov_b(s2_1, q8_1); + const __m256i q8s_2 = __lasx_xvsigncov_b(s2_2, q8_2); + const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); + const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); + const uint16_t ls1 = aux32[0] >> 28; + const uint16_t ls2 = aux32[1] >> 28; + + const __m256i p1 = lasx_madd_h(dot1, __lasx_xvreplgr2vr_h(2*ls1+1)); + const __m256i p2 = lasx_madd_h(dot2, __lasx_xvreplgr2vr_h(2*ls2+1)); + sumi1 = __lasx_xvadd_w(sumi1, p1); + sumi2 = __lasx_xvadd_w(sumi2, p2); + } + + accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); + } + + *s = 0.25f * hsum_float_8(accumf); + +#else + + uint32_t aux32; + + float sumf = 0.f; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict gas = x[i].qs + QK_K/4; + const int8_t * restrict q8 = y[i].qs; + int32_t bsum = 0; + for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { + memcpy(&aux32, gas, sizeof(uint32_t)); gas += sizeof(uint32_t); + const uint32_t ls = 2*(aux32 >> 28) + 1; + int32_t sumi = 0; + for (int l = 0; l < 4; ++l) { + const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*l+0]); + const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*l+1]); + const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*l) & 127]; + for (int j = 0; j < 4; ++j) { + sumi += grid1[j] * q8[j+0] * (signs & kmask_iq2xs[j+0] ? -1 : 1); + sumi += grid2[j] * q8[j+4] * (signs & kmask_iq2xs[j+4] ? -1 : 1); + } + q8 += 8; + } + q3 += 8; + bsum += sumi * ls; + } + sumf += d * bsum; + } + *s = 0.25f * sumf; +#endif +} + +void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_iq3_s * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined(__ARM_NEON) + + typedef union { + uint16x8_t vec_index; + uint16_t index[8]; + } vec_index_t; + + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; + + static const int16_t k_shift[8] = {8, 7, 6, 5, 4, 3, 2, 1}; + + const ggml_uint8x16x2_t mask1 = ggml_vld1q_u8_x2(k_mask1); + const uint8x16_t mask2 = vld1q_u8(k_mask2); + + const int16x8_t hshift = vld1q_s16(k_shift); + const uint16x8_t m256 = vdupq_n_u16(256); + const uint8x16_t m1 = vdupq_n_u8(1); + + uint8x16x2_t vs; + ggml_int8x16x4_t q3s; + ggml_int8x16x4_t q8b; + vec_index_t idx; + + uint32_t scales32[2]; + const uint8_t * scales8 = (const uint8_t *)scales32; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)x[i].signs; + const int8_t * restrict q8 = y[i].qs; + + memcpy(scales32, x[i].scales, 4); + scales32[1] = (((scales32[0] >> 4) & 0x0f0f0f0f) << 1) | 0x01010101; + scales32[0] = ((scales32[0] & 0x0f0f0f0f) << 1) | 0x01010101; + + int sumi1 = 0, sumi2 = 0; + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; + + const uint8x16_t idx_l = vld1q_u8(qs); qs += 16; + idx.vec_index = vorrq_u16(vmovl_u8(vget_low_u8 (idx_l)), vandq_u16(vshlq_u16(vdupq_n_u16(qh[ib32+0]), hshift), m256)); + const uint32x4_t aux32x4_0 = ggml_vld1q_u32(iq3s_grid[idx.index[0]], iq3s_grid[idx.index[1]], + iq3s_grid[idx.index[2]], iq3s_grid[idx.index[3]]); + const uint32x4_t aux32x4_1 = ggml_vld1q_u32(iq3s_grid[idx.index[4]], iq3s_grid[idx.index[5]], + iq3s_grid[idx.index[6]], iq3s_grid[idx.index[7]]); + idx.vec_index = vorrq_u16(vmovl_u8(vget_high_u8(idx_l)), vandq_u16(vshlq_u16(vdupq_n_u16(qh[ib32+1]), hshift), m256)); + const uint32x4_t aux32x4_2 = ggml_vld1q_u32(iq3s_grid[idx.index[0]], iq3s_grid[idx.index[1]], + iq3s_grid[idx.index[2]], iq3s_grid[idx.index[3]]); + const uint32x4_t aux32x4_3 = ggml_vld1q_u32(iq3s_grid[idx.index[4]], iq3s_grid[idx.index[5]], + iq3s_grid[idx.index[6]], iq3s_grid[idx.index[7]]); + + + vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | ((uint32_t) signs[1] << 16))); + vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); + vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); + vs.val[0] = vorrq_u8(vceqq_u8(vs.val[0], mask2), m1); + vs.val[1] = vorrq_u8(vceqq_u8(vs.val[1], mask2), m1); + + q3s.val[0] = vmulq_s8(vreinterpretq_s8_u8(vs.val[0]), vreinterpretq_s8_u32(aux32x4_0)); + q3s.val[1] = vmulq_s8(vreinterpretq_s8_u8(vs.val[1]), vreinterpretq_s8_u32(aux32x4_1)); + + vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | ((uint32_t) signs[3] << 16))); + vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); + vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); + vs.val[0] = vorrq_u8(vceqq_u8(vs.val[0], mask2), m1); + vs.val[1] = vorrq_u8(vceqq_u8(vs.val[1], mask2), m1); + + signs += 4; + + q3s.val[2] = vmulq_s8(vreinterpretq_s8_u8(vs.val[0]), vreinterpretq_s8_u32(aux32x4_2)); + q3s.val[3] = vmulq_s8(vreinterpretq_s8_u8(vs.val[1]), vreinterpretq_s8_u32(aux32x4_3)); + + const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[0], q8b.val[0]), q3s.val[1], q8b.val[1]); + const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[2], q8b.val[2]), q3s.val[3], q8b.val[3]); + + sumi1 += vaddvq_s32(p1) * scales8[ib32/2+0]; + sumi2 += vaddvq_s32(p2) * scales8[ib32/2+4]; + } + sumf += d*(sumi1 + sumi2); + } + *s = sumf; + +#elif defined(__AVX2__) + + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + }; + + const __m256i mask1 = _mm256_loadu_si256((const __m256i*)k_mask1); + const __m256i mask2 = _mm256_loadu_si256((const __m256i*)k_mask2); + + const __m256i idx_shift = _mm256_set_epi32(1, 2, 3, 4, 5, 6, 7, 8); + const __m256i idx_mask = _mm256_set1_epi32(256); + + typedef union { + __m256i vec[2]; + uint32_t index[16]; + } index_t; + + index_t idx; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)x[i].signs; + const int8_t * restrict q8 = y[i].qs; + __m256i sumi1 = _mm256_setzero_si256(); + __m256i sumi2 = _mm256_setzero_si256(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i idx_l = _mm256_cvtepu8_epi16(_mm_loadu_si128((const __m128i *)qs)); qs += 16; + idx.vec[0] = _mm256_set1_epi32(qh[ib32+0]); + idx.vec[1] = _mm256_set1_epi32(qh[ib32+1]); + idx.vec[0] = _mm256_and_si256(_mm256_sllv_epi32(idx.vec[0], idx_shift), idx_mask); + idx.vec[1] = _mm256_and_si256(_mm256_sllv_epi32(idx.vec[1], idx_shift), idx_mask); + idx.vec[0] = _mm256_or_si256(idx.vec[0], _mm256_cvtepi16_epi32(_mm256_castsi256_si128(idx_l))); + idx.vec[1] = _mm256_or_si256(idx.vec[1], _mm256_cvtepi16_epi32(_mm256_extractf128_si256(idx_l, 1))); + + // At leat on my CPU (Ryzen 7950X), using _mm256_i32gather_epi32 is slower than _mm256_set_epi32. Strange. + //const __m256i q2_1 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[0], 4); + //const __m256i q2_2 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[1], 4); + const __m256i q2_1 = _mm256_set_epi32( + iq3s_grid[idx.index[7]], iq3s_grid[idx.index[6]], iq3s_grid[idx.index[5]], iq3s_grid[idx.index[4]], + iq3s_grid[idx.index[3]], iq3s_grid[idx.index[2]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]] + ); + const __m256i q2_2 = _mm256_set_epi32( + iq3s_grid[idx.index[15]], iq3s_grid[idx.index[14]], iq3s_grid[idx.index[13]], iq3s_grid[idx.index[12]], + iq3s_grid[idx.index[11]], iq3s_grid[idx.index[10]], iq3s_grid[idx.index[ 9]], iq3s_grid[idx.index[ 8]] + ); + + __m256i aux256 = _mm256_set1_epi32(signs[0] | (signs[1] << 16)); + aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); + const __m256i s2_1 = _mm256_cmpeq_epi8(aux256, mask2); + const __m256i q8s_1 = _mm256_sub_epi8(_mm256_xor_si256(s2_1, q8_1), s2_1); + + aux256 = _mm256_set1_epi32(signs[2] | (signs[3] << 16)); + aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); + const __m256i s2_2 = _mm256_cmpeq_epi8(aux256, mask2); + const __m256i q8s_2 = _mm256_sub_epi8(_mm256_xor_si256(s2_2, q8_2), s2_2); + + signs += 4; + + const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); + const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); + const uint16_t ls1 = x[i].scales[ib32/2] & 0xf; + const uint16_t ls2 = x[i].scales[ib32/2] >> 4; + const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1)); + const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1)); + sumi1 = _mm256_add_epi32(sumi1, p1); + sumi2 = _mm256_add_epi32(sumi2, p2); + } + + accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); + + } + + *s = hsum_float_8(accumf); + +#elif defined(__AVX__) + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + }; + + const __m128i mask1_0 = _mm_loadu_si128((const __m128i*)k_mask1); + const __m128i mask1_1 = _mm_loadu_si128((const __m128i*)k_mask1 + 1); + const __m128i mask2_0 = _mm_loadu_si128((const __m128i*)k_mask2); + const __m128i mask2_1 = _mm_loadu_si128((const __m128i*)k_mask2 + 1); + + const __m128i idx_mul_0 = _mm_set_epi32(32, 64, 128, 256); + const __m128i idx_mul_1 = _mm_set_epi32(2, 4, 8, 16); + const __m128i idx_mask = _mm_set1_epi32(256); + + typedef union { + __m128i vec[4]; + uint32_t index[16]; + } index_t; + + index_t idx; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)x[i].signs; + const int8_t * restrict q8 = y[i].qs; + __m128i sumi1_0 = _mm_setzero_si128(); + __m128i sumi1_1 = _mm_setzero_si128(); + __m128i sumi2_0 = _mm_setzero_si128(); + __m128i sumi2_1 = _mm_setzero_si128(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m128i q8_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i qs_tmp = _mm_loadu_si128((const __m128i *)qs); + const __m128i idx_l_0 = _mm_cvtepu8_epi16(qs_tmp); + const __m128i idx_l_1 = _mm_cvtepu8_epi16(_mm_srli_si128(qs_tmp, 8)); qs += 16; + idx.vec[0] = _mm_set1_epi32(qh[ib32+0]); + idx.vec[1] = idx.vec[0]; + idx.vec[2] = _mm_set1_epi32(qh[ib32+1]); + idx.vec[3] = idx.vec[2]; + + idx.vec[0] = _mm_and_si128(_mm_mullo_epi32(idx.vec[0], idx_mul_0), idx_mask); + idx.vec[1] = _mm_and_si128(_mm_mullo_epi32(idx.vec[1], idx_mul_1), idx_mask); + idx.vec[2] = _mm_and_si128(_mm_mullo_epi32(idx.vec[2], idx_mul_0), idx_mask); + idx.vec[3] = _mm_and_si128(_mm_mullo_epi32(idx.vec[3], idx_mul_1), idx_mask); + + idx.vec[0] = _mm_or_si128(idx.vec[0], _mm_cvtepi16_epi32(idx_l_0)); + idx.vec[1] = _mm_or_si128(idx.vec[1], _mm_cvtepi16_epi32(_mm_srli_si128(idx_l_0, 8))); + idx.vec[2] = _mm_or_si128(idx.vec[2], _mm_cvtepi16_epi32(idx_l_1)); + idx.vec[3] = _mm_or_si128(idx.vec[3], _mm_cvtepi16_epi32(_mm_srli_si128(idx_l_1, 8))); + + const __m128i q2_1_0 = _mm_set_epi32(iq3s_grid[idx.index[3]], iq3s_grid[idx.index[2]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]]); + const __m128i q2_1_1 = _mm_set_epi32(iq3s_grid[idx.index[7]], iq3s_grid[idx.index[6]], iq3s_grid[idx.index[5]], iq3s_grid[idx.index[4]]); + const __m128i q2_2_0 = _mm_set_epi32(iq3s_grid[idx.index[11]], iq3s_grid[idx.index[10]], iq3s_grid[idx.index[9]], iq3s_grid[idx.index[8]]); + const __m128i q2_2_1 = _mm_set_epi32(iq3s_grid[idx.index[15]], iq3s_grid[idx.index[14]], iq3s_grid[idx.index[13]], iq3s_grid[idx.index[12]]); + + __m128i aux128_0 = _mm_set1_epi32(signs[0] | (signs[1] << 16)); + __m128i aux128_1 = aux128_0; + aux128_0 = _mm_and_si128(_mm_shuffle_epi8(aux128_0,mask1_0), mask2_0); + aux128_1 = _mm_and_si128(_mm_shuffle_epi8(aux128_1,mask1_1), mask2_1); + const __m128i s2_1_0 = _mm_cmpeq_epi8(aux128_0, mask2_0); + const __m128i s2_1_1 = _mm_cmpeq_epi8(aux128_1, mask2_1); + const __m128i q8s_1_0 = _mm_sub_epi8(_mm_xor_si128(s2_1_0, q8_1_0), s2_1_0); + const __m128i q8s_1_1 = _mm_sub_epi8(_mm_xor_si128(s2_1_1, q8_1_1), s2_1_1); + + aux128_0 = _mm_set1_epi32(signs[2] | (signs[3] << 16)); + aux128_1 = aux128_0; + aux128_0 = _mm_and_si128(_mm_shuffle_epi8(aux128_0,mask1_0), mask2_0); + aux128_1 = _mm_and_si128(_mm_shuffle_epi8(aux128_1,mask1_1), mask2_1); + const __m128i s2_2_0 = _mm_cmpeq_epi8(aux128_0, mask2_0); + const __m128i s2_2_1 = _mm_cmpeq_epi8(aux128_1, mask2_1); + const __m128i q8s_2_0 = _mm_sub_epi8(_mm_xor_si128(s2_2_0, q8_2_0), s2_2_0); + const __m128i q8s_2_1 = _mm_sub_epi8(_mm_xor_si128(s2_2_1, q8_2_1), s2_2_1); + + signs += 4; + + const __m128i dot1_0 = _mm_maddubs_epi16(q2_1_0, q8s_1_0); + const __m128i dot1_1 = _mm_maddubs_epi16(q2_1_1, q8s_1_1); + const __m128i dot2_0 = _mm_maddubs_epi16(q2_2_0, q8s_2_0); + const __m128i dot2_1 = _mm_maddubs_epi16(q2_2_1, q8s_2_1); + const uint16_t ls1 = x[i].scales[ib32/2] & 0xf; + const uint16_t ls2 = x[i].scales[ib32/2] >> 4; + const __m128i p1_0 = _mm_madd_epi16(dot1_0, _mm_set1_epi16(2*ls1+1)); + const __m128i p1_1 = _mm_madd_epi16(dot1_1, _mm_set1_epi16(2*ls1+1)); + const __m128i p2_0 = _mm_madd_epi16(dot2_0, _mm_set1_epi16(2*ls2+1)); + const __m128i p2_1 = _mm_madd_epi16(dot2_1, _mm_set1_epi16(2*ls2+1)); + sumi1_0 = _mm_add_epi32(sumi1_0, p1_0); + sumi1_1 = _mm_add_epi32(sumi1_1, p1_1); + sumi2_0 = _mm_add_epi32(sumi2_0, p2_0); + sumi2_1 = _mm_add_epi32(sumi2_1, p2_1); + } + + accumf = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_add_epi32(sumi1_1, sumi2_1), _mm_add_epi32(sumi1_0, sumi2_0)))), accumf); + + } + + *s = hsum_float_8(accumf); + +#elif defined(__POWER9_VECTOR__) + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; + + const vector int v0 = vec_splats((int32_t)0); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + const vector unsigned char mask0 = vec_xl( 0, k_mask1); + const vector unsigned char mask1 = vec_xl(16, k_mask1); + const vector signed char mask2 = (vector signed char)vec_xl( 0, k_mask2); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)(x[i].signs); + const uint8_t * restrict sc = x[i].scales; + const int8_t * restrict q8 = y[i].qs; + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; + + for (int j = 0; j < QK_K/32; j += 2) { + __builtin_prefetch(q3, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector unsigned int aux32x4_0 = {iq3s_grid[q3[ 0] | ((qh[0] << 8) & 256)], iq3s_grid[q3[ 1] | ((qh[0] << 7) & 256)], + iq3s_grid[q3[ 2] | ((qh[0] << 6) & 256)], iq3s_grid[q3[ 3] | ((qh[0] << 5) & 256)]}; + vector unsigned int aux32x4_1 = {iq3s_grid[q3[ 4] | ((qh[0] << 4) & 256)], iq3s_grid[q3[ 5] | ((qh[0] << 3) & 256)], + iq3s_grid[q3[ 6] | ((qh[0] << 2) & 256)], iq3s_grid[q3[ 7] | ((qh[0] << 1) & 256)]}; + vector unsigned int aux32x4_2 = {iq3s_grid[q3[ 8] | ((qh[1] << 8) & 256)], iq3s_grid[q3[ 9] | ((qh[1] << 7) & 256)], + iq3s_grid[q3[10] | ((qh[1] << 6) & 256)], iq3s_grid[q3[11] | ((qh[1] << 5) & 256)]}; + vector unsigned int aux32x4_3 = {iq3s_grid[q3[12] | ((qh[1] << 4) & 256)], iq3s_grid[q3[13] | ((qh[1] << 3) & 256)], + iq3s_grid[q3[14] | ((qh[1] << 2) & 256)], iq3s_grid[q3[15] | ((qh[1] << 1) & 256)]}; + q3 += 16; + qh += 2; + + vector signed char vsigns01 = (vector signed char)vec_splats(*(const uint32_t *)&signs[0]); + vector signed char vsigns02 = (vector signed char)vec_splats(*(const uint32_t *)&signs[2]); + signs += 4; + + vector signed char vsigns0 = vec_perm(vsigns01, vsigns01, mask0); + vector signed char vsigns1 = vec_perm(vsigns01, vsigns01, mask1); + vector signed char vsigns2 = vec_perm(vsigns02, vsigns02, mask0); + vector signed char vsigns3 = vec_perm(vsigns02, vsigns02, mask1); + + vsigns0 = (vector signed char)vec_cmpeq(vec_and(vsigns0, mask2), mask2); + vsigns1 = (vector signed char)vec_cmpeq(vec_and(vsigns1, mask2), mask2); + vsigns2 = (vector signed char)vec_cmpeq(vec_and(vsigns2, mask2), mask2); + vsigns3 = (vector signed char)vec_cmpeq(vec_and(vsigns3, mask2), mask2); + + vector signed char q3x0 = vec_sub(vec_xor(vsigns0, (vector signed char)aux32x4_0), vsigns0); + vector signed char q3x1 = vec_sub(vec_xor(vsigns1, (vector signed char)aux32x4_1), vsigns1); + vector signed char q3x2 = vec_sub(vec_xor(vsigns2, (vector signed char)aux32x4_2), vsigns2); + vector signed char q3x3 = vec_sub(vec_xor(vsigns3, (vector signed char)aux32x4_3), vsigns3); + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q3x0, q8y0), vec_mulo(q3x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q3x1, q8y1), vec_mulo(q3x1, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q3x2, q8y2), vec_mulo(q3x2, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q3x3, q8y3), vec_mulo(q3x3, q8y3)); + + const uint16_t ls0 = (uint16_t)(sc[0] & 0xf); + const uint16_t ls1 = (uint16_t)(sc[0] >> 4); + sc ++; + + vector signed short vscales01 = (vector signed short)vec_splats((uint16_t)(2*ls0+1)); + vector signed short vscales23 = (vector signed short)vec_splats((uint16_t)(2*ls1+1)); + + vsumi0 = vec_msum(qv0, vscales01, vsumi0); + vsumi1 = vec_msum(qv1, vscales01, vsumi1); + vsumi2 = vec_msum(qv2, vscales23, vsumi2); + vsumi3 = vec_msum(qv3, vscales23, vsumi3); + } + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + }; + + const __m256i mask1 = __lasx_xvld((const __m256i*)k_mask1, 0); + const __m256i mask2 = __lasx_xvld((const __m256i*)k_mask2, 0); + + __m256i idx_shift = lasx_set_w(1, 2, 3, 4, 5, 6, 7, 8); + const __m256i idx_mask = __lasx_xvreplgr2vr_w(256); + + typedef union { + __m256i vec[2]; + uint32_t index[16]; + } index_t; + + index_t idx; + + __m256 accumf = (__m256)__lasx_xvldi(0); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)x[i].signs; + const int8_t * restrict q8 = y[i].qs; + __m256i sumi1 = __lasx_xvldi(0); + __m256i sumi2 = __lasx_xvldi(0); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i idx_l = lasx_extu8_16(__lsx_vld(qs, 0)); qs += 16; + idx.vec[0] = __lasx_xvreplgr2vr_w(qh[ib32+0]); + idx.vec[1] = __lasx_xvreplgr2vr_w(qh[ib32+1]); + idx.vec[0] = __lasx_xvand_v(__lasx_xvsll_w(idx.vec[0], idx_shift), idx_mask); + idx.vec[1] = __lasx_xvand_v(__lasx_xvsll_w(idx.vec[1], idx_shift), idx_mask); + idx.vec[0] = __lasx_xvor_v(idx.vec[0], lasx_ext16_32(lasx_extracti128(idx_l, 0))); + idx.vec[1] = __lasx_xvor_v(idx.vec[1], lasx_ext16_32(lasx_extracti128(idx_l, 1))); + + // At leat on my CPU (Ryzen 7950X), using _mm256_i32gather_epi32 is slower than _mm256_set_epi32. Strange. + //const __m256i q2_1 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[0], 4); + //const __m256i q2_2 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[1], 4); + const __m256i q2_1 = lasx_set_w( + iq3s_grid[idx.index[7]], iq3s_grid[idx.index[6]], iq3s_grid[idx.index[5]], iq3s_grid[idx.index[4]], + iq3s_grid[idx.index[3]], iq3s_grid[idx.index[2]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]] + ); + const __m256i q2_2 = lasx_set_w( + iq3s_grid[idx.index[15]], iq3s_grid[idx.index[14]], iq3s_grid[idx.index[13]], iq3s_grid[idx.index[12]], + iq3s_grid[idx.index[11]], iq3s_grid[idx.index[10]], iq3s_grid[idx.index[ 9]], iq3s_grid[idx.index[ 8]] + ); + + __m256i aux256 = __lasx_xvreplgr2vr_w(signs[0] | (signs[1] << 16)); + aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2); + const __m256i s2_1 = __lasx_xvseq_b(aux256, mask2); + const __m256i q8s_1 = __lasx_xvsub_b(__lasx_xvxor_v(s2_1, q8_1), s2_1); + + aux256 = __lasx_xvreplgr2vr_w(signs[2] | (signs[3] << 16)); + aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2); + const __m256i s2_2 = __lasx_xvseq_b(aux256, mask2); + const __m256i q8s_2 = __lasx_xvsub_b(__lasx_xvxor_v(s2_2, q8_2), s2_2); + + signs += 4; + + const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); + const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); + const uint16_t ls1 = x[i].scales[ib32/2] & 0xf; + const uint16_t ls2 = x[i].scales[ib32/2] >> 4; + const __m256i p1 = lasx_madd_h(dot1, __lasx_xvreplgr2vr_h(2*ls1+1)); + const __m256i p2 = lasx_madd_h(dot2, __lasx_xvreplgr2vr_h(2*ls2+1)); + sumi1 = __lasx_xvadd_w(sumi1, p1); + sumi2 = __lasx_xvadd_w(sumi2, p2); + } + + accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); + } + + *s = hsum_float_8(accumf); + +#else + + float sumf = 0.f; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint8_t * restrict signs = x[i].signs; + const int8_t * restrict q8 = y[i].qs; + int32_t bsum = 0; + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const uint32_t ls1 = 2*(x[i].scales[ib32/2] & 0xf) + 1; + const uint32_t ls2 = 2*(x[i].scales[ib32/2] >> 4) + 1; + int32_t sumi = 0; + for (int l = 0; l < 4; ++l) { + const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+0] << (8-2*l)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+0] << (7-2*l)) & 256))); + for (int j = 0; j < 4; ++j) { + sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1); + sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1); + } + q8 += 8; + } + qs += 8; + signs += 4; + bsum += sumi * ls1; + sumi = 0; + for (int l = 0; l < 4; ++l) { + const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+1] << (8-2*l)) & 256))); + const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+1] << (7-2*l)) & 256))); + for (int j = 0; j < 4; ++j) { + sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1); + sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1); + } + q8 += 8; + } + qs += 8; + signs += 4; + bsum += sumi * ls2; + } + sumf += d * bsum; + } + *s = sumf; +#endif +} + +#if defined(__AVX2__) +static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) { + const __m256i ax = _mm256_sign_epi8(x, x); + const __m256i sy = _mm256_sign_epi8(y, x); + return _mm256_maddubs_epi16(ax, sy); +} +#elif defined(__loongarch_asx) +static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) { + const __m256i ax = __lasx_xvsigncov_b(x, x); + const __m256i sy = __lasx_xvsigncov_b(x, y); + __m256i tmp1, tmp2, tmp3; + tmp1 = __lasx_xvmulwev_h_bu_b(ax, sy); + tmp2 = __lasx_xvmulwod_h_bu_b(ax, sy); + tmp3 = __lasx_xvadd_h(tmp1, tmp2); + return __lasx_xvsat_h(tmp3, 15); +} +#endif + +void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_iq1_s * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined __ARM_NEON + + ggml_int8x16x4_t q1b; + ggml_int8x16x4_t q8b; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint16_t * qh = x[i].qh; + + int sumi1 = 0, sumi2 = 0, sumi3 = 0; + + for (int ib = 0; ib < QK_K/32; ib += 2) { + + q1b.val[0] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[0] | ((qh[ib+0] << 8) & 0x700)))), + vld1_s8((const int8_t *)(iq1s_grid + (qs[1] | ((qh[ib+0] << 5) & 0x700))))); + q1b.val[1] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[2] | ((qh[ib+0] << 2) & 0x700)))), + vld1_s8((const int8_t *)(iq1s_grid + (qs[3] | ((qh[ib+0] >> 1) & 0x700))))); + q1b.val[2] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[4] | ((qh[ib+1] << 8) & 0x700)))), + vld1_s8((const int8_t *)(iq1s_grid + (qs[5] | ((qh[ib+1] << 5) & 0x700))))); + q1b.val[3] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[6] | ((qh[ib+1] << 2) & 0x700)))), + vld1_s8((const int8_t *)(iq1s_grid + (qs[7] | ((qh[ib+1] >> 1) & 0x700))))); + qs += 8; + + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; + + const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q1b.val[0], q8b.val[0]), q1b.val[1], q8b.val[1]); + const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q1b.val[2], q8b.val[2]), q1b.val[3], q8b.val[3]); + + const int ls1 = 2*((qh[ib+0] >> 12) & 7) + 1; + const int ls2 = 2*((qh[ib+1] >> 12) & 7) + 1; + sumi1 += vaddvq_s32(p1) * ls1; + sumi2 += vaddvq_s32(p2) * ls2; + sumi3 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * ls1 * (qh[ib+0] & 0x8000 ? -1 : 1) + + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * ls2 * (qh[ib+1] & 0x8000 ? -1 : 1); + + } + + sumf += y[i].d * GGML_FP16_TO_FP32(x[i].d) * (sumi1 + sumi2 + IQ1S_DELTA * sumi3); + } + + *s = sumf; + +#elif defined __AVX2__ + + __m256 accum = _mm256_setzero_ps(); + float accum1 = 0; + for (int i = 0; i < nb; ++i) { + + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint16_t * qh = x[i].qh; + + __m256i sumi = _mm256_setzero_si256(); + int sumi1 = 0; + for (int ib = 0; ib < QK_K/32; ib += 2) { + const __m256i q1b_1 = _mm256_set_epi64x(iq1s_grid[qs[3] | ((qh[ib+0] >> 1) & 0x700)], iq1s_grid[qs[2] | ((qh[ib+0] << 2) & 0x700)], + iq1s_grid[qs[1] | ((qh[ib+0] << 5) & 0x700)], iq1s_grid[qs[0] | ((qh[ib+0] << 8) & 0x700)]); + const __m256i q1b_2 = _mm256_set_epi64x(iq1s_grid[qs[7] | ((qh[ib+1] >> 1) & 0x700)], iq1s_grid[qs[6] | ((qh[ib+1] << 2) & 0x700)], + iq1s_grid[qs[5] | ((qh[ib+1] << 5) & 0x700)], iq1s_grid[qs[4] | ((qh[ib+1] << 8) & 0x700)]); + qs += 8; + const __m256i q8b_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q8b_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + + const __m256i dot1 = mul_add_epi8(q1b_1, q8b_1); + const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2); + const int16_t ls1 = 2*((qh[ib+0] >> 12) & 7) + 1; + const int16_t ls2 = 2*((qh[ib+1] >> 12) & 7) + 1; + const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(ls1)); + const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(ls2)); + + sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p1, p2)); + sumi1 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * (qh[ib+0] & 0x8000 ? -1 : 1) * ls1 + + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * (qh[ib+1] & 0x8000 ? -1 : 1) * ls2; + } + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + accum = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(sumi), accum); + accum1 += d * sumi1; + + } + + *s = hsum_float_8(accum) + IQ1S_DELTA * accum1; + +#elif defined __AVX__ + __m256 accum = _mm256_setzero_ps(); + float accum1 = 0; + for (int i = 0; i < nb; ++i) { + + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint16_t * qh = x[i].qh; + + __m128i sumi1_0 = _mm_setzero_si128(); + __m128i sumi1_1 = _mm_setzero_si128(); + int sumi1 = 0; + for (int ib = 0; ib < QK_K/32; ib += 2) { + const __m128i q1b_1_0 = _mm_set_epi64x(iq1s_grid[qs[1] | ((qh[ib+0] << 5) & 0x700)], iq1s_grid[qs[0] | ((qh[ib+0] << 8) & 0x700)]); + const __m128i q1b_1_1 = _mm_set_epi64x(iq1s_grid[qs[3] | ((qh[ib+0] >> 1) & 0x700)], iq1s_grid[qs[2] | ((qh[ib+0] << 2) & 0x700)]); + const __m128i q1b_2_0 = _mm_set_epi64x(iq1s_grid[qs[5] | ((qh[ib+1] << 5) & 0x700)], iq1s_grid[qs[4] | ((qh[ib+1] << 8) & 0x700)]); + const __m128i q1b_2_1 = _mm_set_epi64x(iq1s_grid[qs[7] | ((qh[ib+1] >> 1) & 0x700)], iq1s_grid[qs[6] | ((qh[ib+1] << 2) & 0x700)]); + qs += 8; + const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + + const __m128i dot1_0 = mul_add_epi8_sse(q1b_1_0, q8b_1_0); + const __m128i dot1_1 = mul_add_epi8_sse(q1b_1_1, q8b_1_1); + const __m128i dot2_0 = mul_add_epi8_sse(q1b_2_0, q8b_2_0); + const __m128i dot2_1 = mul_add_epi8_sse(q1b_2_1, q8b_2_1); + const int16_t ls1 = 2*((qh[ib+0] >> 12) & 7) + 1; + const int16_t ls2 = 2*((qh[ib+1] >> 12) & 7) + 1; + const __m128i p1_0 = _mm_madd_epi16(dot1_0, _mm_set1_epi16(ls1)); + const __m128i p1_1 = _mm_madd_epi16(dot1_1, _mm_set1_epi16(ls1)); + const __m128i p2_0 = _mm_madd_epi16(dot2_0, _mm_set1_epi16(ls2)); + const __m128i p2_1 = _mm_madd_epi16(dot2_1, _mm_set1_epi16(ls2)); + + sumi1_0 = _mm_add_epi32(sumi1_0, _mm_add_epi32(p1_0, p2_0)); + sumi1_1 = _mm_add_epi32(sumi1_1, _mm_add_epi32(p1_1, p2_1)); + sumi1 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * (qh[ib+0] & 0x8000 ? -1 : 1) * ls1 + + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * (qh[ib+1] & 0x8000 ? -1 : 1) * ls2; + } + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + accum = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(sumi1_1, sumi1_0))), accum); + accum1 += d * sumi1; + + } + + *s = hsum_float_8(accum) + IQ1S_DELTA * accum1; + +#elif defined(__POWER9_VECTOR__) + const vector unsigned char v0 = vec_splats((unsigned char)0x0); + const vector unsigned short vsign = vec_splats((unsigned short)0x8000); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + for (int i = 0; i < nb; ++i) { + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); + vector float vyd = vec_splats(y[i].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = vec_splats((int32_t)0); + vector signed int vsumi1 = vec_splats((int32_t)0); + vector signed int vsumi2 = vec_splats((int32_t)0); + vector signed int vsumi3 = vec_splats((int32_t)0); + vector signed int vsumi8 = vec_splats((int32_t)0); + + const uint8_t * restrict q1 = x[i].qs; + const uint16_t * restrict qh = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + const int16_t * restrict qs = y[i].bsums; + + for (int j = 0; j < QK_K/32; j += 2) { + __builtin_prefetch(q1, 0, 1); + __builtin_prefetch(qh, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed long long aux64x2_0 = {*(const int64_t *)(iq1s_grid + (q1[0] | ((qh[0] << 8) & 0x700))), *(const int64_t *)(iq1s_grid + (q1[1] | ((qh[0] << 5) & 0x700)))}; + vector signed long long aux64x2_1 = {*(const int64_t *)(iq1s_grid + (q1[2] | ((qh[0] << 2) & 0x700))), *(const int64_t *)(iq1s_grid + (q1[3] | ((qh[0] >> 1) & 0x700)))}; + vector signed long long aux64x2_2 = {*(const int64_t *)(iq1s_grid + (q1[4] | ((qh[1] << 8) & 0x700))), *(const int64_t *)(iq1s_grid + (q1[5] | ((qh[1] << 5) & 0x700)))}; + vector signed long long aux64x2_3 = {*(const int64_t *)(iq1s_grid + (q1[6] | ((qh[1] << 2) & 0x700))), *(const int64_t *)(iq1s_grid + (q1[7] | ((qh[1] >> 1) & 0x700)))}; + q1 += 8; + + vector signed char q1x0 = (vector signed char)aux64x2_0; + vector signed char q1x1 = (vector signed char)aux64x2_1; + vector signed char q1x2 = (vector signed char)aux64x2_2; + vector signed char q1x3 = (vector signed char)aux64x2_3; + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q1x0, q8y0), vec_mulo(q1x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q1x1, q8y1), vec_mulo(q1x1, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q1x2, q8y2), vec_mulo(q1x2, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q1x3, q8y3), vec_mulo(q1x3, q8y3)); + + const uint16_t ls0 = (uint16_t)((qh[0] >> 12) & 7); + const uint16_t ls1 = (uint16_t)((qh[1] >> 12) & 7); + + vector signed short vscales01 = (vector signed short)vec_splats((uint16_t)(2*ls0+1)); + vector signed short vscales23 = (vector signed short)vec_splats((uint16_t)(2*ls1+1)); + vector signed short vscales = vec_sld(vscales23, vscales01, 8); + + vsumi0 = vec_msum(qv0, vscales01, vsumi0); + vsumi1 = vec_msum(qv1, vscales01, vsumi1); + vsumi2 = vec_msum(qv2, vscales23, vsumi2); + vsumi3 = vec_msum(qv3, vscales23, vsumi3); + + vector signed short q8ysums = vec_xl_len(qs, 8); + qs += 4; + q8ysums = vec_mergeh(q8ysums, (vector signed short)v0); + + vector signed short qxh = (vector signed short)vec_sld(vec_splats(qh[1]), vec_splats(qh[0]), 8); + qh += 2; + vector __bool short vsel = vec_cmpge(qxh, (vector signed short)v0); + + vector signed short q8ysum = vec_sel((vector signed short)vec_xor((vector unsigned short)q8ysums, vsign), q8ysums, vsel); + + vsumi8 = vec_add(vec_mule(q8ysum, vscales), vsumi8); + } + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + + vsumf0 = vec_madd(vec_ctf(vsumi8, 0), vec_mul(vd, vec_splats(IQ1S_DELTA)), vsumf0); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + + __m256 accum = (__m256)__lasx_xvldi(0); + float accum1 = 0; + for (int i = 0; i < nb; ++i) { + + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint16_t * qh = x[i].qh; + + __m256i sumi = __lasx_xvldi(0); + int sumi1 = 0; + for (int ib = 0; ib < QK_K/32; ib += 2) { + __m256i q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[0] | ((qh[ib+0] << 8) & 0x700)], 0); + q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[1] | ((qh[ib+0] << 5) & 0x700)], 1); + q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[2] | ((qh[ib+0] << 2) & 0x700)], 2); + q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[3] | ((qh[ib+0] >> 1) & 0x700)], 3); + + __m256i q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[4] | ((qh[ib+1] << 8) & 0x700)], 0); + q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[5] | ((qh[ib+1] << 5) & 0x700)], 1); + q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[6] | ((qh[ib+1] << 2) & 0x700)], 2); + q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[7] | ((qh[ib+1] >> 1) & 0x700)], 3); + + qs += 8; + const __m256i q8b_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8b_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + + const __m256i dot1 = mul_add_epi8(q1b_1, q8b_1); + const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2); + const int16_t ls1 = 2*((qh[ib+0] >> 12) & 7) + 1; + const int16_t ls2 = 2*((qh[ib+1] >> 12) & 7) + 1; + + __m256i tmp1, tmp5, tmp6; + tmp1 = __lasx_xvreplgr2vr_h(ls1); + tmp5 = __lasx_xvmulwev_w_h(dot1, tmp1); + tmp6 = __lasx_xvmulwod_w_h(dot1, tmp1); + const __m256i p1 = __lasx_xvadd_w(tmp5, tmp6); + + tmp1 = __lasx_xvreplgr2vr_h(ls2); + tmp5 = __lasx_xvmulwev_w_h(dot2, tmp1); + tmp6 = __lasx_xvmulwod_w_h(dot2, tmp1); + const __m256i p2 = __lasx_xvadd_w(tmp5, tmp6); + + sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p1, p2)); + sumi1 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * (qh[ib+0] & 0x8000 ? -1 : 1) * ls1 + + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * (qh[ib+1] & 0x8000 ? -1 : 1) * ls2; + } + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + accum = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), accum); + accum1 += d * sumi1; + } + + *s = hsum_float_8(accum) + IQ1S_DELTA * accum1; + +#else + + float sumf = 0; + for (int i = 0; i < nb; i++) { + + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint16_t * qh = x[i].qh; + + int sumi = 0, sumi1 = 0; + for (int ib = 0; ib < QK_K/32; ++ib) { + const int ls = 2*((qh[ib] >> 12) & 7) + 1; + const int delta = qh[ib] & 0x8000 ? -1 : 1; + int lsum = 0; + for (int l = 0; l < 4; ++l) { + const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((qh[ib] >> 3*l) & 7) << 8))); + for (int j = 0; j < 8; ++j) { + lsum += q8[j] * grid[j]; + } + q8 += 8; + } + sumi += ls * lsum; + sumi1 += ls * delta * (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]); + qs += 4; + } + + sumf += GGML_FP16_TO_FP32(x[i].d) * y[i].d * (sumi + IQ1S_DELTA * sumi1); + } + + *s = sumf; + +#endif +} + +void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(n % QK_K == 0); + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + const block_iq1_m * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + + iq1m_scale_t scale; + +#if defined __ARM_NEON + const int32x4_t mask = vdupq_n_s32(0x7); + const int32x4_t mone = vdupq_n_s32(1); + const int32x4_t mzero = vdupq_n_s32(0); + + ggml_int8x16x4_t deltas; + deltas.val[0] = vcombine_s8(vdup_n_s8(+1), vdup_n_s8(+1)); + deltas.val[1] = vcombine_s8(vdup_n_s8(-1), vdup_n_s8(+1)); + deltas.val[2] = vcombine_s8(vdup_n_s8(+1), vdup_n_s8(-1)); + deltas.val[3] = vcombine_s8(vdup_n_s8(-1), vdup_n_s8(-1)); + + ggml_int8x16x4_t q1b; + ggml_int8x16x4_t q8b; + + uint32_t aux32; + const uint8_t * aux8 = (const uint8_t *)&aux32; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint8_t * qh = x[i].qh; + const uint16_t * sc = (const uint16_t *)x[i].scales; + + scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); + + int32x4_t sumi1 = mzero; + int32x4_t sumi2 = mzero; + + for (int ib = 0; ib < QK_K/32; ib += 2) { + + q1b.val[0] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[0] | ((qh[0] << 8) & 0x700)))), + vld1_s8((const int8_t *)(iq1s_grid + (qs[1] | ((qh[0] << 4) & 0x700))))); + q1b.val[1] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[2] | ((qh[1] << 8) & 0x700)))), + vld1_s8((const int8_t *)(iq1s_grid + (qs[3] | ((qh[1] << 4) & 0x700))))); + q1b.val[2] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[4] | ((qh[2] << 8) & 0x700)))), + vld1_s8((const int8_t *)(iq1s_grid + (qs[5] | ((qh[2] << 4) & 0x700))))); + q1b.val[3] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[6] | ((qh[3] << 8) & 0x700)))), + vld1_s8((const int8_t *)(iq1s_grid + (qs[7] | ((qh[3] << 4) & 0x700))))); + + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; + + const int32x4_t p1 = vpaddq_s32(ggml_vdotq_s32(mzero, q1b.val[0], q8b.val[0]), ggml_vdotq_s32(mzero, q1b.val[1], q8b.val[1])); + const int32x4_t p2 = vpaddq_s32(ggml_vdotq_s32(mzero, q1b.val[2], q8b.val[2]), ggml_vdotq_s32(mzero, q1b.val[3], q8b.val[3])); + const int32x4_t p12 = vpaddq_s32(p1, p2); + + const uint32_t * qh32 = (const uint32_t *)qh; // we are 4-byte aligned, so we can do that + aux32 = ((qh32[0] >> 3) & 0x01010101) | ((qh32[0] >> 6) & 0x02020202); + + const int32x4_t p3 = vpaddq_s32(ggml_vdotq_s32(mzero, deltas.val[aux8[0]], q8b.val[0]), ggml_vdotq_s32(mzero, deltas.val[aux8[1]], q8b.val[1])); + const int32x4_t p4 = vpaddq_s32(ggml_vdotq_s32(mzero, deltas.val[aux8[2]], q8b.val[2]), ggml_vdotq_s32(mzero, deltas.val[aux8[3]], q8b.val[3])); + const int32x4_t p34 = vpaddq_s32(p3, p4); + + int32x4_t scales_4 = ggml_vld1q_u32(sc[ib/2] >> 0, sc[ib/2] >> 3, sc[ib/2] >> 6, sc[ib/2] >> 9); + + scales_4 = vaddq_s32(vshlq_n_s32(vandq_s32(scales_4, mask), 1), mone); + + sumi1 = vmlaq_s32(sumi1, scales_4, p12); + sumi2 = vmlaq_s32(sumi2, scales_4, p34); + + qs += 8; qh += 4; + + } + + sumf += y[i].d * GGML_FP16_TO_FP32(scale.f16) * (vaddvq_s32(sumi1) + IQ1M_DELTA * vaddvq_s32(sumi2)); + } + + *s = sumf; + +#elif defined __AVX2__ + + const __m256i mask = _mm256_set1_epi16(0x7); + const __m256i mone = _mm256_set1_epi16(1); + + __m256 accum1 = _mm256_setzero_ps(); + __m256 accum2 = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint8_t * qh = x[i].qh; + const uint16_t * sc = (const uint16_t *)x[i].scales; + + scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); + + __m256i sumi1 = _mm256_setzero_si256(); + __m256i sumi2 = _mm256_setzero_si256(); + for (int ib = 0; ib < QK_K/32; ib += 2) { + const __m256i q1b_1 = _mm256_set_epi64x( + iq1s_grid[qs[3] | (((uint16_t)qh[1] << 4) & 0x700)], iq1s_grid[qs[2] | (((uint16_t)qh[1] << 8) & 0x700)], + iq1s_grid[qs[1] | (((uint16_t)qh[0] << 4) & 0x700)], iq1s_grid[qs[0] | (((uint16_t)qh[0] << 8) & 0x700)] + ); + const __m256i q1b_2 = _mm256_set_epi64x( + iq1s_grid[qs[7] | (((uint16_t)qh[3] << 4) & 0x700)], iq1s_grid[qs[6] | (((uint16_t)qh[3] << 8) & 0x700)], + iq1s_grid[qs[5] | (((uint16_t)qh[2] << 4) & 0x700)], iq1s_grid[qs[4] | (((uint16_t)qh[2] << 8) & 0x700)] + ); + const __m256i q8b_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + const __m256i q8b_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; + + const __m256i dot1 = mul_add_epi8(q1b_1, q8b_1); + const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2); + + const __m256i delta1 = _mm256_set_epi64x(qh[1] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, + qh[1] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101, + qh[0] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, + qh[0] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101); + const __m256i delta2 = _mm256_set_epi64x(qh[3] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, + qh[3] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101, + qh[2] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, + qh[2] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101); + + const __m256i dot3 = mul_add_epi8(delta1, q8b_1); + const __m256i dot4 = mul_add_epi8(delta2, q8b_2); + + __m256i scale1 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 3), _mm_set1_epi16(sc[ib/2] >> 0)); + __m256i scale2 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 9), _mm_set1_epi16(sc[ib/2] >> 6)); + + scale1 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale1, mask), 1), mone); + scale2 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale2, mask), 1), mone); + const __m256i p1 = _mm256_madd_epi16(dot1, scale1); + const __m256i p2 = _mm256_madd_epi16(dot2, scale2); + const __m256i p3 = _mm256_madd_epi16(dot3, scale1); + const __m256i p4 = _mm256_madd_epi16(dot4, scale2); + + sumi1 = _mm256_add_epi32(sumi1, _mm256_add_epi32(p1, p2)); + sumi2 = _mm256_add_epi32(sumi2, _mm256_add_epi32(p3, p4)); + + qs += 8; qh += 4; + } + + const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(scale.f16)); + + accum1 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi1), accum1); + accum2 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi2), accum2); + } + + *s = hsum_float_8(accum1) + IQ1M_DELTA * hsum_float_8(accum2); + +#elif defined __AVX__ + const __m128i mask = _mm_set1_epi16(0x7); + const __m128i mone = _mm_set1_epi16(1); + + __m256 accum1 = _mm256_setzero_ps(); + __m256 accum2 = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint8_t * qh = x[i].qh; + const uint16_t * sc = (const uint16_t *)x[i].scales; + + scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); + + __m128i sumi1_0 = _mm_setzero_si128(); + __m128i sumi1_1 = _mm_setzero_si128(); + __m128i sumi2_0 = _mm_setzero_si128(); + __m128i sumi2_1 = _mm_setzero_si128(); + for (int ib = 0; ib < QK_K/32; ib += 2) { + const __m128i q1b_1_0 = _mm_set_epi64x( + iq1s_grid[qs[1] | (((uint16_t)qh[0] << 4) & 0x700)], iq1s_grid[qs[0] | (((uint16_t)qh[0] << 8) & 0x700)]); + const __m128i q1b_1_1 = _mm_set_epi64x( + iq1s_grid[qs[3] | (((uint16_t)qh[1] << 4) & 0x700)], iq1s_grid[qs[2] | (((uint16_t)qh[1] << 8) & 0x700)]); + const __m128i q1b_2_0 = _mm_set_epi64x( + iq1s_grid[qs[5] | (((uint16_t)qh[2] << 4) & 0x700)], iq1s_grid[qs[4] | (((uint16_t)qh[2] << 8) & 0x700)]); + const __m128i q1b_2_1 = _mm_set_epi64x( + iq1s_grid[qs[7] | (((uint16_t)qh[3] << 4) & 0x700)], iq1s_grid[qs[6] | (((uint16_t)qh[3] << 8) & 0x700)]); + const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + + const __m128i dot1_0 = mul_add_epi8_sse(q1b_1_0, q8b_1_0); + const __m128i dot1_1 = mul_add_epi8_sse(q1b_1_1, q8b_1_1); + const __m128i dot2_0 = mul_add_epi8_sse(q1b_2_0, q8b_2_0); + const __m128i dot2_1 = mul_add_epi8_sse(q1b_2_1, q8b_2_1); + + const __m128i delta1_0 = _mm_set_epi64x(qh[0] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, + qh[0] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101); + const __m128i delta1_1 = _mm_set_epi64x(qh[1] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, + qh[1] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101); + const __m128i delta2_0 = _mm_set_epi64x(qh[2] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, + qh[2] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101); + const __m128i delta2_1 = _mm_set_epi64x(qh[3] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, + qh[3] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101); + + const __m128i dot3_0 = mul_add_epi8_sse(delta1_0, q8b_1_0); + const __m128i dot3_1 = mul_add_epi8_sse(delta1_1, q8b_1_1); + const __m128i dot4_0 = mul_add_epi8_sse(delta2_0, q8b_2_0); + const __m128i dot4_1 = mul_add_epi8_sse(delta2_1, q8b_2_1); + + __m128i scale1_0 = _mm_set1_epi16(sc[ib/2] >> 0); + __m128i scale1_1 = _mm_set1_epi16(sc[ib/2] >> 3); + __m128i scale2_0 = _mm_set1_epi16(sc[ib/2] >> 6); + __m128i scale2_1 = _mm_set1_epi16(sc[ib/2] >> 9); + + scale1_0 = _mm_add_epi16(_mm_slli_epi16(_mm_and_si128(scale1_0, mask), 1), mone); + scale1_1 = _mm_add_epi16(_mm_slli_epi16(_mm_and_si128(scale1_1, mask), 1), mone); + scale2_0 = _mm_add_epi16(_mm_slli_epi16(_mm_and_si128(scale2_0, mask), 1), mone); + scale2_1 = _mm_add_epi16(_mm_slli_epi16(_mm_and_si128(scale2_1, mask), 1), mone); + const __m128i p1_0 = _mm_madd_epi16(dot1_0, scale1_0); + const __m128i p1_1 = _mm_madd_epi16(dot1_1, scale1_1); + const __m128i p2_0 = _mm_madd_epi16(dot2_0, scale2_0); + const __m128i p2_1 = _mm_madd_epi16(dot2_1, scale2_1); + const __m128i p3_0 = _mm_madd_epi16(dot3_0, scale1_0); + const __m128i p3_1 = _mm_madd_epi16(dot3_1, scale1_1); + const __m128i p4_0 = _mm_madd_epi16(dot4_0, scale2_0); + const __m128i p4_1 = _mm_madd_epi16(dot4_1, scale2_1); + + sumi1_0 = _mm_add_epi32(sumi1_0, _mm_add_epi32(p1_0, p2_0)); + sumi1_1 = _mm_add_epi32(sumi1_1, _mm_add_epi32(p1_1, p2_1)); + sumi2_0 = _mm_add_epi32(sumi2_0, _mm_add_epi32(p3_0, p4_0)); + sumi2_1 = _mm_add_epi32(sumi2_1, _mm_add_epi32(p3_1, p4_1)); + + qs += 8; qh += 4; + } + + const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(scale.f16)); + + accum1 = _mm256_add_ps(_mm256_mul_ps(d, _mm256_cvtepi32_ps(MM256_SET_M128I(sumi1_1, sumi1_0))), accum1); + accum2 = _mm256_add_ps(_mm256_mul_ps(d, _mm256_cvtepi32_ps(MM256_SET_M128I(sumi2_1, sumi2_0))), accum2); + } + + *s = hsum_float_8(accum1) + IQ1M_DELTA * hsum_float_8(accum2); + +#else + + int sum1[2], sum2[2], delta[4]; + + float sumf = 0; + for (int i = 0; i < nb; i++) { + + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint8_t * qh = x[i].qh; + const uint16_t * sc = (const uint16_t *)x[i].scales; + + scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); + + int sumi1 = 0, sumi2 = 0; + for (int ib = 0; ib < QK_K/32; ++ib) { + delta[0] = qh[0] & 0x08 ? -1 : 1; + delta[1] = qh[0] & 0x80 ? -1 : 1; + delta[2] = qh[1] & 0x08 ? -1 : 1; + delta[3] = qh[1] & 0x80 ? -1 : 1; + sum1[0] = sum1[1] = sum2[0] = sum2[1] = 0; + for (int l = 0; l < 4; ++l) { + const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((uint16_t)qh[l/2] << (8 - 4*(l%2))) & 0x700))); + int lsum1 = 0, lsum2 = 0; + for (int j = 0; j < 8; ++j) { + lsum1 += q8[j] * grid[j]; + lsum2 += q8[j]; + } + q8 += 8; + sum1[l/2] += lsum1; + sum2[l/2] += lsum2*delta[l]; + } + + const int ls1 = 2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1; + const int ls2 = 2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1; + + sumi1 += sum1[0] * ls1 + sum1[1] * ls2; + sumi2 += sum2[0] * ls1 + sum2[1] * ls2; + qs += 4; + qh += 2; + } + + sumf += GGML_FP16_TO_FP32(scale.f16) * y[i].d * (sumi1 + IQ1M_DELTA * sumi2); + } + + *s = sumf; + +#endif +} + +void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + assert(n % QK4_NL == 0); + static_assert(QK4_NL == QK8_0, "QK4_NL and QK8_0 must be the same"); + + const block_iq4_nl * restrict x = vx; + const block_q8_0 * restrict y = vy; + + const int nb = n / QK4_NL; + + int ib = 0; + float sumf = 0; + +#if defined __ARM_NEON + const int8x16_t values = vld1q_s8(kvalues_iq4nl); + const uint8x16_t m4b = vdupq_n_u8(0x0f); + uint8x16x2_t q4bits; + int8x16x4_t q4b; + int8x16x4_t q8b; + int32x4_t prod_1, prod_2; + + for (; ib + 1 < nb; ib += 2) { + + q4bits.val[0] = vld1q_u8(x[ib + 0].qs); + q4bits.val[1] = vld1q_u8(x[ib + 1].qs); + q8b.val[0] = vld1q_s8(y[ib + 0].qs); + q8b.val[1] = vld1q_s8(y[ib + 0].qs + 16); + q8b.val[2] = vld1q_s8(y[ib + 1].qs); + q8b.val[3] = vld1q_s8(y[ib + 1].qs + 16); + + q4b.val[0] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[0], m4b)); + q4b.val[1] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4)); + q4b.val[2] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[1], m4b)); + q4b.val[3] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[1], 4)); + + prod_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[0], q8b.val[0]), q4b.val[1], q8b.val[1]); + prod_2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[2], q8b.val[2]), q4b.val[3], q8b.val[3]); + + sumf += + GGML_FP16_TO_FP32(x[ib+0].d) * GGML_FP16_TO_FP32(y[ib + 0].d) * vaddvq_s32(prod_1) + + GGML_FP16_TO_FP32(x[ib+1].d) * GGML_FP16_TO_FP32(y[ib + 1].d) * vaddvq_s32(prod_2); + } + +#elif defined __AVX2__ + + const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl); + const __m128i m4b = _mm_set1_epi8(0x0f); + const __m256i mone = _mm256_set1_epi16(1); + + __m256 accum1 = _mm256_setzero_ps(); + __m256 accum2 = _mm256_setzero_ps(); + for (; ib + 1 < nb; ib += 2) { + const __m128i q4bits_1 = _mm_loadu_si128((const __m128i*)x[ib + 0].qs); + const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)x[ib + 1].qs); + const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)y[ib + 0].qs); + const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)y[ib + 1].qs); + const __m256i q4b_1 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)), + _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b))); + const __m256i q4b_2 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)), + _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b))); + const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); + const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); + const __m256i p_1 = _mm256_madd_epi16(p16_1, mone); + const __m256i p_2 = _mm256_madd_epi16(p16_2, mone); + accum1 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[ib + 0].d)*GGML_FP16_TO_FP32(x[ib + 0].d)), + _mm256_cvtepi32_ps(p_1), accum1); + accum2 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[ib + 1].d)*GGML_FP16_TO_FP32(x[ib + 1].d)), + _mm256_cvtepi32_ps(p_2), accum2); + } + + sumf = hsum_float_8(_mm256_add_ps(accum1, accum2)); + +#elif defined __AVX__ + const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl); + const __m128i m4b = _mm_set1_epi8(0x0f); + + __m256 accum = _mm256_setzero_ps(); + for (; ib + 1 < nb; ib += 2) { + const __m128i q4bits_1 = _mm_loadu_si128((const __m128i *)x[ib + 0].qs); + const __m128i q4bits_2 = _mm_loadu_si128((const __m128i *)x[ib + 1].qs); + const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)y[ib + 0].qs); + const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)y[ib + 0].qs + 1); + const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs); + const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs + 1); + + const __m128i q4b_1_0 = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b)); + const __m128i q4b_1_1 = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)); + const __m128i q4b_2_0 = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b)); + const __m128i q4b_2_1 = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)); + + const __m256 p = mul_sum_i8_quad_float(q4b_1_0, q4b_1_1, q4b_2_0, q4b_2_1, q8b_1_0, q8b_1_1, q8b_2_0, q8b_2_1); + const __m256 deltas = quad_fp16_delta_float(x[ib].d, y[ib].d, x[ib + 1].d, y[ib + 1].d); + accum = _mm256_add_ps(_mm256_mul_ps(deltas, p), accum); + } + + sumf = hsum_float_8(accum); + +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector signed int v0 = vec_splats((int32_t)0); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + + const vector signed char values = vec_xl( 0, kvalues_iq4nl); + +#pragma GCC unroll 4 + for (; ib < nb; ++ib) { + __builtin_prefetch(x[ib].qs, 0, 1); + __builtin_prefetch(y[ib].qs, 0, 1); + + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); + vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); + vector float vd = vec_mul(vxd, vyd); + + vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs); + vector signed char q4x0 = vec_and(qxs, lowMask); + vector signed char q4x1 = vec_sr(qxs, v4); + + q4x0 = vec_perm(values, values, (vector unsigned char)q4x0); + q4x1 = vec_perm(values, values, (vector unsigned char)q4x1); + + vector signed char q8y0 = vec_xl( 0, y[ib].qs); + vector signed char q8y1 = vec_xl(16, y[ib].qs); + + vector signed short qv0 = vec_add(vec_mule(q4x0, q8y0), vec_mulo(q4x0, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q4x1, q8y1), vec_mulo(q4x1, q8y1)); + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + + vsumi0 = vec_sum4s(qv0, vsumi0); + vsumi1 = vec_sum4s(qv1, vsumi1); + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + } + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + sumf = vec_extract(vsumf0, 0); + +#elif defined (__loongarch_asx) + + const __m128i values128 = __lsx_vld((const __m128i*)kvalues_iq4nl, 0); + const __m128i m4b = __lsx_vreplgr2vr_b(0x0f); + const __m256i mone = __lasx_xvreplgr2vr_h(1); + + __m256 accum1 = (__m256)__lasx_xvldi(0); + __m256 accum2 = (__m256)__lasx_xvldi(0); + for (; ib + 1 < nb; ib += 2) { + const __m128i q4bits_1 = __lsx_vld((const __m128i*)x[ib + 0].qs, 0); + const __m128i q4bits_2 = __lsx_vld((const __m128i*)x[ib + 1].qs, 0); + const __m256i q8b_1 = __lasx_xvld((const __m256i *)y[ib + 0].qs, 0); + const __m256i q8b_2 = __lasx_xvld((const __m256i *)y[ib + 1].qs, 0); + const __m256i q4b_1 = lasx_insertf128(lsx_shuffle_b(values128, __lsx_vand_v(__lsx_vsrli_h(q4bits_1, 4), m4b)), + lsx_shuffle_b(values128, __lsx_vand_v(q4bits_1, m4b))); + const __m256i q4b_2 = lasx_insertf128(lsx_shuffle_b(values128, __lsx_vand_v(__lsx_vsrli_h(q4bits_2, 4), m4b)), + lsx_shuffle_b(values128, __lsx_vand_v(q4bits_2, m4b))); + const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); + const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); + const __m256i p_1 = lasx_madd_h(p16_1, mone); + const __m256i p_2 = lasx_madd_h(p16_2, mone); + accum1 = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[ib + 0].d)*GGML_FP16_TO_FP32(x[ib + 0].d)), + __lasx_xvffint_s_w(p_1), accum1); + accum2 = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[ib + 1].d)*GGML_FP16_TO_FP32(x[ib + 1].d)), + __lasx_xvffint_s_w(p_2), accum2); + } + + sumf = hsum_float_8(__lasx_xvfadd_s(accum1, accum2)); + +#endif + for (; ib < nb; ++ib) { + const float d = GGML_FP16_TO_FP32(y[ib].d)*GGML_FP16_TO_FP32(x[ib].d); + int sumi1 = 0, sumi2 = 0; + for (int j = 0; j < QK4_NL/2; ++j) { + sumi1 += y[ib].qs[j+ 0] * kvalues_iq4nl[x[ib].qs[j] & 0xf]; + sumi2 += y[ib].qs[j+QK4_NL/2] * kvalues_iq4nl[x[ib].qs[j] >> 4]; + } + sumf += d * (sumi1 + sumi2); + } + *s = sumf; +} + +void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + assert(n % QK_K == 0); + + const block_iq4_xs * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined __ARM_NEON + const int8x16_t values = vld1q_s8(kvalues_iq4nl); + const uint8x16_t m4b = vdupq_n_u8(0x0f); + ggml_uint8x16x2_t q4bits; + ggml_int8x16x4_t q4b; + ggml_int8x16x4_t q8b; + int32x4_t prod_1, prod_2; + + float sumf = 0; + + for (int ibl = 0; ibl < nb; ++ibl) { + + const int8_t * q8 = y[ibl].qs; + const uint8_t * q4 = x[ibl].qs; + uint16_t h = x[ibl].scales_h; + + int sumi1 = 0, sumi2 = 0; + for (int ib = 0; ib < QK_K/64; ++ib) { + + q4bits = ggml_vld1q_u8_x2(q4); q4 += 32; + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; + + q4b.val[0] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[0], m4b)); + q4b.val[1] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4)); + q4b.val[2] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[1], m4b)); + q4b.val[3] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[1], 4)); + + prod_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[0], q8b.val[0]), q4b.val[1], q8b.val[1]); + prod_2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[2], q8b.val[2]), q4b.val[3], q8b.val[3]); + + int ls1 = ((x[ibl].scales_l[ib] & 0xf) | ((h << 4) & 0x30)) - 32; + int ls2 = ((x[ibl].scales_l[ib] >> 4) | ((h << 2) & 0x30)) - 32; + h >>= 4; + sumi1 += vaddvq_s32(prod_1) * ls1; + sumi2 += vaddvq_s32(prod_2) * ls2; + + } + + sumf += GGML_FP16_TO_FP32(x[ibl].d) * y[ibl].d * (sumi1 + sumi2); + } + + *s = sumf; + +#elif defined __AVX2__ + + const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl); + const __m128i m4b = _mm_set1_epi8(0x0f); + + __m256 accum = _mm256_setzero_ps(); + for (int ibl = 0; ibl < nb; ++ibl) { + const uint8_t * qs = x[ibl].qs; + const int8_t * q8 = y[ibl].qs; + uint16_t sh = x[ibl].scales_h; + __m256i sumi1 = _mm256_setzero_si256(); + __m256i sumi2 = _mm256_setzero_si256(); + for (int ib = 0; ib < QK_K/32; ib += 2) { + const __m128i q4bits_1 = _mm_loadu_si128((const __m128i*)qs); qs += 16; + const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)qs); qs += 16; + const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q4b_1 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)), + _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b))); + const __m256i q4b_2 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)), + _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b))); + const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); + const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); + const int16_t ls1 = ((x[ibl].scales_l[ib/2] & 0xf) | ((sh << 4) & 0x30)) - 32; + const int16_t ls2 = ((x[ibl].scales_l[ib/2] >> 4) | ((sh << 2) & 0x30)) - 32; + sh >>= 4; + const __m256i p_1 = _mm256_madd_epi16(p16_1, _mm256_set1_epi16(ls1)); + const __m256i p_2 = _mm256_madd_epi16(p16_2, _mm256_set1_epi16(ls2)); + sumi1 = _mm256_add_epi32(p_1, sumi1); + sumi2 = _mm256_add_epi32(p_2, sumi2); + } + accum = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(x[ibl].d)*y[ibl].d), + _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accum); + } + + *s = hsum_float_8(accum); + +#elif defined __AVX__ + const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl); + const __m128i m4b = _mm_set1_epi8(0x0f); + + __m256 accum = _mm256_setzero_ps(); + for (int ibl = 0; ibl < nb; ++ibl) { + const uint8_t * qs = x[ibl].qs; + const int8_t * q8 = y[ibl].qs; + uint16_t sh = x[ibl].scales_h; + __m128i sumi1_0 = _mm_setzero_si128(); + __m128i sumi1_1 = _mm_setzero_si128(); + __m128i sumi2_0 = _mm_setzero_si128(); + __m128i sumi2_1 = _mm_setzero_si128(); + for (int ib = 0; ib < QK_K/32; ib += 2) { + const __m128i q4bits_1 = _mm_loadu_si128((const __m128i *)qs); qs += 16; + const __m128i q4bits_2 = _mm_loadu_si128((const __m128i *)qs); qs += 16; + const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; + const __m128i q4b_1_0 = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b)); + const __m128i q4b_1_1 = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)); + const __m128i q4b_2_0 = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b)); + const __m128i q4b_2_1 = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)); + const __m128i p16_1_0 = mul_add_epi8_sse(q4b_1_0, q8b_1_0); + const __m128i p16_1_1 = mul_add_epi8_sse(q4b_1_1, q8b_1_1); + const __m128i p16_2_0 = mul_add_epi8_sse(q4b_2_0, q8b_2_0); + const __m128i p16_2_1 = mul_add_epi8_sse(q4b_2_1, q8b_2_1); + const int16_t ls1 = ((x[ibl].scales_l[ib/2] & 0xf) | ((sh << 4) & 0x30)) - 32; + const int16_t ls2 = ((x[ibl].scales_l[ib/2] >> 4) | ((sh << 2) & 0x30)) - 32; + sh >>= 4; + const __m128i p_1_0 = _mm_madd_epi16(p16_1_0, _mm_set1_epi16(ls1)); + const __m128i p_1_1 = _mm_madd_epi16(p16_1_1, _mm_set1_epi16(ls1)); + const __m128i p_2_0 = _mm_madd_epi16(p16_2_0, _mm_set1_epi16(ls2)); + const __m128i p_2_1 = _mm_madd_epi16(p16_2_1, _mm_set1_epi16(ls2)); + sumi1_0 = _mm_add_epi32(p_1_0, sumi1_0); + sumi1_1 = _mm_add_epi32(p_1_1, sumi1_1); + sumi2_0 = _mm_add_epi32(p_2_0, sumi2_0); + sumi2_1 = _mm_add_epi32(p_2_1, sumi2_1); + } + __m128i sumi12_0 = _mm_add_epi32(sumi1_0, sumi2_0); + __m128i sumi12_1 = _mm_add_epi32(sumi1_1, sumi2_1); + accum = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(x[ibl].d)*y[ibl].d), + _mm256_cvtepi32_ps(MM256_SET_M128I(sumi12_1, sumi12_0))), accum); + } + + *s = hsum_float_8(accum); + +#elif defined(__POWER9_VECTOR__) + const vector signed char lowMask = vec_splats((signed char)0xF); + const vector int v0 = vec_splats((int32_t)0); + const vector unsigned char v4 = vec_splats((unsigned char)0x4); + + vector float vsumf0 = vec_splats(0.0f); + vector float vsumf1 = vec_splats(0.0f); + vector float vsumf2 = vec_splats(0.0f); + vector float vsumf3 = vec_splats(0.0f); + + const vector signed char values = vec_xl( 0, kvalues_iq4nl); + + for (int ibl = 0; ibl < nb; ++ibl) { + + vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ibl].d)); + vector float vyd = vec_splats(y[ibl].d); + vector float vd = vec_mul(vxd, vyd); + + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; + + uint16_t h = x[ibl].scales_h; + + const uint8_t * restrict q4 = x[ibl].qs; + const uint8_t * restrict sc = x[ibl].scales_l; + const int8_t * restrict q8 = y[ibl].qs; + + for (int ib = 0; ib < QK_K/64; ib ++ ) { + __builtin_prefetch(q4, 0, 1); + __builtin_prefetch(q8, 0, 1); + + vector signed char qxs0 = (vector signed char)vec_xl( 0, q4); + vector signed char qxs1 = (vector signed char)vec_xl(16, q4); + q4 += 32; + + vector signed char q4x00 = (vector signed char)vec_and(qxs0, lowMask); + vector signed char q4x01 = (vector signed char)vec_sr(qxs0, v4); + vector signed char q4x10 = (vector signed char)vec_and(qxs1, lowMask); + vector signed char q4x11 = (vector signed char)vec_sr(qxs1, v4); + + q4x00 = vec_perm(values, values, (vector unsigned char)q4x00); + q4x01 = vec_perm(values, values, (vector unsigned char)q4x01); + q4x10 = vec_perm(values, values, (vector unsigned char)q4x10); + q4x11 = vec_perm(values, values, (vector unsigned char)q4x11); + + vector signed char q8y0 = vec_xl( 0, q8); + vector signed char q8y1 = vec_xl(16, q8); + vector signed char q8y2 = vec_xl(32, q8); + vector signed char q8y3 = vec_xl(48, q8); + q8 += 64; + + vector signed short qv0 = vec_add(vec_mule(q4x00, q8y0), vec_mulo(q4x00, q8y0)); + vector signed short qv1 = vec_add(vec_mule(q4x01, q8y1), vec_mulo(q4x01, q8y1)); + vector signed short qv2 = vec_add(vec_mule(q4x10, q8y2), vec_mulo(q4x10, q8y2)); + vector signed short qv3 = vec_add(vec_mule(q4x11, q8y3), vec_mulo(q4x11, q8y3)); + + const uint16_t ls0 = (uint16_t)(((sc[0] & 0xf) | ((h << 4) & 0x30)) - 32); + const uint16_t ls1 = (uint16_t)(((sc[0] >> 4) | ((h << 2) & 0x30)) - 32); + h >>= 4; + sc ++; + + vector signed short vscales01 = vec_splats((int16_t)ls0); + vector signed short vscales23 = vec_splats((int16_t)ls1); + + vsumi0 = vec_msum(qv0, vscales01, vsumi0); + vsumi1 = vec_msum(qv1, vscales01, vsumi1); + vsumi2 = vec_msum(qv2, vscales23, vsumi2); + vsumi3 = vec_msum(qv3, vscales23, vsumi3); + } + + vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); + vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); + vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); + vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); + } + + vsumf0 = vec_add(vsumf0, vsumf2); + vsumf1 = vec_add(vsumf1, vsumf3); + + vsumf0 = vec_add(vsumf0, vsumf1); + + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); + vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); + + *s = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + + const __m128i values128 = __lsx_vld((const __m128i*)kvalues_iq4nl, 0); + const __m128i m4b = __lsx_vreplgr2vr_b(0x0f); + + __m256 accum = (__m256)__lasx_xvldi(0); + __m256i tmp1; + __m128i tmp0, tmp2, tmp3, tmp4, mask_8f, mask; + + mask_8f = __lsx_vreplgr2vr_b(0x8f); + for (int ibl = 0; ibl < nb; ++ibl) { + const uint8_t * qs = x[ibl].qs; + const int8_t * q8 = y[ibl].qs; + uint16_t sh = x[ibl].scales_h; + __m256i sumi1 = __lasx_xvldi(0); + __m256i sumi2 = __lasx_xvldi(0); + __m128i zero = __lsx_vldi(0); + for (int ib = 0; ib < QK_K/32; ib += 2) { + const __m128i q4bits_1 = __lsx_vld((const __m128i*)qs, 0); qs += 16; + const __m128i q4bits_2 = __lsx_vld((const __m128i*)qs, 0); qs += 16; + const __m256i q8b_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8b_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + tmp2 = __lsx_vand_v(__lsx_vand_v(__lsx_vsrli_h(q4bits_1, 4), m4b), mask_8f); + tmp0 = __lsx_vori_b(tmp2, 0x10); + mask = __lsx_vsle_b(zero, tmp2); + tmp3 = __lsx_vand_v(tmp0, mask); + tmp3 = __lsx_vshuf_b(values128, zero, tmp3); + + tmp2 = __lsx_vand_v(__lsx_vand_v(q4bits_1, m4b), mask_8f); + tmp0 = __lsx_vori_b(tmp2, 0x10); + mask = __lsx_vsle_b(zero, tmp2); + tmp4 = __lsx_vand_v(tmp0, mask); + tmp4 = __lsx_vshuf_b(values128, zero, tmp4); + + const __m256i q4b_1 = lasx_insertf128(tmp3, tmp4); + + tmp2 = __lsx_vand_v(__lsx_vand_v(__lsx_vsrli_h(q4bits_2, 4), m4b), mask_8f); + tmp0 = __lsx_vori_b(tmp2, 0x10); + mask = __lsx_vsle_b(zero, tmp2); + tmp3 = __lsx_vand_v(tmp0, mask); + tmp3 = __lsx_vshuf_b(values128, zero, tmp3); + + tmp2 = __lsx_vand_v(__lsx_vand_v(q4bits_2, m4b), mask_8f); + tmp0 = __lsx_vori_b(tmp2, 0x10); + mask = __lsx_vsle_b(zero, tmp2); + tmp4 = __lsx_vand_v(tmp0, mask); + tmp4 = __lsx_vshuf_b(values128, zero, tmp4); + + const __m256i q4b_2 = lasx_insertf128(tmp3, tmp4); + + const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); + const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); + const int16_t ls1 = ((x[ibl].scales_l[ib/2] & 0xf) | ((sh << 4) & 0x30)) - 32; + const int16_t ls2 = ((x[ibl].scales_l[ib/2] >> 4) | ((sh << 2) & 0x30)) - 32; + sh >>= 4; + __m256i tmp5, tmp6; + tmp1 = __lasx_xvreplgr2vr_h(ls1); + tmp5 = __lasx_xvmulwev_w_h(p16_1, tmp1); + tmp6 = __lasx_xvmulwod_w_h(p16_1, tmp1); + const __m256i p_1 = __lasx_xvadd_w(tmp5, tmp6); + tmp1 = __lasx_xvreplgr2vr_h(ls2); + tmp5 = __lasx_xvmulwev_w_h(p16_2, tmp1); + tmp6 = __lasx_xvmulwod_w_h(p16_2, tmp1); + const __m256i p_2 = __lasx_xvadd_w(tmp5, tmp6); + sumi1 = __lasx_xvadd_w(p_1, sumi1); + sumi2 = __lasx_xvadd_w(p_2, sumi2); + } + accum = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ibl].d)*y[ibl].d), + __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accum); + } + + *s = hsum_float_8(accum); + +#else + float sumf = 0; + for (int ibl = 0; ibl < nb; ++ibl) { + const float d4d8 = GGML_FP16_TO_FP32(x[ibl].d) * y[ibl].d; + uint16_t h = x[ibl].scales_h; + const uint8_t * qs = x[ibl].qs; + const int8_t * q8 = y[ibl].qs; + for (int ib = 0; ib < QK_K/32; ib += 2) { + const uint8_t ls1 = (x[ibl].scales_l[ib/2] & 0xf) | ((h << 4) & 0x30); + const uint8_t ls2 = (x[ibl].scales_l[ib/2] >> 4) | ((h << 2) & 0x30); + h >>= 4; + const float d1 = d4d8*(ls1 - 32); + const float d2 = d4d8*(ls2 - 32); + int sumi1 = 0, sumi2 = 0; + for (int j = 0; j < 16; ++j) { + sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf]; + sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >> 4]; + } + sumf += d1 * (sumi1 + sumi2); + qs += 16; + q8 += 32; + sumi1 = sumi2 = 0; + for (int j = 0; j < 16; ++j) { + sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf]; + sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >> 4]; + } + sumf += d2 * (sumi1 + sumi2); + qs += 16; + q8 += 32; + } + } + *s = sumf; +#endif +} + +// ============================ 4-bit non-linear quants + +void quantize_row_iq4_nl(const float * restrict x, void * restrict y, int64_t k) { + assert(k % QK4_NL == 0); + quantize_row_iq4_nl_ref(x, y, k); +} + +void quantize_row_iq4_xs(const float * restrict x, void * restrict y, int64_t k) { + assert(k % QK_K == 0); + quantize_iq4_xs(x, y, 1, k, NULL); +} diff --git a/ggml/src/ggml-cpu/ggml-cpu-quants.h b/ggml/src/ggml-cpu/ggml-cpu-quants.h new file mode 100644 index 000000000..e33d9d473 --- /dev/null +++ b/ggml/src/ggml-cpu/ggml-cpu-quants.h @@ -0,0 +1,63 @@ +#pragma once + +#define GGML_COMMON_DECL_C +#include "ggml-common.h" + +#include "ggml.h" + +// GGML CPU internal header + +#ifdef __cplusplus +extern "C" { +#endif + +// Quantization +void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q4_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q5_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q5_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); + +void quantize_row_q2_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q3_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q4_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q5_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); + +void quantize_row_tq1_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_tq2_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); + +void quantize_row_iq4_nl (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +void quantize_row_iq4_xs (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); + +// Dot product +void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); + +void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); + +void ggml_vec_dot_tq1_0_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_tq2_0_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); + +void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq2_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq1_m_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq4_nl_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq4_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); + +#ifdef __cplusplus +} +#endif diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c new file mode 100644 index 000000000..f12b62e3b --- /dev/null +++ b/ggml/src/ggml-cpu/ggml-cpu.c @@ -0,0 +1,14088 @@ +#define _CRT_SECURE_NO_DEPRECATE // Disables "unsafe" warnings on Windows +#define _USE_MATH_DEFINES // For M_PI on MSVC + +#include "ggml-backend-impl.h" +#include "ggml-backend.h" +#include "ggml-cpu-aarch64.h" +#include "ggml-cpu-impl.h" +#include "ggml-cpu.h" +#include "ggml-impl.h" +#include "ggml-quants.h" +#include "ggml-cpu-quants.h" +#include "ggml-threading.h" +#include "amx/amx.h" +#include "ggml.h" + +#if defined(_MSC_VER) || defined(__MINGW32__) +#include // using malloc.h with MSC/MINGW +#elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__) +#include +#endif + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#if defined(__gnu_linux__) +#include +#endif + +#ifdef GGML_USE_OPENMP +#include +#endif + +#if defined(__ARM_FEATURE_SVE) || defined(__ARM_FEATURE_MATMUL_INT8) +#undef GGML_USE_LLAMAFILE +#endif + +#ifdef GGML_USE_LLAMAFILE +#include "llamafile/sgemm.h" +#endif + +#if defined(_MSC_VER) +// disable "possible loss of data" to avoid hundreds of casts +// we should just be careful :) +#pragma warning(disable: 4244 4267) + +// disable POSIX deprecation warnings +// these functions are never going away, anyway +#pragma warning(disable: 4996) + +// unreachable code because of multiple instances of code after GGML_ABORT +#pragma warning(disable: 4702) +#endif + +// Note: once we move threading into a separate C++ file +// will use std::hardware_destructive_interference_size instead of hardcoding it here +// and we'll use C++ attribute syntax. +#define GGML_CACHE_LINE 64 + +#if defined(__clang__) || defined(__GNUC__) +#define GGML_CACHE_ALIGN __attribute__((aligned(GGML_CACHE_LINE))) +#endif + +#if defined(__has_feature) +#if __has_feature(thread_sanitizer) +#define GGML_TSAN_ENABLED 1 +#endif +#else // __has_feature +#if defined(__SANITIZE_THREAD__) +#define GGML_TSAN_ENABLED 1 +#endif +#endif // __has_feature + +#define UNUSED GGML_UNUSED +#define SWAP(x, y, T) do { T SWAP = x; (x) = y; (y) = SWAP; } while (0) + +#if defined(GGML_USE_ACCELERATE) +#include +#endif + +// floating point type used to accumulate sums +typedef double ggml_float; + +#define GGML_GELU_FP16 +#define GGML_GELU_QUICK_FP16 + +#define GGML_SOFT_MAX_UNROLL 4 +#define GGML_VEC_DOT_UNROLL 2 +#define GGML_VEC_MAD_UNROLL 32 + +// +// global data +// + +// precomputed gelu table for f16 (128 KB) +static ggml_fp16_t ggml_table_gelu_f16[1 << 16]; + +// precomputed quick gelu table for f16 (128 KB) +static ggml_fp16_t ggml_table_gelu_quick_f16[1 << 16]; + +#if defined(__ARM_ARCH) +struct ggml_arm_arch_features_type { + int has_neon; + int has_dotprod; + int has_i8mm; + int has_sve; + int sve_cnt; +} ggml_arm_arch_features = {-1, -1, -1, -1, 0}; +#endif + + +#if defined(_WIN32) + +#define WIN32_LEAN_AND_MEAN +#ifndef NOMINMAX + #define NOMINMAX +#endif +#include + + +#if !defined(__clang__) +#define GGML_CACHE_ALIGN __declspec(align(GGML_CACHE_LINE)) + +typedef volatile LONG atomic_int; +typedef atomic_int atomic_bool; +typedef atomic_int atomic_flag; + +#define ATOMIC_FLAG_INIT 0 + +typedef enum { + memory_order_relaxed, + memory_order_consume, + memory_order_acquire, + memory_order_release, + memory_order_acq_rel, + memory_order_seq_cst +} memory_order; + +static void atomic_store(atomic_int * ptr, LONG val) { + InterlockedExchange(ptr, val); +} +static void atomic_store_explicit(atomic_int * ptr, LONG val, memory_order mo) { + // TODO: add support for explicit memory order + InterlockedExchange(ptr, val); +} +static LONG atomic_load(atomic_int * ptr) { + return InterlockedCompareExchange(ptr, 0, 0); +} +static LONG atomic_load_explicit(atomic_int * ptr, memory_order mo) { + // TODO: add support for explicit memory order + return InterlockedCompareExchange(ptr, 0, 0); +} +static LONG atomic_fetch_add(atomic_int * ptr, LONG inc) { + return InterlockedExchangeAdd(ptr, inc); +} +static LONG atomic_fetch_add_explicit(atomic_int * ptr, LONG inc, memory_order mo) { + // TODO: add support for explicit memory order + return InterlockedExchangeAdd(ptr, inc); +} +static atomic_bool atomic_flag_test_and_set(atomic_flag * ptr) { + return InterlockedExchange(ptr, 1); +} +static void atomic_flag_clear(atomic_flag * ptr) { + InterlockedExchange(ptr, 0); +} +static void atomic_thread_fence(memory_order mo) { + MemoryBarrier(); +} +#else // clang +#include +#endif + +typedef HANDLE pthread_t; + +typedef DWORD thread_ret_t; +static int pthread_create(pthread_t * out, void * unused, thread_ret_t(*func)(void *), void * arg) { + (void) unused; + HANDLE handle = CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE) func, arg, 0, NULL); + if (handle == NULL) + { + return EAGAIN; + } + + *out = handle; + return 0; +} + +static int pthread_join(pthread_t thread, void * unused) { + (void) unused; + int ret = (int) WaitForSingleObject(thread, INFINITE); + CloseHandle(thread); + return ret; +} + +static int sched_yield (void) { + Sleep (0); + return 0; +} +#else + +#include +#include +#include +#if defined(__FreeBSD__) +#include +#endif + +typedef void * thread_ret_t; + +#include +#include +#include + +#endif + +typedef pthread_t ggml_thread_t; + +#ifdef GGML_USE_CPU_HBM +#include +#endif + +#if defined(__APPLE__) +#include +#include +#include +#endif + +// +// cache line +// + +#if defined(__cpp_lib_hardware_interference_size) +#define CACHE_LINE_SIZE hardware_destructive_interference_size +#else +#if defined(__POWER9_VECTOR__) +#define CACHE_LINE_SIZE 128 +#else +#define CACHE_LINE_SIZE 64 +#endif +#endif + +static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float); + + +static void ggml_vec_dot_f32(int n, float * restrict s, size_t bs, const float * restrict x, size_t bx, const float * restrict y, size_t by, int nrc); +static void ggml_vec_dot_f16(int n, float * restrict s, size_t bs, ggml_fp16_t * restrict x, size_t bx, ggml_fp16_t * restrict y, size_t by, int nrc); +static void ggml_vec_dot_bf16(int n, float * restrict s, size_t bs, ggml_bf16_t * restrict x, size_t bx, ggml_bf16_t * restrict y, size_t by, int nrc); + +static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = { + [GGML_TYPE_F32] = { + .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f32, + .vec_dot_type = GGML_TYPE_F32, + .nrows = 1, + }, + [GGML_TYPE_F16] = { + .from_float = (ggml_from_float_t) ggml_fp32_to_fp16_row, + .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f16, + .vec_dot_type = GGML_TYPE_F16, + .nrows = 1, + }, + [GGML_TYPE_Q4_0] = { + .from_float = quantize_row_q4_0, + .vec_dot = ggml_vec_dot_q4_0_q8_0, + .vec_dot_type = GGML_TYPE_Q8_0, +#if defined (__ARM_FEATURE_MATMUL_INT8) + .nrows = 2, +#else + .nrows = 1, +#endif + }, + [GGML_TYPE_Q4_1] = { + .from_float = quantize_row_q4_1, + .vec_dot = ggml_vec_dot_q4_1_q8_1, + .vec_dot_type = GGML_TYPE_Q8_1, +#if defined (__ARM_FEATURE_MATMUL_INT8) + .nrows = 2, +#else + .nrows = 1, +#endif + }, + [GGML_TYPE_Q5_0] = { + .from_float = quantize_row_q5_0, + .vec_dot = ggml_vec_dot_q5_0_q8_0, + .vec_dot_type = GGML_TYPE_Q8_0, + .nrows = 1, + }, + [GGML_TYPE_Q5_1] = { + .from_float = quantize_row_q5_1, + .vec_dot = ggml_vec_dot_q5_1_q8_1, + .vec_dot_type = GGML_TYPE_Q8_1, + .nrows = 1, + }, + [GGML_TYPE_Q8_0] = { + .from_float = quantize_row_q8_0, + .from_float_to_mat = quantize_mat_q8_0, + .vec_dot = ggml_vec_dot_q8_0_q8_0, + .vec_dot_type = GGML_TYPE_Q8_0, +#if defined (__ARM_FEATURE_MATMUL_INT8) + .nrows = 2, +#else + .nrows = 1, +#endif + }, + [GGML_TYPE_Q8_1] = { + .from_float = quantize_row_q8_1, + .vec_dot_type = GGML_TYPE_Q8_1, + .nrows = 1, + }, + [GGML_TYPE_Q2_K] = { + .from_float = quantize_row_q2_K, + .vec_dot = ggml_vec_dot_q2_K_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_Q3_K] = { + .from_float = quantize_row_q3_K, + .vec_dot = ggml_vec_dot_q3_K_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_Q4_K] = { + .from_float = quantize_row_q4_K, + .vec_dot = ggml_vec_dot_q4_K_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_Q5_K] = { + .from_float = quantize_row_q5_K, + .vec_dot = ggml_vec_dot_q5_K_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_Q6_K] = { + .from_float = quantize_row_q6_K, + .vec_dot = ggml_vec_dot_q6_K_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_IQ2_XXS] = { + .from_float = NULL, + .vec_dot = ggml_vec_dot_iq2_xxs_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_IQ2_XS] = { + .from_float = NULL, + .vec_dot = ggml_vec_dot_iq2_xs_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_IQ3_XXS] = { + // NOTE: from_float for iq3 and iq2_s was removed because these quants require initialization in ggml_quantize_init + //.from_float = quantize_row_iq3_xxs, + .vec_dot = ggml_vec_dot_iq3_xxs_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_IQ3_S] = { + //.from_float = quantize_row_iq3_s, + .vec_dot = ggml_vec_dot_iq3_s_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_IQ2_S] = { + //.from_float = quantize_row_iq2_s, + .vec_dot = ggml_vec_dot_iq2_s_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_IQ1_S] = { + .from_float = NULL, + .vec_dot = ggml_vec_dot_iq1_s_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_IQ1_M] = { + .from_float = NULL, + .vec_dot = ggml_vec_dot_iq1_m_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_IQ4_NL] = { + .from_float = quantize_row_iq4_nl, + .vec_dot = ggml_vec_dot_iq4_nl_q8_0, + .vec_dot_type = GGML_TYPE_Q8_0, + .nrows = 1, + }, + [GGML_TYPE_IQ4_XS] = { + .from_float = quantize_row_iq4_xs, + .vec_dot = ggml_vec_dot_iq4_xs_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_Q8_K] = { + .from_float = quantize_row_q8_K, + }, + [GGML_TYPE_BF16] = { + .from_float = (ggml_from_float_t) ggml_fp32_to_bf16_row, + .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_bf16, + .vec_dot_type = GGML_TYPE_BF16, + .nrows = 1, + }, + [GGML_TYPE_Q4_0_4_4] = { + .from_float = NULL, + .vec_dot = NULL, + .vec_dot_type = GGML_TYPE_Q8_0, + .nrows = 1, + .ncols = 4, + .gemv = ggml_gemv_q4_0_4x4_q8_0, + .gemm = ggml_gemm_q4_0_4x4_q8_0, + }, + [GGML_TYPE_Q4_0_4_8] = { + .from_float = NULL, + .vec_dot = NULL, + .vec_dot_type = GGML_TYPE_Q8_0, + .nrows = 1, + .ncols = 4, + .gemv = ggml_gemv_q4_0_4x8_q8_0, + .gemm = ggml_gemm_q4_0_4x8_q8_0, + }, + [GGML_TYPE_Q4_0_8_8] = { + .from_float = NULL, + .vec_dot = NULL, + .vec_dot_type = GGML_TYPE_Q8_0, + .nrows = 1, + .ncols = 8, + .gemv = ggml_gemv_q4_0_8x8_q8_0, + .gemm = ggml_gemm_q4_0_8x8_q8_0, + }, + [GGML_TYPE_TQ1_0] = { + .from_float = quantize_row_tq1_0, + .vec_dot = ggml_vec_dot_tq1_0_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_TQ2_0] = { + .from_float = quantize_row_tq2_0, + .vec_dot = ggml_vec_dot_tq2_0_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + .nrows = 1, + }, + [GGML_TYPE_IQ4_NL_4_4] = { + .from_float = NULL, + .vec_dot = NULL, + .vec_dot_type = GGML_TYPE_Q8_0, + .nrows = 1, + .ncols = 4, + .gemv = ggml_gemv_iq4_nl_4x4_q8_0, + .gemm = ggml_gemm_iq4_nl_4x4_q8_0, + }, +}; + +const struct ggml_type_traits_cpu * ggml_get_type_traits_cpu(enum ggml_type type) { + return &type_traits_cpu[type]; +} + +// +// simd mappings +// + +// we define a common set of C macros which map to specific intrinsics based on the current architecture +// we then implement the fundamental computation operations below using only these macros +// adding support for new architectures requires to define the corresponding SIMD macros +// +// GGML_F32_STEP / GGML_F16_STEP +// number of elements to process in a single step +// +// GGML_F32_EPR / GGML_F16_EPR +// number of elements to fit in a single register +// + +#if defined(__ARM_NEON) && defined(__ARM_FEATURE_FMA) + +#define GGML_SIMD + +// F32 NEON + +#define GGML_F32_STEP 16 +#define GGML_F32_EPR 4 + +#define GGML_F32x4 float32x4_t +#define GGML_F32x4_ZERO vdupq_n_f32(0.0f) +#define GGML_F32x4_SET1(x) vdupq_n_f32(x) +#define GGML_F32x4_LOAD vld1q_f32 +#define GGML_F32x4_STORE vst1q_f32 +#define GGML_F32x4_FMA(a, b, c) vfmaq_f32(a, b, c) +#define GGML_F32x4_ADD vaddq_f32 +#define GGML_F32x4_MUL vmulq_f32 +#define GGML_F32x4_REDUCE_ONE(x) vaddvq_f32(x) +#define GGML_F32x4_REDUCE(res, x) \ +{ \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \ + } \ + (res) = GGML_F32x4_REDUCE_ONE((x)[0]); \ +} + +#define GGML_F32_VEC GGML_F32x4 +#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD +#define GGML_F32_VEC_STORE GGML_F32x4_STORE +#define GGML_F32_VEC_FMA GGML_F32x4_FMA +#define GGML_F32_VEC_ADD GGML_F32x4_ADD +#define GGML_F32_VEC_MUL GGML_F32x4_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE + +// F16 NEON + +#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) + #define GGML_F16_STEP 32 + #define GGML_F16_EPR 8 + + #define GGML_F16x8 float16x8_t + #define GGML_F16x8_ZERO vdupq_n_f16(0.0f) + #define GGML_F16x8_SET1(x) vdupq_n_f16(x) + #define GGML_F16x8_LOAD(x) vld1q_f16((const ggml_fp16_internal_t *)(x)) + #define GGML_F16x8_STORE vst1q_f16 + #define GGML_F16x8_FMA(a, b, c) vfmaq_f16(a, b, c) + #define GGML_F16x8_ADD vaddq_f16 + #define GGML_F16x8_MUL vmulq_f16 + #define GGML_F16x8_REDUCE(res, x) \ + do { \ + int offset = GGML_F16_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + (x)[i] = vaddq_f16((x)[i], (x)[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + (x)[i] = vaddq_f16((x)[i], (x)[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + (x)[i] = vaddq_f16((x)[i], (x)[offset+i]); \ + } \ + const float32x4_t t0 = vcvt_f32_f16(vget_low_f16 ((x)[0])); \ + const float32x4_t t1 = vcvt_f32_f16(vget_high_f16((x)[0])); \ + (res) = (ggml_float) vaddvq_f32(vaddq_f32(t0, t1)); \ + } while (0) + + #define GGML_F16_VEC GGML_F16x8 + #define GGML_F16_VEC_ZERO GGML_F16x8_ZERO + #define GGML_F16_VEC_SET1 GGML_F16x8_SET1 + #define GGML_F16_VEC_LOAD(p, i) GGML_F16x8_LOAD(p) + #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE((ggml_fp16_internal_t *)(p), (r)[i]) + #define GGML_F16_VEC_FMA GGML_F16x8_FMA + #define GGML_F16_VEC_ADD GGML_F16x8_ADD + #define GGML_F16_VEC_MUL GGML_F16x8_MUL + #define GGML_F16_VEC_REDUCE GGML_F16x8_REDUCE +#else + // if FP16 vector arithmetic is not supported, we use FP32 instead + // and take advantage of the vcvt_ functions to convert to/from FP16 + + #define GGML_F16_STEP 16 + #define GGML_F16_EPR 4 + + #define GGML_F32Cx4 float32x4_t + #define GGML_F32Cx4_ZERO vdupq_n_f32(0.0f) + #define GGML_F32Cx4_SET1(x) vdupq_n_f32(x) + #define GGML_F32Cx4_LOAD(x) vcvt_f32_f16(vld1_f16((const ggml_fp16_internal_t *)(x))) + #define GGML_F32Cx4_STORE(x, y) vst1_f16(x, vcvt_f16_f32(y)) + #define GGML_F32Cx4_FMA(a, b, c) vfmaq_f32(a, b, c) + #define GGML_F32Cx4_ADD vaddq_f32 + #define GGML_F32Cx4_MUL vmulq_f32 + #define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE + + #define GGML_F16_VEC GGML_F32Cx4 + #define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO + #define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 + #define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) + #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE((ggml_fp16_internal_t *)(p), r[i]) + #define GGML_F16_VEC_FMA GGML_F32Cx4_FMA + #define GGML_F16_VEC_ADD GGML_F32Cx4_ADD + #define GGML_F16_VEC_MUL GGML_F32Cx4_MUL + #define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE +#endif + +#elif defined(__AVX512F__) + +#define GGML_SIMD + +// F32 AVX512 + +#define GGML_F32_STEP 64 +#define GGML_F32_EPR 16 + +#define GGML_F32x16 __m512 +#define GGML_F32x16_ZERO _mm512_setzero_ps() +#define GGML_F32x16_SET1(x) _mm512_set1_ps(x) +#define GGML_F32x16_LOAD _mm512_loadu_ps +#define GGML_F32x16_STORE _mm512_storeu_ps +// _mm512_fmadd_ps is defined in AVX512F so no guard is required +#define GGML_F32x16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a) +#define GGML_F32x16_ADD _mm512_add_ps +#define GGML_F32x16_MUL _mm512_mul_ps +#define GGML_F32x16_REDUCE(res, x) \ +do { \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ps(x[i], x[offset+i]); \ + } \ + res = (ggml_float) _mm512_reduce_add_ps(x[0]); \ +} while (0) + +// TODO: is this optimal ? + +#define GGML_F32_VEC GGML_F32x16 +#define GGML_F32_VEC_ZERO GGML_F32x16_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x16_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x16_LOAD +#define GGML_F32_VEC_STORE GGML_F32x16_STORE +#define GGML_F32_VEC_FMA GGML_F32x16_FMA +#define GGML_F32_VEC_ADD GGML_F32x16_ADD +#define GGML_F32_VEC_MUL GGML_F32x16_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x16_REDUCE + +// F16 AVX512 + +// F16 AVX + +#define GGML_F16_STEP 64 +#define GGML_F16_EPR 16 + +// AVX512 has FP16 extension (AVX512_FP16) but I don't have it on my machine so I use FP32 instead + +#define GGML_F32Cx16 __m512 +#define GGML_F32Cx16_ZERO _mm512_setzero_ps() +#define GGML_F32Cx16_SET1(x) _mm512_set1_ps(x) + +// unlike _mm256_cvt intrinsics that require F16C, _mm512_cvt is defined in AVX512F +// so F16C guard isn't required +#define GGML_F32Cx16_LOAD(x) _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(x))) +#define GGML_F32Cx16_STORE(x, y) _mm256_storeu_si256((__m256i *)(x), _mm512_cvtps_ph(y, 0)) + +#define GGML_F32Cx16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a) +#define GGML_F32Cx16_ADD _mm512_add_ps +#define GGML_F32Cx16_MUL _mm512_mul_ps +#define GGML_F32Cx16_REDUCE(res, x) \ +do { \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm512_add_ps(x[i], x[offset+i]); \ + } \ + res = (ggml_float) _mm512_reduce_add_ps(x[0]); \ +} while (0) + +#define GGML_F16_VEC GGML_F32Cx16 +#define GGML_F16_VEC_ZERO GGML_F32Cx16_ZERO +#define GGML_F16_VEC_SET1 GGML_F32Cx16_SET1 +#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx16_LOAD(p) +#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx16_STORE(p, r[i]) +#define GGML_F16_VEC_FMA GGML_F32Cx16_FMA +#define GGML_F16_VEC_ADD GGML_F32Cx16_ADD +#define GGML_F16_VEC_MUL GGML_F32Cx16_MUL + +#define GGML_F16_VEC_REDUCE GGML_F32Cx16_REDUCE +#elif defined(__AVX__) + +#define GGML_SIMD + +// F32 AVX + +#define GGML_F32_STEP 32 +#define GGML_F32_EPR 8 + +#define GGML_F32x8 __m256 +#define GGML_F32x8_ZERO _mm256_setzero_ps() +#define GGML_F32x8_SET1(x) _mm256_set1_ps(x) +#define GGML_F32x8_LOAD _mm256_loadu_ps +#define GGML_F32x8_STORE _mm256_storeu_ps +#if defined(__FMA__) + #define GGML_F32x8_FMA(a, b, c) _mm256_fmadd_ps(b, c, a) +#else + #define GGML_F32x8_FMA(a, b, c) _mm256_add_ps(_mm256_mul_ps(b, c), a) +#endif +#define GGML_F32x8_ADD _mm256_add_ps +#define GGML_F32x8_MUL _mm256_mul_ps +#define GGML_F32x8_REDUCE(res, x) \ +do { \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm256_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm256_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm256_add_ps(x[i], x[offset+i]); \ + } \ + const __m128 t0 = _mm_add_ps(_mm256_castps256_ps128(x[0]), \ + _mm256_extractf128_ps(x[0], 1)); \ + const __m128 t1 = _mm_hadd_ps(t0, t0); \ + res = (ggml_float) _mm_cvtss_f32(_mm_hadd_ps(t1, t1)); \ +} while (0) +// TODO: is this optimal ? + +#define GGML_F32_VEC GGML_F32x8 +#define GGML_F32_VEC_ZERO GGML_F32x8_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x8_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x8_LOAD +#define GGML_F32_VEC_STORE GGML_F32x8_STORE +#define GGML_F32_VEC_FMA GGML_F32x8_FMA +#define GGML_F32_VEC_ADD GGML_F32x8_ADD +#define GGML_F32_VEC_MUL GGML_F32x8_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE + +// F16 AVX + +#define GGML_F16_STEP 32 +#define GGML_F16_EPR 8 + +// F16 arithmetic is not supported by AVX, so we use F32 instead + +#define GGML_F32Cx8 __m256 +#define GGML_F32Cx8_ZERO _mm256_setzero_ps() +#define GGML_F32Cx8_SET1(x) _mm256_set1_ps(x) + +#if defined(__F16C__) +// the _mm256_cvt intrinsics require F16C +#define GGML_F32Cx8_LOAD(x) _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)(x))) +#define GGML_F32Cx8_STORE(x, y) _mm_storeu_si128((__m128i *)(x), _mm256_cvtps_ph(y, 0)) +#else +static inline __m256 __avx_f32cx8_load(const ggml_fp16_t * x) { + float tmp[8]; + + for (int i = 0; i < 8; i++) { + tmp[i] = GGML_FP16_TO_FP32(x[i]); + } + + return _mm256_loadu_ps(tmp); +} +static inline void __avx_f32cx8_store(ggml_fp16_t *x, __m256 y) { + float arr[8]; + + _mm256_storeu_ps(arr, y); + + for (int i = 0; i < 8; i++) + x[i] = GGML_FP32_TO_FP16(arr[i]); +} +#define GGML_F32Cx8_LOAD(x) __avx_f32cx8_load(x) +#define GGML_F32Cx8_STORE(x, y) __avx_f32cx8_store(x, y) +#endif + +#define GGML_F32Cx8_FMA GGML_F32x8_FMA +#define GGML_F32Cx8_ADD _mm256_add_ps +#define GGML_F32Cx8_MUL _mm256_mul_ps +#define GGML_F32Cx8_REDUCE GGML_F32x8_REDUCE + +#define GGML_F16_VEC GGML_F32Cx8 +#define GGML_F16_VEC_ZERO GGML_F32Cx8_ZERO +#define GGML_F16_VEC_SET1 GGML_F32Cx8_SET1 +#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx8_LOAD(p) +#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i]) +#define GGML_F16_VEC_FMA GGML_F32Cx8_FMA +#define GGML_F16_VEC_ADD GGML_F32Cx8_ADD +#define GGML_F16_VEC_MUL GGML_F32Cx8_MUL +#define GGML_F16_VEC_REDUCE GGML_F32Cx8_REDUCE + +#elif defined(__POWER9_VECTOR__) + +#define GGML_SIMD + +// F32 POWER9 + +#define GGML_F32_STEP 32 +#define GGML_F32_EPR 4 + +#define GGML_F32x4 vector float +#define GGML_F32x4_ZERO 0.0f +#define GGML_F32x4_SET1 vec_splats +#define GGML_F32x4_LOAD(p) vec_xl(0, p) +#define GGML_F32x4_STORE(p, r) vec_xst(r, 0, p) +#define GGML_F32x4_FMA(a, b, c) vec_madd(b, c, a) +#define GGML_F32x4_ADD vec_add +#define GGML_F32x4_MUL vec_mul +#define GGML_F32x4_REDUCE(res, x) \ +{ \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = vec_add(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = vec_add(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = vec_add(x[i], x[offset+i]); \ + } \ + res = vec_extract(x[0], 0) + \ + vec_extract(x[0], 1) + \ + vec_extract(x[0], 2) + \ + vec_extract(x[0], 3); \ +} + +#define GGML_F32_VEC GGML_F32x4 +#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD +#define GGML_F32_VEC_STORE GGML_F32x4_STORE +#define GGML_F32_VEC_FMA GGML_F32x4_FMA +#define GGML_F32_VEC_ADD GGML_F32x4_ADD +#define GGML_F32_VEC_MUL GGML_F32x4_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE + +// F16 POWER9 +#define GGML_F16_STEP GGML_F32_STEP +#define GGML_F16_EPR GGML_F32_EPR +#define GGML_F16_VEC GGML_F32x4 +#define GGML_F16_VEC_ZERO GGML_F32x4_ZERO +#define GGML_F16_VEC_SET1 GGML_F32x4_SET1 +#define GGML_F16_VEC_FMA GGML_F32x4_FMA +#define GGML_F16_VEC_ADD GGML_F32x4_ADD +#define GGML_F16_VEC_MUL GGML_F32x4_MUL +#define GGML_F16_VEC_REDUCE GGML_F32x4_REDUCE +// Use vec_xl, not vec_ld, in case the load address is not aligned. +#define GGML_F16_VEC_LOAD(p, i) (i & 0x1) ? \ + vec_extract_fp32_from_shorth(vec_xl(0, p - GGML_F16_EPR)) : \ + vec_extract_fp32_from_shortl(vec_xl(0, p)) +#define GGML_ENDIAN_BYTE(i) ((unsigned char *)&(uint16_t){1})[i] +#define GGML_F16_VEC_STORE(p, r, i) \ + if (i & 0x1) \ + vec_xst(vec_pack_to_short_fp32(r[i - GGML_ENDIAN_BYTE(1)], \ + r[i - GGML_ENDIAN_BYTE(0)]), \ + 0, p - GGML_F16_EPR) + +#elif defined(__wasm_simd128__) + +#define GGML_SIMD + +// F32 WASM + +#define GGML_F32_STEP 16 +#define GGML_F32_EPR 4 + +#define GGML_F32x4 v128_t +#define GGML_F32x4_ZERO wasm_f32x4_splat(0.0f) +#define GGML_F32x4_SET1(x) wasm_f32x4_splat(x) +#define GGML_F32x4_LOAD wasm_v128_load +#define GGML_F32x4_STORE wasm_v128_store +#define GGML_F32x4_FMA(a, b, c) wasm_f32x4_add(wasm_f32x4_mul(b, c), a) +#define GGML_F32x4_ADD wasm_f32x4_add +#define GGML_F32x4_MUL wasm_f32x4_mul +#define GGML_F32x4_REDUCE(res, x) \ +{ \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ + } \ + res = wasm_f32x4_extract_lane(x[0], 0) + \ + wasm_f32x4_extract_lane(x[0], 1) + \ + wasm_f32x4_extract_lane(x[0], 2) + \ + wasm_f32x4_extract_lane(x[0], 3); \ +} + +#define GGML_F32_VEC GGML_F32x4 +#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD +#define GGML_F32_VEC_STORE GGML_F32x4_STORE +#define GGML_F32_VEC_FMA GGML_F32x4_FMA +#define GGML_F32_VEC_ADD GGML_F32x4_ADD +#define GGML_F32_VEC_MUL GGML_F32x4_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE + +// F16 WASM + +#define GGML_F16_STEP 16 +#define GGML_F16_EPR 4 + +inline static v128_t __wasm_f16x4_load(const ggml_fp16_t * p) { + float tmp[4]; + + tmp[0] = GGML_FP16_TO_FP32(p[0]); + tmp[1] = GGML_FP16_TO_FP32(p[1]); + tmp[2] = GGML_FP16_TO_FP32(p[2]); + tmp[3] = GGML_FP16_TO_FP32(p[3]); + + return wasm_v128_load(tmp); +} + +inline static void __wasm_f16x4_store(ggml_fp16_t * p, v128_t x) { + float tmp[4]; + + wasm_v128_store(tmp, x); + + p[0] = GGML_FP32_TO_FP16(tmp[0]); + p[1] = GGML_FP32_TO_FP16(tmp[1]); + p[2] = GGML_FP32_TO_FP16(tmp[2]); + p[3] = GGML_FP32_TO_FP16(tmp[3]); +} + +#define GGML_F16x4 v128_t +#define GGML_F16x4_ZERO wasm_f32x4_splat(0.0f) +#define GGML_F16x4_SET1(x) wasm_f32x4_splat(x) +#define GGML_F16x4_LOAD(x) __wasm_f16x4_load(x) +#define GGML_F16x4_STORE(x, y) __wasm_f16x4_store(x, y) +#define GGML_F16x4_FMA GGML_F32x4_FMA +#define GGML_F16x4_ADD wasm_f32x4_add +#define GGML_F16x4_MUL wasm_f32x4_mul +#define GGML_F16x4_REDUCE(res, x) \ +{ \ + int offset = GGML_F16_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ + } \ + res = wasm_f32x4_extract_lane(x[0], 0) + \ + wasm_f32x4_extract_lane(x[0], 1) + \ + wasm_f32x4_extract_lane(x[0], 2) + \ + wasm_f32x4_extract_lane(x[0], 3); \ +} + +#define GGML_F16_VEC GGML_F16x4 +#define GGML_F16_VEC_ZERO GGML_F16x4_ZERO +#define GGML_F16_VEC_SET1 GGML_F16x4_SET1 +#define GGML_F16_VEC_LOAD(p, i) GGML_F16x4_LOAD(p) +#define GGML_F16_VEC_STORE(p, r, i) GGML_F16x4_STORE(p, r[i]) +#define GGML_F16_VEC_FMA GGML_F16x4_FMA +#define GGML_F16_VEC_ADD GGML_F16x4_ADD +#define GGML_F16_VEC_MUL GGML_F16x4_MUL +#define GGML_F16_VEC_REDUCE GGML_F16x4_REDUCE + +#elif defined(__SSE3__) + +#define GGML_SIMD + +// F32 SSE + +#define GGML_F32_STEP 32 +#define GGML_F32_EPR 4 + +#define GGML_F32x4 __m128 +#define GGML_F32x4_ZERO _mm_setzero_ps() +#define GGML_F32x4_SET1(x) _mm_set1_ps(x) +#define GGML_F32x4_LOAD _mm_loadu_ps +#define GGML_F32x4_STORE _mm_storeu_ps +#if defined(__FMA__) + // TODO: Does this work? + #define GGML_F32x4_FMA(a, b, c) _mm_fmadd_ps(b, c, a) +#else + #define GGML_F32x4_FMA(a, b, c) _mm_add_ps(_mm_mul_ps(b, c), a) +#endif +#define GGML_F32x4_ADD _mm_add_ps +#define GGML_F32x4_MUL _mm_mul_ps +#define GGML_F32x4_REDUCE(res, x) \ +{ \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm_add_ps(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm_add_ps(x[i], x[offset+i]); \ + } \ + const __m128 t0 = _mm_hadd_ps(x[0], x[0]); \ + res = (ggml_float) _mm_cvtss_f32(_mm_hadd_ps(t0, t0)); \ +} +// TODO: is this optimal ? + +#define GGML_F32_VEC GGML_F32x4 +#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD +#define GGML_F32_VEC_STORE GGML_F32x4_STORE +#define GGML_F32_VEC_FMA GGML_F32x4_FMA +#define GGML_F32_VEC_ADD GGML_F32x4_ADD +#define GGML_F32_VEC_MUL GGML_F32x4_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE + +// F16 SSE + +#define GGML_F16_STEP 32 +#define GGML_F16_EPR 4 + +static inline __m128 __sse_f16x4_load(ggml_fp16_t *x) { + float tmp[4]; + + tmp[0] = GGML_FP16_TO_FP32(x[0]); + tmp[1] = GGML_FP16_TO_FP32(x[1]); + tmp[2] = GGML_FP16_TO_FP32(x[2]); + tmp[3] = GGML_FP16_TO_FP32(x[3]); + + return _mm_loadu_ps(tmp); +} + +static inline void __sse_f16x4_store(ggml_fp16_t *x, __m128 y) { + float arr[4]; + + _mm_storeu_ps(arr, y); + + x[0] = GGML_FP32_TO_FP16(arr[0]); + x[1] = GGML_FP32_TO_FP16(arr[1]); + x[2] = GGML_FP32_TO_FP16(arr[2]); + x[3] = GGML_FP32_TO_FP16(arr[3]); +} + +#define GGML_F32Cx4 __m128 +#define GGML_F32Cx4_ZERO _mm_setzero_ps() +#define GGML_F32Cx4_SET1(x) _mm_set1_ps(x) +#define GGML_F32Cx4_LOAD(x) __sse_f16x4_load(x) +#define GGML_F32Cx4_STORE(x, y) __sse_f16x4_store(x, y) +#define GGML_F32Cx4_FMA GGML_F32x4_FMA +#define GGML_F32Cx4_ADD _mm_add_ps +#define GGML_F32Cx4_MUL _mm_mul_ps +#define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE + +#define GGML_F16_VEC GGML_F32Cx4 +#define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO +#define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 +#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) +#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i]) +#define GGML_F16_VEC_FMA GGML_F32Cx4_FMA +#define GGML_F16_VEC_ADD GGML_F32Cx4_ADD +#define GGML_F16_VEC_MUL GGML_F32Cx4_MUL +#define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE + +#elif defined(__loongarch_asx) + +#define GGML_SIMD + +// F32 LASX +#define GGML_F32_STEP 32 +#define GGML_F32_EPR 8 + +#define GGML_F32x8 __m256 +#define GGML_F32x8_ZERO (__m256)__lasx_xvldi(0) +#define GGML_F32x8_SET1(x) (__m256)__lasx_xvreplfr2vr_s((x)) +#define GGML_F32x8_LOAD(x) (__m256)__lasx_xvld((x), 0) +#define GGML_F32x8_STORE(x,y) __lasx_xvst((y), (x), 0) +#define GGML_F32x8_FMA(a, b, c) __lasx_xvfmadd_s(b, c, a) +#define GGML_F32x8_ADD __lasx_xvfadd_s +#define GGML_F32x8_MUL __lasx_xvfmul_s +#define GGML_F32x8_REDUCE(res, x) \ +do { \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ + } \ + float *tmp_p = (float *)&x[0]; \ + res = tmp_p[0] + tmp_p[1] + tmp_p[2] + tmp_p[3] + tmp_p[4] + tmp_p[5] + tmp_p[6] + tmp_p[7]; \ +} while (0) +// TODO: is this optimal ? + +#define GGML_F32_VEC GGML_F32x8 +#define GGML_F32_VEC_ZERO GGML_F32x8_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x8_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x8_LOAD +#define GGML_F32_VEC_STORE GGML_F32x8_STORE +#define GGML_F32_VEC_FMA GGML_F32x8_FMA +#define GGML_F32_VEC_ADD GGML_F32x8_ADD +#define GGML_F32_VEC_MUL GGML_F32x8_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE + +// F16 LASX + +#define GGML_F16_STEP 32 +#define GGML_F16_EPR 8 + +// F16 arithmetic is not supported by AVX, so we use F32 instead + +#define GGML_F32Cx8 __m256 +#define GGML_F32Cx8_ZERO (__m256)__lasx_xvldi(0) +#define GGML_F32Cx8_SET1(x) (__m256)__lasx_xvreplgr2vr_w((x)) + +static inline __m256 __lasx_f32cx8_load(const ggml_fp16_t * x) { + float tmp[8]; + + for (int i = 0; i < 8; i++) { + tmp[i] = GGML_FP16_TO_FP32(x[i]); + } + + return (__m256)__lasx_xvld(tmp, 0); +} +static inline void __lasx_f32cx8_store(ggml_fp16_t * x, __m256 y) { + float arr[8]; + + __lasx_xvst(y, arr, 0); + + for (int i = 0; i < 8; i++) { + x[i] = GGML_FP32_TO_FP16(arr[i]); + } +} +#define GGML_F32Cx8_LOAD(x) __lasx_f32cx8_load(x) +#define GGML_F32Cx8_STORE(x, y) __lasx_f32cx8_store(x, y) + +#define GGML_F32Cx8_FMA GGML_F32x8_FMA +#define GGML_F32Cx8_ADD __lasx_xvfadd_s +#define GGML_F32Cx8_MUL __lasx_xvfmul_s +#define GGML_F32Cx8_REDUCE GGML_F32x8_REDUCE + +#define GGML_F16_VEC GGML_F32Cx8 +#define GGML_F16_VEC_ZERO GGML_F32Cx8_ZERO +#define GGML_F16_VEC_SET1 GGML_F32Cx8_SET1 +#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx8_LOAD(p) +#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i]) +#define GGML_F16_VEC_FMA GGML_F32Cx8_FMA +#define GGML_F16_VEC_ADD GGML_F32Cx8_ADD +#define GGML_F16_VEC_MUL GGML_F32Cx8_MUL +#define GGML_F16_VEC_REDUCE GGML_F32Cx8_REDUCE + +#elif defined(__loongarch_sx) + +#define GGML_SIMD + +// F32 LSX + +#define GGML_F32_STEP 32 +#define GGML_F32_EPR 4 + +#define GGML_F32x4 __m128 +#define GGML_F32x4_ZERO __lsx_vldi(0) +#define GGML_F32x4_SET1(x) __lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0) +#define GGML_F32x4_LOAD(x) __lsx_vld((x), 0) +#define GGML_F32x4_STORE((x),(y)) __lsx_vst((y), (x), 0) +#define GGML_F32x4_FMA(a, b, c) __lsx_vfmadd_s(b, c, a) +#define GGML_F32x4_ADD __lsx_vfadd_s +#define GGML_F32x4_MUL __lsx_vfmul_s +#define GGML_F32x4_REDUCE(res, x) \ +{ \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lsx_vfadd_s(x[i], x[offset + i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lsx_vfadd_s(x[i], x[offset + i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lsx_vfadd_s(x[i], x[offset + i]); \ + } \ + __m128i tmp = __lsx_vsrli_d((__m128i) x[0], 32); \ + tmp = (__m128i) __lsx_vfadd_s((__m128) tmp, x[0]); \ + tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \ + const __m128 t0 = __lsx_vshuf4i_w(tmp, 0x88); \ + tmp = __lsx_vsrli_d((__m128i) t0, 32); \ + tmp = (__m128i) __lsx_vfadd_s((__m128) tmp, t0); \ + tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \ + res = (ggml_float) __lsx_vpickve2gr_w(__lsx_vshuf4i_w(tmp, 0x88), 0); \ +} + +#define GGML_F32_VEC GGML_F32x4 +#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD +#define GGML_F32_VEC_STORE GGML_F32x4_STORE +#define GGML_F32_VEC_FMA GGML_F32x4_FMA +#define GGML_F32_VEC_ADD GGML_F32x4_ADD +#define GGML_F32_VEC_MUL GGML_F32x4_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE + +// F16 LSX + +#define GGML_F16_STEP 32 +#define GGML_F16_EPR 4 + +static inline __m128 __lsx_f16x4_load(const ggml_fp16_t * x) { + float tmp[4]; + + tmp[0] = GGML_FP16_TO_FP32(x[0]); + tmp[1] = GGML_FP16_TO_FP32(x[1]); + tmp[2] = GGML_FP16_TO_FP32(x[2]); + tmp[3] = GGML_FP16_TO_FP32(x[3]); + + return __lsx_vld(tmp, 0); +} + +static inline void __lsx_f16x4_store(ggml_fp16_t * x, __m128 y) { + float arr[4]; + + __lsx_vst(y, arr, 0); + + x[0] = GGML_FP32_TO_FP16(arr[0]); + x[1] = GGML_FP32_TO_FP16(arr[1]); + x[2] = GGML_FP32_TO_FP16(arr[2]); + x[3] = GGML_FP32_TO_FP16(arr[3]); +} + +#define GGML_F32Cx4 __m128 +#define GGML_F32Cx4_ZERO __lsx_vldi(0) +#define GGML_F32Cx4_SET1(x) __lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0) +#define GGML_F32Cx4_LOAD(x) __lsx_f16x4_load(x) +#define GGML_F32Cx4_STORE(x, y) __lsx_f16x4_store(x, y) +#define GGML_F32Cx4_FMA GGML_F32x4_FMA +#define GGML_F32Cx4_ADD __lsx_vfadd_s +#define GGML_F32Cx4_MUL __lsx_vfmul_s +#define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE + +#define GGML_F16_VEC GGML_F32Cx4 +#define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO +#define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 +#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) +#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i]) +#define GGML_F16_VEC_FMA GGML_F32Cx4_FMA +#define GGML_F16_VEC_ADD GGML_F32Cx4_ADD +#define GGML_F16_VEC_MUL GGML_F32Cx4_MUL +#define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE + +#endif + +// GGML_F32_ARR / GGML_F16_ARR +// number of registers to use per step +#ifdef GGML_SIMD +#define GGML_F32_ARR (GGML_F32_STEP/GGML_F32_EPR) +#define GGML_F16_ARR (GGML_F16_STEP/GGML_F16_EPR) +#endif + +// +// Threading defs +// + +typedef pthread_t ggml_thread_t; + +#if defined(_WIN32) + +typedef CONDITION_VARIABLE ggml_cond_t; +typedef SRWLOCK ggml_mutex_t; + +#define ggml_mutex_init(m) InitializeSRWLock(m) +#define ggml_mutex_destroy(m) +#define ggml_mutex_lock(m) AcquireSRWLockExclusive(m) +#define ggml_mutex_unlock(m) ReleaseSRWLockExclusive(m) +#define ggml_mutex_lock_shared(m) AcquireSRWLockShared(m) +#define ggml_mutex_unlock_shared(m) ReleaseSRWLockShared(m) + +#define ggml_cond_init(c) InitializeConditionVariable(c) +#define ggml_cond_destroy(c) +#define ggml_cond_wait(c, m) SleepConditionVariableSRW(c, m, INFINITE, CONDITION_VARIABLE_LOCKMODE_SHARED) +#define ggml_cond_broadcast(c) WakeAllConditionVariable(c) + +#define ggml_thread_create pthread_create +#define ggml_thread_join pthread_join + +#else + +typedef pthread_cond_t ggml_cond_t; +typedef pthread_mutex_t ggml_mutex_t; + +#define ggml_mutex_init(m) pthread_mutex_init(m, NULL) +#define ggml_mutex_destroy(m) pthread_mutex_destroy(m) +#define ggml_mutex_lock(m) pthread_mutex_lock(m) +#define ggml_mutex_unlock(m) pthread_mutex_unlock(m) +#define ggml_mutex_lock_shared(m) pthread_mutex_lock(m) +#define ggml_mutex_unlock_shared(m) pthread_mutex_unlock(m) + +#define ggml_lock_init(x) UNUSED(x) +#define ggml_lock_destroy(x) UNUSED(x) +#if defined(__x86_64__) || (defined(_MSC_VER) && defined(_M_AMD64)) +#define ggml_lock_lock(x) _mm_pause() +#else +#define ggml_lock_lock(x) UNUSED(x) +#endif +#define ggml_lock_unlock(x) UNUSED(x) + +#define GGML_LOCK_INITIALIZER 0 +#define ggml_cond_init(c) pthread_cond_init(c, NULL) +#define ggml_cond_destroy(c) pthread_cond_destroy(c) +#define ggml_cond_wait(c, m) pthread_cond_wait(c, m) +#define ggml_cond_broadcast(c) pthread_cond_broadcast(c) + +#define ggml_thread_create pthread_create +#define ggml_thread_join pthread_join + +#endif + +// Threadpool def +struct ggml_threadpool { + ggml_mutex_t mutex; // mutex for cond.var + ggml_cond_t cond; // cond.var for waiting for new work + + struct ggml_cgraph * cgraph; + struct ggml_cplan * cplan; + + // synchronization primitives + atomic_int n_graph; // incremented when there is work to be done (i.e each graph) + atomic_int GGML_CACHE_ALIGN n_barrier; + atomic_int GGML_CACHE_ALIGN n_barrier_passed; + atomic_int current_chunk; // currently processing chunk during Mat_Mul, shared between all the threads. + + // these are atomic as an annotation for thread-sanitizer + atomic_bool stop; // Used for stopping the threadpool altogether + atomic_bool pause; // Used for pausing the threadpool or individual threads + atomic_bool abort; // Used for aborting processing of a graph + + struct ggml_compute_state * workers; // per thread state + int n_threads_max; // number of threads in the pool + atomic_int n_threads_cur; // number of threads used in the current graph + + int32_t prio; // Scheduling priority + uint32_t poll; // Polling level (0 - no polling) + + enum ggml_status ec; +}; + +// Per-thread state +struct ggml_compute_state { +#ifndef GGML_USE_OPENMP + ggml_thread_t thrd; + bool cpumask[GGML_MAX_N_THREADS]; + int last_graph; + bool pending; +#endif + struct ggml_threadpool * threadpool; + int ith; +}; + +// +// fundamental operations +// + +inline static void ggml_vec_set_i8(const int n, int8_t * x, const int8_t v) { for (int i = 0; i < n; ++i) x[i] = v; } +inline static void ggml_vec_set_i16(const int n, int16_t * x, const int16_t v) { for (int i = 0; i < n; ++i) x[i] = v; } + +inline static void ggml_vec_set_i32(const int n, int32_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; } +inline static void ggml_vec_cpy_i32(const int n, int32_t * y, const int32_t * x) { for (int i = 0; i < n; ++i) y[i] = x[i]; } + +inline static void ggml_vec_set_f16(const int n, ggml_fp16_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; } +inline static void ggml_vec_set_bf16(const int n, ggml_bf16_t * x, const ggml_bf16_t v) { for (int i = 0; i < n; ++i) x[i] = v; } +inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] + y[i]; } +inline static void ggml_vec_add1_f32(const int n, float * z, const float * x, const float v) { for (int i = 0; i < n; ++i) z[i] = x[i] + v; } +inline static void ggml_vec_acc_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] += x[i]; } +inline static void ggml_vec_acc1_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] += v; } +inline static void ggml_vec_sub_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] - y[i]; } +inline static void ggml_vec_set_f32 (const int n, float * x, const float v) { for (int i = 0; i < n; ++i) x[i] = v; } +inline static void ggml_vec_cpy_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]; } +inline static void ggml_vec_neg_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = -x[i]; } +inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]*y[i]; } +inline static void ggml_vec_div_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]/y[i]; } + +static void ggml_vec_dot_f32(int n, float * restrict s, size_t bs, const float * restrict x, size_t bx, const float * restrict y, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + +#if defined(GGML_SIMD) + float sumf = 0.0f; + const int np = (n & ~(GGML_F32_STEP - 1)); + + GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO }; + + GGML_F32_VEC ax[GGML_F32_ARR]; + GGML_F32_VEC ay[GGML_F32_ARR]; + + for (int i = 0; i < np; i += GGML_F32_STEP) { + for (int j = 0; j < GGML_F32_ARR; j++) { + ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR); + ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); + + sum[j] = GGML_F32_VEC_FMA(sum[j], ax[j], ay[j]); + } + } + + // reduce sum0..sum3 to sum0 + GGML_F32_VEC_REDUCE(sumf, sum); + + // leftovers + for (int i = np; i < n; ++i) { + sumf += x[i]*y[i]; + } +#else + // scalar + ggml_float sumf = 0.0; + for (int i = 0; i < n; ++i) { + sumf += (ggml_float)(x[i]*y[i]); + } +#endif + + *s = sumf; +} + +static void ggml_vec_dot_bf16(int n, float * restrict s, size_t bs, ggml_bf16_t * restrict x, size_t bx, ggml_bf16_t * restrict y, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + int i = 0; + ggml_float sumf = 0; + +#if defined(__AVX512BF16__) + __m512 c1 = _mm512_setzero_ps(); + __m512 c2 = _mm512_setzero_ps(); + for (; i + 64 <= n; i += 64) { + c1 = _mm512_dpbf16_ps(c1, m512bh(_mm512_loadu_si512((x + i))), + m512bh(_mm512_loadu_si512((y + i)))); + c2 = _mm512_dpbf16_ps(c2, m512bh(_mm512_loadu_si512((x + i + 32))), + m512bh(_mm512_loadu_si512((y + i + 32)))); + } + sumf += (ggml_float)_mm512_reduce_add_ps(c1); + sumf += (ggml_float)_mm512_reduce_add_ps(c2); + +#elif defined(__AVX512F__) +#define LOAD(p) _mm512_castsi512_ps(_mm512_slli_epi32(_mm512_cvtepu16_epi32(_mm256_loadu_si256((const __m256i *)(p))), 16)) + __m512 c1 = _mm512_setzero_ps(); + __m512 c2 = _mm512_setzero_ps(); + for (; i + 32 <= n; i += 32) { + c1 = _mm512_add_ps(_mm512_mul_ps(LOAD(x + i), LOAD(y + i)), c1); + c2 = _mm512_add_ps(_mm512_mul_ps(LOAD(x + i + 16), LOAD(y + i + 16)), c2); + } + sumf += (ggml_float)_mm512_reduce_add_ps(c1); + sumf += (ggml_float)_mm512_reduce_add_ps(c2); + +#undef LOAD +#elif defined(__AVX2__) || defined(__AVX__) +#if defined(__AVX2__) +#define LOAD(p) _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_cvtepu16_epi32(_mm_loadu_si128((const __m128i *)(p))), 16)) +#else +#define LOAD(p) _mm256_castsi256_ps(_mm256_insertf128_si256(_mm256_castsi128_si256(_mm_slli_epi32(_mm_cvtepu16_epi32(_mm_loadu_si128((const __m128i *)(p))), 16)), (_mm_slli_epi32(_mm_cvtepu16_epi32(_mm_bsrli_si128(_mm_loadu_si128((const __m128i *)(p)), 8)), 16)), 1)) +#endif + __m256 c1 = _mm256_setzero_ps(); + __m256 c2 = _mm256_setzero_ps(); + __m256 c3 = _mm256_setzero_ps(); + __m256 c4 = _mm256_setzero_ps(); + for (; i + 32 <= n; i += 32) { + c1 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i), LOAD(y + i)), c1); + c2 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 8), LOAD(y + i + 8)), c2); + c3 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 16), LOAD(y + i + 16)), c3); + c4 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 24), LOAD(y + i + 24)), c4); + } + __m128 g; + c1 = _mm256_add_ps(_mm256_add_ps(c1, c3), + _mm256_add_ps(c2, c4)); + g = _mm_add_ps(_mm256_extractf128_ps(c1, 1), + _mm256_castps256_ps128(c1)); + g = _mm_add_ps(g, _mm_movehl_ps(g, g)); + g = _mm_add_ss(g, _mm_movehdup_ps(g)); + sumf += (ggml_float)_mm_cvtss_f32(g); + +#undef LOAD +#endif + + for (; i < n; ++i) { + sumf += (ggml_float)(GGML_BF16_TO_FP32(x[i]) * + GGML_BF16_TO_FP32(y[i])); + } + *s = sumf; +} + +static void ggml_vec_dot_f16(int n, float * restrict s, size_t bs, ggml_fp16_t * restrict x, size_t bx, ggml_fp16_t * restrict y, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + + ggml_float sumf = 0.0; + +#if defined(GGML_SIMD) + const int np = (n & ~(GGML_F16_STEP - 1)); + + GGML_F16_VEC sum[GGML_F16_ARR] = { GGML_F16_VEC_ZERO }; + + GGML_F16_VEC ax[GGML_F16_ARR]; + GGML_F16_VEC ay[GGML_F16_ARR]; + + for (int i = 0; i < np; i += GGML_F16_STEP) { + for (int j = 0; j < GGML_F16_ARR; j++) { + ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j); + ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); + + sum[j] = GGML_F16_VEC_FMA(sum[j], ax[j], ay[j]); + } + } + + // reduce sum0..sum3 to sum0 + GGML_F16_VEC_REDUCE(sumf, sum); + + // leftovers + for (int i = np; i < n; ++i) { + sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i])); + } +#else + for (int i = 0; i < n; ++i) { + sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i])); + } +#endif + + *s = sumf; +} + +// compute GGML_VEC_DOT_UNROLL dot products at once +// xs - x row stride in bytes +inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * restrict s, void * restrict xv, ggml_fp16_t * restrict y) { + ggml_float sumf[GGML_VEC_DOT_UNROLL] = { 0.0 }; + + ggml_fp16_t * restrict x[GGML_VEC_DOT_UNROLL]; + + for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) { + x[i] = (ggml_fp16_t *) ((char *) xv + i*xs); + } + +#if defined(GGML_SIMD) + const int np = (n & ~(GGML_F16_STEP - 1)); + + GGML_F16_VEC sum[GGML_VEC_DOT_UNROLL][GGML_F16_ARR] = { { GGML_F16_VEC_ZERO } }; + + GGML_F16_VEC ax[GGML_F16_ARR]; + GGML_F16_VEC ay[GGML_F16_ARR]; + + for (int i = 0; i < np; i += GGML_F16_STEP) { + for (int j = 0; j < GGML_F16_ARR; j++) { + ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); + + for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) { + ax[j] = GGML_F16_VEC_LOAD(x[k] + i + j*GGML_F16_EPR, j); + + sum[k][j] = GGML_F16_VEC_FMA(sum[k][j], ax[j], ay[j]); + } + } + } + + // reduce sum0..sum3 to sum0 + for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) { + GGML_F16_VEC_REDUCE(sumf[k], sum[k]); + } + + // leftovers + for (int i = np; i < n; ++i) { + for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) { + sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i])); + } + } +#else + for (int i = 0; i < n; ++i) { + for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) { + sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i])); + } + } +#endif + + for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) { + s[i] = sumf[i]; + } +} + +inline static void ggml_vec_mad_f32(const int n, float * restrict y, const float * restrict x, const float v) { +#if defined(GGML_SIMD) + const int np = (n & ~(GGML_F32_STEP - 1)); + + GGML_F32_VEC vx = GGML_F32_VEC_SET1(v); + + GGML_F32_VEC ax[GGML_F32_ARR]; + GGML_F32_VEC ay[GGML_F32_ARR]; + + for (int i = 0; i < np; i += GGML_F32_STEP) { + for (int j = 0; j < GGML_F32_ARR; j++) { + ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR); + ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); + ay[j] = GGML_F32_VEC_FMA(ay[j], ax[j], vx); + + GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); + } + } + + // leftovers + for (int i = np; i < n; ++i) { + y[i] += x[i]*v; + } +#else + // scalar + for (int i = 0; i < n; ++i) { + y[i] += x[i]*v; + } +#endif +} + +inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * restrict y, const ggml_fp16_t * restrict x, const float v) { +#if defined(GGML_SIMD) + const int np = (n & ~(GGML_F16_STEP - 1)); + + GGML_F16_VEC vx = GGML_F16_VEC_SET1(v); + + GGML_F16_VEC ax[GGML_F16_ARR]; + GGML_F16_VEC ay[GGML_F16_ARR]; + + for (int i = 0; i < np; i += GGML_F16_STEP) { + for (int j = 0; j < GGML_F16_ARR; j++) { + ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j); + ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); + ay[j] = GGML_F16_VEC_FMA(ay[j], ax[j], vx); + + GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j); + } + } + + // leftovers + for (int i = np; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v); + } +#else + // scalar + for (int i = 0; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v); + } +#endif +} + +// xs and vs are byte strides of x and v +inline static void ggml_vec_mad_f32_unroll(const int n, const int xs, const int vs, float * restrict y, const float * restrict xv, const float * restrict vv) { + + const float * restrict x[GGML_VEC_MAD_UNROLL]; + const float * restrict v[GGML_VEC_MAD_UNROLL]; + + for (int i = 0; i < GGML_VEC_MAD_UNROLL; ++i) { + x[i] = (const float *) ((const char *) xv + i*xs); + v[i] = (const float *) ((const char *) vv + i*vs); + } + +#if defined(GGML_SIMD) + const int np = (n & ~(GGML_F32_STEP - 1)); + + GGML_F32_VEC vx[GGML_VEC_MAD_UNROLL]; + + for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { + vx[k] = GGML_F32_VEC_SET1(v[k][0]); + } + + GGML_F32_VEC ax[GGML_VEC_MAD_UNROLL][GGML_F32_ARR]; + GGML_F32_VEC ay[GGML_F32_ARR]; + + for (int i = 0; i < np; i += GGML_F32_STEP) { + for (int j = 0; j < GGML_F32_ARR; j++) { + ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); + + for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { + ax[k][j] = GGML_F32_VEC_LOAD(x[k] + i + j*GGML_F32_EPR); + ay[j] = GGML_F32_VEC_FMA(ay[j], ax[k][j], vx[k]); + } + + GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); + } + } + + // leftovers + for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { + for (int i = np; i < n; ++i) { + y[i] += x[k][i]*v[k][0]; + } + } +#else + // scalar + for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { + for (int i = 0; i < n; ++i) { + y[i] += x[k][i]*v[k][0]; + } + } +#endif +} + +//inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] *= v; } +inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { +#if defined(GGML_USE_ACCELERATE) + vDSP_vsmul(y, 1, &v, y, 1, n); +#elif defined(GGML_SIMD) + const int np = (n & ~(GGML_F32_STEP - 1)); + + GGML_F32_VEC vx = GGML_F32_VEC_SET1(v); + + GGML_F32_VEC ay[GGML_F32_ARR]; + + for (int i = 0; i < np; i += GGML_F32_STEP) { + for (int j = 0; j < GGML_F32_ARR; j++) { + ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); + ay[j] = GGML_F32_VEC_MUL(ay[j], vx); + + GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); + } + } + + // leftovers + for (int i = np; i < n; ++i) { + y[i] *= v; + } +#else + // scalar + for (int i = 0; i < n; ++i) { + y[i] *= v; + } +#endif +} + +inline static void ggml_vec_scale_f16(const int n, ggml_fp16_t * y, const float v) { +#if defined(GGML_SIMD) + const int np = (n & ~(GGML_F16_STEP - 1)); + + GGML_F16_VEC vx = GGML_F16_VEC_SET1(v); + + GGML_F16_VEC ay[GGML_F16_ARR]; + + for (int i = 0; i < np; i += GGML_F16_STEP) { + for (int j = 0; j < GGML_F16_ARR; j++) { + ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); + ay[j] = GGML_F16_VEC_MUL(ay[j], vx); + + GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j); + } + } + + // leftovers + for (int i = np; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i])*v); + } +#else + // scalar + for (int i = 0; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i])*v); + } +#endif +} + +inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, 0, x, 0, x, 0, 1); *s = sqrtf(*s); } +inline static void ggml_vec_sqr_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]*x[i]; } +inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrtf(x[i]); } +inline static void ggml_vec_log_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = logf(x[i]); } +inline static void ggml_vec_sin_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sinf(x[i]); } +inline static void ggml_vec_cos_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = cosf(x[i]); } +inline static void ggml_vec_abs_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fabsf(x[i]); } +inline static void ggml_vec_sgn_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : ((x[i] < 0.f) ? -1.f : 0.f); } +inline static void ggml_vec_step_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : 0.f; } +inline static void ggml_vec_tanh_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = tanhf(x[i]); } +inline static void ggml_vec_elu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expm1f(x[i]); } +inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; } +inline static void ggml_vec_leaky_relu_f32 (const int n, float * y, const float * x, const float ns) { for (int i = 0; i < n; ++i) y[i] = ((x[i] > 0.f) ? x[i] : 0.f) + ns * ((x[i] < 0.0f) ? x[i] : 0.f); } +inline static void ggml_vec_sigmoid_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = 1.f / (1.f + expf(-x[i])); } +// TODO: optimize performance +inline static void ggml_vec_hardswish_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i] * fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); } +inline static void ggml_vec_hardsigmoid_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); } +inline static void ggml_vec_exp_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = expf(x[i]); } + +static const float GELU_COEF_A = 0.044715f; +static const float GELU_QUICK_COEF = -1.702f; +static const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; + +inline static float ggml_gelu_f32(float x) { + return 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x))); +} + +inline static void ggml_vec_gelu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { + const uint16_t * i16 = (const uint16_t *) x; + for (int i = 0; i < n; ++i) { + y[i] = ggml_table_gelu_f16[i16[i]]; + } +} + +#ifdef GGML_GELU_FP16 +inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { + uint16_t t; + for (int i = 0; i < n; ++i) { + if (x[i] <= -10.0f) { + y[i] = 0.0f; + } else if (x[i] >= 10.0f) { + y[i] = x[i]; + } else { + ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); + memcpy(&t, &fp16, sizeof(uint16_t)); + y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_f16[t]); + } + } +} +#else +inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { + for (int i = 0; i < n; ++i) { + y[i] = ggml_gelu_f32(x[i]); + } +} +#endif + +inline static float ggml_gelu_quick_f32(float x) { + return x*(1.0f/(1.0f+expf(GELU_QUICK_COEF*x))); +} + +//inline static void ggml_vec_gelu_quick_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { +// const uint16_t * i16 = (const uint16_t *) x; +// for (int i = 0; i < n; ++i) { +// y[i] = ggml_table_gelu_quick_f16[i16[i]]; +// } +//} + +#ifdef GGML_GELU_QUICK_FP16 +inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) { + uint16_t t; + for (int i = 0; i < n; ++i) { + ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); + memcpy(&t, &fp16, sizeof(uint16_t)); + y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_quick_f16[t]); + } +} +#else +inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) { + for (int i = 0; i < n; ++i) { + y[i] = ggml_gelu_quick_f32(x[i]); + } +} +#endif + +// Sigmoid Linear Unit (SiLU) function +inline static float ggml_silu_f32(float x) { + return x/(1.0f + expf(-x)); +} + +#if __FINITE_MATH_ONLY__ +#error "some routines in ggml.c require non-finite math arithmetics -- pass -fno-finite-math-only to the compiler to fix" +#error "ref: https://github.com/ggerganov/llama.cpp/pull/7154#issuecomment-2143844461" +#endif + +#if defined(__ARM_NEON) && defined(__aarch64__) + +// adapted from arm limited optimized routine +// the maximum error is 1.45358 plus 0.5 ulps +// numbers above 88.38 will flush to infinity +// numbers beneath -103.97 will flush to zero +inline static float32x4_t ggml_v_expf(float32x4_t x) { + const float32x4_t r = vdupq_n_f32(0x1.8p23f); + const float32x4_t z = vfmaq_f32(r, x, vdupq_n_f32(0x1.715476p+0f)); + const float32x4_t n = vsubq_f32(z, r); + const float32x4_t b = vfmsq_f32(vfmsq_f32(x, n, vdupq_n_f32(0x1.62e4p-1f)), n, + vdupq_n_f32(0x1.7f7d1cp-20f)); + const uint32x4_t e = vshlq_n_u32(vreinterpretq_u32_f32(z), 23); + const float32x4_t k = vreinterpretq_f32_u32(vaddq_u32(e, vreinterpretq_u32_f32(vdupq_n_f32(1)))); + const uint32x4_t c = vcagtq_f32(n, vdupq_n_f32(126)); + const float32x4_t u = vmulq_f32(b, b); + const float32x4_t j = vfmaq_f32( + vmulq_f32(vdupq_n_f32(0x1.ffffecp-1f), b), + vfmaq_f32(vfmaq_f32(vdupq_n_f32(0x1.fffdb6p-2f), vdupq_n_f32(0x1.555e66p-3f), b), + vfmaq_f32(vdupq_n_f32(0x1.573e2ep-5f), vdupq_n_f32(0x1.0e4020p-7f), b), u), u); + if (!vpaddd_u64(vreinterpretq_u64_u32(c))) + return vfmaq_f32(k, j, k); + const uint32x4_t d = vandq_u32(vclezq_f32(n), vdupq_n_u32(0x82000000)); + const float32x4_t s1 = vreinterpretq_f32_u32(vaddq_u32(d, vdupq_n_u32(0x7f000000))); + const float32x4_t s2 = vreinterpretq_f32_u32(vsubq_u32(e, d)); + return vbslq_f32(vcagtq_f32(n, vdupq_n_f32(192)), vmulq_f32(s1, s1), + vbslq_f32(c, vmulq_f32(vfmaq_f32(s2, s2, j), s1), vfmaq_f32(k, k, j))); +} + +// computes silu x/(1+exp(-x)) in single precision vector +inline static float32x4_t ggml_v_silu(float32x4_t x) { + const float32x4_t one = vdupq_n_f32(1.0f); + const float32x4_t zero = vdupq_n_f32(0.0f); + const float32x4_t neg_x = vsubq_f32(zero, x); + const float32x4_t exp_neg_x = ggml_v_expf(neg_x); + const float32x4_t one_plus_exp_neg_x = vaddq_f32(one, exp_neg_x); + return vdivq_f32(x, one_plus_exp_neg_x); +} + +#elif defined(__AVX512F__) && defined(__AVX512DQ__) + +// adapted from arm limited optimized routine +// the maximum error is 1.45358 plus 0.5 ulps +// numbers above 88.38 will flush to infinity +// numbers beneath -103.97 will flush to zero +inline static __m512 ggml_v_expf(__m512 x) { + const __m512 r = _mm512_set1_ps(0x1.8p23f); + const __m512 z = _mm512_fmadd_ps(x, _mm512_set1_ps(0x1.715476p+0f), r); + const __m512 n = _mm512_sub_ps(z, r); + const __m512 b = + _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.7f7d1cp-20f), + _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.62e4p-1f), x)); + const __mmask16 d = + _mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(192), _CMP_GT_OQ); + const __m512 u = _mm512_mul_ps(b, b); + const __m512 j = _mm512_fmadd_ps( + _mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_set1_ps(0x1.0e4020p-7f), b, + _mm512_set1_ps(0x1.573e2ep-5f)), + u, + _mm512_fmadd_ps(_mm512_set1_ps(0x1.555e66p-3f), b, + _mm512_set1_ps(0x1.fffdb6p-2f))), + u, + _mm512_fmadd_ps(_mm512_set1_ps(0x1.ffffecp-1f), b, _mm512_set1_ps(1.0F))); + const __m512 res = _mm512_scalef_ps(j, n); + if (_mm512_kortestz(d, d)) + return res; + const __m512 zero = _mm512_setzero_ps(); + const __m512 alt = _mm512_mask_blend_ps( + _mm512_cmp_ps_mask(n, zero, _CMP_LE_OQ), _mm512_set1_ps(INFINITY), zero); + return _mm512_mask_blend_ps(d, res, alt); +} + +// computes silu x/(1+exp(-x)) in single precision vector +inline static __m512 ggml_v_silu(__m512 x) { + const __m512 one = _mm512_set1_ps(1); + const __m512 zero = _mm512_setzero_ps(); + const __m512 neg_x = _mm512_sub_ps(zero, x); + const __m512 exp_neg_x = ggml_v_expf(neg_x); + const __m512 one_plus_exp_neg_x = _mm512_add_ps(one, exp_neg_x); + return _mm512_div_ps(x, one_plus_exp_neg_x); +} + +#elif defined(__AVX2__) && defined(__FMA__) + +// adapted from arm limited optimized routine +// the maximum error is 1.45358 plus 0.5 ulps +// numbers above 88.38 will flush to infinity +// numbers beneath -103.97 will flush to zero +inline static __m256 ggml_v_expf(__m256 x) { + const __m256 r = _mm256_set1_ps(0x1.8p23f); + const __m256 z = _mm256_fmadd_ps(x, _mm256_set1_ps(0x1.715476p+0f), r); + const __m256 n = _mm256_sub_ps(z, r); + const __m256 b = _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.7f7d1cp-20f), + _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.62e4p-1f), x)); + const __m256i e = _mm256_slli_epi32(_mm256_castps_si256(z), 23); + const __m256 k = _mm256_castsi256_ps( + _mm256_add_epi32(e, _mm256_castps_si256(_mm256_set1_ps(1)))); + const __m256i c = _mm256_castps_si256( + _mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), + _mm256_set1_ps(126), _CMP_GT_OQ)); + const __m256 u = _mm256_mul_ps(b, b); + const __m256 j = _mm256_fmadd_ps(_mm256_fmadd_ps(_mm256_fmadd_ps(_mm256_set1_ps(0x1.0e4020p-7f), b, + _mm256_set1_ps(0x1.573e2ep-5f)), u, + _mm256_fmadd_ps(_mm256_set1_ps(0x1.555e66p-3f), b, + _mm256_set1_ps(0x1.fffdb6p-2f))), + u, _mm256_mul_ps(_mm256_set1_ps(0x1.ffffecp-1f), b)); + if (!_mm256_movemask_ps(_mm256_castsi256_ps(c))) + return _mm256_fmadd_ps(j, k, k); + const __m256i g = _mm256_and_si256( + _mm256_castps_si256(_mm256_cmp_ps(n, _mm256_setzero_ps(), _CMP_LE_OQ)), + _mm256_set1_epi32(0x82000000u)); + const __m256 s1 = + _mm256_castsi256_ps(_mm256_add_epi32(g, _mm256_set1_epi32(0x7f000000u))); + const __m256 s2 = _mm256_castsi256_ps(_mm256_sub_epi32(e, g)); + const __m256i d = _mm256_castps_si256( + _mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), + _mm256_set1_ps(192), _CMP_GT_OQ)); + return _mm256_or_ps( + _mm256_and_ps(_mm256_castsi256_ps(d), _mm256_mul_ps(s1, s1)), + _mm256_andnot_ps( + _mm256_castsi256_ps(d), + _mm256_or_ps( + _mm256_and_ps(_mm256_castsi256_ps(c), + _mm256_mul_ps(_mm256_fmadd_ps(s2, j, s2), s1)), + _mm256_andnot_ps(_mm256_castsi256_ps(c), _mm256_fmadd_ps(k, j, k))))); +} + +// computes silu x/(1+exp(-x)) in single precision vector +inline static __m256 ggml_v_silu(__m256 x) { + const __m256 one = _mm256_set1_ps(1); + const __m256 zero = _mm256_setzero_ps(); + const __m256 neg_x = _mm256_sub_ps(zero, x); + const __m256 exp_neg_x = ggml_v_expf(neg_x); + const __m256 one_plus_exp_neg_x = _mm256_add_ps(one, exp_neg_x); + return _mm256_div_ps(x, one_plus_exp_neg_x); +} + +#elif defined(__SSE2__) // __AVX2__ / __ARM_NEON + +#if defined(__FMA__) +#define MADD128(x, y, z) _mm_fmadd_ps(x, y, z) +#define NMADD128(x, y, z) _mm_fnmadd_ps(x, y, z) +#else +#define MADD128(x, y, z) _mm_add_ps(_mm_mul_ps(x, y), z) +#define NMADD128(x, y, z) _mm_sub_ps(z, _mm_mul_ps(x, y)) +#endif + +// adapted from arm limited optimized routine +// the maximum error is 1.45358 plus 0.5 ulps +// numbers above 88.38 will flush to infinity +// numbers beneath -103.97 will flush to zero +inline static __m128 ggml_v_expf(__m128 x) { + const __m128 r = _mm_set1_ps(0x1.8p23f); + const __m128 z = MADD128(x, _mm_set1_ps(0x1.715476p+0f), r); + const __m128 n = _mm_sub_ps(z, r); + const __m128 b = + NMADD128(n, _mm_set1_ps(0x1.7f7d1cp-20f), NMADD128(n, _mm_set1_ps(0x1.62e4p-1f), x)); + const __m128i e = _mm_slli_epi32(_mm_castps_si128(z), 23); + const __m128 k = _mm_castsi128_ps(_mm_add_epi32(e, _mm_castps_si128(_mm_set1_ps(1)))); + const __m128i c = + _mm_castps_si128(_mm_cmpgt_ps(_mm_andnot_ps(_mm_set1_ps(-0.f), n), _mm_set1_ps(126))); + const __m128 u = _mm_mul_ps(b, b); + const __m128 j = + MADD128(MADD128(MADD128(_mm_set1_ps(0x1.0e4020p-7f), b, _mm_set1_ps(0x1.573e2ep-5f)), u, + MADD128(_mm_set1_ps(0x1.555e66p-3f), b, _mm_set1_ps(0x1.fffdb6p-2f))), + u, _mm_mul_ps(_mm_set1_ps(0x1.ffffecp-1f), b)); + if (!_mm_movemask_epi8(c)) + return MADD128(j, k, k); + const __m128i g = _mm_and_si128(_mm_castps_si128(_mm_cmple_ps(n, _mm_setzero_ps())), + _mm_set1_epi32(0x82000000u)); + const __m128 s1 = _mm_castsi128_ps(_mm_add_epi32(g, _mm_set1_epi32(0x7f000000u))); + const __m128 s2 = _mm_castsi128_ps(_mm_sub_epi32(e, g)); + const __m128i d = + _mm_castps_si128(_mm_cmpgt_ps(_mm_andnot_ps(_mm_set1_ps(-0.f), n), _mm_set1_ps(192))); + return _mm_or_ps( + _mm_and_ps(_mm_castsi128_ps(d), _mm_mul_ps(s1, s1)), + _mm_andnot_ps(_mm_castsi128_ps(d), + _mm_or_ps(_mm_and_ps(_mm_castsi128_ps(c), _mm_mul_ps(MADD128(s2, j, s2), s1)), + _mm_andnot_ps(_mm_castsi128_ps(c), MADD128(k, j, k))))); +} + +// computes silu x/(1+exp(-x)) in single precision vector +inline static __m128 ggml_v_silu(__m128 x) { + const __m128 one = _mm_set1_ps(1); + const __m128 zero = _mm_setzero_ps(); + const __m128 neg_x = _mm_sub_ps(zero, x); + const __m128 exp_neg_x = ggml_v_expf(neg_x); + const __m128 one_plus_exp_neg_x = _mm_add_ps(one, exp_neg_x); + return _mm_div_ps(x, one_plus_exp_neg_x); +} + +#endif // __ARM_NEON / __AVX2__ / __SSE2__ + +static void ggml_vec_silu_f32(const int n, float * y, const float * x) { + int i = 0; +#if defined(__AVX512F__) && defined(__AVX512DQ__) + for (; i + 15 < n; i += 16) { + _mm512_storeu_ps(y + i, ggml_v_silu(_mm512_loadu_ps(x + i))); + } +#elif defined(__AVX2__) && defined(__FMA__) + for (; i + 7 < n; i += 8) { + _mm256_storeu_ps(y + i, ggml_v_silu(_mm256_loadu_ps(x + i))); + } +#elif defined(__SSE2__) + for (; i + 3 < n; i += 4) { + _mm_storeu_ps(y + i, ggml_v_silu(_mm_loadu_ps(x + i))); + } +#elif defined(__ARM_NEON) && defined(__aarch64__) + for (; i + 3 < n; i += 4) { + vst1q_f32(y + i, ggml_v_silu(vld1q_f32(x + i))); + } +#endif + for (; i < n; ++i) { + y[i] = ggml_silu_f32(x[i]); + } +} + +static ggml_float ggml_vec_soft_max_f32(const int n, float * y, const float * x, float max) { + int i = 0; + ggml_float sum = 0; +#if defined(__AVX512F__) && defined(__AVX512DQ__) + for (; i + 15 < n; i += 16) { + __m512 val = ggml_v_expf(_mm512_sub_ps(_mm512_loadu_ps(x + i), + _mm512_set1_ps(max))); + _mm512_storeu_ps(y + i, val); + sum += (ggml_float)_mm512_reduce_add_ps(val); + } +#elif defined(__AVX2__) && defined(__FMA__) + for (; i + 7 < n; i += 8) { + __m256 val = ggml_v_expf(_mm256_sub_ps(_mm256_loadu_ps(x + i), + _mm256_set1_ps(max))); + _mm256_storeu_ps(y + i, val); + __m128 val2 = _mm_add_ps(_mm256_extractf128_ps(val, 1), + _mm256_castps256_ps128(val)); + val2 = _mm_add_ps(val2, _mm_movehl_ps(val2, val2)); + val2 = _mm_add_ss(val2, _mm_movehdup_ps(val2)); + sum += (ggml_float)_mm_cvtss_f32(val2); + } +#elif defined(__SSE2__) + for (; i + 3 < n; i += 4) { + __m128 val = ggml_v_expf(_mm_sub_ps(_mm_loadu_ps(x + i), + _mm_set1_ps(max))); + _mm_storeu_ps(y + i, val); +#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) + val = _mm_add_ps(val, _mm_movehl_ps(val, val)); + val = _mm_add_ss(val, _mm_movehdup_ps(val)); +#else + __m128 tmp = _mm_shuffle_ps(val, val, _MM_SHUFFLE(2, 3, 0, 1)); + val = _mm_add_ps(val, tmp); + tmp = _mm_movehl_ps(tmp, val); + val = _mm_add_ss(val, tmp); +#endif + sum += (ggml_float)_mm_cvtss_f32(val); + } +#elif defined(__ARM_NEON) && defined(__aarch64__) + for (; i + 3 < n; i += 4) { + float32x4_t val = ggml_v_expf(vsubq_f32(vld1q_f32(x + i), + vdupq_n_f32(max))); + vst1q_f32(y + i, val); + sum += (ggml_float)vaddvq_f32(val); + } +#endif + for (; i < n; ++i) { + float val = expf(x[i] - max); + sum += (ggml_float)val; + y[i] = val; + } + return sum; +} + +static ggml_float ggml_vec_log_soft_max_f32(const int n, float * y, const float * x, float max) { + // log(soft_max) = log(soft_max_i / soft_max_sum) = log(soft_max_i) - log(soft_max_sum) = (logit_i - max) - log(soft_max_i) + + int i = 0; + ggml_float sum = 0; + for (; i < n; ++i) { + float val = x[i] - max; + y[i] = val; + sum += (ggml_float)expf(val); + } + return sum = (ggml_float)logf(sum); +} + +inline static float ggml_silu_backward_f32(float x, float dy) { + const float s = 1.0f/(1.0f + expf(-x)); + return dy*s*(1.0f + x*(1.0f - s)); +} + +inline static void ggml_vec_silu_backward_f32(const int n, float * dx, const float * x, const float * dy) { + for (int i = 0; i < n; ++i) { + dx[i] = ggml_silu_backward_f32(x[i], dy[i]); + } +} + +inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) { +#ifndef GGML_USE_ACCELERATE + ggml_float sum = 0.0; + for (int i = 0; i < n; ++i) { + sum += (ggml_float)x[i]; + } + *s = sum; +#else + vDSP_sve(x, 1, s, n); +#endif +} + +inline static void ggml_vec_sum_f32_ggf(const int n, ggml_float * s, const float * x) { + ggml_float sum = 0.0; + for (int i = 0; i < n; ++i) { + sum += (ggml_float)x[i]; + } + *s = sum; +} + +inline static void ggml_vec_sum_f16_ggf(const int n, float * s, const ggml_fp16_t * x) { + float sum = 0.0f; + for (int i = 0; i < n; ++i) { + sum += GGML_FP16_TO_FP32(x[i]); + } + *s = sum; +} + +inline static void ggml_vec_sum_bf16_ggf(const int n, float * s, const ggml_bf16_t * x) { + float sum = 0.0f; + for (int i = 0; i < n; ++i) { + sum += GGML_BF16_TO_FP32(x[i]); + } + *s = sum; +} + +inline static void ggml_vec_max_f32(const int n, float * s, const float * x) { +#ifndef GGML_USE_ACCELERATE + float max = -INFINITY; + for (int i = 0; i < n; ++i) { + max = MAX(max, x[i]); + } + *s = max; +#else + vDSP_maxv(x, 1, s, n); +#endif +} + +inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x) { + ggml_vec_norm_f32(n, s, x); + *s = 1.f/(*s); +} + +inline static void ggml_vec_argmax_f32(const int n, int * s, const float * x) { + float max = -INFINITY; + int idx = 0; + for (int i = 0; i < n; ++i) { + max = MAX(max, x[i]); + if (max == x[i]) { idx = i; } + } + *s = idx; +} + +// Helpers for polling loops +#if defined(__aarch64__) && ( defined(__clang__) || defined(__GNUC__) ) +static inline void ggml_thread_cpu_relax(void) { + __asm__ volatile("yield" ::: "memory"); +} +#elif defined(__x86_64__) +static inline void ggml_thread_cpu_relax(void) { + _mm_pause(); +} +#else +static inline void ggml_thread_cpu_relax(void) {;} +#endif + +// +// NUMA support +// + +#define GGML_NUMA_MAX_NODES 8 +#define GGML_NUMA_MAX_CPUS 512 + +struct ggml_numa_node { + uint32_t cpus[GGML_NUMA_MAX_CPUS]; // hardware threads on this node + uint32_t n_cpus; +}; + +struct ggml_numa_nodes { + enum ggml_numa_strategy numa_strategy; + struct ggml_numa_node nodes[GGML_NUMA_MAX_NODES]; + uint32_t n_nodes; + uint32_t total_cpus; // hardware threads on system + uint32_t current_node; // node on which main process is execting +#if defined(__gnu_linux__) + cpu_set_t cpuset; // cpuset from numactl +#else + uint32_t cpuset; // no NUMA support outside of Linux at this time. Use a portable datatype +#endif +}; + +// +// ggml state +// + +struct ggml_state { + struct ggml_numa_nodes numa; +}; + +static struct ggml_state g_state = {0}; + +void ggml_barrier(struct ggml_threadpool * tp) { + int n_threads = atomic_load_explicit(&tp->n_threads_cur, memory_order_relaxed); + if (n_threads == 1) { + return; + } + +#ifdef GGML_USE_OPENMP + #pragma omp barrier +#else + int n_passed = atomic_load_explicit(&tp->n_barrier_passed, memory_order_relaxed); + + // enter barrier (full seq-cst fence) + int n_barrier = atomic_fetch_add_explicit(&tp->n_barrier, 1, memory_order_seq_cst); + + if (n_barrier == (n_threads - 1)) { + // last thread + atomic_store_explicit(&tp->n_barrier, 0, memory_order_relaxed); + + // exit barrier (fill seq-cst fence) + atomic_fetch_add_explicit(&tp->n_barrier_passed, 1, memory_order_seq_cst); + return; + } + + // wait for other threads + while (atomic_load_explicit(&tp->n_barrier_passed, memory_order_relaxed) == n_passed) { + ggml_thread_cpu_relax(); + } + + // exit barrier (full seq-cst fence) + // TSAN doesn't support standalone fence yet, we use a dummy read-modify-write instead + #ifdef GGML_TSAN_ENABLED + atomic_fetch_add_explicit(&tp->n_barrier_passed, 0, memory_order_seq_cst); + #else + atomic_thread_fence(memory_order_seq_cst); + #endif +#endif +} + +#if defined(__gnu_linux__) +static cpu_set_t ggml_get_numa_affinity(void) { + cpu_set_t cpuset; + pthread_t thread; + thread = pthread_self(); + CPU_ZERO(&cpuset); + pthread_getaffinity_np(thread, sizeof(cpu_set_t), &cpuset); + return cpuset; +} +#else +static uint32_t ggml_get_numa_affinity(void) { + return 0; // no NUMA support +} +#endif + +void ggml_numa_init(enum ggml_numa_strategy numa_flag) { + if (g_state.numa.n_nodes > 0) { + fprintf(stderr, "ggml_numa_init: NUMA already initialized\n"); + + return; + } + +#if defined(__gnu_linux__) + struct stat st; + char path[256]; + int rv; + + // set numa scheme + g_state.numa.numa_strategy = numa_flag; + + GGML_PRINT_DEBUG("numa strategy %u\n",g_state.numa.numa_strategy); + + g_state.numa.cpuset = ggml_get_numa_affinity(); + + // enumerate nodes + while (g_state.numa.n_nodes < GGML_NUMA_MAX_NODES) { + rv = snprintf(path, sizeof(path), "/sys/devices/system/node/node%u", g_state.numa.n_nodes); + GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path)); + if (stat(path, &st) != 0) { break; } + ++g_state.numa.n_nodes; + } + + // enumerate CPUs + while (g_state.numa.total_cpus < GGML_NUMA_MAX_CPUS) { + rv = snprintf(path, sizeof(path), "/sys/devices/system/cpu/cpu%u", g_state.numa.total_cpus); + GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path)); + if (stat(path, &st) != 0) { break; } + ++g_state.numa.total_cpus; + } + + GGML_PRINT_DEBUG("found %u numa nodes, %u CPUs\n", g_state.numa.n_nodes, g_state.numa.total_cpus); + + // figure out which node we're on + uint current_cpu; + int getcpu_ret = 0; +#if __GLIBC__ > 2 || (__GLIBC__ == 2 && __GLIBC_MINOR__ > 33) || defined(__COSMOPOLITAN__) + getcpu_ret = getcpu(¤t_cpu, &g_state.numa.current_node); +#else + // old glibc doesn't have a wrapper for this call. Fall back on direct syscall +# if !defined(SYS_getcpu) && defined(SYS_get_cpu) +# define SYS_getcpu SYS_get_cpu // some older glibc versions use this name +# endif + getcpu_ret = syscall(SYS_getcpu, ¤t_cpu, &g_state.numa.current_node); +#endif + + if (g_state.numa.n_nodes < 1 || g_state.numa.total_cpus < 1 || getcpu_ret != 0) { + g_state.numa.n_nodes = 0; + return; + } + + GGML_PRINT_DEBUG("found our process on numa node %u, CPU %u\n", g_state.numa.current_node, current_cpu); + + for (uint32_t n = 0; n < g_state.numa.n_nodes; ++n) { + struct ggml_numa_node * node = &g_state.numa.nodes[n]; + GGML_PRINT_DEBUG("CPUs on node %u:", n); + node->n_cpus = 0; + for (uint32_t c = 0; c < g_state.numa.total_cpus; ++c) { + rv = snprintf(path, sizeof(path), "/sys/devices/system/node/node%u/cpu%u", n, c); + GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path)); + if (stat(path, &st) == 0) { + node->cpus[node->n_cpus++] = c; + GGML_PRINT_DEBUG(" %u", c); + } + } + GGML_PRINT_DEBUG("\n"); + } + + if (ggml_is_numa()) { + FILE *fptr = fopen("/proc/sys/kernel/numa_balancing", "r"); + if (fptr != NULL) { + char buf[42]; + if (fgets(buf, sizeof(buf), fptr) && strncmp(buf, "0\n", sizeof(buf)) != 0) { + GGML_LOG_WARN("/proc/sys/kernel/numa_balancing is enabled, this has been observed to impair performance\n"); + } + fclose(fptr); + } + } +#else + UNUSED(numa_flag); + // TODO +#endif +} + +bool ggml_is_numa(void) { + return g_state.numa.n_nodes > 1; +} + +#if defined(__ARM_ARCH) + +#if defined(__linux__) && defined(__aarch64__) +#include +#elif defined(__APPLE__) +#include +#endif + +#if !defined(HWCAP2_I8MM) +#define HWCAP2_I8MM (1 << 13) +#endif + +static void ggml_init_arm_arch_features(void) { +#if defined(__linux__) && defined(__aarch64__) + uint32_t hwcap = getauxval(AT_HWCAP); + uint32_t hwcap2 = getauxval(AT_HWCAP2); + + ggml_arm_arch_features.has_neon = !!(hwcap & HWCAP_ASIMD); + ggml_arm_arch_features.has_dotprod = !!(hwcap && HWCAP_ASIMDDP); + ggml_arm_arch_features.has_i8mm = !!(hwcap2 & HWCAP2_I8MM); + ggml_arm_arch_features.has_sve = !!(hwcap & HWCAP_SVE); + +#if defined(__ARM_FEATURE_SVE) + ggml_arm_arch_features.sve_cnt = PR_SVE_VL_LEN_MASK & prctl(PR_SVE_GET_VL); +#endif +#elif defined(__APPLE__) + int oldp = 0; + size_t size = sizeof(oldp); + if (sysctlbyname("hw.optional.AdvSIMD", &oldp, &size, NULL, 0) != 0) { + oldp = 0; + } + ggml_arm_arch_features.has_neon = oldp; + + if (sysctlbyname("hw.optional.arm.FEAT_DotProd", &oldp, &size, NULL, 0) != 0) { + oldp = 0; + } + ggml_arm_arch_features.has_dotprod = oldp; + + if (sysctlbyname("hw.optional.arm.FEAT_I8MM", &oldp, &size, NULL, 0) != 0) { + oldp = 0; + } + ggml_arm_arch_features.has_i8mm = oldp; + + ggml_arm_arch_features.has_sve = 0; + ggml_arm_arch_features.sve_cnt = 0; +#else +// Run-time CPU feature detection not implemented for this platform, fallback to compile time +#if defined(__ARM_NEON) + ggml_arm_arch_features.has_neon = 1; +#else + ggml_arm_arch_features.has_neon = 0; +#endif + +#if defined(__ARM_FEATURE_MATMUL_INT8) + ggml_arm_arch_features.has_i8mm = 1; +#else + ggml_arm_arch_features.has_i8mm = 0; +#endif + +#if defined(__ARM_FEATURE_SVE) + ggml_arm_arch_features.has_sve = 1; + ggml_arm_arch_features.sve_cnt = 16; +#else + ggml_arm_arch_features.has_sve = 0; + ggml_arm_arch_features.sve_cnt = 0; +#endif +#endif +} +#endif + +struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value) { + GGML_ASSERT(!ggml_get_no_alloc(ctx)); + + struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 1); + + ggml_set_i32(result, value); + + return result; +} + +struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value) { + GGML_ASSERT(!ggml_get_no_alloc(ctx)); + + struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1); + + ggml_set_f32(result, value); + + return result; +} + +struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value) { + const int n = ggml_nrows(tensor); + const int nc = tensor->ne[0]; + const size_t n1 = tensor->nb[1]; + + char * const data = tensor->data; + + switch (tensor->type) { + case GGML_TYPE_I8: + { + assert(tensor->nb[0] == sizeof(int8_t)); + for (int i = 0; i < n; i++) { + ggml_vec_set_i8(nc, (int8_t *)(data + i*n1), value); + } + } break; + case GGML_TYPE_I16: + { + assert(tensor->nb[0] == sizeof(int16_t)); + for (int i = 0; i < n; i++) { + ggml_vec_set_i16(nc, (int16_t *)(data + i*n1), value); + } + } break; + case GGML_TYPE_I32: + { + assert(tensor->nb[0] == sizeof(int32_t)); + for (int i = 0; i < n; i++) { + ggml_vec_set_i32(nc, (int32_t *)(data + i*n1), value); + } + } break; + case GGML_TYPE_F16: + { + assert(tensor->nb[0] == sizeof(ggml_fp16_t)); + for (int i = 0; i < n; i++) { + ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value)); + } + } break; + case GGML_TYPE_BF16: + { + assert(tensor->nb[0] == sizeof(ggml_fp16_t)); + for (int i = 0; i < n; i++) { + ggml_vec_set_bf16(nc, (ggml_bf16_t *)(data + i*n1), GGML_FP32_TO_BF16(value)); + } + } break; + case GGML_TYPE_F32: + { + assert(tensor->nb[0] == sizeof(float)); + for (int i = 0; i < n; i++) { + ggml_vec_set_f32(nc, (float *)(data + i*n1), value); + } + } break; + default: + { + GGML_ABORT("fatal error"); + } + } + + return tensor; +} + +struct ggml_tensor * ggml_set_f32(struct ggml_tensor * tensor, float value) { + const int n = ggml_nrows(tensor); + const int nc = tensor->ne[0]; + const size_t n1 = tensor->nb[1]; + + char * const data = tensor->data; + + switch (tensor->type) { + case GGML_TYPE_I8: + { + assert(tensor->nb[0] == sizeof(int8_t)); + for (int i = 0; i < n; i++) { + ggml_vec_set_i8(nc, (int8_t *)(data + i*n1), value); + } + } break; + case GGML_TYPE_I16: + { + assert(tensor->nb[0] == sizeof(int16_t)); + for (int i = 0; i < n; i++) { + ggml_vec_set_i16(nc, (int16_t *)(data + i*n1), value); + } + } break; + case GGML_TYPE_I32: + { + assert(tensor->nb[0] == sizeof(int32_t)); + for (int i = 0; i < n; i++) { + ggml_vec_set_i32(nc, (int32_t *)(data + i*n1), value); + } + } break; + case GGML_TYPE_F16: + { + assert(tensor->nb[0] == sizeof(ggml_fp16_t)); + for (int i = 0; i < n; i++) { + ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value)); + } + } break; + case GGML_TYPE_BF16: + { + assert(tensor->nb[0] == sizeof(ggml_bf16_t)); + for (int i = 0; i < n; i++) { + ggml_vec_set_bf16(nc, (ggml_bf16_t *)(data + i*n1), GGML_FP32_TO_BF16(value)); + } + } break; + case GGML_TYPE_F32: + { + assert(tensor->nb[0] == sizeof(float)); + for (int i = 0; i < n; i++) { + ggml_vec_set_f32(nc, (float *)(data + i*n1), value); + } + } break; + default: + { + GGML_ABORT("fatal error"); + } + } + + return tensor; +} + +int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i) { + if (!ggml_is_contiguous(tensor)) { + int64_t id[4] = { 0, 0, 0, 0 }; + ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]); + return ggml_get_i32_nd(tensor, id[0], id[1], id[2], id[3]); + } + switch (tensor->type) { + case GGML_TYPE_I8: + { + GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); + return ((int8_t *)(tensor->data))[i]; + } + case GGML_TYPE_I16: + { + GGML_ASSERT(tensor->nb[0] == sizeof(int16_t)); + return ((int16_t *)(tensor->data))[i]; + } + case GGML_TYPE_I32: + { + GGML_ASSERT(tensor->nb[0] == sizeof(int32_t)); + return ((int32_t *)(tensor->data))[i]; + } + case GGML_TYPE_F16: + { + GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); + return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]); + } + case GGML_TYPE_BF16: + { + GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t)); + return GGML_BF16_TO_FP32(((ggml_bf16_t *)(tensor->data))[i]); + } + case GGML_TYPE_F32: + { + GGML_ASSERT(tensor->nb[0] == sizeof(float)); + return ((float *)(tensor->data))[i]; + } + default: + { + GGML_ABORT("fatal error"); + } + } +} + +void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value) { + if (!ggml_is_contiguous(tensor)) { + int64_t id[4] = { 0, 0, 0, 0 }; + ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]); + ggml_set_i32_nd(tensor, id[0], id[1], id[2], id[3], value); + return; + } + switch (tensor->type) { + case GGML_TYPE_I8: + { + GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); + ((int8_t *)(tensor->data))[i] = value; + } break; + case GGML_TYPE_I16: + { + GGML_ASSERT(tensor->nb[0] == sizeof(int16_t)); + ((int16_t *)(tensor->data))[i] = value; + } break; + case GGML_TYPE_I32: + { + GGML_ASSERT(tensor->nb[0] == sizeof(int32_t)); + ((int32_t *)(tensor->data))[i] = value; + } break; + case GGML_TYPE_F16: + { + GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); + ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value); + } break; + case GGML_TYPE_BF16: + { + GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t)); + ((ggml_bf16_t *)(tensor->data))[i] = GGML_FP32_TO_BF16(value); + } break; + case GGML_TYPE_F32: + { + GGML_ASSERT(tensor->nb[0] == sizeof(float)); + ((float *)(tensor->data))[i] = value; + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +int32_t ggml_get_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3) { + void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; + switch (tensor->type) { + case GGML_TYPE_I8: + return ((int8_t *) data)[0]; + case GGML_TYPE_I16: + return ((int16_t *) data)[0]; + case GGML_TYPE_I32: + return ((int32_t *) data)[0]; + case GGML_TYPE_F16: + return GGML_FP16_TO_FP32(((ggml_fp16_t *) data)[0]); + case GGML_TYPE_BF16: + return GGML_BF16_TO_FP32(((ggml_bf16_t *) data)[0]); + case GGML_TYPE_F32: + return ((float *) data)[0]; + default: + GGML_ABORT("fatal error"); + } +} + +void ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, int32_t value) { + void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; + switch (tensor->type) { + case GGML_TYPE_I8: + { + ((int8_t *)(data))[0] = value; + } break; + case GGML_TYPE_I16: + { + ((int16_t *)(data))[0] = value; + } break; + case GGML_TYPE_I32: + { + ((int32_t *)(data))[0] = value; + } break; + case GGML_TYPE_F16: + { + ((ggml_fp16_t *)(data))[0] = GGML_FP32_TO_FP16(value); + } break; + case GGML_TYPE_BF16: + { + ((ggml_bf16_t *)(data))[0] = GGML_FP32_TO_BF16(value); + } break; + case GGML_TYPE_F32: + { + ((float *)(data))[0] = value; + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i) { + if (!ggml_is_contiguous(tensor)) { + int64_t id[4] = { 0, 0, 0, 0 }; + ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]); + return ggml_get_f32_nd(tensor, id[0], id[1], id[2], id[3]); + } + switch (tensor->type) { + case GGML_TYPE_I8: + { + return ((int8_t *)(tensor->data))[i]; + } + case GGML_TYPE_I16: + { + return ((int16_t *)(tensor->data))[i]; + } + case GGML_TYPE_I32: + { + return ((int32_t *)(tensor->data))[i]; + } + case GGML_TYPE_F16: + { + return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]); + } + case GGML_TYPE_BF16: + { + return GGML_BF16_TO_FP32(((ggml_bf16_t *)(tensor->data))[i]); + } + case GGML_TYPE_F32: + { + return ((float *)(tensor->data))[i]; + } + default: + { + GGML_ABORT("fatal error"); + } + } +} + +void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value) { + if (!ggml_is_contiguous(tensor)) { + int64_t id[4] = { 0, 0, 0, 0 }; + ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]); + ggml_set_f32_nd(tensor, id[0], id[1], id[2], id[3], value); + return; + } + switch (tensor->type) { + case GGML_TYPE_I8: + { + ((int8_t *)(tensor->data))[i] = value; + } break; + case GGML_TYPE_I16: + { + ((int16_t *)(tensor->data))[i] = value; + } break; + case GGML_TYPE_I32: + { + ((int32_t *)(tensor->data))[i] = value; + } break; + case GGML_TYPE_F16: + { + ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value); + } break; + case GGML_TYPE_BF16: + { + ((ggml_bf16_t *)(tensor->data))[i] = GGML_FP32_TO_BF16(value); + } break; + case GGML_TYPE_F32: + { + ((float *)(tensor->data))[i] = value; + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +float ggml_get_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3) { + void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; + switch (tensor->type) { + case GGML_TYPE_I8: + return ((int8_t *) data)[0]; + case GGML_TYPE_I16: + return ((int16_t *) data)[0]; + case GGML_TYPE_I32: + return ((int32_t *) data)[0]; + case GGML_TYPE_F16: + return GGML_FP16_TO_FP32(((ggml_fp16_t *) data)[0]); + case GGML_TYPE_BF16: + return GGML_BF16_TO_FP32(((ggml_bf16_t *) data)[0]); + case GGML_TYPE_F32: + return ((float *) data)[0]; + default: + GGML_ABORT("fatal error"); + } +} + +void ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, float value) { + void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; + switch (tensor->type) { + case GGML_TYPE_I8: + { + ((int8_t *)(data))[0] = value; + } break; + case GGML_TYPE_I16: + { + ((int16_t *)(data))[0] = value; + } break; + case GGML_TYPE_I32: + { + ((int32_t *)(data))[0] = value; + } break; + case GGML_TYPE_F16: + { + ((ggml_fp16_t *)(data))[0] = GGML_FP32_TO_FP16(value); + } break; + case GGML_TYPE_BF16: + { + ((ggml_bf16_t *)(data))[0] = GGML_FP32_TO_BF16(value); + } break; + case GGML_TYPE_F32: + { + ((float *)(data))[0] = value; + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +//////////////////////////////////////////////////////////////////////////////// + +// ggml_compute_forward_dup + +static void ggml_compute_forward_dup_same_cont( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); + GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); + GGML_ASSERT(src0->type == dst->type); + + const size_t nb0 = ggml_type_size(src0->type); + + const int ith = params->ith; // thread index + const int nth = params->nth; // number of threads + + // parallelize by elements + const int ne = ggml_nelements(dst); + const int dr = (ne + nth - 1) / nth; + const int ie0 = dr * ith; + const int ie1 = MIN(ie0 + dr, ne); + + if (ie0 < ie1) { + memcpy( + ((char *) dst->data + ie0*nb0), + ((char *) src0->data + ie0*nb0), + (ie1 - ie0) * nb0); + } +} + +static void ggml_compute_forward_dup_f16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); + + GGML_TENSOR_UNARY_OP_LOCALS + + const int ith = params->ith; // thread index + const int nth = params->nth; // number of threads + + // parallelize by rows + const int nr = ne01; + // number of rows per thread + const int dr = (nr + nth - 1) / nth; + // row range for this thread + const int ir0 = dr * ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (src0->type == dst->type && + ne00 == ne0 && + nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { + // copy by rows + const size_t rs = ne00*nb00; + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = ir0; i01 < ir1; i01++) { + memcpy( + ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), + ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), + rs); + } + } + } + return; + } + + // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy + + if (ggml_is_contiguous(dst)) { + if (nb00 == sizeof(ggml_fp16_t)) { + if (dst->type == GGML_TYPE_F16) { + size_t id = 0; + const size_t rs = ne00 * nb00; + char * dst_ptr = (char *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; + memcpy(dst_ptr + id, src0_ptr, rs); + id += rs; + } + id += rs * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_F32) { + size_t id = 0; + float * dst_ptr = (float *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + for (int i00 = 0; i00 < ne00; i00++) { + dst_ptr[id] = GGML_FP16_TO_FP32(src0_ptr[i00]); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (ggml_get_type_traits_cpu(dst->type)->from_float) { + ggml_from_float_t const quantize_row_q = ggml_get_type_traits_cpu(dst->type)->from_float; + float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; + + size_t id = 0; + size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); + char * dst_ptr = (char *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + + for (int i00 = 0; i00 < ne00; i00++) { + src0_f32[i00] = GGML_FP16_TO_FP32(src0_ptr[i00]); + } + + quantize_row_q(src0_f32, dst_ptr + id, ne00); + id += rs; + } + id += rs * (ne01 - ir1); + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } + } else { + //printf("%s: this is not optimal - fix me\n", __func__); + + if (dst->type == GGML_TYPE_F32) { + size_t id = 0; + float * dst_ptr = (float *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_F16) { + size_t id = 0; + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = *src0_ptr; + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } + } + return; + } + + // dst counters + int64_t i10 = 0; + int64_t i11 = 0; + int64_t i12 = 0; + int64_t i13 = 0; + + if (dst->type == GGML_TYPE_F16) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + memcpy(dst_ptr, src0_ptr, sizeof(ggml_fp16_t)); + + if (++i10 == ne00) { + i10 = 0; + if (++i11 == ne01) { + i11 = 0; + if (++i12 == ne02) { + i12 = 0; + if (++i13 == ne03) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else if (dst->type == GGML_TYPE_F32) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + *(float *) dst_ptr = GGML_FP16_TO_FP32(*(const ggml_fp16_t *) src0_ptr); + + if (++i10 == ne0) { + i10 = 0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } +} + +static void ggml_compute_forward_dup_bf16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); + + GGML_TENSOR_UNARY_OP_LOCALS + + const int ith = params->ith; // thread index + const int nth = params->nth; // number of threads + + // parallelize by rows + const int nr = ne01; + // number of rows per thread + const int dr = (nr + nth - 1) / nth; + // row range for this thread + const int ir0 = dr * ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (src0->type == dst->type && + ne00 == ne0 && + nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { + // copy by rows + const size_t rs = ne00*nb00; + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = ir0; i01 < ir1; i01++) { + memcpy( + ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), + ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), + rs); + } + } + } + return; + } + + // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy + + if (ggml_is_contiguous(dst)) { + if (nb00 == sizeof(ggml_bf16_t)) { + if (dst->type == GGML_TYPE_BF16) { + size_t id = 0; + const size_t rs = ne00 * nb00; + char * dst_ptr = (char *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; + memcpy(dst_ptr + id, src0_ptr, rs); + id += rs; + } + id += rs * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_F16) { + size_t id = 0; + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + for (int i00 = 0; i00 < ne00; i00++) { + dst_ptr[id] = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(src0_ptr[i00])); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_F32) { + size_t id = 0; + float * dst_ptr = (float *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + for (int i00 = 0; i00 < ne00; i00++) { + dst_ptr[id] = GGML_BF16_TO_FP32(src0_ptr[i00]); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (ggml_get_type_traits_cpu(dst->type)->from_float) { + ggml_from_float_t const quantize_row_q = ggml_get_type_traits_cpu(dst->type)->from_float; + float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; + + size_t id = 0; + size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); + char * dst_ptr = (char *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + + for (int i00 = 0; i00 < ne00; i00++) { + src0_f32[i00] = GGML_BF16_TO_FP32(src0_ptr[i00]); + } + + quantize_row_q(src0_f32, dst_ptr + id, ne00); + id += rs; + } + id += rs * (ne01 - ir1); + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } + } else { + //printf("%s: this is not optimal - fix me\n", __func__); + + if (dst->type == GGML_TYPE_F32) { + size_t id = 0; + float * dst_ptr = (float *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = GGML_BF16_TO_FP32(*src0_ptr); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_BF16) { + size_t id = 0; + ggml_bf16_t * dst_ptr = (ggml_bf16_t *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = *src0_ptr; + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_F16) { + size_t id = 0; + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(*src0_ptr)); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } + } + return; + } + + // dst counters + int64_t i10 = 0; + int64_t i11 = 0; + int64_t i12 = 0; + int64_t i13 = 0; + + if (dst->type == GGML_TYPE_BF16) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + memcpy(dst_ptr, src0_ptr, sizeof(ggml_bf16_t)); + + if (++i10 == ne00) { + i10 = 0; + if (++i11 == ne01) { + i11 = 0; + if (++i12 == ne02) { + i12 = 0; + if (++i13 == ne03) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else if (dst->type == GGML_TYPE_F16) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + *(ggml_fp16_t *) dst_ptr = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(*(const ggml_bf16_t *) src0_ptr)); + + if (++i10 == ne0) { + i10 = 0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else if (dst->type == GGML_TYPE_F32) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + *(float *) dst_ptr = GGML_BF16_TO_FP32(*(const ggml_bf16_t *) src0_ptr); + + if (++i10 == ne0) { + i10 = 0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } +} + +static void ggml_compute_forward_dup_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); + + GGML_TENSOR_UNARY_OP_LOCALS + + const int ith = params->ith; // thread index + const int nth = params->nth; // number of threads + + // parallelize by rows + const int nr = ne01; + // number of rows per thread + const int dr = (nr + nth - 1) / nth; + // row range for this thread + const int ir0 = dr * ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (src0->type == dst->type && + ne00 == ne0 && + nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { + // copy by rows + const size_t rs = ne00*nb00; + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = ir0; i01 < ir1; i01++) { + memcpy( + ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), + ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), + rs); + } + } + } + return; + } + + if (ggml_is_contiguous(dst)) { + // TODO: simplify + if (nb00 == sizeof(float)) { + if (dst->type == GGML_TYPE_F32) { + size_t id = 0; + const size_t rs = ne00 * nb00; + char * dst_ptr = (char *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; + memcpy(dst_ptr + id, src0_ptr, rs); + id += rs; + } + id += rs * (ne01 - ir1); + } + } + } else if (ggml_get_type_traits_cpu(dst->type)->from_float) { + ggml_from_float_t const quantize_row_q = ggml_get_type_traits_cpu(dst->type)->from_float; + + size_t id = 0; + size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); + char * dst_ptr = (char *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const float * src0_ptr = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + quantize_row_q(src0_ptr, dst_ptr + id, ne00); + id += rs; + } + id += rs * (ne01 - ir1); + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } + } else { + //printf("%s: this is not optimal - fix me\n", __func__); + + if (dst->type == GGML_TYPE_F32) { + size_t id = 0; + float * dst_ptr = (float *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = *src0_ptr; + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_F16) { + size_t id = 0; + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_BF16) { + size_t id = 0; + ggml_bf16_t * dst_ptr = (ggml_bf16_t *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = GGML_FP32_TO_BF16(*src0_ptr); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } + } + + return; + } + + // dst counters + + int64_t i10 = 0; + int64_t i11 = 0; + int64_t i12 = 0; + int64_t i13 = 0; + + if (dst->type == GGML_TYPE_F32) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + memcpy(dst_ptr, src0_ptr, sizeof(float)); + + if (++i10 == ne0) { + i10 = 0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else if (dst->type == GGML_TYPE_F16) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + *(ggml_fp16_t *) dst_ptr = GGML_FP32_TO_FP16(*(const float *) src0_ptr); + + if (++i10 == ne0) { + i10 = 0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else if (dst->type == GGML_TYPE_BF16) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + *(ggml_bf16_t *) dst_ptr = GGML_FP32_TO_BF16(*(const float *) src0_ptr); + + if (++i10 == ne0) { + i10 = 0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else { + GGML_ABORT("fatal error"); // TODO: implement + } +} + +// A simplified version of ggml_compute_forward_dup that doesn't do float upcasting, and just plain old memcpy. +static void ggml_compute_forward_dup_bytes( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); + GGML_ASSERT(src0->type == dst->type); + + GGML_TENSOR_UNARY_OP_LOCALS; + + if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst)) { + ggml_compute_forward_dup_same_cont(params, dst); + return; + } + + const size_t type_size = ggml_type_size(src0->type); + const int ith = params->ith; // thread index + const int nth = params->nth; // number of threads + + + // parallelize by rows + const int nr = ne01; + // number of rows per thread + const int dr = (nr + nth - 1) / nth; + // row range for this thread + const int ir0 = dr * ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (src0->type == dst->type && + ne00 == ne0 && + nb00 == type_size && nb0 == type_size) { + // copy by rows + const size_t rs = ne00 * type_size; + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = ir0; i01 < ir1; i01++) { + memcpy( + ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), + ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), + rs); + } + } + } + return; + } + + if (ggml_is_contiguous(dst)) { + size_t id = 0; + char * dst_ptr = (char *) dst->data; + const size_t rs = ne00 * type_size; + + if (nb00 == type_size) { + // src0 is contigous on first dimension, copy by rows + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int64_t i01 = ir0; i01 < ir1; i01++) { + const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; + memcpy(dst_ptr + id, src0_ptr, rs); + id += rs; + } + id += rs * (ne01 - ir1); + } + } + } else { + //printf("%s: this is not optimal - fix me\n", __func__); + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = (char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03; + memcpy(dst_ptr + id, src0_ptr, type_size); + + id += type_size; + } + } + id += rs * (ne01 - ir1); + } + } + } + + return; + } + + // dst counters + + int64_t i10 = 0; + int64_t i11 = 0; + int64_t i12 = 0; + int64_t i13 = 0; + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + memcpy(dst_ptr, src0_ptr, type_size); + + if (++i10 == ne0) { + i10 = 0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } +} + +static void ggml_compute_forward_dup( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (src0->type == dst->type) { + ggml_compute_forward_dup_bytes(params, dst); + return; + } + + switch (src0->type) { + case GGML_TYPE_F16: + { + ggml_compute_forward_dup_f16(params, dst); + } break; + case GGML_TYPE_BF16: + { + ggml_compute_forward_dup_bf16(params, dst); + } break; + case GGML_TYPE_F32: + { + ggml_compute_forward_dup_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_add + +static void ggml_compute_forward_add_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_BINARY_OP_LOCALS + + GGML_ASSERT( nb0 == sizeof(float)); + GGML_ASSERT(nb00 == sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (nb10 == sizeof(float)) { + for (int ir = ir0; ir < ir1; ++ir) { + // src1 is broadcastable across src0 and dst in i1, i2, i3 + const int64_t i03 = ir/(ne02*ne01); + const int64_t i02 = (ir - i03*ne02*ne01)/ne01; + const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); + + const int64_t i13 = i03 % ne13; + const int64_t i12 = i02 % ne12; + const int64_t i11 = i01 % ne11; + const int64_t nr0 = ne00 / ne10; + + float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); + float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); + float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); + + for (int64_t r = 0; r < nr0; ++r) { +#ifdef GGML_USE_ACCELERATE + vDSP_vadd(src0_ptr + r*ne10, 1, src1_ptr, 1, dst_ptr + r*ne10, 1, ne10); +#else + ggml_vec_add_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr); +#endif + } + } + } else { + // src1 is not contiguous + for (int ir = ir0; ir < ir1; ++ir) { + // src1 is broadcastable across src0 and dst in i1, i2, i3 + const int64_t i03 = ir/(ne02*ne01); + const int64_t i02 = (ir - i03*ne02*ne01)/ne01; + const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); + + const int64_t i13 = i03 % ne13; + const int64_t i12 = i02 % ne12; + const int64_t i11 = i01 % ne11; + + float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); + float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); + + for (int64_t i0 = 0; i0 < ne0; ++i0) { + const int64_t i10 = i0 % ne10; + float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10); + + dst_ptr[i0] = src0_ptr[i0] + *src1_ptr; + } + } + } +} + +static void ggml_compute_forward_add_f16_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_BINARY_OP_LOCALS + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + if (dst->type == GGML_TYPE_F32) { + GGML_ASSERT( nb0 == sizeof(float)); + } + else { + GGML_ASSERT(dst->type == GGML_TYPE_F16); + GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); + } + + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (nb10 == sizeof(float)) { + if (dst->type == GGML_TYPE_F16) { + for (int ir = ir0; ir < ir1; ++ir) { + // src0, src1 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); + ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); + + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + src1_ptr[i]); + } + } + } else { + for (int ir = ir0; ir < ir1; ++ir) { + // src0, src1 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + float * dst_ptr = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); + ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); + + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP16_TO_FP32(src0_ptr[i]) + src1_ptr[i]; + } + } + } + } + else { + // src1 is not contiguous + GGML_ABORT("fatal error"); + } +} + +static void ggml_compute_forward_add_bf16_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_BINARY_OP_LOCALS + + GGML_ASSERT(src0->type == GGML_TYPE_BF16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + if (dst->type == GGML_TYPE_F32) { + GGML_ASSERT( nb0 == sizeof(float)); + } + else { + GGML_ASSERT(dst->type == GGML_TYPE_BF16); + GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); + } + + GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (nb10 == sizeof(float)) { + if (dst->type == GGML_TYPE_BF16) { + for (int ir = ir0; ir < ir1; ++ir) { + // src0, src1 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); + ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); + + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + src1_ptr[i]); + } + } + } else { + for (int ir = ir0; ir < ir1; ++ir) { + // src0, src1 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + float * dst_ptr = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); + ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); + + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_BF16_TO_FP32(src0_ptr[i]) + src1_ptr[i]; + } + } + } + } + else { + // src1 is not contiguous + GGML_ABORT("fatal error"); + } +} + +static void ggml_compute_forward_add_f16_f16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_BINARY_OP_LOCALS + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F16); + GGML_ASSERT(dst->type == GGML_TYPE_F16); + + GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (nb10 == sizeof(ggml_fp16_t)) { + for (int ir = ir0; ir < ir1; ++ir) { + // src0, src1 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); + ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + ggml_fp16_t * src1_ptr = (ggml_fp16_t *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); + + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + GGML_FP16_TO_FP32(src1_ptr[i])); + } + } + } + else { + // src1 is not contiguous + GGML_ABORT("fatal error"); + } +} + +static void ggml_compute_forward_add_bf16_bf16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_BINARY_OP_LOCALS + + GGML_ASSERT(src0->type == GGML_TYPE_BF16); + GGML_ASSERT(src1->type == GGML_TYPE_BF16); + GGML_ASSERT(dst->type == GGML_TYPE_BF16); + + GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); + GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (nb10 == sizeof(ggml_bf16_t)) { + for (int ir = ir0; ir < ir1; ++ir) { + // src0, src1 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); + ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + ggml_bf16_t * src1_ptr = (ggml_bf16_t *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); + + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + GGML_BF16_TO_FP32(src1_ptr[i])); + } + } + } + else { + // src1 is not contiguous + GGML_ABORT("fatal error"); + } +} + +static void ggml_compute_forward_add_q_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_BINARY_OP_LOCALS + + const int ith = params->ith; + const int nth = params->nth; + + const enum ggml_type type = src0->type; + const enum ggml_type dtype = dst->type; + ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; + ggml_from_float_t const quantize_row_q = ggml_get_type_traits_cpu(dtype)->from_float; + + // we don't support permuted src0 or src1 + GGML_ASSERT(nb00 == ggml_type_size(type)); + GGML_ASSERT(nb10 == sizeof(float)); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + GGML_ASSERT(ggml_is_quantized(src0->type)); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + float * wdata = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 indices + const int i03 = ir/(ne02*ne01); + const int i02 = (ir - i03*ne02*ne01)/ne01; + const int i01 = (ir - i03*ne02*ne01 - i02*ne01); + + // src1 and dst are same shape as src0 => same indices + const int i13 = i03; + const int i12 = i02; + const int i11 = i01; + + const int i3 = i03; + const int i2 = i02; + const int i1 = i01; + + void * src0_row = (void *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)); + float * src1_row = (float *)((char *) src1->data + (i11*nb11 + i12*nb12 + i13*nb13)); + void * dst_row = (void *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); + + assert(ne00 % 32 == 0); + + // unquantize row from src0 to temp buffer + dequantize_row_q(src0_row, wdata, ne00); + // add src1 + ggml_vec_acc_f32(ne00, wdata, src1_row); + // quantize row to dst + if (quantize_row_q != NULL) { + quantize_row_q(wdata, dst_row, ne00); + } else { + memcpy(dst_row, wdata, ne0*nb0); + } + } +} + +static void ggml_compute_forward_add( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + if (src1->type == GGML_TYPE_F32) { + ggml_compute_forward_add_f32(params, dst); + } + else { + GGML_ABORT("fatal error"); + } + } break; + case GGML_TYPE_F16: + { + if (src1->type == GGML_TYPE_F16) { + ggml_compute_forward_add_f16_f16(params, dst); + } + else if (src1->type == GGML_TYPE_F32) { + ggml_compute_forward_add_f16_f32(params, dst); + } + else { + GGML_ABORT("fatal error"); + } + } break; + case GGML_TYPE_BF16: + { + if (src1->type == GGML_TYPE_BF16) { + ggml_compute_forward_add_bf16_bf16(params, dst); + } + else if (src1->type == GGML_TYPE_F32) { + ggml_compute_forward_add_bf16_f32(params, dst); + } + else { + GGML_ABORT("fatal error"); + } + } break; + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_TQ1_0: + case GGML_TYPE_TQ2_0: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_Q4_0_4_4: + case GGML_TYPE_Q4_0_4_8: + case GGML_TYPE_Q4_0_8_8: + { + ggml_compute_forward_add_q_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_add1 + +static void ggml_compute_forward_add1_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_scalar(src1)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT( nb0 == sizeof(float)); + GGML_ASSERT(nb00 == sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + +#ifdef GGML_USE_ACCELERATE + UNUSED(ggml_vec_add1_f32); + + vDSP_vadd( + (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), 1, + (float *) ((char *) src1->data), 0, + (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), 1, + ne0); +#else + ggml_vec_add1_f32(ne0, + (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), + (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), + *(float *) src1->data); +#endif + } +} + +static void ggml_compute_forward_add1_f16_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_scalar(src1)); + + // scalar to add + const float v = *(float *) src1->data; + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F16); + + GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); + ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + v); + } + } +} + +static void ggml_compute_forward_add1_f16_f16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_scalar(src1)); + + // scalar to add + const float v = GGML_FP16_TO_FP32(*(ggml_fp16_t *) src1->data); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F16); + GGML_ASSERT(dst->type == GGML_TYPE_F16); + + GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); + ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + v); + } + } +} + +static void ggml_compute_forward_add1_q_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_scalar(src1)); + + // scalar to add + const float v = *(float *) src1->data; + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_UNARY_OP_LOCALS + + const enum ggml_type type = src0->type; + ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; + ggml_from_float_t const quantize_row_q = ggml_get_type_traits_cpu(type)->from_float; + + // we don't support permuted src0 + GGML_ASSERT(nb00 == ggml_type_size(type)); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + GGML_ASSERT(ggml_is_quantized(src0->type)); + GGML_ASSERT(dst->type == src0->type); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + float * wdata = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32) * ith; + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + void * src0_row = (void *) ((char *) src0->data + (i1*nb01 + i2*nb02 + i3*nb03)); + void * dst_row = (void *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb0 )); + + assert(ne0 % 32 == 0); + + // unquantize row from src0 to temp buffer + dequantize_row_q(src0_row, wdata, ne0); + // add src1 + ggml_vec_acc1_f32(ne0, wdata, v); + // quantize row to dst + quantize_row_q(wdata, dst_row, ne0); + } +} + +static void ggml_compute_forward_add1_bf16_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_scalar(src1)); + + // scalar to add + const float v = *(float *) src1->data; + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT(src0->type == GGML_TYPE_BF16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_BF16); + + GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); + GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); + ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + v); + } + } +} + +static void ggml_compute_forward_add1_bf16_bf16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_scalar(src1)); + + // scalar to add + const float v = GGML_BF16_TO_FP32(*(ggml_bf16_t *) src1->data); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT(src0->type == GGML_TYPE_BF16); + GGML_ASSERT(src1->type == GGML_TYPE_BF16); + GGML_ASSERT(dst->type == GGML_TYPE_BF16); + + GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); + GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); + ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + v); + } + } +} + +static void ggml_compute_forward_add1( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_add1_f32(params, dst); + } break; + case GGML_TYPE_F16: + { + if (src1->type == GGML_TYPE_F16) { + ggml_compute_forward_add1_f16_f16(params, dst); + } + else if (src1->type == GGML_TYPE_F32) { + ggml_compute_forward_add1_f16_f32(params, dst); + } + else { + GGML_ABORT("fatal error"); + } + } break; + case GGML_TYPE_BF16: + { + if (src1->type == GGML_TYPE_BF16) { + ggml_compute_forward_add1_bf16_bf16(params, dst); + } + else if (src1->type == GGML_TYPE_F32) { + ggml_compute_forward_add1_bf16_f32(params, dst); + } + else { + GGML_ABORT("fatal error"); + } + } break; + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q8_1: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_TQ1_0: + case GGML_TYPE_TQ2_0: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_Q4_0_4_4: + case GGML_TYPE_Q4_0_4_8: + case GGML_TYPE_Q4_0_8_8: + { + ggml_compute_forward_add1_q_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_acc + +static void ggml_compute_forward_acc_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); + + // view src0 and dst with these strides and data offset inbytes during acc + // nb0 is implicitly element_size because src0 and dst are contiguous + size_t nb1 = ((int32_t *) dst->op_params)[0]; + size_t nb2 = ((int32_t *) dst->op_params)[1]; + size_t nb3 = ((int32_t *) dst->op_params)[2]; + size_t offset = ((int32_t *) dst->op_params)[3]; + bool inplace = (bool) ((int32_t *) dst->op_params)[4]; + + if (!inplace) { + if (params->ith == 0) { + // memcpy needs to be synchronized across threads to avoid race conditions. + // => do it in INIT phase + memcpy( + ((char *) dst->data), + ((char *) src0->data), + ggml_nbytes(dst)); + } + ggml_barrier(params->threadpool); + } + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src1); + const int nc = src1->ne[0]; + + GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) + GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) + + // src0 and dst as viewed during acc + const size_t nb0 = ggml_element_size(src0); + + const size_t nb00 = nb0; + const size_t nb01 = nb1; + const size_t nb02 = nb2; + const size_t nb03 = nb3; + + GGML_ASSERT(offset + (ne10 == 0 ? 0 : ne10-1)*nb0 + (ne11 == 0 ? 0 : ne11-1)*nb1 + (ne12 == 0 ? 0 : ne12-1)*nb2 + (ne13 == 0 ? 0 : ne13-1)*nb3 < ggml_nbytes(dst)); + GGML_ASSERT(offset + (ne10 == 0 ? 0 : ne10-1)*nb00 + (ne11 == 0 ? 0 : ne11-1)*nb01 + (ne12 == 0 ? 0 : ne12-1)*nb02 + (ne13 == 0 ? 0 : ne13-1)*nb03 < ggml_nbytes(src0)); + + GGML_ASSERT(nb10 == sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are viewed with shape of src1 and offset + // => same indices + const int i3 = ir/(ne12*ne11); + const int i2 = (ir - i3*ne12*ne11)/ne11; + const int i1 = (ir - i3*ne12*ne11 - i2*ne11); + +#ifdef GGML_USE_ACCELERATE + vDSP_vadd( + (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + offset), 1, + (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1, + (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), 1, nc); +#else + ggml_vec_add_f32(nc, + (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), + (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + offset), + (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); +#endif + } +} + +static void ggml_compute_forward_acc( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_acc_f32(params, dst); + } break; + case GGML_TYPE_F16: + case GGML_TYPE_BF16: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q8_1: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_TQ1_0: + case GGML_TYPE_TQ2_0: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_Q4_0_4_4: + case GGML_TYPE_Q4_0_4_8: + case GGML_TYPE_Q4_0_8_8: + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_sub + +static void ggml_compute_forward_sub_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + assert(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_BINARY_OP_LOCALS + + GGML_ASSERT( nb0 == sizeof(float)); + GGML_ASSERT(nb00 == sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (nb10 == sizeof(float)) { + for (int ir = ir0; ir < ir1; ++ir) { + // src1 is broadcastable across src0 and dst in i1, i2, i3 + const int64_t i03 = ir/(ne02*ne01); + const int64_t i02 = (ir - i03*ne02*ne01)/ne01; + const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); + + const int64_t i13 = i03 % ne13; + const int64_t i12 = i02 % ne12; + const int64_t i11 = i01 % ne11; + const int64_t nr0 = ne00 / ne10; + + float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); + float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); + float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); + + for (int64_t r = 0; r < nr0; ++r) { +#ifdef GGML_USE_ACCELERATE + vDSP_vsub(src1_ptr, 1, src0_ptr + r*ne10, 1, dst_ptr + r*ne10, 1, ne10); +#else + ggml_vec_sub_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr); +#endif + } + } + } else { + // src1 is not contiguous + for (int ir = ir0; ir < ir1; ++ir) { + // src1 is broadcastable across src0 and dst in i1, i2, i3 + const int64_t i03 = ir/(ne02*ne01); + const int64_t i02 = (ir - i03*ne02*ne01)/ne01; + const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); + + const int64_t i13 = i03 % ne13; + const int64_t i12 = i02 % ne12; + const int64_t i11 = i01 % ne11; + + float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); + float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); + + for (int64_t i0 = 0; i0 < ne0; ++i0) { + const int64_t i10 = i0 % ne10; + float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10); + + dst_ptr[i0] = src0_ptr[i0] - *src1_ptr; + } + } + } +} + +static void ggml_compute_forward_sub( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_sub_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_mul + +static void ggml_compute_forward_mul_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t nr = ggml_nrows(src0); + + GGML_TENSOR_BINARY_OP_LOCALS + + GGML_ASSERT( nb0 == sizeof(float)); + GGML_ASSERT(nb00 == sizeof(float)); + + if (nb10 == sizeof(float)) { + for (int64_t ir = ith; ir < nr; ir += nth) { + // src0 and dst are same shape => same indices + const int64_t i03 = ir/(ne02*ne01); + const int64_t i02 = (ir - i03*ne02*ne01)/ne01; + const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); + + const int64_t i13 = i03 % ne13; + const int64_t i12 = i02 % ne12; + const int64_t i11 = i01 % ne11; + const int64_t nr0 = ne00 / ne10; + + float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); + float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); + float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); + + for (int64_t r = 0 ; r < nr0; ++r) { +#ifdef GGML_USE_ACCELERATE + UNUSED(ggml_vec_mul_f32); + + vDSP_vmul(src0_ptr + r*ne10, 1, src1_ptr, 1, dst_ptr + r*ne10, 1, ne10); +#else + ggml_vec_mul_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr); +#endif + } + } + } else { + // src1 is not contiguous + for (int64_t ir = ith; ir < nr; ir += nth) { + // src0 and dst are same shape => same indices + // src1 is broadcastable across src0 and dst in i1, i2, i3 + const int64_t i03 = ir/(ne02*ne01); + const int64_t i02 = (ir - i03*ne02*ne01)/ne01; + const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); + + const int64_t i13 = i03 % ne13; + const int64_t i12 = i02 % ne12; + const int64_t i11 = i01 % ne11; + + float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); + float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); + + for (int64_t i0 = 0; i0 < ne00; ++i0) { + const int64_t i10 = i0 % ne10; + float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10); + + dst_ptr[i0] = src0_ptr[i0] * (*src1_ptr); + } + } + } +} + +static void ggml_compute_forward_mul( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src1->type == GGML_TYPE_F32 && "only f32 src1 supported for now"); + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_mul_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_div + +static void ggml_compute_forward_div_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t nr = ggml_nrows(src0); + + GGML_TENSOR_BINARY_OP_LOCALS + + GGML_ASSERT( nb0 == sizeof(float)); + GGML_ASSERT(nb00 == sizeof(float)); + + if (nb10 == sizeof(float)) { + for (int64_t ir = ith; ir < nr; ir += nth) { + // src0 and dst are same shape => same indices + const int64_t i03 = ir/(ne02*ne01); + const int64_t i02 = (ir - i03*ne02*ne01)/ne01; + const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); + + const int64_t i13 = i03 % ne13; + const int64_t i12 = i02 % ne12; + const int64_t i11 = i01 % ne11; + const int64_t nr0 = ne00 / ne10; + + float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); + float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); + float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); + + for (int64_t r = 0; r < nr0; ++r) { +#ifdef GGML_USE_ACCELERATE + UNUSED(ggml_vec_div_f32); + + vDSP_vdiv(src1_ptr, 1, src0_ptr + r*ne10, 1, dst_ptr + r*ne10, 1, ne10); +#else + ggml_vec_div_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr); +#endif + } + } + } else { + // src1 is not contiguous + for (int64_t ir = ith; ir < nr; ir += nth) { + // src0 and dst are same shape => same indices + // src1 is broadcastable across src0 and dst in i1, i2, i3 + const int64_t i03 = ir/(ne02*ne01); + const int64_t i02 = (ir - i03*ne02*ne01)/ne01; + const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); + + const int64_t i13 = i03 % ne13; + const int64_t i12 = i02 % ne12; + const int64_t i11 = i01 % ne11; + + float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); + float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); + + for (int64_t i0 = 0; i0 < ne00; ++i0) { + const int64_t i10 = i0 % ne10; + float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10); + + dst_ptr[i0] = src0_ptr[i0] / (*src1_ptr); + } + } + } +} + +static void ggml_compute_forward_div( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_div_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_sqr + +static void ggml_compute_forward_sqr_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + assert( dst->nb[0] == sizeof(float)); + assert(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < n; i++) { + ggml_vec_sqr_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_sqr( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_sqr_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_sqrt + +static void ggml_compute_forward_sqrt_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + assert( dst->nb[0] == sizeof(float)); + assert(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < n; i++) { + ggml_vec_sqrt_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_sqrt( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_sqrt_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_log + +static void ggml_compute_forward_log_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + GGML_ASSERT( dst->nb[0] == sizeof(float)); + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < n; i++) { + ggml_vec_log_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_log( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_log_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_sin + +static void ggml_compute_forward_sin_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + GGML_ASSERT( dst->nb[0] == sizeof(float)); + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < n; i++) { + ggml_vec_sin_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_sin( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_sin_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_cos + +static void ggml_compute_forward_cos_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + GGML_ASSERT( dst->nb[0] == sizeof(float)); + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < n; i++) { + ggml_vec_cos_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_cos( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_cos_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_sum + +static void ggml_compute_forward_sum_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_scalar(dst)); + assert(src0->nb[0] == sizeof(float)); + + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) + GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) + + ggml_float sum = 0; + ggml_float row_sum = 0; + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + ggml_vec_sum_f32_ggf(ne00, + &row_sum, + (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03)); + sum += row_sum; + } + } + } + ((float *) dst->data)[0] = sum; +} + +static void ggml_compute_forward_sum_f16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_scalar(dst)); + + assert(src0->nb[0] == sizeof(ggml_fp16_t)); + + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) + GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) + + float sum = 0; + float row_sum = 0; + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + ggml_vec_sum_f16_ggf(ne00, + &row_sum, + (ggml_fp16_t *) ((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03)); + sum += row_sum; + } + } + } + ((ggml_fp16_t *) dst->data)[0] = GGML_FP32_TO_FP16(sum); +} + +static void ggml_compute_forward_sum_bf16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_scalar(dst)); + + assert(src0->nb[0] == sizeof(ggml_bf16_t)); + + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) + GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) + + float sum = 0; + float row_sum = 0; + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + ggml_vec_sum_bf16_ggf(ne00, + &row_sum, + (ggml_bf16_t *) ((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03)); + sum += row_sum; + } + } + } + ((ggml_bf16_t *) dst->data)[0] = GGML_FP32_TO_BF16(sum); +} + +static void ggml_compute_forward_sum( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_sum_f32(params, dst); + } break; + case GGML_TYPE_F16: + { + ggml_compute_forward_sum_f16(params, dst); + } break; + case GGML_TYPE_BF16: + { + ggml_compute_forward_sum_bf16(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_sum_rows + +static void ggml_compute_forward_sum_rows_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + GGML_ASSERT(dst->nb[0] == sizeof(float)); + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT(ne0 == 1); + GGML_ASSERT(ne1 == ne01); + GGML_ASSERT(ne2 == ne02); + GGML_ASSERT(ne3 == ne03); + + for (int64_t i3 = 0; i3 < ne03; i3++) { + for (int64_t i2 = 0; i2 < ne02; i2++) { + for (int64_t i1 = 0; i1 < ne01; i1++) { + float * src_row = (float *) ((char *) src0->data + i1*nb01 + i2*nb02 + i3*nb03); + float * dst_row = (float *) ((char *) dst->data + i1*nb1 + i2*nb2 + i3*nb3); + float row_sum = 0; + ggml_vec_sum_f32(ne00, &row_sum, src_row); + dst_row[0] = row_sum; + } + } + } +} + +static void ggml_compute_forward_sum_rows( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_sum_rows_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_mean + +static void ggml_compute_forward_mean_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(src0->nb[0] == sizeof(float)); + + GGML_TENSOR_UNARY_OP_LOCALS + + assert(ne0 == 1); + assert(ne1 == ne01); + assert(ne2 == ne02); + assert(ne3 == ne03); + + UNUSED(ne0); + UNUSED(ne1); + UNUSED(ne2); + UNUSED(ne3); + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + ggml_vec_sum_f32(ne00, + (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), + (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03)); + + *(float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3) /= (float) ne00; + } + } + } +} + +static void ggml_compute_forward_mean( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_mean_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_argmax + +static void ggml_compute_forward_argmax_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(src0->nb[0] == sizeof(float)); + assert(dst->nb[0] == sizeof(float)); + + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + + const size_t nb01 = src0->nb[1]; + const size_t nb0 = dst->nb[0]; + + for (int64_t i1 = 0; i1 < ne01; i1++) { + float * src = (float *) ((char *) src0->data + i1*nb01); + int32_t * dst_ = (int32_t *) ((char *) dst->data + i1*nb0); + int v = 0; + ggml_vec_argmax_f32(ne00, &v, src); + dst_[0] = v; + } +} + +static void ggml_compute_forward_argmax( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_argmax_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_count_equal + +static void ggml_compute_forward_count_equal_i32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS; + + GGML_ASSERT(src0->type == GGML_TYPE_I32); + GGML_ASSERT(src1->type == GGML_TYPE_I32); + GGML_ASSERT(ggml_are_same_shape(src0, src1)); + GGML_ASSERT(ggml_is_scalar(dst)); + GGML_ASSERT(dst->type == GGML_TYPE_I64); + + const int64_t nr = ggml_nrows(src0); + + const int ith = params->ith; + const int nth = params->nth; + + int64_t * sums = (int64_t *) params->wdata; + int64_t sum_thread = 0; + + // rows per thread + const int64_t dr = (nr + nth - 1)/nth; + + // row range for this thread + const int64_t ir0 = dr*ith; + const int64_t ir1 = MIN(ir0 + dr, nr); + + for (int64_t ir = ir0; ir < ir1; ++ir) { + const int64_t i03 = ir / (ne02*ne01); + const int64_t i02 = (ir - i03*ne03) / ne01; + const int64_t i01 = ir - i03*ne03 - i02*ne02; + + const char * data0 = (const char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01; + const char * data1 = (const char *) src1->data + i03*nb13 + i02*nb12 + i01*nb11; + + for (int64_t i00 = 0; i00 < ne00; ++i00) { + const int32_t val0 = *((const int32_t *) (data0 + i00*nb00)); + const int32_t val1 = *((const int32_t *) (data1 + i00*nb10)); + + sum_thread += val0 == val1; + } + } + if (ith != 0) { + sums[ith] = sum_thread; + } + ggml_barrier(params->threadpool); + + if (ith != 0) { + return; + } + + for (int ith_other = 1; ith_other < nth; ++ith_other) { + sum_thread += sums[ith_other]; + } + *((int64_t *) dst->data) = sum_thread; +} + +static void ggml_compute_forward_count_equal( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_I32: + { + ggml_compute_forward_count_equal_i32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_repeat + +static void ggml_compute_forward_repeat_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + GGML_ASSERT(ggml_can_repeat(src0, dst)); + + GGML_TENSOR_UNARY_OP_LOCALS + + // guaranteed to be an integer due to the check in ggml_can_repeat + const int nr0 = (int)(ne0/ne00); + const int nr1 = (int)(ne1/ne01); + const int nr2 = (int)(ne2/ne02); + const int nr3 = (int)(ne3/ne03); + + // TODO: support for transposed / permuted tensors + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb00 == sizeof(float)); + + // TODO: maybe this is not optimal? + for (int i3 = 0; i3 < nr3; i3++) { + for (int k3 = 0; k3 < ne03; k3++) { + for (int i2 = 0; i2 < nr2; i2++) { + for (int k2 = 0; k2 < ne02; k2++) { + for (int i1 = 0; i1 < nr1; i1++) { + for (int k1 = 0; k1 < ne01; k1++) { + for (int i0 = 0; i0 < nr0; i0++) { + ggml_vec_cpy_f32(ne00, + (float *) ((char *) dst->data + (i3*ne03 + k3)*nb3 + (i2*ne02 + k2)*nb2 + (i1*ne01 + k1)*nb1 + (i0*ne00)*nb0), + (float *) ((char *) src0->data + ( k3)*nb03 + ( k2)*nb02 + ( k1)*nb01)); + } + } + } + } + } + } + } +} + +static void ggml_compute_forward_repeat_f16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + GGML_ASSERT(ggml_can_repeat(src0, dst)); + + GGML_TENSOR_UNARY_OP_LOCALS + + // guaranteed to be an integer due to the check in ggml_can_repeat + const int nr0 = (int)(ne0/ne00); + const int nr1 = (int)(ne1/ne01); + const int nr2 = (int)(ne2/ne02); + const int nr3 = (int)(ne3/ne03); + + // TODO: support for transposed / permuted tensors + GGML_ASSERT(nb0 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + + // TODO: maybe this is not optimal? + for (int i3 = 0; i3 < nr3; i3++) { + for (int k3 = 0; k3 < ne03; k3++) { + for (int i2 = 0; i2 < nr2; i2++) { + for (int k2 = 0; k2 < ne02; k2++) { + for (int i1 = 0; i1 < nr1; i1++) { + for (int k1 = 0; k1 < ne01; k1++) { + for (int i0 = 0; i0 < nr0; i0++) { + ggml_fp16_t * y = (ggml_fp16_t *) ((char *) dst->data + (i3*ne03 + k3)*nb3 + (i2*ne02 + k2)*nb2 + (i1*ne01 + k1)*nb1 + (i0*ne00)*nb0); + ggml_fp16_t * x = (ggml_fp16_t *) ((char *) src0->data + ( k3)*nb03 + ( k2)*nb02 + ( k1)*nb01); + // ggml_vec_cpy_f16(ne00, y, x) + for (int i = 0; i < ne00; ++i) { + y[i] = x[i]; + } + } + } + } + } + } + } + } +} + +static void ggml_compute_forward_repeat( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F16: + case GGML_TYPE_BF16: + case GGML_TYPE_I16: + { + ggml_compute_forward_repeat_f16(params, dst); + } break; + case GGML_TYPE_F32: + case GGML_TYPE_I32: + { + ggml_compute_forward_repeat_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_repeat_back + +static void ggml_compute_forward_repeat_back_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + GGML_ASSERT(ggml_can_repeat(dst, src0)); + + GGML_TENSOR_UNARY_OP_LOCALS + + // guaranteed to be an integer due to the check in ggml_can_repeat + const int nr0 = (int)(ne00/ne0); + const int nr1 = (int)(ne01/ne1); + const int nr2 = (int)(ne02/ne2); + const int nr3 = (int)(ne03/ne3); + + // TODO: support for transposed / permuted tensors + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb00 == sizeof(float)); + + if (ggml_is_contiguous(dst)) { + ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); + } else { + for (int k3 = 0; k3 < ne3; k3++) { + for (int k2 = 0; k2 < ne2; k2++) { + for (int k1 = 0; k1 < ne1; k1++) { + ggml_vec_set_f32(ne0, + (float *) ((char *) dst->data + k1*nb1 + k2*nb2 + k3*nb3), + 0); + } + } + } + } + + // TODO: maybe this is not optimal? + for (int i3 = 0; i3 < nr3; i3++) { + for (int k3 = 0; k3 < ne3; k3++) { + for (int i2 = 0; i2 < nr2; i2++) { + for (int k2 = 0; k2 < ne2; k2++) { + for (int i1 = 0; i1 < nr1; i1++) { + for (int k1 = 0; k1 < ne1; k1++) { + for (int i0 = 0; i0 < nr0; i0++) { + ggml_vec_acc_f32(ne0, + (float *) ((char *) dst->data + ( k3)*nb3 + ( k2)*nb2 + ( k1)*nb1), + (float *) ((char *) src0->data + (i3*ne3 + k3)*nb03 + (i2*ne2 + k2)*nb02 + (i1*ne1 + k1)*nb01 + (i0*ne0)*nb00)); + } + } + } + } + } + } + } +} + +static void ggml_compute_forward_repeat_back( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_repeat_back_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_concat + +static void ggml_compute_forward_concat_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_BINARY_OP_LOCALS + + const int32_t dim = ggml_get_op_params_i32(dst, 0); + + GGML_ASSERT(dim >= 0 && dim < 4); + + int64_t o[4] = {0, 0, 0, 0}; + o[dim] = src0->ne[dim]; + + const float * x; + + // TODO: smarter multi-theading + for (int i3 = 0; i3 < ne3; i3++) { + for (int i2 = ith; i2 < ne2; i2 += nth) { + for (int i1 = 0; i1 < ne1; i1++) { + for (int i0 = 0; i0 < ne0; i0++) { + if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { + x = (const float *) ((const char *)src0->data + (i0 )*nb00 + (i1 )*nb01 + (i2 )*nb02 + (i3 )*nb03); + } else { + x = (const float *) ((const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13); + } + + float * y = (float *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); + + *y = *x; + } + } + } + } +} + +static void ggml_compute_forward_concat( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + case GGML_TYPE_I32: + { + ggml_compute_forward_concat_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_abs + +static void ggml_compute_forward_abs_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + ggml_vec_abs_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_abs( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_abs_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_sgn + +static void ggml_compute_forward_sgn_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + ggml_vec_sgn_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_sgn( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_sgn_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_neg + +static void ggml_compute_forward_neg_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + ggml_vec_neg_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_neg( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_neg_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_step + +static void ggml_compute_forward_step_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + ggml_vec_step_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_step( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_step_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_tanh + +static void ggml_compute_forward_tanh_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + ggml_vec_tanh_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_tanh( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_tanh_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_elu + +static void ggml_compute_forward_elu_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + ggml_vec_elu_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_elu( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_elu_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_relu + +static void ggml_compute_forward_relu_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + ggml_vec_relu_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_relu( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_relu_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_sigmoid + +static void ggml_compute_forward_sigmoid_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + ggml_vec_sigmoid_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_sigmoid( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_sigmoid_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_gelu + +static void ggml_compute_forward_gelu_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src0->ne[0]; + const int nr = ggml_nrows(src0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int i1 = ir0; i1 < ir1; i1++) { + ggml_vec_gelu_f32(nc, + (float *) ((char *) dst->data + i1*( dst->nb[1])), + (float *) ((char *) src0->data + i1*(src0->nb[1]))); + +#ifndef NDEBUG + for (int k = 0; k < nc; k++) { + const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; + UNUSED(x); + assert(!isnan(x)); + assert(!isinf(x)); + } +#endif + } +} + +static void ggml_compute_forward_gelu( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_gelu_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_gelu_quick + +static void ggml_compute_forward_gelu_quick_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src0->ne[0]; + const int nr = ggml_nrows(src0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int i1 = ir0; i1 < ir1; i1++) { + ggml_vec_gelu_quick_f32(nc, + (float *) ((char *) dst->data + i1*( dst->nb[1])), + (float *) ((char *) src0->data + i1*(src0->nb[1]))); + +#ifndef NDEBUG + for (int k = 0; k < nc; k++) { + const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; + UNUSED(x); + assert(!isnan(x)); + assert(!isinf(x)); + } +#endif + } +} + +static void ggml_compute_forward_gelu_quick( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_gelu_quick_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_silu + +static void ggml_compute_forward_silu_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src0->ne[0]; + const int nr = ggml_nrows(src0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int i1 = ir0; i1 < ir1; i1++) { + ggml_vec_silu_f32(nc, + (float *) ((char *) dst->data + i1*( dst->nb[1])), + (float *) ((char *) src0->data + i1*(src0->nb[1]))); + +#ifndef NDEBUG + for (int k = 0; k < nc; k++) { + const float x = ((float *) ((char *) dst->data + i1*(dst->nb[1])))[k]; + UNUSED(x); + assert(!isnan(x)); + assert(!isinf(x)); + } +#endif + } +} + +static void ggml_compute_forward_silu( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_silu_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} +// ggml_compute_forward_leaky_relu + +static void ggml_compute_forward_leaky_relu_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + float negative_slope; + memcpy(&negative_slope, dst->op_params, sizeof(float)); + + assert(dst->nb[0] == sizeof(float)); + assert(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < n; i++) { + ggml_vec_leaky_relu_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1])), negative_slope); + } +} + +static void ggml_compute_forward_leaky_relu( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_leaky_relu_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_silu_back + +static void ggml_compute_forward_silu_back_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * grad = dst->src[1]; + + assert(ggml_is_contiguous_1(grad)); + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + assert(ggml_are_same_shape(src0, grad)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src0->ne[0]; + const int nr = ggml_nrows(src0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int i1 = ir0; i1 < ir1; i1++) { + ggml_vec_silu_backward_f32(nc, + (float *) ((char *) dst->data + i1*( dst->nb[1])), + (float *) ((char *) src0->data + i1*(src0->nb[1])), + (float *) ((char *) grad->data + i1*(grad->nb[1]))); + +#ifndef NDEBUG + for (int k = 0; k < nc; k++) { + const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; + UNUSED(x); + assert(!isnan(x)); + assert(!isinf(x)); + } +#endif + } +} + +static void ggml_compute_forward_silu_back( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_silu_back_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + + +static void ggml_compute_forward_hardswish_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + ggml_vec_hardswish_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} +static void ggml_compute_forward_hardswish( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_hardswish_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +static void ggml_compute_forward_hardsigmoid_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + ggml_vec_hardsigmoid_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_hardsigmoid( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_hardsigmoid_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +static void ggml_compute_forward_exp_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + ggml_vec_exp_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_exp( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_exp_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + + +// ggml_compute_forward_norm + +static void ggml_compute_forward_norm_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_UNARY_OP_LOCALS + + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); + + GGML_ASSERT(eps > 0.0f); + + // TODO: optimize + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = ith; i01 < ne01; i01 += nth) { + const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + + ggml_float sum = 0.0; + for (int64_t i00 = 0; i00 < ne00; i00++) { + sum += (ggml_float)x[i00]; + } + + float mean = sum/ne00; + + float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); + + ggml_float sum2 = 0.0; + for (int64_t i00 = 0; i00 < ne00; i00++) { + float v = x[i00] - mean; + y[i00] = v; + sum2 += (ggml_float)(v*v); + } + + float variance = sum2/ne00; + const float scale = 1.0f/sqrtf(variance + eps); + + ggml_vec_scale_f32(ne00, y, scale); + } + } + } +} + +static void ggml_compute_forward_norm( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_norm_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_group_rms_norm + +static void ggml_compute_forward_rms_norm_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_UNARY_OP_LOCALS + + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); + + GGML_ASSERT(eps > 0.0f); + + // TODO: optimize + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = ith; i01 < ne01; i01 += nth) { + const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + + ggml_float sum = 0.0; + for (int64_t i00 = 0; i00 < ne00; i00++) { + sum += (ggml_float)(x[i00] * x[i00]); + } + + const float mean = sum/ne00; + + float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); + + memcpy(y, x, ne00 * sizeof(float)); + // for (int i00 = 0; i00 < ne00; i00++) { + // y[i00] = x[i00]; + // } + + const float scale = 1.0f/sqrtf(mean + eps); + + ggml_vec_scale_f32(ne00, y, scale); + } + } + } +} + +static void ggml_compute_forward_rms_norm( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_rms_norm_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +static void ggml_compute_forward_rms_norm_back_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst) && ggml_are_same_shape(src0, src1)); + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_BINARY_OP_LOCALS + + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); + + // TODO: optimize + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = ith; i01 < ne01; i01 += nth) { + // src1 is same shape as src0 => same indices + const int64_t i11 = i01; + const int64_t i12 = i02; + const int64_t i13 = i03; + + const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + const float * dz = (float *) ((char *) src1->data + i11*nb11 + i12*nb12 + i13*nb13); + + ggml_float sum_xx = 0.0; + ggml_float sum_xdz = 0.0; + + for (int64_t i00 = 0; i00 < ne00; i00++) { + sum_xx += (ggml_float)(x[i00] * x[i00]); + sum_xdz += (ggml_float)(x[i00] * dz[i00]); + } + + //const float mean = (float)(sum_xx)/ne00; + const float mean_eps = (float)(sum_xx)/ne00 + eps; + const float sum_eps = (float)(sum_xx) + eps*ne00; + //const float mean_xdz = (float)(sum_xdz)/ne00; + // we could cache rms from forward pass to improve performance. + // to do this implement ggml_rms and compose ggml_rms_norm using ggml_rms. + //const float rms = sqrtf(mean_eps); + const float rrms = 1.0f / sqrtf(mean_eps); + //const float scale = -rrms/(ne00 * mean_eps); // -1/(n*rms**3) + + { + // z = rms_norm(x) + // + // rms_norm(src0) = + // scale( + // src0, + // div( + // 1, + // sqrt( + // add( + // scale( + // sum( + // sqr( + // src0)), + // (1.0/N)), + // eps)))); + + // postorder: + // ## op args grad + // 00 param src0 grad[#00] + // 01 const 1 + // 02 sqr (#00) grad[#02] + // 03 sum (#02) grad[#03] + // 04 const 1/N + // 05 scale (#03, #04) grad[#05] + // 06 const eps + // 07 add (#05, #06) grad[#07] + // 08 sqrt (#07) grad[#08] + // 09 div (#01,#08) grad[#09] + // 10 scale (#00,#09) grad[#10] + // + // backward pass, given grad[#10] + // #10: scale + // grad[#00] += scale(grad[#10],#09) + // grad[#09] += sum(mul(grad[#10],#00)) + // #09: div + // grad[#08] += neg(mul(grad[#09], div(#09,#08))) + // #08: sqrt + // grad[#07] += mul(grad[#08], div(0.5, #08)) + // #07: add + // grad[#05] += grad[#07] + // #05: scale + // grad[#03] += scale(grad[#05],#04) + // #03: sum + // grad[#02] += repeat(grad[#03], #02) + // #02: + // grad[#00] += scale(mul(#00, grad[#02]), 2.0) + // + // substitute and simplify: + // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, grad[#02]), 2.0) + // grad[#02] = repeat(grad[#03], #02) + // grad[#02] = repeat(scale(grad[#05],#04), #02) + // grad[#02] = repeat(scale(grad[#07],#04), #02) + // grad[#02] = repeat(scale(mul(grad[#08], div(0.5, #08)),#04), #02) + // grad[#02] = repeat(scale(mul(neg(mul(grad[#09], div(#09,#08))), div(0.5, #08)),#04), #02) + // grad[#02] = repeat(scale(mul(neg(mul(sum(mul(grad[#10],#00)), div(#09,#08))), div(0.5, #08)),#04), #02) + // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(#09,#08) * div(0.5, #08) * (1/N)), #02) + // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(div(#01,#08),#08) * div(0.5, #08) * (1/N)), #02) + // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(1,#08*#08) * div(0.5, #08) * (1/N)), #02) + // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N)), #02) + // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, grad[#02]), 2.0) + // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, repeat(-(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N)), #02)), 2.0) + // grad[#00] = scale(grad(#10), #09) + scale(scale(#00, -(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N))), 2.0) + // grad[#00] = scale(grad(#10), #09) + scale(#00, -(sum(mul(grad[#10],#00)) * div(1,#07) * div(1,#08) * (1/N))) + // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,#07*#08) * (-1/N)) + // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,#07*#08) * (-1/N)) + // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,mean_eps*rms) * (-1/N)) + // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*mean_eps)) + // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*(sum_xx/N+eps))) + // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*sum_xx+rms*N*eps)) + // grad[#00] = scale(dz, rrms) + scale(x, sum(mul(dz,x)) * div(-1,rms*N*mean_eps)) + // grad[#00] = scale(dz, rrms) + scale(x, sum_xdz * div(-1,rms*N*mean_eps)) + // a = b*c + d*e + // a = b*c*f/f + d*e*f/f + // a = (b*c*f + d*e*f)*(1/f) + // a = (b*c*(1/c) + d*e*(1/c))*(1/(1/c)) + // a = (b + d*e/c)*c + // b = dz, c = rrms, d = x, e = sum_xdz * div(-1,rms*N*mean_eps) + // a = (dz + x*sum_xdz * div(-1,rms*N*mean_eps)/rrms)*rrms + // a = (dz + x*sum_xdz * div(-1,rms*N*mean_eps)*rms)*rrms + // a = (dz + x*sum_xdz * div(-rms,rms*N*mean_eps))*rrms + // a = (dz + x*sum_xdz * div(-1,N*mean_eps))*rrms + // a = (dz + x*div(-sum_xdz,N*mean_eps))*rrms + // a = (dz + x*div(-mean_xdz,mean_eps))*rrms + // grad[#00] = scale(dz + scale(x, div(-mean_xdz,mean_eps)),rrms) + // grad[#00] = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) + // dx = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) + } + // dx = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) + // post-order: + // dx := x + // dx := scale(dx,-mean_xdz/mean_eps) + // dx := add(dx, dz) + // dx := scale(dx, rrms) + float * dx = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); + + ggml_vec_cpy_f32 (ne00, dx, x); + // ggml_vec_scale_f32(ne00, dx, -mean_xdz/mean_eps); + ggml_vec_scale_f32(ne00, dx, (float)(-sum_xdz)/sum_eps); + ggml_vec_acc_f32 (ne00, dx, dz); + ggml_vec_scale_f32(ne00, dx, rrms); + } + } + } +} + +static void ggml_compute_forward_rms_norm_back( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_rms_norm_back_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_group_norm + +static void ggml_compute_forward_group_norm_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_UNARY_OP_LOCALS + + // TODO: optimize + + float eps; + memcpy(&eps, dst->op_params + 1, sizeof(float)); + + int n_channels = src0->ne[2]; + int n_groups = dst->op_params[0]; + int n_channels_per_group = (n_channels + n_groups - 1) / n_groups; + for (int i = ith; i < n_groups; i += nth) { + int start = i * n_channels_per_group; + int end = start + n_channels_per_group; + if (end > n_channels) { + end = n_channels; + } + int step = end - start; + + for (int64_t i03 = 0; i03 < ne03; i03++) { + ggml_float sum = 0.0; + for (int64_t i02 = start; i02 < end; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03); + + ggml_float sumr = 0.0; + for (int64_t i00 = 0; i00 < ne00; i00++) { + sumr += (ggml_float)x[i00]; + } + sum += sumr; + } + } + const float mean = sum / (ne00 * ne01 * step); + + ggml_float sum2 = 0.0; + for (int64_t i02 = start; i02 < end; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03); + + float * y = (float *)((char *) dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3); + + ggml_float sumr = 0.0; + for (int64_t i00 = 0; i00 < ne00; i00++) { + float v = x[i00] - mean; + y[i00] = v; + sumr += (ggml_float)(v * v); + } + sum2 += sumr; + } + } + const float variance = sum2 / (ne00 * ne01 * step); + const float scale = 1.0f / sqrtf(variance + eps); + + for (int64_t i02 = start; i02 < end; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + float * y = (float *)((char *) dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3); + ggml_vec_scale_f32(ne00, y, scale); + } + } + } + } +} + +static void ggml_compute_forward_group_norm( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_group_norm_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_mul_mat + +static void ggml_compute_forward_mul_mat_one_chunk( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const enum ggml_type type, + const int64_t num_rows_per_vec_dot, + const int64_t ir0_start, + const int64_t ir0_end, + const int64_t ir1_start, + const int64_t ir1_end) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS + + const bool src1_cont = ggml_is_contiguous(src1); + + ggml_vec_dot_t const vec_dot = type_traits_cpu[type].vec_dot; + enum ggml_type const vec_dot_type = type_traits_cpu[type].vec_dot_type; + + // broadcast factors + const int64_t r2 = ne12 / ne02; + const int64_t r3 = ne13 / ne03; + + //printf("ir0_start = %6lld, ir0_end = %6lld, ir1_start = %6lld, ir1_end = %6lld\n", ir0_start, ir0_end, ir1_start, ir1_end); + + // threads with no work simply yield (not sure if it helps) + if (ir0_start >= ir0_end || ir1_start >= ir1_end) { + return; + } + + const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; + const size_t row_size = ggml_row_size(vec_dot_type, ne10); + + assert(ne12 % ne02 == 0); + assert(ne13 % ne03 == 0); + + // block-tiling attempt + const int64_t blck_0 = 16; + const int64_t blck_1 = 16; + + const size_t src1_col_stride = src1_cont || src1->type != vec_dot_type ? row_size : nb11; + + // attempt to reduce false-sharing (does not seem to make a difference) + // 16 * 2, accounting for mmla kernels + float tmp[32]; + + for (int64_t iir1 = ir1_start; iir1 < ir1_end; iir1 += blck_1) { + for (int64_t iir0 = ir0_start; iir0 < ir0_end; iir0 += blck_0) { + for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir1_end; ir1 += num_rows_per_vec_dot) { + const int64_t i13 = (ir1 / (ne12 * ne1)); + const int64_t i12 = (ir1 - i13 * ne12 * ne1) / ne1; + const int64_t i11 = (ir1 - i13 * ne12 * ne1 - i12 * ne1); + + // broadcast src0 into src1 + const int64_t i03 = i13 / r3; + const int64_t i02 = i12 / r2; + + const int64_t i1 = i11; + const int64_t i2 = i12; + const int64_t i3 = i13; + + const char * src0_row = (const char*)src0->data + (0 + i02 * nb02 + i03 * nb03); + + // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides + // if it is, then we have either copied the data to params->wdata and made it contiguous or we are using + // the original src1 data pointer, so we should index using the indices directly + // TODO: this is a bit of a hack, we should probably have a better way to handle this + const char * src1_col = (const char*)wdata + + (src1_cont || src1->type != vec_dot_type + ? (i11 + i12 * ne11 + i13 * ne12 * ne11) * row_size + : (i11 * nb11 + i12 * nb12 + i13 * nb13)); + float * dst_col = (float*)((char*)dst->data + (i1 * nb1 + i2 * nb2 + i3 * nb3)); + + //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ++ir0) { + // vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col); + //} + + for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ir0 += num_rows_per_vec_dot) { + vec_dot(ne00, &tmp[ir0 - iir0], (num_rows_per_vec_dot > 1 ? 16 : 0), src0_row + ir0 * nb01, (num_rows_per_vec_dot > 1 ? nb01 : 0), src1_col, (num_rows_per_vec_dot > 1 ? src1_col_stride : 0), num_rows_per_vec_dot); + } + + for (int cn = 0; cn < num_rows_per_vec_dot; ++cn) { + memcpy(&dst_col[iir0 + cn * nb1 / nb0], tmp + (cn * 16), (MIN(iir0 + blck_0, ir0_end) - iir0) * sizeof(float)); + } + } + } + } +} + +static void ggml_compute_forward_mul_mat( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS + + const int ith = params->ith; + const int nth = params->nth; + + enum ggml_type type = src0->type; + + if (src0->buffer && ggml_backend_cpu_buft_is_aarch64(src0->buffer->buft)) { + type = (enum ggml_type)(intptr_t)src0->extra; + } + +#if defined(__AMX_INT8__) && defined(__AVX512VNNI__) + if (src0->buffer && ggml_backend_amx_buft_is_amx(src0->buffer->buft)) { + ggml_backend_amx_mul_mat(params, dst); + return; + } +#endif + + enum ggml_type const vec_dot_type = type_traits_cpu[type].vec_dot_type; + ggml_from_float_t const from_float = type_traits_cpu[vec_dot_type].from_float; + ggml_from_float_to_mat_t const from_float_to_mat = type_traits_cpu[vec_dot_type].from_float_to_mat; + int64_t const vec_dot_num_rows = type_traits_cpu[type].nrows; + int64_t const matmul_num_cols = type_traits_cpu[type].ncols; + int64_t const blck_size_interleave = ggml_get_type_traits(type)->blck_size_interleave; + ggml_gemv_t const gemv = type_traits_cpu[type].gemv; + ggml_gemm_t const gemm = type_traits_cpu[type].gemm; + + GGML_ASSERT(ne0 == ne01); + GGML_ASSERT(ne1 == ne11); + GGML_ASSERT(ne2 == ne12); + GGML_ASSERT(ne3 == ne13); + + // we don't support permuted src0 or src1 + GGML_ASSERT(nb00 == ggml_type_size(type)); + GGML_ASSERT(nb10 == ggml_type_size(src1->type)); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + // nb01 >= nb00 - src0 is not transposed + // compute by src0 rows + +#if GGML_USE_LLAMAFILE + // broadcast factors + const int64_t r2 = ne12 / ne02; + const int64_t r3 = ne13 / ne03; + + const bool src1_cont = ggml_is_contiguous(src1); + + if (src1_cont) { + for (int64_t i13 = 0; i13 < ne13; i13++) + for (int64_t i12 = 0; i12 < ne12; i12++) + if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(type), + (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03, + nb01/ggml_type_size(type), + (const char *)src1->data + i12*nb12 + i13*nb13, + nb11/ggml_type_size(src1->type), + (char *)dst->data + i12*nb2 + i13*nb3, + nb1/ggml_type_size(dst->type), + ith, nth, + type, + src1->type, + dst->type)) + goto UseGgmlGemm1; + return; + } +UseGgmlGemm1:; +#endif + + if (src1->type != vec_dot_type) { + char * wdata = params->wdata; + + const size_t nbw1 = ggml_row_size(vec_dot_type, ne10); + const size_t nbw2 = nbw1*ne11; + const size_t nbw3 = nbw2*ne12; + + assert(params->wsize >= ne13*nbw3); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + for (int64_t i13 = 0; i13 < ne13; ++i13) { + for (int64_t i12 = 0; i12 < ne12; ++i12) { + int64_t i11_processed = 0; + if ((ggml_n_dims(src1) == 2) && from_float_to_mat && gemm) { + for (int64_t i11 = ith * 4; i11 < ne11 - ne11 % 4; i11 += nth * 4) { + from_float_to_mat((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), + (void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1), + 4, ne10, blck_size_interleave); + } + i11_processed = ne11 - ne11 % 4; + } + for (int64_t i11 = i11_processed + ith; i11 < ne11; i11 += nth) { + from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), + (void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1), + ne10); + } + } + } + } + + if (ith == 0) { + // Every thread starts at ith, so the first unprocessed chunk is nth. This save a bit of coordination right at the start. + atomic_store_explicit(¶ms->threadpool->current_chunk, nth, memory_order_relaxed); + } + + ggml_barrier(params->threadpool); + +#if GGML_USE_LLAMAFILE + if (src1->type != vec_dot_type) { + const void* wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; + const size_t row_size = ggml_row_size(vec_dot_type, ne10); + + for (int64_t i13 = 0; i13 < ne13; i13++) + for (int64_t i12 = 0; i12 < ne12; i12++) + if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(type), + (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03, + nb01/ggml_type_size(type), + (const char *)wdata + (i12*ne11 + i13*ne12*ne11)*row_size, + row_size/ggml_type_size(vec_dot_type), + (char *)dst->data + i12*nb2 + i13*nb3, + nb1/ggml_type_size(dst->type), + ith, nth, + type, + vec_dot_type, + dst->type)) + goto UseGgmlGemm2; + return; + } +UseGgmlGemm2:; +#endif + + // This is the size of the first dimension of the result, so we can iterate that way. (see the ASSERT above, these are the same numbers) + const int64_t nr0 = ne0; + + // This is the size of the rest of the dimensions of the result + const int64_t nr1 = ne1 * ne2 * ne3; + + // Now select a reasonable chunk size. + int chunk_size = 16; + + // We need to step up the size if it's small + if (nr0 == 1 || nr1 == 1) { + chunk_size = 64; + } + + // distribute the work across the inner or outer loop based on which one is larger + // The number of chunks in the 0/1 dim. + // CEIL(nr0/chunk_size) + int64_t nchunk0 = (nr0 + chunk_size - 1) / chunk_size; + int64_t nchunk1 = (nr1 + chunk_size - 1) / chunk_size; + + // If the chunking is poor for the number of threads on this setup, scrap the whole plan. Re-chunk it by thread. + // Also, chunking by thread was measured to have perform better on NUMA systems. See https://github.com/ggerganov/llama.cpp/pull/6915 + // In theory, chunking should be just as useful on NUMA and non NUMA systems, but testing disagreed with that. + if (nchunk0 * nchunk1 < nth * 4 || ggml_is_numa()) { + // distribute the thread work across the inner or outer loop based on which one is larger + nchunk0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows + nchunk1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows + } + + // The number of elements in each chunk + const int64_t dr0 = (nr0 + nchunk0 - 1) / nchunk0; + const int64_t dr1 = (nr1 + nchunk1 - 1) / nchunk1; + + if ((ggml_n_dims(src0) == 2) && gemv) { + const void * src1_wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; + const size_t src1_col_stride = ggml_is_contiguous(src1) || src1->type != vec_dot_type ? ggml_row_size(vec_dot_type, ne10) : nb11; + int64_t src0_start = (ith * ne01) / nth; + int64_t src0_end = ((ith + 1) * ne01) / nth; + src0_start = (src0_start % matmul_num_cols) ? src0_start + matmul_num_cols - (src0_start % matmul_num_cols): src0_start; + src0_end = (src0_end % matmul_num_cols) ? src0_end + matmul_num_cols - (src0_end % matmul_num_cols): src0_end; + if (src0_start >= src0_end) return; + + // If there are more than three rows in src1, use gemm; otherwise, use gemv. + if (gemm && (ne11 > 3)) { + gemm(ne00, (float *)((char *) dst->data) + src0_start, ne01, (const char *) src0->data + src0_start * nb01, + (const char *) src1_wdata, ne11 - ne11 % 4, src0_end - src0_start); + } + for (int iter = gemm ? ne11 - ne11 % 4 : 0; iter < ne11; iter++) { + gemv(ne00, (float *)((char *) dst->data + (iter * nb1)) + src0_start, ne01, + (const char *) src0->data + src0_start * nb01, (const char *) src1_wdata + (src1_col_stride * iter), 1, + src0_end - src0_start); + } + return; + } + + // The first chunk comes from our thread_id, the rest will get auto-assigned. + int current_chunk = ith; + + while (current_chunk < nchunk0 * nchunk1) { + const int64_t ith0 = current_chunk % nchunk0; + const int64_t ith1 = current_chunk / nchunk0; + + const int64_t ir0_start = dr0 * ith0; + const int64_t ir0_end = MIN(ir0_start + dr0, nr0); + + const int64_t ir1_start = dr1 * ith1; + const int64_t ir1_end = MIN(ir1_start + dr1, nr1); + + // dot kernels can handle 1 row and col at a time, but mmla kernels can process 2 rows and cols + int64_t num_rows_per_vec_dot = vec_dot_num_rows; + + // these checks are needed to avoid crossing dim1 boundaries + // can be optimized, but the logic would become more complicated, so keeping it like this for simplicity + if ((nr0 % 2 != 0) || (ne11 % 2 != 0) || ((ir0_end - ir0_start) % 2 != 0) || ((ir1_end - ir1_start) % 2 != 0)) { + num_rows_per_vec_dot = 1; + } + + ggml_compute_forward_mul_mat_one_chunk(params, dst, type, num_rows_per_vec_dot, ir0_start, ir0_end, ir1_start, ir1_end); + + if (nth >= nchunk0 * nchunk1) { + break; + } + + current_chunk = atomic_fetch_add_explicit(¶ms->threadpool->current_chunk, 1, memory_order_relaxed); + } +} + +// ggml_compute_forward_mul_mat_id + +static void ggml_compute_forward_mul_mat_id( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + const struct ggml_tensor * ids = dst->src[2]; + + GGML_TENSOR_BINARY_OP_LOCALS + + const int ith = params->ith; + const int nth = params->nth; + + const enum ggml_type type = src0->type; + + const bool src1_cont = ggml_is_contiguous(src1); + + ggml_vec_dot_t const vec_dot = type_traits_cpu[type].vec_dot; + enum ggml_type const vec_dot_type = type_traits_cpu[type].vec_dot_type; + ggml_from_float_t const from_float = type_traits_cpu[vec_dot_type].from_float; + int64_t const matmul_num_cols = type_traits_cpu[type].ncols; + ggml_gemv_t const gemv = type_traits_cpu[type].gemv; + + // we don't support permuted src0 or src1 + GGML_ASSERT(nb00 == ggml_type_size(type)); + GGML_ASSERT(nb10 == ggml_type_size(src1->type)); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + // row groups + const int n_ids = ids->ne[0]; // n_expert_used + const int n_as = ne02; // n_expert + + char * wdata_src1_end = (src1->type == vec_dot_type) ? + (char *) params->wdata : + (char *) params->wdata + GGML_PAD(ggml_row_size(vec_dot_type, ggml_nelements(src1)), sizeof(int64_t)); + + struct mmid_row_mapping { + int32_t i1; + int32_t i2; + }; + + int64_t * matrix_row_counts = (int64_t *) (wdata_src1_end); // [n_as] + struct mmid_row_mapping * matrix_rows = (struct mmid_row_mapping *)(matrix_row_counts + n_as); // [n_as][ne11] + + if (src1->type != vec_dot_type) { + char * wdata = params->wdata; + + const size_t nbw1 = ggml_row_size(vec_dot_type, ne10); + const size_t nbw2 = nbw1*ne11; + const size_t nbw3 = nbw2*ne12; + + assert(params->wsize >= ne13*nbw3); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + for (int64_t i13 = 0; i13 < ne13; ++i13) { + for (int64_t i12 = 0; i12 < ne12; ++i12) { + for (int64_t i11 = ith; i11 < ne11; i11 += nth) { + from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), + (void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1), + ne10); + } + } + } + } + +#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne12 + (i1)] + + if (ith == 0) { + // initialize matrix_row_counts + memset(matrix_row_counts, 0, n_as*sizeof(int64_t)); + + // group rows by src0 matrix + for (int64_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) { + for (int id = 0; id < n_ids; ++id) { + const int32_t i02 = *(const int32_t *) ((const char *) ids->data + iid1*ids->nb[1] + id*ids->nb[0]); + + assert(i02 >= 0 && i02 < n_as); + + MMID_MATRIX_ROW(i02, matrix_row_counts[i02]) = (struct mmid_row_mapping) {id, iid1}; + matrix_row_counts[i02] += 1; + } + } + } + + ggml_barrier(params->threadpool); + + // compute each matrix multiplication in sequence + for (int cur_a = 0; cur_a < n_as; ++cur_a) { + const int64_t cne1 = matrix_row_counts[cur_a]; + + if (cne1 == 0) { + continue; + } + + const char * src0_cur = (const char *) src0->data + cur_a*nb02; + + const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; + const size_t row_size = ggml_row_size(vec_dot_type, ne10); + + const int64_t nr0 = ne01; // src0 rows + const int64_t nr1 = cne1; // src1 rows + + if (((ggml_n_dims(src0) - 1) == 2) && gemv) { + int64_t src0_cur_start = (ith * ne01) / nth; + int64_t src0_cur_end = ((ith + 1) * ne01) / nth; + src0_cur_start = (src0_cur_start % matmul_num_cols) ? src0_cur_start + matmul_num_cols - (src0_cur_start % matmul_num_cols): src0_cur_start; + src0_cur_end = (src0_cur_end % matmul_num_cols) ? src0_cur_end + matmul_num_cols - (src0_cur_end % matmul_num_cols): src0_cur_end; + if (src0_cur_start >= src0_cur_end) return; + + for (int ir1 = 0; ir1 < nr1; ir1++) { + struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, ir1); + const int id = row_mapping.i1; // selected expert index + + const int64_t i11 = id % ne11; + const int64_t i12 = row_mapping.i2; // row index in src1 + + const int64_t i1 = id; // selected expert index + const int64_t i2 = i12; // row + + const char * src1_col = (const char *) wdata + + (src1_cont || src1->type != vec_dot_type + ? (i11 + i12 * ne11) * row_size + : (i11 * nb11 + i12 * nb12)); + + gemv(ne00, (float *)((char *) dst->data + (i1 * nb1 + i2 * nb2)) + src0_cur_start, ne01, + (const char *) src0_cur + src0_cur_start * nb01, src1_col, 1, src0_cur_end - src0_cur_start); + } + continue; + } + + // distribute the thread work across the inner or outer loop based on which one is larger + + const int64_t nth0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows + const int64_t nth1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows + + const int64_t ith0 = ith % nth0; + const int64_t ith1 = ith / nth0; + + const int64_t dr0 = (nr0 + nth0 - 1)/nth0; + const int64_t dr1 = (nr1 + nth1 - 1)/nth1; + + const int64_t ir010 = dr0*ith0; + const int64_t ir011 = MIN(ir010 + dr0, nr0); + + const int64_t ir110 = dr1*ith1; + const int64_t ir111 = MIN(ir110 + dr1, nr1); + + // threads with no work simply yield (not sure if it helps) + //if (ir010 >= ir011 || ir110 >= ir111) { + // sched_yield(); + // continue; + //} + + // block-tiling attempt + const int64_t blck_0 = 16; + const int64_t blck_1 = 16; + + // attempt to reduce false-sharing (does not seem to make a difference) + float tmp[16]; + + for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) { + for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) { + for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) { + const int64_t _i12 = ir1; // logical row index for this expert + + struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, _i12); + const int id = row_mapping.i1; // selected expert index + + const int64_t i11 = id % ne11; + const int64_t i12 = row_mapping.i2; // row index in src1 + + const int64_t i1 = id; // selected expert index + const int64_t i2 = i12; // row + + // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides + // if it is, then we have either copied the data to params->wdata and made it contiguous or we are using + // the original src1 data pointer, so we should index using the indices directly + // TODO: this is a bit of a hack, we should probably have a better way to handle this + const char * src1_col = (const char *) wdata + + (src1_cont || src1->type != vec_dot_type + ? (i11 + i12*ne11)*row_size + : (i11*nb11 + i12*nb12)); + + float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2)); + + //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) { + // vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col); + //} + + for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) { + vec_dot(ne00, &tmp[ir0 - iir0], 0, src0_cur + ir0*nb01, 0, src1_col, 0, 1); + } + + memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float)); + } + } + } + } + +#undef MMID_MATRIX_ROW +} + +// ggml_compute_forward_out_prod + +static void ggml_compute_forward_out_prod_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS + + GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_ASSERT(ne0 == ne00); + GGML_ASSERT(ne1 == ne10); + GGML_ASSERT(ne2 == ne02); + GGML_ASSERT(ne02 == ne12); + GGML_ASSERT(ne3 == ne13); + GGML_ASSERT(ne03 == ne13); + + // we don't support permuted src0 or src1 + GGML_ASSERT(nb00 == sizeof(float)); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + // GGML_ASSERT(nb0 <= nb1); + // GGML_ASSERT(nb1 <= nb2); + // GGML_ASSERT(nb2 <= nb3); + + // nb01 >= nb00 - src0 is not transposed + // compute by src0 rows + + if (ith == 0) { + ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); + } + ggml_barrier(params->threadpool); + + // dst[:,:,:,:] = 0 + // for i2,i3: + // for i1: + // for i01: + // for i0: + // dst[i0,i1,i2,i3] += src0[i0,i01,i2,i3] * src1[i1,i01,i2,i3] + + // parallelize by last three dimensions + + // total rows in dst + const int64_t nr = ne1*ne2*ne3; + + // rows per thread + const int64_t dr = (nr + nth - 1)/nth; + + // row range for this thread + const int64_t ir0 = dr*ith; + const int64_t ir1 = MIN(ir0 + dr, nr); + + // block-tiling attempt + const int64_t blck_0 = MAX(GGML_VEC_MAD_UNROLL, 32); + const int64_t blck_1 = 16; + + for (int64_t bir = ir0; bir < ir1; bir += blck_1) { + const int64_t bir1 = MIN(bir + blck_1, ir1); + for (int64_t bi01 = 0; bi01 < ne01; bi01 += blck_0) { + const int64_t bne01 = MIN(bi01 + blck_0, ne01); + for (int64_t ir = bir; ir < bir1; ++ir) { + // dst indices + const int64_t i3 = ir/(ne2*ne1); + const int64_t i2 = (ir - i3*ne2*ne1)/ne1; + const int64_t i1 = (ir - i3*ne2*ne1 - i2*ne1); + + const int64_t i02 = i2; + const int64_t i03 = i3; + + //const int64_t i10 = i1; + const int64_t i12 = i2; + const int64_t i13 = i3; + +#if GGML_VEC_MAD_UNROLL > 2 + const int64_t bne01_unroll = bne01 - (bne01 % GGML_VEC_MAD_UNROLL); + for (int64_t i01 = bi01; i01 < bne01_unroll; i01 += GGML_VEC_MAD_UNROLL) { + const int64_t i11 = i01; + + float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); + float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); + float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); + + ggml_vec_mad_f32_unroll(ne0, nb01, nb11, d, s0, s1); + } + for (int64_t i01 = bne01_unroll; i01 < bne01; ++i01) { + const int64_t i11 = i01; + + float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); + float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); + float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); + + ggml_vec_mad_f32(ne0, d, s0, *s1); + } +#else + for (int64_t i01 = bi01; i01 < bne01; ++i01) { + const int64_t i11 = i01; + + float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); + float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); + float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); + + ggml_vec_mad_f32(ne0, d, s0, *s1); + } +#endif + } + } + } +} + +static void ggml_compute_forward_out_prod_q_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS; + + const int ith = params->ith; + const int nth = params->nth; + + const enum ggml_type type = src0->type; + ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; + + GGML_ASSERT(ne02 == ne12); + GGML_ASSERT(ne03 == ne13); + GGML_ASSERT(ne2 == ne12); + GGML_ASSERT(ne3 == ne13); + + // we don't support permuted src0 dim0 + GGML_ASSERT(nb00 == ggml_type_size(type)); + + // dst dim0 cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + // GGML_ASSERT(nb0 <= nb1); + // GGML_ASSERT(nb1 <= nb2); + // GGML_ASSERT(nb2 <= nb3); + + GGML_ASSERT(ne0 == ne00); + GGML_ASSERT(ne1 == ne10); + GGML_ASSERT(ne2 == ne02); + GGML_ASSERT(ne3 == ne03); + + // nb01 >= nb00 - src0 is not transposed + // compute by src0 rows + + if (ith == 0) { + ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); + } + ggml_barrier(params->threadpool); + + // parallelize by last three dimensions + + // total rows in dst + const int64_t nr = ne1*ne2*ne3; + + // rows per thread + const int64_t dr = (nr + nth - 1)/nth; + + // row range for this thread + const int64_t ir0 = dr*ith; + const int64_t ir1 = MIN(ir0 + dr, nr); + + // dst[:,:,:,:] = 0 + // for i2,i3: + // for i1: + // for i01: + // for i0: + // dst[i0,i1,i2,i3] += src0[i0,i01,i2,i3] * src1[i1,i01,i2,i3] + + float * wdata = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32) * ith; + + for (int64_t ir = ir0; ir < ir1; ++ir) { + // dst indices + const int64_t i3 = ir/(ne2*ne1); + const int64_t i2 = (ir - i3*ne2*ne1)/ne1; + const int64_t i1 = (ir - i3*ne2*ne1 - i2*ne1); + + const int64_t i02 = i2; + const int64_t i03 = i3; + + //const int64_t i10 = i1; + const int64_t i12 = i2; + const int64_t i13 = i3; + + for (int64_t i01 = 0; i01 < ne01; ++i01) { + const int64_t i11 = i01; + + float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); + float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); + float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); + + dequantize_row_q(s0, wdata, ne0); + ggml_vec_mad_f32(ne0, d, wdata, *s1); + } + } +} + +static void ggml_compute_forward_out_prod( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_TQ1_0: + case GGML_TYPE_TQ2_0: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_Q4_0_4_4: + case GGML_TYPE_Q4_0_4_8: + case GGML_TYPE_Q4_0_8_8: + { + ggml_compute_forward_out_prod_q_f32(params, dst); + } break; + case GGML_TYPE_F16: + { + GGML_ABORT("fatal error"); // todo + // ggml_compute_forward_out_prod_f16_f32(params, dst); + } + case GGML_TYPE_F32: + { + ggml_compute_forward_out_prod_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_scale + +static void ggml_compute_forward_scale_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguous(dst)); + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + + // scale factor + float v; + memcpy(&v, dst->op_params, sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src0->ne[0]; + const int nr = ggml_nrows(src0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + const size_t nb01 = src0->nb[1]; + + const size_t nb1 = dst->nb[1]; + + for (int i1 = ir0; i1 < ir1; i1++) { + if (dst->data != src0->data) { + // src0 is same shape as dst => same indices + memcpy((char *)dst->data + i1*nb1, (char *)src0->data + i1*nb01, nc * sizeof(float)); + } + ggml_vec_scale_f32(nc, (float *) ((char *) dst->data + i1*nb1), v); + } +} + +static void ggml_compute_forward_scale( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_scale_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_set + +static void ggml_compute_forward_set_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); + + // view src0 and dst with these strides and data offset inbytes during set + // nb0 is implicitly element_size because src0 and dst are contiguous + size_t nb1 = ((int32_t *) dst->op_params)[0]; + size_t nb2 = ((int32_t *) dst->op_params)[1]; + size_t nb3 = ((int32_t *) dst->op_params)[2]; + size_t offset = ((int32_t *) dst->op_params)[3]; + bool inplace = (bool) ((int32_t *) dst->op_params)[4]; + + if (!inplace) { + if (params->ith == 0) { + // memcpy needs to be synchronized across threads to avoid race conditions. + // => do it in INIT phase + memcpy( + ((char *) dst->data), + ((char *) src0->data), + ggml_nbytes(dst)); + } + ggml_barrier(params->threadpool); + } + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src1); + const int nc = src1->ne[0]; + + GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) + GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) + + // src0 and dst as viewed during set + const size_t nb0 = ggml_element_size(src0); + + const int im0 = (ne10 == 0 ? 0 : ne10-1); + const int im1 = (ne11 == 0 ? 0 : ne11-1); + const int im2 = (ne12 == 0 ? 0 : ne12-1); + const int im3 = (ne13 == 0 ? 0 : ne13-1); + + GGML_ASSERT(offset + im0*nb0 + im1*nb1 + im2*nb2 + im3*nb3 <= ggml_nbytes(dst)); + + GGML_ASSERT(nb10 == sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are viewed with shape of src1 and offset + // => same indices + const int i3 = ir/(ne12*ne11); + const int i2 = (ir - i3*ne12*ne11)/ne11; + const int i1 = (ir - i3*ne12*ne11 - i2*ne11); + + ggml_vec_cpy_f32(nc, + (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), + (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); + } +} + +static void ggml_compute_forward_set_i32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); + + // view src0 and dst with these strides and data offset inbytes during set + // nb0 is implicitly element_size because src0 and dst are contiguous + size_t nb1 = ((int32_t *) dst->op_params)[0]; + size_t nb2 = ((int32_t *) dst->op_params)[1]; + size_t nb3 = ((int32_t *) dst->op_params)[2]; + size_t offset = ((int32_t *) dst->op_params)[3]; + bool inplace = (bool) ((int32_t *) dst->op_params)[4]; + + if (!inplace) { + if (params->ith == 0) { + // memcpy needs to be synchronized across threads to avoid race conditions. + // => do it in INIT phase + memcpy( + ((char *) dst->data), + ((char *) src0->data), + ggml_nbytes(dst)); + } + ggml_barrier(params->threadpool); + } + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src1); + const int nc = src1->ne[0]; + + GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) + GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) + + // src0 and dst as viewed during set + const size_t nb0 = ggml_element_size(src0); + + const int im0 = (ne10 == 0 ? 0 : ne10-1); + const int im1 = (ne11 == 0 ? 0 : ne11-1); + const int im2 = (ne12 == 0 ? 0 : ne12-1); + const int im3 = (ne13 == 0 ? 0 : ne13-1); + + GGML_ASSERT(offset + im0*nb0 + im1*nb1 + im2*nb2 + im3*nb3 <= ggml_nbytes(dst)); + + GGML_ASSERT(nb10 == sizeof(int32_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are viewed with shape of src1 and offset + // => same indices + const int i3 = ir/(ne12*ne11); + const int i2 = (ir - i3*ne12*ne11)/ne11; + const int i1 = (ir - i3*ne12*ne11 - i2*ne11); + + ggml_vec_cpy_i32(nc, + (int32_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), + (int32_t *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); + } +} + +static void ggml_compute_forward_set( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_set_f32(params, dst); + } break; + case GGML_TYPE_I32: + { + ggml_compute_forward_set_i32(params, dst); + } break; + case GGML_TYPE_F16: + case GGML_TYPE_BF16: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q8_1: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_TQ1_0: + case GGML_TYPE_TQ2_0: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_Q4_0_4_4: + case GGML_TYPE_Q4_0_4_8: + case GGML_TYPE_Q4_0_8_8: + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_cpy + +static void ggml_compute_forward_cpy( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + ggml_compute_forward_dup(params, dst); +} + +// ggml_compute_forward_cont + +static void ggml_compute_forward_cont( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + ggml_compute_forward_dup(params, dst); +} + +// ggml_compute_forward_reshape + +static void ggml_compute_forward_reshape( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + // NOP + UNUSED(params); + UNUSED(dst); +} + +// ggml_compute_forward_view + +static void ggml_compute_forward_view( + const struct ggml_compute_params * params, + const struct ggml_tensor * dst) { + // NOP + UNUSED(params); + UNUSED(dst); +} + +// ggml_compute_forward_permute + +static void ggml_compute_forward_permute( + const struct ggml_compute_params * params, + const struct ggml_tensor * dst) { + // NOP + UNUSED(params); + UNUSED(dst); +} + +// ggml_compute_forward_transpose + +static void ggml_compute_forward_transpose( + const struct ggml_compute_params * params, + const struct ggml_tensor * dst) { + // NOP + UNUSED(params); + UNUSED(dst); +} + +// ggml_compute_forward_get_rows + +static void ggml_compute_forward_get_rows_q( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS + + const int64_t nc = ne00; + const int64_t nr = ggml_nelements(src1); + + const enum ggml_type type = src0->type; + ggml_to_float_t const dequantize_row_q = ggml_get_type_traits(type)->to_float; + + assert(ne0 == nc); + assert(ne02 == ne11); + assert(nb00 == ggml_type_size(type)); + assert(ggml_nrows(dst) == nr); + + const int ith = params->ith; + const int nth = params->nth; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int64_t i = ir0; i < ir1; ++i) { + const int64_t i12 = i/(ne11*ne10); + const int64_t i11 = (i - i12*ne11*ne10)/ne10; + const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); + const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); + + GGML_ASSERT(i01 >= 0 && i01 < ne01); + + dequantize_row_q( + (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), + (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); + } +} + +static void ggml_compute_forward_get_rows_f16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS + + const int64_t nc = ne00; + const int64_t nr = ggml_nelements(src1); + + assert(ne0 == nc); + assert(ne02 == ne11); + assert(nb00 == sizeof(ggml_fp16_t)); + assert(ggml_nrows(dst) == nr); + + const int ith = params->ith; + const int nth = params->nth; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int64_t i = ir0; i < ir1; ++i) { + const int64_t i12 = i/(ne11*ne10); + const int64_t i11 = (i - i12*ne11*ne10)/ne10; + const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); + const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); + + GGML_ASSERT(i01 >= 0 && i01 < ne01); + + ggml_fp16_to_fp32_row( + (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), + (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); + } +} + +static void ggml_compute_forward_get_rows_bf16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS + + const int64_t nc = ne00; + const int64_t nr = ggml_nelements(src1); + + assert(ne0 == nc); + assert(ne02 == ne11); + assert(nb00 == sizeof(ggml_bf16_t)); + assert(ggml_nrows(dst) == nr); + + const int ith = params->ith; + const int nth = params->nth; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int64_t i = ir0; i < ir1; ++i) { + const int64_t i12 = i/(ne11*ne10); + const int64_t i11 = (i - i12*ne11*ne10)/ne10; + const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); + const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); + + GGML_ASSERT(i01 >= 0 && i01 < ne01); + + ggml_bf16_to_fp32_row( + (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), + (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); + } +} + +static void ggml_compute_forward_get_rows_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS + + const int64_t nc = ne00; + const int64_t nr = ggml_nelements(src1); + + assert(ne0 == nc); + assert(ne02 == ne11); + assert(nb00 == sizeof(float)); + assert(ggml_nrows(dst) == nr); + + const int ith = params->ith; + const int nth = params->nth; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int64_t i = ir0; i < ir1; ++i) { + const int64_t i12 = i/(ne11*ne10); + const int64_t i11 = (i - i12*ne11*ne10)/ne10; + const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); + const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); + + GGML_ASSERT(i01 >= 0 && i01 < ne01); + + ggml_vec_cpy_f32(nc, + (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), + (float *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03)); + } +} + +static void ggml_compute_forward_get_rows( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q8_1: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_TQ1_0: + case GGML_TYPE_TQ2_0: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_Q4_0_4_4: + case GGML_TYPE_Q4_0_4_8: + case GGML_TYPE_Q4_0_8_8: + { + ggml_compute_forward_get_rows_q(params, dst); + } break; + case GGML_TYPE_F16: + { + ggml_compute_forward_get_rows_f16(params, dst); + } break; + case GGML_TYPE_BF16: + { + ggml_compute_forward_get_rows_bf16(params, dst); + } break; + case GGML_TYPE_F32: + case GGML_TYPE_I32: + { + ggml_compute_forward_get_rows_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } + + //static bool first = true; + //printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]); + //if (first) { + // first = false; + //} else { + // for (int k = 0; k < dst->ne[1]; ++k) { + // for (int j = 0; j < dst->ne[0]/16; ++j) { + // for (int i = 0; i < 16; ++i) { + // printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]); + // } + // printf("\n"); + // } + // printf("\n"); + // } + // printf("\n"); + // exit(0); + //} +} + +// ggml_compute_forward_get_rows_back + +static void ggml_compute_forward_get_rows_back_f32_f16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + if (params->ith != 0) { + return; + } + + GGML_ASSERT(ggml_is_contiguous(dst)); + + // ggml_compute_forward_dup_same_cont(params, opt0, dst); + + memset(dst->data, 0, ggml_nbytes(dst)); + + const int nc = src0->ne[0]; + const int nr = ggml_nelements(src1); + + GGML_ASSERT( dst->ne[0] == nc); + GGML_ASSERT(src0->nb[0] == sizeof(ggml_fp16_t)); + + for (int i = 0; i < nr; ++i) { + const int r = ((int32_t *) src1->data)[i]; + + for (int j = 0; j < nc; ++j) { + ggml_fp16_t v = ((ggml_fp16_t *) ((char *) src0->data + i*src0->nb[1]))[j]; + ((float *) ((char *) dst->data + r*dst->nb[1]))[j] += GGML_FP16_TO_FP32(v); + } + } +} + +static void ggml_compute_forward_get_rows_back_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + if (params->ith != 0) { + return; + } + + GGML_ASSERT(ggml_is_contiguous(dst)); + + // ggml_compute_forward_dup_same_cont(params, opt0, dst); + + memset(dst->data, 0, ggml_nbytes(dst)); + + const int nc = src0->ne[0]; + const int nr = ggml_nelements(src1); + + GGML_ASSERT( dst->ne[0] == nc); + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < nr; ++i) { + const int r = ((int32_t *) src1->data)[i]; + + ggml_vec_add_f32(nc, + (float *) ((char *) dst->data + r*dst->nb[1]), + (float *) ((char *) dst->data + r*dst->nb[1]), + (float *) ((char *) src0->data + i*src0->nb[1])); + } +} + +static void ggml_compute_forward_get_rows_back( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F16: + { + ggml_compute_forward_get_rows_back_f32_f16(params, dst); + } break; + case GGML_TYPE_F32: + { + ggml_compute_forward_get_rows_back_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } + + //static bool first = true; + //printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]); + //if (first) { + // first = false; + //} else { + // for (int k = 0; k < dst->ne[1]; ++k) { + // for (int j = 0; j < dst->ne[0]/16; ++j) { + // for (int i = 0; i < 16; ++i) { + // printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]); + // } + // printf("\n"); + // } + // printf("\n"); + // } + // printf("\n"); + // exit(0); + //} +} + +// ggml_compute_forward_diag + +static void ggml_compute_forward_diag_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + // TODO: handle transposed/permuted matrices + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT(ne00 == ne0); + GGML_ASSERT(ne00 == ne1); + GGML_ASSERT(ne01 == 1); + GGML_ASSERT(ne02 == ne2); + GGML_ASSERT(ne03 == ne3); + + GGML_ASSERT(nb00 == sizeof(float)); + GGML_ASSERT(nb0 == sizeof(float)); + + for (int i3 = 0; i3 < ne3; i3++) { + for (int i2 = 0; i2 < ne2; i2++) { + for (int i1 = 0; i1 < ne1; i1++) { + float * d = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); + float * s = (float *)((char *) src0->data + i3*nb03 + i2*nb02); + for (int i0 = 0; i0 < i1; i0++) { + d[i0] = 0; + } + d[i1] = s[i1]; + for (int i0 = i1+1; i0 < ne0; i0++) { + d[i0] = 0; + } + } + } + } +} + +static void ggml_compute_forward_diag( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_diag_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_diag_mask_inf + +static void ggml_compute_forward_diag_mask_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const float value) { + + const struct ggml_tensor * src0 = dst->src[0]; + + const int ith = params->ith; + const int nth = params->nth; + + const int n_past = ((int32_t *) dst->op_params)[0]; + const bool inplace = src0->data == dst->data; + + GGML_ASSERT(n_past >= 0); + + if (!inplace) { + if (ith == 0) { + // memcpy needs to be synchronized across threads to avoid race conditions. + // => do it in INIT phase + GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); + GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); + memcpy( + ((char *) dst->data), + ((char *) src0->data), + ggml_nbytes(dst)); + } + ggml_barrier(params->threadpool); + } + + // TODO: handle transposed/permuted matrices + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + const int nr = src0->ne[1]; + const int nz = n/nr; + + GGML_ASSERT( dst->nb[0] == sizeof(float)); + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + for (int k = 0; k < nz; k++) { + for (int j = ith; j < nr; j += nth) { + for (int i = n_past; i < nc; i++) { + if (i > n_past + j) { + *(float *)((char *) dst->data + k*dst->nb[2] + j*dst->nb[1] + i*dst->nb[0]) = value; + } + } + } + } +} + +static void ggml_compute_forward_diag_mask_inf( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_diag_mask_f32(params, dst, -INFINITY); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +static void ggml_compute_forward_diag_mask_zero( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_diag_mask_f32(params, dst, 0); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_soft_max + +static void ggml_compute_forward_soft_max_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + assert(ggml_is_contiguous(dst)); + assert(ggml_are_same_shape(src0, dst)); + + float scale = 1.0f; + float max_bias = 0.0f; + + memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); + memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); + + // TODO: handle transposed/permuted matrices + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_UNARY_OP_LOCALS + + //const int64_t ne11 = src1 ? src1->ne[1] : 1; + + // TODO: is this supposed to be ceil instead of floor? + // https://huggingface.co/mosaicml/mpt-7b/blob/main/attention.py#L370 + const uint32_t n_head = ne02; + const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + const int nc = src0->ne[0]; + const int nr = ggml_nrows(src0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + float * wp = (float *) params->wdata + (nc + CACHE_LINE_SIZE_F32) * ith; + + const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16); + + for (int i1 = ir0; i1 < ir1; i1++) { + // ALiBi + const uint32_t h = (i1/ne01)%ne02; // head + const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f; + + float * sp = (float *)((char *) src0->data + i1*src0->nb[1]); + float * dp = (float *)((char *) dst->data + i1*dst->nb[1]); + + // broadcast the mask across rows + ggml_fp16_t * mp_f16 = src1 ? (ggml_fp16_t *)((char *) src1->data) + (i1%ne01)*ne00 : NULL; + float * mp_f32 = src1 ? (float *)((char *) src1->data) + (i1%ne01)*ne00 : NULL; + + ggml_vec_cpy_f32 (nc, wp, sp); + ggml_vec_scale_f32(nc, wp, scale); + if (mp_f32) { + if (use_f16) { + for (int i = 0; i < nc; ++i) { + wp[i] += slope*GGML_FP16_TO_FP32(mp_f16[i]); + } + } else { + for (int i = 0; i < nc; ++i) { + wp[i] += slope*mp_f32[i]; + } + } + } + +#ifndef NDEBUG + for (int i = 0; i < nc; ++i) { + //printf("p[%d] = %f\n", i, p[i]); + assert(!isnan(wp[i])); + } +#endif + + float max = -INFINITY; + ggml_vec_max_f32(nc, &max, wp); + + ggml_float sum = ggml_vec_soft_max_f32(nc, dp, wp, max); + assert(sum > 0.0); + + sum = 1.0/sum; + ggml_vec_scale_f32(nc, dp, sum); + +#ifndef NDEBUG + for (int i = 0; i < nc; ++i) { + assert(!isnan(dp[i])); + assert(!isinf(dp[i])); + } +#endif + } +} + +static void ggml_compute_forward_soft_max( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_soft_max_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + + +// ggml_compute_forward_soft_max_back + +static void ggml_compute_forward_soft_max_back_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguous(src1)); + GGML_ASSERT(ggml_is_contiguous(dst)); + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_are_same_shape(src1, dst)); + + // TODO: handle transposed/permuted matrices + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src0->ne[0]; + const int nr = ggml_nrows(src0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int i1 = ir0; i1 < ir1; i1++) { + float *dy = (float *)((char *) src0->data + i1*src0->nb[1]); + float *y = (float *)((char *) src1->data + i1*src1->nb[1]); + float *dx = (float *)((char *) dst->data + i1*dst->nb[1]); + +#ifndef NDEBUG + for (int i = 0; i < nc; ++i) { + //printf("p[%d] = %f\n", i, p[i]); + assert(!isnan(dy[i])); + assert(!isnan(y[i])); + } +#endif + // Jii = yi - yi*yi + // Jij = -yi*yj + // J = diag(y)-y.T*y + // dx = J * dy + // dxk = sum_i(Jki * dyi) + // dxk = sum_i(-yk*yi * dyi) - (-yk*yk)*dyk + (yk - yk*yk)*dyk + // dxk = sum_i(-yk*yi * dyi) + yk*yk*dyk + yk*dyk - yk*yk*dyk + // dxk = sum_i(-yk*yi * dyi) + yk*dyk + // dxk = -yk * sum_i(yi * dyi) + yk*dyk + // dxk = -yk * dot(y, dy) + yk*dyk + // dxk = yk * (- dot(y, dy) + dyk) + // dxk = yk * (dyk - dot(y, dy)) + // + // post-order: + // dot_y_dy := dot(y, dy) + // dx := dy + // dx := dx - dot_y_dy + // dx := dx * y + + // linear runtime, no additional memory + float dot_y_dy = 0; + ggml_vec_dot_f32 (nc, &dot_y_dy, 0, y, 0, dy, 0, 1); + ggml_vec_cpy_f32 (nc, dx, dy); + ggml_vec_acc1_f32(nc, dx, -dot_y_dy); + ggml_vec_mul_f32 (nc, dx, dx, y); + +#ifndef NDEBUG + for (int i = 0; i < nc; ++i) { + assert(!isnan(dx[i])); + assert(!isinf(dx[i])); + } +#endif + } +} + +static void ggml_compute_forward_soft_max_back( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_soft_max_back_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_clamp + +static void ggml_compute_forward_clamp_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + float min; + float max; + memcpy(&min, (float *) dst->op_params + 0, sizeof(float)); + memcpy(&max, (float *) dst->op_params + 1, sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + const size_t nb00 = src0->nb[0]; + const size_t nb01 = src0->nb[1]; + + const size_t nb0 = dst->nb[0]; + const size_t nb1 = dst->nb[1]; + + GGML_ASSERT( nb0 == sizeof(float)); + GGML_ASSERT(nb00 == sizeof(float)); + + for (int j = ith; j < n; j += nth) { + float * dst_ptr = (float *) ((char *) dst->data + j*nb1); + float * src0_ptr = (float *) ((char *) src0->data + j*nb01); + + for (int i = 0; i < nc; i++) { + dst_ptr[i] = MAX(MIN(src0_ptr[i], max), min); + } + } +} + +static void ggml_compute_forward_clamp( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_clamp_f32(params, dst); + } break; + case GGML_TYPE_F16: + case GGML_TYPE_BF16: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q8_1: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_TQ1_0: + case GGML_TYPE_TQ2_0: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_Q8_K: + case GGML_TYPE_Q4_0_4_4: + case GGML_TYPE_Q4_0_4_8: + case GGML_TYPE_Q4_0_8_8: + case GGML_TYPE_IQ4_NL_4_4: + case GGML_TYPE_I8: + case GGML_TYPE_I16: + case GGML_TYPE_I32: + case GGML_TYPE_I64: + case GGML_TYPE_F64: + case GGML_TYPE_COUNT: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_rope + +static float rope_yarn_ramp(const float low, const float high, const int i0) { + const float y = (i0 / 2 - low) / MAX(0.001f, high - low); + return 1 - MIN(1, MAX(0, y)); +} + +// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn +// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. +static void rope_yarn( + float theta_extrap, float freq_scale, float corr_dims[2], int64_t i0, float ext_factor, float mscale, + float * cos_theta, float * sin_theta) { + // Get n-d rotational scaling corrected for extrapolation + float theta_interp = freq_scale * theta_extrap; + float theta = theta_interp; + if (ext_factor != 0.0f) { + float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor; + theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; + + // Get n-d magnitude scaling corrected for interpolation + mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale); + } + *cos_theta = cosf(theta) * mscale; + *sin_theta = sinf(theta) * mscale; +} + +static void ggml_rope_cache_init( + float theta_base, float freq_scale, const float * freq_factors, float corr_dims[2], int64_t ne0, float ext_factor, float mscale, + float * cache, float sin_sign, float theta_scale) { + // ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py + float theta = theta_base; + for (int64_t i0 = 0; i0 < ne0; i0 += 2) { + const float ff = freq_factors ? freq_factors[i0/2] : 1.0f; + rope_yarn( + theta/ff, freq_scale, corr_dims, i0, ext_factor, mscale, &cache[i0 + 0], &cache[i0 + 1] + ); + cache[i0 + 1] *= sin_sign; + + theta *= theta_scale; + } +} + +static void ggml_compute_forward_rope_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const bool forward) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + const struct ggml_tensor * src2 = dst->src[2]; + + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + + //const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; + //const int n_ctx = ((int32_t *) dst->op_params)[3]; + const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; + + memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); + + GGML_TENSOR_UNARY_OP_LOCALS + + //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); + //printf("n_past = %d, ne2 = %d\n", n_past, ne2); + + GGML_ASSERT(nb00 == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(dst); + + GGML_ASSERT(n_dims <= ne0); + GGML_ASSERT(n_dims % 2 == 0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + // row index used to determine which thread to use + int ir = 0; + + const float theta_scale = powf(freq_base, -2.0f/n_dims); + + float corr_dims[2]; + ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); + + const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; + + const float * freq_factors = NULL; + if (src2 != NULL) { + GGML_ASSERT(src2->type == GGML_TYPE_F32); + GGML_ASSERT(src2->ne[0] >= n_dims / 2); + freq_factors = (const float *) src2->data; + } + + // backward process uses inverse rotation by cos and sin. + // cos and sin build a rotation matrix, where the inverse is the transpose. + // this essentially just switches the sign of sin. + const float sin_sign = forward ? 1.0f : -1.0f; + + const int32_t * pos = (const int32_t *) src1->data; + + for (int64_t i3 = 0; i3 < ne3; i3++) { + for (int64_t i2 = 0; i2 < ne2; i2++) { + const int64_t p = pos[i2]; + + float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith; + ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); + + for (int64_t i1 = 0; i1 < ne1; i1++) { + if (ir++ < ir0) continue; + if (ir > ir1) break; + + if (!is_neox) { + for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { + const float cos_theta = cache[i0 + 0]; + const float sin_theta = cache[i0 + 1]; + + const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + const float x0 = src[0]; + const float x1 = src[1]; + + dst_data[0] = x0*cos_theta - x1*sin_theta; + dst_data[1] = x0*sin_theta + x1*cos_theta; + } + } else { + for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { + const int64_t ic = i0/2; + + const float cos_theta = cache[i0 + 0]; + const float sin_theta = cache[i0 + 1]; + + const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); + float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); + + const float x0 = src[0]; + const float x1 = src[n_dims/2]; + + dst_data[0] = x0*cos_theta - x1*sin_theta; + dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta; + } + } + + for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) { + const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + dst_data[0] = src[0]; + dst_data[1] = src[1]; + } + } + } + } +} + +// TODO: deduplicate f16/f32 code +static void ggml_compute_forward_rope_f16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const bool forward) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + const struct ggml_tensor * src2 = dst->src[2]; + + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + + //const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; + //const int n_ctx = ((int32_t *) dst->op_params)[3]; + const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; + memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); + + GGML_TENSOR_UNARY_OP_LOCALS + + //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); + //printf("n_past = %d, ne2 = %d\n", n_past, ne2); + + GGML_ASSERT(nb0 == sizeof(ggml_fp16_t)); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(dst); + + GGML_ASSERT(n_dims <= ne0); + GGML_ASSERT(n_dims % 2 == 0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + // row index used to determine which thread to use + int ir = 0; + + const float theta_scale = powf(freq_base, -2.0f/n_dims); + + float corr_dims[2]; + ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); + + const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; + + const float * freq_factors = NULL; + if (src2 != NULL) { + GGML_ASSERT(src2->type == GGML_TYPE_F32); + GGML_ASSERT(src2->ne[0] >= n_dims / 2); + freq_factors = (const float *) src2->data; + } + + // backward process uses inverse rotation by cos and sin. + // cos and sin build a rotation matrix, where the inverse is the transpose. + // this essentially just switches the sign of sin. + const float sin_sign = forward ? 1.0f : -1.0f; + + const int32_t * pos = (const int32_t *) src1->data; + + for (int64_t i3 = 0; i3 < ne3; i3++) { + for (int64_t i2 = 0; i2 < ne2; i2++) { + const int64_t p = pos[i2]; + + float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith; + ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); + + for (int64_t i1 = 0; i1 < ne1; i1++) { + if (ir++ < ir0) continue; + if (ir > ir1) break; + + if (!is_neox) { + for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { + const float cos_theta = cache[i0 + 0]; + const float sin_theta = cache[i0 + 1]; + + const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + const float x0 = GGML_FP16_TO_FP32(src[0]); + const float x1 = GGML_FP16_TO_FP32(src[1]); + + dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); + dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); + } + } else { + for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { + const int64_t ic = i0/2; + + const float cos_theta = cache[i0 + 0]; + const float sin_theta = cache[i0 + 1]; + + const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); + ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); + + const float x0 = GGML_FP16_TO_FP32(src[0]); + const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]); + + dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); + dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); + } + } + + for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) { + const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + dst_data[0] = src[0]; + dst_data[1] = src[1]; + } + } + } + } +} + +static void ggml_compute_forward_rope( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F16: + { + ggml_compute_forward_rope_f16(params, dst, true); + } break; + case GGML_TYPE_F32: + { + ggml_compute_forward_rope_f32(params, dst, true); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_rope_back + +static void ggml_compute_forward_rope_back( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F16: + { + ggml_compute_forward_rope_f16(params, dst, false); + } break; + case GGML_TYPE_F32: + { + ggml_compute_forward_rope_f32(params, dst, false); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_conv_transpose_1d + +static void ggml_compute_forward_conv_transpose_1d_f16_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + GGML_TENSOR_BINARY_OP_LOCALS + + const int ith = params->ith; + const int nth = params->nth; + + const int nk = ne00*ne01*ne02; + + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb10 == sizeof(float)); + + if (ith == 0) { + memset(params->wdata, 0, params->wsize); + + // permute kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout) + { + ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; + + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i02*nb02 + i01*nb01); + ggml_fp16_t * dst_data = wdata + i01*ne00*ne02; + for (int64_t i00 = 0; i00 < ne00; i00++) { + dst_data[i00*ne02 + i02] = src[i00]; + } + } + } + } + + // permute source data (src1) from (L x Cin) to (Cin x L) + { + ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + nk; + ggml_fp16_t * dst_data = wdata; + + for (int64_t i11 = 0; i11 < ne11; i11++) { + const float * const src = (float *)((char *) src1->data + i11*nb11); + for (int64_t i10 = 0; i10 < ne10; i10++) { + dst_data[i10*ne11 + i11] = GGML_FP32_TO_FP16(src[i10]); + } + } + } + + // need to zero dst since we are accumulating into it + memset(dst->data, 0, ggml_nbytes(dst)); + } + ggml_barrier(params->threadpool); + + const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; + + // total rows in dst + const int nr = ne1; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; + ggml_fp16_t * const wdata_src = wdata + nk; + + for (int i1 = ir0; i1 < ir1; i1++) { + float * dst_data = (float *)((char *) dst->data + i1*nb1); + ggml_fp16_t * wdata_kernel = wdata + i1*ne02*ne00; + for (int i10 = 0; i10 < ne10; i10++) { + const int i1n = i10*ne11; + for (int i00 = 0; i00 < ne00; i00++) { + float v = 0; + ggml_vec_dot_f16(ne02, &v, 0, + (ggml_fp16_t *) wdata_src + i1n, 0, + (ggml_fp16_t *) wdata_kernel + i00*ne02, 0, 1); + dst_data[i10*s0 + i00] += v; + } + } + } +} + +static void ggml_compute_forward_conv_transpose_1d_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + GGML_TENSOR_BINARY_OP_LOCALS + + const int ith = params->ith; + const int nth = params->nth; + + const int nk = ne00*ne01*ne02; + + GGML_ASSERT(nb00 == sizeof(float)); + GGML_ASSERT(nb10 == sizeof(float)); + + if (ith == 0) { + memset(params->wdata, 0, params->wsize); + + // prepare kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout) + { + float * const wdata = (float *) params->wdata + 0; + + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + const float * const src = (float *)((char *) src0->data + i02*nb02 + i01*nb01); + float * dst_data = wdata + i01*ne00*ne02; + for (int64_t i00 = 0; i00 < ne00; i00++) { + dst_data[i00*ne02 + i02] = src[i00]; + } + } + } + } + + // prepare source data (src1) + { + float * const wdata = (float *) params->wdata + nk; + float * dst_data = wdata; + + for (int64_t i11 = 0; i11 < ne11; i11++) { + const float * const src = (float *)((char *) src1->data + i11*nb11); + for (int64_t i10 = 0; i10 < ne10; i10++) { + dst_data[i10*ne11 + i11] = src[i10]; + } + } + } + + // need to zero dst since we are accumulating into it + memset(dst->data, 0, ggml_nbytes(dst)); + } + ggml_barrier(params->threadpool); + + const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; + + // total rows in dst + const int nr = ne1; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + float * const wdata = (float *) params->wdata + 0; + float * const wdata_src = wdata + nk; + + for (int i1 = ir0; i1 < ir1; i1++) { + float * dst_data = (float *)((char *) dst->data + i1*nb1); + float * wdata_kernel = wdata + i1*ne02*ne00; + for (int i10 = 0; i10 < ne10; i10++) { + const int i1n = i10*ne11; + for (int i00 = 0; i00 < ne00; i00++) { + float v = 0; + ggml_vec_dot_f32(ne02, &v, 0, + wdata_src + i1n, 0, + wdata_kernel + i00*ne02, 0, 1); + dst_data[i10*s0 + i00] += v; + } + } + } +} + +static void ggml_compute_forward_conv_transpose_1d( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F16: + { + ggml_compute_forward_conv_transpose_1d_f16_f32(params, dst); + } break; + case GGML_TYPE_F32: + { + ggml_compute_forward_conv_transpose_1d_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_im2col_f32 +// src0: kernel [OC, IC, KH, KW] +// src1: image [N, IC, IH, IW] +// dst: result [N, OH, OW, IC*KH*KW] +static void ggml_compute_forward_im2col_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + GGML_TENSOR_BINARY_OP_LOCALS; + + const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; + const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; + const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; + const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; + const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; + const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t N = is_2D ? ne13 : ne12; + const int64_t IC = is_2D ? ne12 : ne11; + const int64_t IH = is_2D ? ne11 : 1; + const int64_t IW = ne10; + + const int64_t KH = is_2D ? ne01 : 1; + const int64_t KW = ne00; + + const int64_t OH = is_2D ? ne2 : 1; + const int64_t OW = ne1; + + int ofs0 = is_2D ? nb13 : nb12; + int ofs1 = is_2D ? nb12 : nb11; + + GGML_ASSERT(nb10 == sizeof(float)); + + // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] + { + float * const wdata = (float *) dst->data; + + for (int64_t in = 0; in < N; in++) { + for (int64_t ioh = 0; ioh < OH; ioh++) { // 1 + for (int64_t iow = 0; iow < OW; iow++) { + for (int64_t iic = ith; iic < IC; iic += nth) { + + // micro kernel + float * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] + const float * const src_data = (float *)((char *) src1->data + in*ofs0 + iic*ofs1); // [IH, IW] + + for (int64_t ikh = 0; ikh < KH; ikh++) { // 1 + for (int64_t ikw = 0; ikw < KW; ikw++) { + const int64_t iiw = iow*s0 + ikw*d0 - p0; + const int64_t iih = ioh*s1 + ikh*d1 - p1; + + if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { + dst_data[iic*(KH*KW) + ikh*KW + ikw] = 0; + } else { + dst_data[iic*(KH*KW) + ikh*KW + ikw] = (src_data[iih*IW + iiw]); + } + } + } + } + } + } + } + } +} + + +// ggml_compute_forward_im2col_f16 +// src0: kernel [OC, IC, KH, KW] +// src1: image [N, IC, IH, IW] +// dst: result [N, OH, OW, IC*KH*KW] +static void ggml_compute_forward_im2col_f16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F16); + + GGML_TENSOR_BINARY_OP_LOCALS; + + const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; + const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; + const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; + const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; + const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; + const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t N = is_2D ? ne13 : ne12; + const int64_t IC = is_2D ? ne12 : ne11; + const int64_t IH = is_2D ? ne11 : 1; + const int64_t IW = ne10; + + const int64_t KH = is_2D ? ne01 : 1; + const int64_t KW = ne00; + + const int64_t OH = is_2D ? ne2 : 1; + const int64_t OW = ne1; + + int ofs0 = is_2D ? nb13 : nb12; + int ofs1 = is_2D ? nb12 : nb11; + + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb10 == sizeof(float)); + + // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] + { + ggml_fp16_t * const wdata = (ggml_fp16_t *) dst->data; + + for (int64_t in = 0; in < N; in++) { + for (int64_t ioh = 0; ioh < OH; ioh++) { // 1 + for (int64_t iow = 0; iow < OW; iow++) { + for (int64_t iic = ith; iic < IC; iic += nth) { + + // micro kernel + ggml_fp16_t * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] + const float * const src_data = (float *)((char *) src1->data + in*ofs0 + iic*ofs1); // [IH, IW] + + for (int64_t ikh = 0; ikh < KH; ikh++) { // 1 + for (int64_t ikw = 0; ikw < KW; ikw++) { + const int64_t iiw = iow*s0 + ikw*d0 - p0; + const int64_t iih = ioh*s1 + ikh*d1 - p1; + + if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { + dst_data[iic*(KH*KW) + ikh*KW + ikw] = 0; + } else { + dst_data[iic*(KH*KW) + ikh*KW + ikw] = GGML_FP32_TO_FP16(src_data[iih*IW + iiw]); + } + } + } + } + } + } + } + } +} + +static void ggml_compute_forward_im2col( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + switch (dst->type) { + case GGML_TYPE_F16: + { + ggml_compute_forward_im2col_f16(params, dst); + } break; + case GGML_TYPE_F32: + { + ggml_compute_forward_im2col_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_im2col_back_f32 + +static void ggml_compute_forward_im2col_back_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + GGML_TENSOR_BINARY_OP_LOCALS; + + const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; + const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; + const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; + const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; + const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; + const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t N = is_2D ? ne3 : ne2; + const int64_t IC = is_2D ? ne2 : ne1; + const int64_t IH = is_2D ? ne1 : 1; + const int64_t IW = ne0; + + const int64_t KH = is_2D ? ne01 : 1; + const int64_t KW = ne00; + + const int64_t OH = is_2D ? ne12 : 1; + const int64_t OW = ne11; + + int ofs0 = is_2D ? nb3 : nb2; + int ofs1 = is_2D ? nb2 : nb1; + + GGML_ASSERT(nb0 == sizeof(float)); + + // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] + { + float * const wdata = (float *) dst->data; + + for (int64_t in = 0; in < N; in++) { + for (int64_t iic = ith; iic < IC; iic += nth) { + for (int64_t iih = 0; iih < IH; iih++) { + for (int64_t iiw = 0; iiw < IW; iiw++) { + + // micro kernel + float grad = 0.0f; + for (int64_t ikh = 0; ikh < KH; ikh++) { + for (int64_t ikw = 0; ikw < KW; ikw++) { + // For s0 > 1 some values were skipped over in the forward pass. + // These values have tmpw % s0 != 0 and need to be skipped in the backwards pass as well. + const int64_t tmpw = (iiw + p0 - ikw*d0); + if (tmpw % s0 != 0) { + continue; + } + const int64_t iow = tmpw / s0; + + // Equivalent logic as above except for s1. + int64_t ioh; + if (is_2D) { + const int64_t tmph = iih + p1 - ikh*d1; + + if (tmph % s1 != 0) { + continue; + } + + ioh = tmph / s1; + } else { + ioh = 0; + } + + if (iow < 0 || iow >= OW || ioh < 0 || ioh >= OH) { + continue; + } + + const float * const src_data = (const float *) src1->data + + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] + grad += src_data[iic*(KH*KW) + ikh*KW + ikw]; + } + } + float * dst_data = (float *)((char *) wdata + (in*ofs0 + iic*ofs1)); // [IH, IW] + dst_data[iih*IW + iiw] = grad; + } + } + } + } + } +} + +// ggml_compute_forward_conv_transpose_2d + +static void ggml_compute_forward_conv_transpose_2d( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + GGML_TENSOR_BINARY_OP_LOCALS + + const int ith = params->ith; + const int nth = params->nth; + + const int nk = ne00*ne01*ne02*ne03; + + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb10 == sizeof(float)); + + if (ith == 0) { + memset(params->wdata, 0, params->wsize); + + // permute kernel data (src0) from (Kw x Kh x Cout x Cin) to (Cin x Kw x Kh x Cout) + { + ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i03*nb03 + i02*nb02); + ggml_fp16_t * dst_data = wdata + i02*ne01*ne00*ne03; + for (int64_t i01 = 0; i01 < ne01; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + dst_data[i01*ne00*ne03 + i00*ne03 + i03] = src[i01 * ne00 + i00]; + } + } + } + } + } + + // permute source data (src1) from (Sw x Sh x Cin) to (Cin x Sw x Sh) + { + ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + nk; + for (int i12 = 0; i12 < ne12; i12++) { + for (int i11 = 0; i11 < ne11; i11++) { + const float * const src = (float *)((char *) src1->data + i12*nb12 + i11*nb11); + ggml_fp16_t * dst_data = wdata + i11*ne10*ne12; + for (int i10 = 0; i10 < ne10; i10++) { + dst_data[i10*ne12 + i12] = GGML_FP32_TO_FP16(src[i10]); + } + } + } + } + + memset(dst->data, 0, ggml_nbytes(dst)); + } + ggml_barrier(params->threadpool); + + const int32_t stride = ggml_get_op_params_i32(dst, 0); + + // total patches in dst + const int np = ne2; + + // patches per thread + const int dp = (np + nth - 1)/nth; + + // patch range for this thread + const int ip0 = dp*ith; + const int ip1 = MIN(ip0 + dp, np); + + ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; + ggml_fp16_t * const wdata_src = wdata + nk; + + for (int i2 = ip0; i2 < ip1; i2++) { // Cout + float * dst_data = (float *)((char *) dst->data + i2*nb2); + ggml_fp16_t * wdata_kernel = wdata + i2*ne01*ne00*ne03; + for (int i11 = 0; i11 < ne11; i11++) { + for (int i10 = 0; i10 < ne10; i10++) { + const int i1n = i11*ne10*ne12 + i10*ne12; + for (int i01 = 0; i01 < ne01; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + float v = 0; + ggml_vec_dot_f16(ne03, &v, 0, + wdata_src + i1n, 0, + wdata_kernel + i01*ne00*ne03 + i00*ne03, 0, 1); + dst_data[(i11*stride + i01)*ne0 + i10*stride + i00] += v; + } + } + } + } + } +} + +// ggml_compute_forward_pool_1d_sk_p0 + +static void ggml_compute_forward_pool_1d_sk_p0( + const struct ggml_compute_params * params, + const enum ggml_op_pool op, + const int k, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src = dst->src[0]; + + assert(src->type == GGML_TYPE_F32 || src->type == GGML_TYPE_F16); + + if (params->ith != 0) { + return; + } + + const char * cdata = (const char *)src->data; + const char * const data_end = cdata + ggml_nbytes(src); + float * drow = (float *)dst->data; + + const int64_t rs = dst->ne[0]; + + while (cdata < data_end) { + const void * srow = (const void *)cdata; + int j = 0; + for (int64_t i = 0; i < rs; ++i) { + switch (op) { + case GGML_OP_POOL_AVG: drow[i] = 0; break; + case GGML_OP_POOL_MAX: drow[i] = -FLT_MAX; break; + case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); + } + for (int ki = 0; ki < k; ++ki) { + const float srow_j = (src->type == GGML_TYPE_F32) ? ((const float*)srow)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t*)srow)[j]); + switch (op) { + case GGML_OP_POOL_AVG: drow[i] += srow_j; break; + case GGML_OP_POOL_MAX: if (srow_j > drow[i]) drow[i] = srow_j; break; + case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); + } + ++j; + } + switch (op) { + case GGML_OP_POOL_AVG: drow[i] /= k; break; + case GGML_OP_POOL_MAX: break; + case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); + } + } + + cdata += src->nb[1]; + drow += rs; + } +} + +// ggml_compute_forward_pool_1d + +static void ggml_compute_forward_pool_1d( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const int32_t * opts = (const int32_t *)dst->op_params; + enum ggml_op_pool op = opts[0]; + const int k0 = opts[1]; + const int s0 = opts[2]; + const int p0 = opts[3]; + GGML_ASSERT(p0 == 0); // padding not supported + GGML_ASSERT(k0 == s0); // only s = k supported + + ggml_compute_forward_pool_1d_sk_p0(params, op, k0, dst); +} + +// ggml_compute_forward_pool_2d + +static void ggml_compute_forward_pool_2d( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src = dst->src[0]; + + assert(src->type == GGML_TYPE_F32 || src->type == GGML_TYPE_F16); + + if (params->ith != 0) { + return; + } + + const int32_t * opts = (const int32_t *)dst->op_params; + enum ggml_op_pool op = opts[0]; + const int k0 = opts[1]; + const int k1 = opts[2]; + const int s0 = opts[3]; + const int s1 = opts[4]; + const int p0 = opts[5]; + const int p1 = opts[6]; + const char * cdata = (const char*)src->data; + const char * const data_end = cdata + ggml_nbytes(src); + + const int64_t px = dst->ne[0]; + const int64_t py = dst->ne[1]; + const int64_t pa = px * py; + + float * dplane = (float *)dst->data; + + const int ka = k0 * k1; + const int offset0 = -p0; + const int offset1 = -p1; + + while (cdata < data_end) { + for (int oy = 0; oy < py; ++oy) { + float * const drow = dplane + oy * px; + for (int ox = 0; ox < px; ++ox) { + float * const out = drow + ox; + switch (op) { + case GGML_OP_POOL_AVG: *out = 0; break; + case GGML_OP_POOL_MAX: *out = -FLT_MAX; break; + case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); + } + + const int ix = offset0 + ox * s0; + const int iy = offset1 + oy * s1; + + for (int ky = 0; ky < k1; ++ky) { + if (iy + ky < 0 || iy + ky >= src->ne[1]) continue; + const void * srow = (const void *)(cdata + src->nb[1] * (iy + ky)); + for (int kx = 0; kx < k0; ++kx) { + int j = ix + kx; + if (j < 0 || j >= src->ne[0]) continue; + const float srow_j = (src->type == GGML_TYPE_F32) ? ((const float*)srow)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t*)srow)[j]); + switch (op) { + case GGML_OP_POOL_AVG: *out += srow_j; break; + case GGML_OP_POOL_MAX: if (srow_j > *out) *out = srow_j; break; + case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); + } + } + } + switch (op) { + case GGML_OP_POOL_AVG: *out /= ka; break; + case GGML_OP_POOL_MAX: break; + case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); + } + } + } + + cdata += src->nb[2]; + dplane += pa; + } +} + +// ggml_compute_forward_pool_2d_back + +static void ggml_compute_forward_pool_2d_back( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src = dst->src[0]; + const struct ggml_tensor * dstf = dst->src[1]; // forward tensor of dst + + assert(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); + + if (params->ith != 0) { + return; + } + + const int32_t * opts = (const int32_t *)dst->op_params; + enum ggml_op_pool op = opts[0]; + const int k0 = opts[1]; + const int k1 = opts[2]; + const int s0 = opts[3]; + const int s1 = opts[4]; + const int p0 = opts[5]; + const int p1 = opts[6]; + + char * cdata = (char *) dst->data; + const char * cdataf = (const char *) dstf->data; + const char * const data_end = cdata + ggml_nbytes(dst); + + GGML_ASSERT(params->ith == 0); + memset(cdata, 0, ggml_nbytes(dst)); + + const int64_t px = src->ne[0]; + const int64_t py = src->ne[1]; + const int64_t pa = px * py; + + const float * splane = (const float *) src->data; + + const int ka = k0 * k1; + const int offset0 = -p0; + const int offset1 = -p1; + + while (cdata < data_end) { + for (int oy = 0; oy < py; ++oy) { + const float * const srow = splane + oy * px; + for (int ox = 0; ox < px; ++ox) { + const float grad0 = srow[ox]; + + const int ix = offset0 + ox * s0; + const int iy = offset1 + oy * s1; + + if (op == GGML_OP_POOL_MAX) { + float maxval = -FLT_MAX; + int kxmax = -1; + int kymax = -1; + + for (int ky = 0; ky < k1; ++ky) { + if (iy + ky < 0 || iy + ky >= dst->ne[1]) { + continue; + } + const void * drowf = (const void *)(cdataf + dst->nb[1] * (iy + ky)); + for (int kx = 0; kx < k0; ++kx) { + int j = ix + kx; + if (j < 0 || j >= dst->ne[0]) { + continue; + } + + const float val = dst->type == GGML_TYPE_F32 ? + ((const float *) drowf)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t *) drowf)[j]); + if (val <= maxval) { + continue; + } + + maxval = val; + kxmax = kx; + kymax = ky; + } + } + + if (kxmax == -1 || kymax == -1) { + continue; + } + + void * drow = (void *)(cdata + dst->nb[1] * (iy + kymax)); + const int j = ix + kxmax; + if (dst->type == GGML_TYPE_F32) { + ((float *) drow)[j] += grad0; + } else { + ((ggml_fp16_t *) drow)[j] = GGML_FP32_TO_FP16(grad0 + GGML_FP16_TO_FP32(((const ggml_fp16_t *) drow)[j])); + } + } else if (op == GGML_OP_POOL_AVG) { + const float grad = grad0 / ka; + + for (int ky = 0; ky < k1; ++ky) { + if (iy + ky < 0 || iy + ky >= dst->ne[1]) { + continue; + } + void * drow = (void *)(cdata + dst->nb[1] * (iy + ky)); + for (int kx = 0; kx < k0; ++kx) { + int j = ix + kx; + if (j < 0 || j >= dst->ne[0]) { + continue; + } + + if (dst->type == GGML_TYPE_F32) { + ((float *) drow)[j] += grad; + } else { + ((ggml_fp16_t *) drow)[j] += GGML_FP32_TO_FP16(grad); + } + } + } + } else { + GGML_ASSERT(false); + } + } + } + + cdata += dst->nb[2]; + cdataf += dst->nb[2]; + splane += pa; + } +} + +// ggml_compute_forward_upscale + +static void ggml_compute_forward_upscale_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_UNARY_OP_LOCALS + + const float sf0 = (float)ne0/src0->ne[0]; + const float sf1 = (float)ne1/src0->ne[1]; + const float sf2 = (float)ne2/src0->ne[2]; + const float sf3 = (float)ne3/src0->ne[3]; + + // TODO: optimize + + for (int64_t i3 = 0; i3 < ne3; i3++) { + const int64_t i03 = i3 / sf3; + for (int64_t i2 = ith; i2 < ne2; i2 += nth) { + const int64_t i02 = i2 / sf2; + for (int64_t i1 = 0; i1 < ne1; i1++) { + const int64_t i01 = i1 / sf1; + for (int64_t i0 = 0; i0 < ne0; i0++) { + const int64_t i00 = i0 / sf0; + + const float * x = (float *)((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + float * y = (float *)((char *) dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); + + *y = *x; + } + } + } + } +} + +static void ggml_compute_forward_upscale( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_upscale_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + + +// ggml_compute_forward_pad + +static void ggml_compute_forward_pad_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + GGML_ASSERT( dst->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_UNARY_OP_LOCALS + + float * dst_ptr = (float *) dst->data; + + // TODO: optimize + + for (int64_t i2 = 0; i2 < ne2; ++i2) { + for (int64_t i1 = ith; i1 < ne1; i1 += nth) { + for (int64_t i0 = 0; i0 < ne0; ++i0) { + for (int64_t i3 = 0; i3 < ne3; ++i3) { + const int64_t dst_idx = i3*(ne0*ne1*ne2) + i2*(ne0*ne1) + i1*ne0 + i0; + + const float * src_ptr = (const float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + + if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { + dst_ptr[dst_idx] = *src_ptr; + } else { + dst_ptr[dst_idx] = 0; + } + } + } + } + } +} + +static void ggml_compute_forward_pad( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_pad_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_pad_reflect_1d + +static void ggml_compute_forward_pad_reflect_1d( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + const int ith = params->ith; + const int nth = params->nth; + + const int32_t * opts = (const int32_t *) dst->op_params; + const int p0 = opts[0]; + const int p1 = opts[1]; + + GGML_TENSOR_UNARY_OP_LOCALS + + for (int64_t i3 = 0; i3 < ne3; i3++) { + for (int64_t i2 = 0; i2 < ne2; i2++) { + for (int64_t i1 = ith; i1 < ne1; i1 += nth) { + float * left = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + p0*nb0); + float * right = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + (ne0-p1-1)*nb0); + + ggml_vec_cpy_f32(ne00, left, (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01)); + + for (int i0 = 1; i0 <= p0; i0++) { left[-i0] = left[i0]; } + for (int i0 = 1; i0 <= p1; i0++) { right[i0] = right[-i0]; } + } + } + } +} + +// ggml_compute_forward_arange + +static void ggml_compute_forward_arange_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + GGML_ASSERT(dst->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + const float start = ggml_get_op_params_f32(dst, 0); + const float stop = ggml_get_op_params_f32(dst, 1); + const float step = ggml_get_op_params_f32(dst, 2); + + const int64_t steps = (int64_t) ceilf((stop - start) / step); + + GGML_ASSERT(ggml_nelements(dst) == steps); + + for (int64_t i = ith; i < steps; i+= nth) { + float value = start + step * i; + ((float *)dst->data)[i] = value; + } +} + +static void ggml_compute_forward_arange( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + switch (dst->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_arange_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +static void ggml_compute_forward_timestep_embedding_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(src0->nb[0] == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + GGML_TENSOR_UNARY_OP_LOCALS + + const int dim = ggml_get_op_params_i32(dst, 0); + const int max_period = ggml_get_op_params_i32(dst, 1); + + int half = dim / 2; + + for (int64_t i = 0; i < ne00; i++) { + float * embed_data = (float *)((char *) dst->data + i*nb1); + for (int64_t j = ith; j < half; j += nth) { + float timestep = ((float *)src0->data)[i]; + float freq = (float)expf(-logf(max_period) * j / half); + float arg = timestep * freq; + embed_data[j] = cosf(arg); + embed_data[j + half] = sinf(arg); + } + if (dim % 2 != 0 && ith == 0) { + embed_data[dim] = 0.f; + } + } +} + +static void ggml_compute_forward_timestep_embedding( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_timestep_embedding_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_argsort + +static void ggml_compute_forward_argsort_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT(nb0 == sizeof(float)); + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t nr = ggml_nrows(src0); + + enum ggml_sort_order order = (enum ggml_sort_order) ggml_get_op_params_i32(dst, 0); + + for (int64_t i = ith; i < nr; i += nth) { + int32_t * dst_data = (int32_t *)((char *) dst->data + i*nb1); + const float * src_data = (float *)((char *) src0->data + i*nb01); + + for (int64_t j = 0; j < ne0; j++) { + dst_data[j] = j; + } + + // C doesn't have a functional sort, so we do a bubble sort instead + for (int64_t j = 0; j < ne0; j++) { + for (int64_t k = j + 1; k < ne0; k++) { + if ((order == GGML_SORT_ORDER_ASC && src_data[dst_data[j]] > src_data[dst_data[k]]) || + (order == GGML_SORT_ORDER_DESC && src_data[dst_data[j]] < src_data[dst_data[k]])) { + int32_t tmp = dst_data[j]; + dst_data[j] = dst_data[k]; + dst_data[k] = tmp; + } + } + } + } +} + +static void ggml_compute_forward_argsort( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_argsort_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_flash_attn_ext + +static void ggml_compute_forward_flash_attn_ext_f16( + const struct ggml_compute_params * params, + const struct ggml_tensor * q, + const struct ggml_tensor * k, + const struct ggml_tensor * v, + const struct ggml_tensor * mask, + struct ggml_tensor * dst) { + + GGML_TENSOR_LOCALS(int64_t, neq, q, ne) + GGML_TENSOR_LOCALS(size_t, nbq, q, nb) + GGML_TENSOR_LOCALS(int64_t, nek, k, ne) + GGML_TENSOR_LOCALS(size_t, nbk, k, nb) + GGML_TENSOR_LOCALS(int64_t, nev, v, ne) + GGML_TENSOR_LOCALS(size_t, nbv, v, nb) + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) + GGML_TENSOR_LOCALS(size_t, nb, dst, nb) + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t D = neq0; + const int64_t N = neq1; + + GGML_ASSERT(ne0 == D); + GGML_ASSERT(ne2 == N); + + // input tensor rows must be contiguous + GGML_ASSERT(nbq0 == ggml_type_size(q->type)); + GGML_ASSERT(nbk0 == ggml_type_size(k->type)); + GGML_ASSERT(nbv0 == ggml_type_size(v->type)); + + GGML_ASSERT(neq0 == D); + GGML_ASSERT(nek0 == D); + GGML_ASSERT(nev0 == D); + + GGML_ASSERT(neq1 == N); + GGML_ASSERT(nev0 == D); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + // broadcast factors + const int64_t rk2 = neq2/nek2; + const int64_t rk3 = neq3/nek3; + + const int64_t rv2 = neq2/nev2; + const int64_t rv3 = neq3/nev3; + + // parallelize by q rows using ggml_vec_dot_f32 + + // total rows in q + const int nr = neq1*neq2*neq3; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + float scale = 1.0f; + float max_bias = 0.0f; + float logit_softcap = 0.0f; + + memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); + memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); + memcpy(&logit_softcap, (float *) dst->op_params + 2, sizeof(float)); + + if (logit_softcap != 0) { + scale /= logit_softcap; + } + + const uint32_t n_head = neq2; + const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + enum ggml_type const k_vec_dot_type = type_traits_cpu[k->type].vec_dot_type; + ggml_from_float_t const q_to_vec_dot = type_traits_cpu[k_vec_dot_type].from_float; + ggml_vec_dot_t const kq_vec_dot = type_traits_cpu[k->type].vec_dot; + ggml_to_float_t const v_to_float = ggml_get_type_traits(v->type)->to_float; + + GGML_ASSERT(q_to_vec_dot && "fattn: unsupported K-type"); + GGML_ASSERT(v_to_float && "fattn: unsupported V-type"); + + // loop over n_batch and n_head + for (int ir = ir0; ir < ir1; ++ir) { + // q indices + const int iq3 = ir/(neq2*neq1); + const int iq2 = (ir - iq3*neq2*neq1)/neq1; + const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1); + + const uint32_t h = iq2; // head index + const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f; + + float S = 0.0f; // sum + float M = -INFINITY; // maximum KQ value + + float * VKQ32 = (float *) params->wdata + ith*(3*D + CACHE_LINE_SIZE_F32); // FP32 VKQ accumulator + float * V32 = (VKQ32 + 1*D); // (temporary) FP32 V buffer + ggml_fp16_t * VKQ16 = (ggml_fp16_t *) (VKQ32 + 1*D); // (temporary) FP16 VKQ accumulator + ggml_fp16_t * Q_q = (ggml_fp16_t *) (VKQ32 + 2*D); // (temporary) buffer for Q converted to quantized/FP16 + + if (v->type == GGML_TYPE_F16) { + memset(VKQ16, 0, D*sizeof(ggml_fp16_t)); + } else { + memset(VKQ32, 0, D*sizeof(float)); + } + + const ggml_fp16_t * mp = mask ? (ggml_fp16_t *)((char *) mask->data + iq1*mask->nb[1]) : NULL; + + // k indices + const int ik3 = iq3 / rk3; + const int ik2 = iq2 / rk2; + + // v indices + const int iv3 = iq3 / rv3; + const int iv2 = iq2 / rv2; + + const float * pq = (const float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)); + q_to_vec_dot(pq, Q_q, D); + + // online softmax / attention + // loop over n_kv and n_head_kv + // ref: https://arxiv.org/pdf/2112.05682.pdf + for (int64_t ic = 0; ic < nek1; ++ic) { + const float mv = mp ? slope*GGML_FP16_TO_FP32(mp[ic]) : 0.0f; + if (mv == -INFINITY) { + continue; + } + + float s; // KQ value + + const char * k_data = (const char *) k->data + ( ic*nbk1 + ik2*nbk2 + ik3*nbk3); + kq_vec_dot(D, &s, 0, k_data, 0, Q_q, 0, 1); + + s = s*scale; // scale KQ value + + if (logit_softcap != 0.0f) { + s = logit_softcap*tanhf(s); + } + + s += mv; // apply mask + + const float Mold = M; + + float ms = 1.0f; // upon new higher max val, scale VKQ and KQ sum with this value + float vs = 1.0f; // post-softmax KQ value, expf(s - M) + + const char * v_data = ((const char *) v->data + (ic*nbv1 + iv2*nbv2 + iv3*nbv3)); + + if (v->type == GGML_TYPE_F16) { + if (s > M) { + // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f + M = s; + ms = expf(Mold - M); + + // V = V*expf(Mold - M) + ggml_vec_scale_f16(D, VKQ16, ms); + } else { + // no new maximum, ms == 1.0f, vs != 1.0f + vs = expf(s - M); + } + + // V += v*expf(s - M) + ggml_vec_mad_f16(D, VKQ16, (const ggml_fp16_t *) v_data, vs); + } else { + if (s > M) { + // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f + M = s; + ms = expf(Mold - M); + + // V = V*expf(Mold - M) + ggml_vec_scale_f32(D, VKQ32, ms); + } else { + // no new maximum, ms == 1.0f, vs != 1.0f + vs = expf(s - M); + } + + v_to_float(v_data, V32, D); + + // V += v*expf(s - M) + ggml_vec_mad_f32(D, VKQ32, V32, vs); + } + + S = S*ms + vs; // scale and increment sum with partial sum + } + + if (v->type == GGML_TYPE_F16) { + for (int64_t d = 0; d < D; ++d) { + VKQ32[d] = GGML_FP16_TO_FP32(VKQ16[d]); + } + } + + // V /= S + const float S_inv = 1.0f/S; + ggml_vec_scale_f32(D, VKQ32, S_inv); + + // dst indices + const int i1 = iq1; + const int i2 = iq2; + const int i3 = iq3; + + // original + //memcpy((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3), V, nev0*sizeof(float)); + + // permute(0, 2, 1, 3) + memcpy((char *) dst->data + (i3*ne2*ne1 + i2 + i1*ne1)*nb1, VKQ32, nb1); + } +} + +static void ggml_compute_forward_flash_attn_ext( + const struct ggml_compute_params * params, + const struct ggml_tensor * q, + const struct ggml_tensor * k, + const struct ggml_tensor * v, + const struct ggml_tensor * mask, + struct ggml_tensor * dst) { + switch (dst->op_params[3]) { + case GGML_PREC_DEFAULT: + case GGML_PREC_F32: + { + // uses F32 accumulators + ggml_compute_forward_flash_attn_ext_f16(params, q, k, v, mask, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_flash_attn_back + +static void ggml_compute_forward_flash_attn_back_f32( + const struct ggml_compute_params * params, + const bool masked, + struct ggml_tensor * dst) { + + const struct ggml_tensor * q = dst->src[0]; + const struct ggml_tensor * k = dst->src[1]; + const struct ggml_tensor * v = dst->src[2]; + const struct ggml_tensor * d = dst->src[3]; + + GGML_TENSOR_LOCALS(int64_t, neq, q, ne) + GGML_TENSOR_LOCALS(size_t, nbq, q, nb) + GGML_TENSOR_LOCALS(int64_t, nek, k, ne) + GGML_TENSOR_LOCALS(size_t, nbk, k, nb) + GGML_TENSOR_LOCALS(int64_t, nev, v, ne) + GGML_TENSOR_LOCALS(size_t, nbv, v, nb) + GGML_TENSOR_LOCALS(int64_t, ned, d, ne) + GGML_TENSOR_LOCALS(size_t, nbd, d, nb) + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) + GGML_TENSOR_LOCALS(size_t, nb, dst, nb) + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t D = neq0; + const int64_t N = neq1; + const int64_t P = nek1 - N; + const int64_t M = P + N; + + const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL); + const int mxDM = MAX(D, Mup); + + // GGML_ASSERT(ne0 == D); + // GGML_ASSERT(ne1 == N); + GGML_ASSERT(P >= 0); + + GGML_ASSERT(nbq0 == sizeof(float)); + GGML_ASSERT(nbk0 == sizeof(float)); + GGML_ASSERT(nbv0 == sizeof(float)); + + GGML_ASSERT(neq0 == D); + GGML_ASSERT(nek0 == D); + GGML_ASSERT(nev1 == D); + GGML_ASSERT(ned0 == D); + + GGML_ASSERT(neq1 == N); + GGML_ASSERT(nek1 == N + P); + GGML_ASSERT(nev1 == D); + GGML_ASSERT(ned1 == N); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + if (ith == 0) { + memset(dst->data, 0, nb0*ne0*ne1*ne2*ne3); + } + ggml_barrier(params->threadpool); + + const int64_t elem_q = ggml_nelements(q); + const int64_t elem_k = ggml_nelements(k); + + enum ggml_type result_type = dst->type; + GGML_ASSERT(ggml_blck_size(result_type) == 1); + const size_t tsize = ggml_type_size(result_type); + + const size_t offs_q = 0; + const size_t offs_k = offs_q + GGML_PAD(elem_q * tsize, GGML_MEM_ALIGN); + const size_t offs_v = offs_k + GGML_PAD(elem_k * tsize, GGML_MEM_ALIGN); + + void * grad_q = (char *) dst->data; + void * grad_k = (char *) dst->data + offs_k; + void * grad_v = (char *) dst->data + offs_v; + + const size_t nbgq1 = nb0*neq0; + const size_t nbgq2 = nb0*neq0*neq1; + const size_t nbgq3 = nb0*neq0*neq1*neq2; + + const size_t nbgk1 = nb0*nek0; + const size_t nbgk2 = nb0*nek0*nek1; + const size_t nbgk3 = nb0*nek0*nek1*neq2; + + const size_t nbgv1 = nb0*nev0; + const size_t nbgv2 = nb0*nev0*nev1; + const size_t nbgv3 = nb0*nev0*nev1*neq2; + + // parallelize by k rows using ggml_vec_dot_f32 + + // total rows in k + const int nr = nek2*nek3; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + const float scale = 1.0f/sqrtf(D); + + //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale); + + // how often k2 (and v2) is repeated in q2 + int nrep = neq2/nek2; + + for (int ir = ir0; ir < ir1; ++ir) { + // q indices + const int ik3 = ir/(nek2); + const int ik2 = ir - ik3*nek2; + + const int iq3 = ik3; + const int id3 = ik3; + const int iv3 = ik3; + const int iv2 = ik2; + + for (int irep = 0; irep < nrep; ++irep) { + const int iq2 = ik2 + irep*nek2; + const int id2 = iq2; + + // (ik2 + irep*nek2) % nek2 == ik2 + for (int iq1 = 0; iq1 < neq1; ++iq1) { + const int id1 = iq1; + + // not sure about CACHE_LINE_SIZE_F32.. + // - maybe it must not be multiplied by 2 and excluded from .. in SM 1*(..) offset? + float * S = (float *) params->wdata + ith*2*(mxDM + CACHE_LINE_SIZE_F32) + 0*(mxDM+CACHE_LINE_SIZE_F32); + float * SM = (float *) params->wdata + ith*2*(mxDM + CACHE_LINE_SIZE_F32) + 1*(mxDM+CACHE_LINE_SIZE_F32); + + for (int i = M; i < Mup; ++i) { + S[i] = -INFINITY; + } + + const int64_t masked_begin = masked ? (P + iq1 + 1) : M; + for (int64_t ic = 0; ic < masked_begin; ++ic) { + // k indices + const int ik1 = ic; + + // S indices + const int i1 = ik1; + + ggml_vec_dot_f32(neq0, + S + i1, 0, + (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), 0, + (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), 0, 1); + } + + // scale + ggml_vec_scale_f32(masked_begin, S, scale); + + for (int64_t i = masked_begin; i < M; i++) { + S[i] = -INFINITY; + } + + // softmax + // exclude known -INF S[..] values from max and loop + // dont forget to set their SM values to zero + { + float max = -INFINITY; + ggml_vec_max_f32(masked_begin, &max, S); + + ggml_float sum = 0.0; + { +#ifdef GGML_SOFT_MAX_ACCELERATE + max = -max; + vDSP_vsadd(SM, 1, &max, SM, 1, Mup); + vvexpf(SM, SM, &Mup); + ggml_vec_sum_f32(Mup, &sum, SM); +#else + sum = ggml_vec_soft_max_f32(Mup, SM, S, max); +#endif + } + + assert(sum > 0.0); + + sum = 1.0/sum; + ggml_vec_scale_f32(masked_begin, SM, sum); + + } + + // step-by-step explanation + { + // forward-process shape grads from backward process + // parallel_for ik2,ik3: + // for irep: + // iq2 = ik2 + irep*nek2 + // k[:D,:M,:,:] [D,M,:,:] grad[k][:D,:M,ik2,ik3] += grad[kcur] + // q[:D,:N,:,:] [D,N,:,:] grad[q][:D,iq1,iq2,iq3] += grad[qcur] + // v[:M,:D,:,:] [M,D,:,:] grad[v][:M,:D,iv2,iv3] += grad[vcur] + // for iq1: + // kcur = k[:D,:M,ik2,ik3] [D,M,1,1] grad[kcur] = grad[S1].T @ qcur + // qcur = q[:D,iq1,iq2,iq3] [D,1,1,1] grad[qcur] = grad[S1] @ kcur + // vcur = v[:M,:D,iv2,iv3] [M,D,1,1] grad[vcur] = grad[S5].T @ S4 + // S0 = -Inf [D,1,1,1] + // ~S1[i] = dot(kcur[:D,i], qcur) + // S1 = qcur @ kcur.T [M,1,1,1] grad[S1] = grad[S2] * scale + // S2 = S1 * scale [M,1,1,1] grad[S2] = diag_mask_zero(grad[S3], P) + // S3 = diag_mask_inf(S2, P) [M,1,1,1] grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) + // S4 = softmax(S3) [M,1,1,1] grad[S4] = grad[S5] @ vcur + // ~S5[i] = dot(vcur[:,i], S4) + // S5 = S4 @ vcur.T [D,1,1,1] grad[S5] = d[:D,id1,id2,id3] + // ~dst[i,iq1,iq2,iq3] = S5[i] ^ + // dst[:D,iq1,iq2,iq3] = S5 | grad[dst[:D,iq1,iq2,iq3]] = d[:D,id1,id2,id3] + // dst backward-/ grad[dst] = d + // + // output gradients with their dependencies: + // + // grad[kcur] = grad[S1].T @ qcur + // grad[S1] = diag_mask_zero(grad[S3], P) * scale + // grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) + // grad[S4] = grad[S5] @ vcur + // grad[S4] = d[:D,id1,id2,id3] @ vcur + // grad[qcur] = grad[S1] @ kcur + // grad[vcur] = grad[S5].T @ S4 + // grad[vcur] = d[:D,id1,id2,id3].T @ S4 + // + // in post-order: + // + // S1 = qcur @ kcur.T + // S2 = S1 * scale + // S3 = diag_mask_inf(S2, P) + // S4 = softmax(S3) + // grad[S4] = d[:D,id1,id2,id3] @ vcur + // grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) + // grad[S1] = diag_mask_zero(grad[S3], P) * scale + // grad[qcur] = grad[S1] @ kcur + // grad[kcur] = grad[S1].T @ qcur + // grad[vcur] = d[:D,id1,id2,id3].T @ S4 + // + // using less variables (SM=S4): + // + // S = diag_mask_inf(qcur @ kcur.T * scale, P) + // SM = softmax(S) + // S = d[:D,iq1,iq2,iq3] @ vcur + // dot_SM_gradSM = dot(SM, S) + // S = SM * (S - dot(SM, S)) + // S = diag_mask_zero(S, P) * scale + // + // grad[q][:D,iq1,iq2,iq3] += S @ kcur + // grad[k][:D,:M,ik2,ik3] += S.T @ qcur + // grad[v][:M,:D,iv2,iv3] += d[:D,id1,id2,id3].T @ SM + } + + // S = gradSM = d[:D,id1,id2,id3] @ vcur[:,:,iv2,iv3] + // S = d[:D,id1,id2,id3] @ vcur[:,:,iv2,iv3] + // for ic: + // S[:M] += vcur[:M,ic,iv2,iv3] * d[ic,id1,id2,id3] + // exclude known future zero S[..] values from operation + ggml_vec_set_f32(masked_begin, S, 0); + for (int64_t ic = 0; ic < D; ++ic) { + ggml_vec_mad_f32(masked_begin, + S, + (float *) ((char *) v->data + ( ic*nbv1 + iv2*nbv2 + iv3*nbv3)), + *(float *) ((char *) d->data + (ic*nbd0 + id1*nbd1 + id2*nbd2 + id3*nbd3))); + } + + // S = SM * (S - dot(SM, S)) + float dot_SM_gradSM = 0; + ggml_vec_dot_f32 (masked_begin, &dot_SM_gradSM, 0, SM, 0, S, 0, 1); + ggml_vec_acc1_f32(M, S, -dot_SM_gradSM); + ggml_vec_mul_f32 (masked_begin, S, S, SM); + + // S = diag_mask_zero(S, P) * scale + // already done by above ggml_vec_set_f32 + + // exclude known zero S[..] values from operation + ggml_vec_scale_f32(masked_begin, S, scale); + + // S shape [M,1] + // SM shape [M,1] + // kcur shape [D,M] + // qcur shape [D,1] + // vcur shape [M,D] + + // grad[q][:D,iq1,iq2,iq3] += S @ kcur + // grad[q][:D,iq1,iq2,iq3] += shape[M,1] @ shape[D,M] + // for ic: + // grad[q][:D,iq1,iq2,iq3] += S[ic] * kcur[:D,ic,ik2,ik3] + // exclude known zero S[..] values from loop + for (int64_t ic = 0; ic < masked_begin; ++ic) { + ggml_vec_mad_f32(D, + (float *) ((char *) grad_q + (iq1*nbgq1 + iq2*nbgq2 + iq3*nbgq3)), + (float *) ((char *) k->data + (ic*nbk1 + ik2*nbk2 + ik3*nbk3)), + S[ic]); + } + + // grad[k][:D,:M,iq2,iq3] += S.T @ qcur + // for ic: + // grad[k][:D,ic,iq2,iq3] += S.T[0,ic] * qcur[:D,0] + // grad[k][:D,ic,iq2,iq3] += S[ic] * qcur[:D,0] + // exclude known zero S[..] values from loop + for (int64_t ic = 0; ic < masked_begin; ++ic) { + ggml_vec_mad_f32(D, + (float *) ((char *) grad_k + (ic*nbgk1 + ik2*nbgk2 + ik3*nbgk3)), + (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), + S[ic]); + } + + // grad[v][:M,:D,iv2,iv3] += d[:D,id1,id2,id3].T @ SM + // for ic: + // grad[v][:M,ic,iv2,iv3] += d[:D,id1,id2,id3].T[0,ic] * SM[:M] + // grad[v][:M,ic,iv2,iv3] += d[ic,id1,id2,id3] * SM[:M] + // exclude known zero SM[..] values from mad + for (int64_t ic = 0; ic < D; ++ic) { + ggml_vec_mad_f32(masked_begin, + (float *) ((char *) grad_v + ( ic*nbgv1 + iv2*nbgv2 + iv3*nbgv3)), + SM, + *(float *) ((char *) d->data + (ic*nbd0 + id1*nbd1 + id2*nbd2 + id3*nbd3))); + } + } + } + } +} + +static void ggml_compute_forward_flash_attn_back( + const struct ggml_compute_params * params, + const bool masked, + struct ggml_tensor * dst) { + + const struct ggml_tensor * q = dst->src[0]; + + switch (q->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_flash_attn_back_f32(params, masked, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_ssm_conv + +static void ggml_compute_forward_ssm_conv_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + const struct ggml_tensor * src0 = dst->src[0]; // conv_x + const struct ggml_tensor * src1 = dst->src[1]; // conv1d.weight + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src1->ne[0]; // d_conv + const int ncs = src0->ne[0]; // d_conv - 1 + n_t + const int nr = src0->ne[1]; // d_inner + const int n_t = dst->ne[1]; // tokens per sequence + const int n_s = dst->ne[2]; // number of sequences in the batch + + GGML_ASSERT( dst->ne[0] == nr); + GGML_ASSERT(src0->nb[0] == sizeof(float)); + GGML_ASSERT(src1->nb[0] == sizeof(float)); + GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + const int ir = ir1 - ir0; + + for (int i3 = 0; i3 < n_s; ++i3) { + for (int i2 = 0; i2 < n_t; ++i2) { + // {d_conv - 1 + n_t, d_inner, n_seqs} + // sliding window + const float * s = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i2*(src0->nb[0]) + i3*(src0->nb[2])); // {d_conv, d_inner, n_s} + const float * c = (const float *) ((const char *) src1->data + ir0*(src1->nb[1])); // {d_conv, d_inner} + float * x = (float *) ((char *) dst->data + ir0*(dst->nb[0]) + i2*(dst->nb[1]) + i3*(dst->nb[2])); // {d_inner, n_t, n_s} + + // TODO: transpose the output for smaller strides for big batches? + // d_inner + for (int i1 = 0; i1 < ir; ++i1) { + // rowwise dot product + // NOTE: not using ggml_vec_dot_f32, because its sum is in double precision + float sumf = 0.0f; + + // d_conv + for (int i0 = 0; i0 < nc; ++i0) { + sumf += s[i0 + i1*ncs] * c[i0 + i1*nc]; + } + x[i1] = sumf; + } + } + } +} + +static void ggml_compute_forward_ssm_conv( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + switch (dst->src[0]->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_ssm_conv_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_ssm_scan + +static void ggml_compute_forward_ssm_scan_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + const struct ggml_tensor * src0 = dst->src[0]; // s + const struct ggml_tensor * src1 = dst->src[1]; // x + const struct ggml_tensor * src2 = dst->src[2]; // dt + const struct ggml_tensor * src3 = dst->src[3]; // A + const struct ggml_tensor * src4 = dst->src[4]; // B + const struct ggml_tensor * src5 = dst->src[5]; // C + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t nc = src0->ne[0]; // d_state + const int64_t nr = src0->ne[1]; // d_inner + const int64_t n_t = src1->ne[1]; // number of tokens per sequence + const int64_t n_s = src0->ne[2]; // number of sequences in the batch + + GGML_ASSERT(ggml_nelements(src1) + ggml_nelements(src0) == ggml_nelements(dst)); + GGML_ASSERT(src0->nb[0] == sizeof(float)); + GGML_ASSERT(src1->nb[0] == sizeof(float)); + GGML_ASSERT(src2->nb[0] == sizeof(float)); + GGML_ASSERT(src3->nb[0] == sizeof(float)); + GGML_ASSERT(src4->nb[0] == sizeof(float)); + GGML_ASSERT(src5->nb[0] == sizeof(float)); + // required for the dot product between s and C + GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float)); + // required for per-sequence offsets for states + GGML_ASSERT(src0->nb[2] == src0->ne[0]*src0->ne[1]*sizeof(float)); + // required to get correct offset for state destination (i.e. src1->nb[3]) + GGML_ASSERT(src1->nb[3] == src1->ne[0]*src1->ne[1]*src1->ne[2]*sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + const int ir = ir1 - ir0; + + for (int i3 = 0; i3 < n_s; ++i3) { + for (int i2 = 0; i2 < n_t; ++i2) { + const float * s0 = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2])); // {d_state, d_inner, n_s} + const float * x = (const float *) ((const char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s} + const float * dt = (const float *) ((const char *) src2->data + ir0*(src2->nb[0]) + i2*(src2->nb[1]) + i3*(src2->nb[2])); // {d_inner, n_t, n_s} + const float * A = (const float *) ((const char *) src3->data + ir0*(src3->nb[1])); // {d_state, d_inner} + const float * B = (const float *) ((const char *) src4->data + i2*(src4->nb[1]) + i3*(src4->nb[2])); // {d_state, n_t, n_s} + const float * C = (const float *) ((const char *) src5->data + i2*(src5->nb[1]) + i3*(src5->nb[2])); // {d_state, n_t, n_s} + float * y = ( float *) (( char *) dst->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s} + float * s = ( float *) (( char *) dst->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]) + src1->nb[3]); // {d_state, d_inner, n_s} + + // use the output as the source for the next token-wise iterations + if (i2 > 0) { s0 = s; } + + // d_inner + for (int i1 = 0; i1 < ir; ++i1) { + // ref: https://github.com/state-spaces/mamba/blob/34076d664838588a3c97727b263478ab9f621a07/mamba_ssm/ops/triton/selective_state_update.py#L78 + float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1]; + float x_dt = x[i1] * dt_soft_plus; + float sumf = 0.0f; + // d_state + for (int i0 = 0; i0 < nc; ++i0) { + int i = i0 + i1*nc; + // state = prev_state * dA + dB * x + float state = (s0[i] * expf(dt_soft_plus * A[i])) + (B[i0] * x_dt); + // y = rowwise_dotprod(state, C) + sumf += state * C[i0]; + s[i] = state; + } + y[i1] = sumf; + } + } + } +} + +static void ggml_compute_forward_ssm_scan( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + switch (dst->src[0]->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_ssm_scan_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_win_part + +static void ggml_compute_forward_win_part_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + UNUSED(params); + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) + + const int32_t nep0 = ((const int32_t *)(dst->op_params))[0]; + const int32_t nep1 = ((const int32_t *)(dst->op_params))[1]; + const int32_t w = ((const int32_t *)(dst->op_params))[2]; + + assert(ne00 == ne0); + assert(ne3 == nep0*nep1); + + // TODO: optimize / multi-thread + for (int py = 0; py < nep1; ++py) { + for (int px = 0; px < nep0; ++px) { + const int64_t i3 = py*nep0 + px; + for (int64_t i2 = 0; i2 < ne2; ++i2) { + for (int64_t i1 = 0; i1 < ne1; ++i1) { + for (int64_t i0 = 0; i0 < ne0; ++i0) { + const int64_t i02 = py*w + i2; + const int64_t i01 = px*w + i1; + const int64_t i00 = i0; + + const int64_t i = i3*ne2*ne1*ne0 + i2*ne1*ne0 + i1*ne0 + i0; + const int64_t j = i02*ne01*ne00 + i01*ne00 + i00; + + if (py*w + i2 >= ne02 || px*w + i1 >= ne01) { + ((float *) dst->data)[i] = 0.0f; + } else { + ((float *) dst->data)[i] = ((float *) src0->data)[j]; + } + } + } + } + } + } +} + +static void ggml_compute_forward_win_part( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_win_part_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_win_unpart + +static void ggml_compute_forward_win_unpart_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + UNUSED(params); + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) + + const int32_t w = ((const int32_t *)(dst->op_params))[0]; + + // padding + const int px = (w - ne1%w)%w; + //const int py = (w - ne2%w)%w; + + const int npx = (px + ne1)/w; + //const int npy = (py + ne2)/w; + + assert(ne0 == ne00); + + // TODO: optimize / multi-thread + for (int64_t i2 = 0; i2 < ne2; ++i2) { + for (int64_t i1 = 0; i1 < ne1; ++i1) { + for (int64_t i0 = 0; i0 < ne0; ++i0) { + const int ip2 = i2/w; + const int ip1 = i1/w; + + const int64_t i02 = i2%w; + const int64_t i01 = i1%w; + const int64_t i00 = i0; + + const int64_t i = (ip2*npx + ip1)*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00 + i00; + const int64_t j = i2*ne1*ne0 + i1*ne0 + i0; + + ((float *) dst->data)[j] = ((float *) src0->data)[i]; + } + } + } +} + +static void ggml_compute_forward_win_unpart( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_win_unpart_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +//gmml_compute_forward_unary + +static void ggml_compute_forward_unary( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const enum ggml_unary_op op = ggml_get_unary_op(dst); + + switch (op) { + case GGML_UNARY_OP_ABS: + { + ggml_compute_forward_abs(params, dst); + } break; + case GGML_UNARY_OP_SGN: + { + ggml_compute_forward_sgn(params, dst); + } break; + case GGML_UNARY_OP_NEG: + { + ggml_compute_forward_neg(params, dst); + } break; + case GGML_UNARY_OP_STEP: + { + ggml_compute_forward_step(params, dst); + } break; + case GGML_UNARY_OP_TANH: + { + ggml_compute_forward_tanh(params, dst); + } break; + case GGML_UNARY_OP_ELU: + { + ggml_compute_forward_elu(params, dst); + } break; + case GGML_UNARY_OP_RELU: + { + ggml_compute_forward_relu(params, dst); + } break; + case GGML_UNARY_OP_SIGMOID: + { + ggml_compute_forward_sigmoid(params, dst); + } break; + case GGML_UNARY_OP_GELU: + { + ggml_compute_forward_gelu(params, dst); + } break; + case GGML_UNARY_OP_GELU_QUICK: + { + ggml_compute_forward_gelu_quick(params, dst); + } break; + case GGML_UNARY_OP_SILU: + { + ggml_compute_forward_silu(params, dst); + } break; + case GGML_UNARY_OP_HARDSWISH: + { + ggml_compute_forward_hardswish(params, dst); + } break; + case GGML_UNARY_OP_HARDSIGMOID: + { + ggml_compute_forward_hardsigmoid(params, dst); + } break; + case GGML_UNARY_OP_EXP: + { + ggml_compute_forward_exp(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_get_rel_pos + +static void ggml_compute_forward_get_rel_pos_f16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + UNUSED(params); + + const struct ggml_tensor * src0 = dst->src[0]; + + // ref: https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/modeling/image_encoder.py#L292-L322 + + GGML_TENSOR_UNARY_OP_LOCALS + + const int64_t w = ne1; + + ggml_fp16_t * src0_data = (ggml_fp16_t *) src0->data; + ggml_fp16_t * dst_data = (ggml_fp16_t *) dst->data; + + for (int64_t i2 = 0; i2 < ne2; ++i2) { + for (int64_t i1 = 0; i1 < ne1; ++i1) { + const int64_t pos = (w - i1 - 1) + i2; + for (int64_t i0 = 0; i0 < ne0; ++i0) { + dst_data[i2*ne1*ne0 + i1*ne0 + i0] = src0_data[pos*ne00 + i0]; + } + } + } +} + +static void ggml_compute_forward_get_rel_pos( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F16: + case GGML_TYPE_BF16: + { + ggml_compute_forward_get_rel_pos_f16(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_add_rel_pos + +static void ggml_compute_forward_add_rel_pos_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + const struct ggml_tensor * src2 = dst->src[2]; + + const bool inplace = (bool) ((int32_t *) dst->op_params)[0]; + if (!inplace) { + if (params->ith == 0) { + memcpy((char *) dst->data, (char *) src0->data, ggml_nbytes(dst)); + } + ggml_barrier(params->threadpool); + } + // ref: https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/modeling/image_encoder.py#L357-L359 + + float * src1_data = (float *) src1->data; + float * src2_data = (float *) src2->data; + float * dst_data = (float *) dst->data; + + const int64_t ne10 = src1->ne[0]; + const int64_t ne11 = src1->ne[1]; + const int64_t ne12 = src1->ne[2]; + const int64_t ne13 = src1->ne[3]; + + const int ith = params->ith; + const int nth = params->nth; + + // total patches in dst + const int np = ne13; + + // patches per thread + const int dp = (np + nth - 1)/nth; + + // patch range for this thread + const int ip0 = dp*ith; + const int ip1 = MIN(ip0 + dp, np); + + for (int64_t i13 = ip0; i13 < ip1; ++i13) { + for (int64_t i12 = 0; i12 < ne12; ++i12) { + for (int64_t i11 = 0; i11 < ne11; ++i11) { + const int64_t jp1 = i13*ne12*ne11*ne10 + i12*ne11*ne10 + i11*ne10; + for (int64_t i10 = 0; i10 < ne10; ++i10) { + const int64_t jp0 = jp1 + i10; + const float src1_e = src1_data[jp0]; + const float src2_e = src2_data[jp0]; + + const int64_t jdh = jp0 * ne10; + const int64_t jdw = jdh - (ne10 - 1) * i10; + + for (int64_t j = 0; j < ne10; ++j) { + dst_data[jdh + j ] += src2_e; + dst_data[jdw + j*ne10] += src1_e; + } + } + } + } + } +} + +static void ggml_compute_forward_add_rel_pos( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_add_rel_pos_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_rwkv_wkv6 + +static void ggml_compute_forward_rwkv_wkv6_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + const int64_t T = dst->src[1]->ne[3]; + const int64_t C = dst->ne[0]; + const int64_t HEADS = dst->src[1]->ne[2]; + const int64_t n_seqs = dst->src[5]->ne[1]; + const int64_t head_size = C / HEADS; + + float * dst_data = (float *) dst->data; + float * state = ((float *) dst->data) + C * T; + + const int ith = params->ith; + const int nth = params->nth; + + if (ith >= HEADS) { + return; + } + + const int h_start = (HEADS * ith) / nth; + const int h_end = ((HEADS * (ith + 1)) / nth < HEADS) ? + (HEADS * (ith + 1)) / nth : HEADS; + + float * k = (float *) dst->src[0]->data; + float * v = (float *) dst->src[1]->data; + float * r = (float *) dst->src[2]->data; + float * time_faaaa = (float *) dst->src[3]->data; + float * time_decay = (float *) dst->src[4]->data; + + size_t t_stride = HEADS * head_size; // Same to C + + size_t h_stride = C / HEADS; + GGML_ASSERT(C % HEADS == 0); // C must be divisible by HEADS + size_t h_stride_2d = head_size * head_size; + + if (ith == 0) { + memset(dst_data, 0, T * C * sizeof(float)); + } + ggml_barrier(params->threadpool); + + + #if defined(__AVX__) && !defined(__AVX512F__) + #define GGML_F32X GGML_F32x8 + #define GGML_F32X_SET1 GGML_F32x8_SET1 + #define GGML_F32X_LOAD GGML_F32x8_LOAD + #define GGML_F32X_STORE GGML_F32x8_STORE + #define GGML_F32X_MUL GGML_F32x8_MUL + #define GGML_F32X_FMA GGML_F32x8_FMA + #define WKV_VECTOR_SIZE 8 + #elif defined(__AVX512F__) + #define GGML_F32X GGML_F32x16 + #define GGML_F32X_SET1 GGML_F32x16_SET1 + #define GGML_F32X_LOAD GGML_F32x16_LOAD + #define GGML_F32X_STORE GGML_F32x16_STORE + #define GGML_F32X_MUL GGML_F32x16_MUL + #define GGML_F32X_FMA GGML_F32x16_FMA + #define WKV_VECTOR_SIZE 16 + #elif defined(__ARM_NEON) && defined(__aarch64__) + #define GGML_F32X GGML_F32x4 + #define GGML_F32X_SET1 GGML_F32x4_SET1 + #define GGML_F32X_LOAD GGML_F32x4_LOAD + #define GGML_F32X_STORE GGML_F32x4_STORE + #define GGML_F32X_MUL GGML_F32x4_MUL + #define GGML_F32X_FMA GGML_F32x4_FMA + #define WKV_VECTOR_SIZE 4 + #endif + + #ifdef WKV_VECTOR_SIZE + const int64_t vec_count = head_size / WKV_VECTOR_SIZE; + + for (int64_t t = 0; t < T; t++) { + size_t t_offset = t * t_stride; + size_t state_offset = head_size * C * (t / (T / n_seqs)); + float * state_cur = state + state_offset; + float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[5]->data + state_offset; + + for (int64_t h = h_start; h < h_end; h++) { + size_t h_offset = h * h_stride; + size_t t_h_offset = t_offset + h_offset; + size_t h_2d_offset = h * h_stride_2d; + + for (int64_t i = 0; i < head_size; i++) { + size_t t_h_i_offset = t_h_offset + i; + size_t h_i_offset = h_offset + i; + size_t h_2d_i_offset = h_2d_offset + i * h_stride; + + float k_val = k[t_h_i_offset]; + float r_val = r[t_h_i_offset]; + float time_faaaa_val = time_faaaa[h_i_offset]; + float time_decay_val = time_decay[t_h_i_offset]; + + // Broadcast scalar values to vectors + GGML_F32X k_vec = GGML_F32X_SET1(k_val); + GGML_F32X r_vec = GGML_F32X_SET1(r_val); + GGML_F32X time_faaaa_vec = GGML_F32X_SET1(time_faaaa_val); + GGML_F32X time_decay_vec = GGML_F32X_SET1(time_decay_val); + + for (int64_t j = 0; j < vec_count; j++) { + size_t base_j = j * WKV_VECTOR_SIZE; + size_t t_h_j_offset = t_h_offset + base_j; + size_t h_2d_i_j_offset = h_2d_i_offset + base_j; + + // Load x elements at once + GGML_F32X v_vec = GGML_F32X_LOAD(&v[t_h_j_offset]); + GGML_F32X prev_state_vec = GGML_F32X_LOAD(&state_prev[h_2d_i_j_offset]); + GGML_F32X dst_vec = GGML_F32X_LOAD(&dst_data[t_h_j_offset]); + + // Compute kv = v * k + GGML_F32X kv_vec = GGML_F32X_MUL(v_vec, k_vec); + + // Compute temp = kv * time_faaaa + prev_state + GGML_F32X temp_vec = GGML_F32X_FMA(prev_state_vec, kv_vec, time_faaaa_vec); + + // Update dst: dst += temp * r + dst_vec = GGML_F32X_FMA(dst_vec, temp_vec, r_vec); + GGML_F32X_STORE(&dst_data[t_h_j_offset], dst_vec); + + // Update state: state = prev_state * time_decay + kv + GGML_F32X new_state_vec = GGML_F32X_FMA(kv_vec, prev_state_vec, time_decay_vec); + GGML_F32X_STORE(&state_cur[h_2d_i_j_offset], new_state_vec); + } + + // Handle remaining elements, this will not be used. + for (int64_t j = vec_count * WKV_VECTOR_SIZE; j < head_size; j++) { + size_t t_h_j_offset = t_h_offset + j; + size_t h_2d_i_j_offset = h_2d_i_offset + j; + float v_val = v[t_h_j_offset]; + float kv_val = v_val * k_val; + float prev_state_val = state_prev[h_2d_i_j_offset]; + float temp_val = kv_val * time_faaaa_val + prev_state_val; + dst_data[t_h_j_offset] += temp_val * r_val; + state_cur[h_2d_i_j_offset] = prev_state_val * time_decay_val + kv_val; + } + } + } + } + + #else + // basically fused operations: + // dst = r @ (time_faaaa * (k @ v) + state), + // state = time_decay * state + (k @ v), + // recursive through each token + for (int64_t t = 0; t < T; t++) { + size_t t_offset = t * t_stride; + size_t state_offset = head_size * C * (t / (T / n_seqs)); + float * state_cur = state + state_offset; + float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[5]->data + state_offset; + + for (int64_t h = h_start; h < h_end; h++) { + size_t h_offset = h * h_stride; + size_t t_h_offset = t_offset + h_offset; + size_t h_2d_offset = h * h_stride_2d; + + for (int64_t i = 0; i < head_size; i++) { + size_t t_h_i_offset = t_h_offset + i; + size_t h_i_offset = h_offset + i; + size_t h_2d_i_offset = h_2d_offset + i * h_stride; + + float k_val = k[t_h_i_offset]; + float r_val = r[t_h_i_offset]; + float time_faaaa_val = time_faaaa[h_i_offset]; + // RWKV v6: different time_decay for each token. + float time_decay_val = time_decay[t_h_i_offset]; + + for (int64_t j = 0; j < head_size; j++) { + size_t t_h_j_offset = t_h_offset + j; + size_t h_2d_i_j_offset = h_2d_i_offset + j; + + float v_val = v[t_h_j_offset]; + float kv_val = v_val * k_val; + float prev_state_val = state_prev[h_2d_i_j_offset]; + float temp_val = kv_val * time_faaaa_val + prev_state_val; + dst_data[t_h_j_offset] += temp_val * r_val; + state_cur[h_2d_i_j_offset] = prev_state_val * time_decay_val + kv_val; + } + } + } + } + #endif +} + + +static void ggml_compute_forward_rwkv_wkv6( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_rwkv_wkv6_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_map_unary + +static void ggml_compute_forward_map_unary_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const ggml_unary_op_f32_t fun) { + + const struct ggml_tensor * src0 = dst->src[0]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + fun(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_map_unary( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const ggml_unary_op_f32_t fun) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_map_unary_f32(params, dst, fun); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_map_binary + +static void ggml_compute_forward_map_binary_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const ggml_binary_op_f32_t fun) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + if (params->ith != 0) { + return; + } + + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(src1)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + for (int i = 0; i < n; i++) { + fun(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1])), + (float *) ((char *) src1->data + i*(src1->nb[1]))); + } +} + +static void ggml_compute_forward_map_binary( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const ggml_binary_op_f32_t fun) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_map_binary_f32(params, dst, fun); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_map_custom1 + +static void ggml_compute_forward_map_custom1_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const ggml_custom1_op_f32_t fun) { + + const struct ggml_tensor * a = dst->src[0]; + + if (params->ith != 0) { + return; + } + + fun(dst, a); +} + +// ggml_compute_forward_map_custom2 + +static void ggml_compute_forward_map_custom2_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const ggml_custom2_op_f32_t fun) { + + const struct ggml_tensor * a = dst->src[0]; + const struct ggml_tensor * b = dst->src[1]; + + if (params->ith != 0) { + return; + } + + fun(dst, a, b); +} + +// ggml_compute_forward_map_custom3 + +static void ggml_compute_forward_map_custom3_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const ggml_custom3_op_f32_t fun) { + + const struct ggml_tensor * a = dst->src[0]; + const struct ggml_tensor * b = dst->src[1]; + const struct ggml_tensor * c = dst->src[1]; + + if (params->ith != 0) { + return; + } + + fun(dst, a, b, c); +} + +// ggml_compute_forward_map_custom1 + +static void ggml_compute_forward_map_custom1( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * a = dst->src[0]; + + struct ggml_map_custom1_op_params p; + memcpy(&p, dst->op_params, sizeof(p)); + + p.fun(dst, a, params->ith, params->nth, p.userdata); +} + +// ggml_compute_forward_map_custom2 + +static void ggml_compute_forward_map_custom2( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * a = dst->src[0]; + const struct ggml_tensor * b = dst->src[1]; + + struct ggml_map_custom2_op_params p; + memcpy(&p, dst->op_params, sizeof(p)); + + p.fun(dst, a, b, params->ith, params->nth, p.userdata); +} + +// ggml_compute_forward_map_custom3 + +static void ggml_compute_forward_map_custom3( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * a = dst->src[0]; + const struct ggml_tensor * b = dst->src[1]; + const struct ggml_tensor * c = dst->src[2]; + + struct ggml_map_custom3_op_params p; + memcpy(&p, dst->op_params, sizeof(p)); + + p.fun(dst, a, b, c, params->ith, params->nth, p.userdata); +} + +// ggml_compute_forward_cross_entropy_loss + +static void ggml_compute_forward_cross_entropy_loss_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type)); + GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type)); + GGML_ASSERT(ggml_are_same_shape(src0, src1)); + GGML_ASSERT(ggml_is_scalar(dst)); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + // TODO: handle transposed/permuted matrices + const int64_t nc = src0->ne[0]; + const int64_t nr = ggml_nrows(src0); + + const int ith = params->ith; + const int nth = params->nth; + + float * sums = (float *) params->wdata; + float * st = ((float *) params->wdata) + nth + ith*nc; + float sum_thread = 0.0f; + + GGML_ASSERT(params->wsize >= sizeof(float) * (nth + nth * nc)); + + // rows per thread + const int64_t dr = (nr + nth - 1)/nth; + + // row range for this thread + const int64_t ir0 = dr*ith; + const int64_t ir1 = MIN(ir0 + dr, nr); + + for (int64_t i1 = ir0; i1 < ir1; ++i1) { + const float * s0 = (const float *)((const char *) src0->data + i1*src0->nb[1]); + const float * s1 = (const float *)((const char *) src1->data + i1*src1->nb[1]); + +#ifndef NDEBUG + for (int64_t i = 0; i < nc; ++i) { + //printf("p[%d] = %f\n", i, p[i]); + assert(!isnan(s0[i])); + assert(!isnan(s1[i])); + } +#endif + + float max = -INFINITY; + ggml_vec_max_f32(nc, &max, s0); + const ggml_float sum_softmax = ggml_vec_log_soft_max_f32(nc, st, s0, max); + assert(sum_softmax >= 0.0); + + ggml_vec_add1_f32(nc, st, st, -sum_softmax); + ggml_vec_mul_f32(nc, st, st, s1); + + float sum_st = 0.0f; + ggml_vec_sum_f32(nc, &sum_st, st); + sum_thread += sum_st; + +#ifndef NDEBUG + for (int64_t i = 0; i < nc; ++i) { + assert(!isnan(st[i])); + assert(!isinf(st[i])); + } +#endif + } + sums[ith] = sum_thread; + ggml_barrier(params->threadpool); + + if (ith == 0) { + float * dp = (float *) dst->data; + ggml_vec_sum_f32(nth, dp, sums); + dp[0] *= -1.0f / (float) nr; + } +} + +static void ggml_compute_forward_cross_entropy_loss( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_cross_entropy_loss_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +// ggml_compute_forward_cross_entropy_loss_back + +static void ggml_compute_forward_cross_entropy_loss_back_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + const struct ggml_tensor * opt0 = dst->src[2]; + + GGML_ASSERT(ggml_is_contiguous(dst)); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguous(src1)); + GGML_ASSERT(ggml_is_contiguous(opt0)); + GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); + + const int64_t ith = params->ith; + const int64_t nth = params->nth; + + // TODO: handle transposed/permuted matrices + const int64_t nc = src0->ne[0]; + const int64_t nr = ggml_nrows(src0); + + // rows per thread + const int64_t dr = (nr + nth - 1)/nth; + + // row range for this thread + const int64_t ir0 = dr*ith; + const int64_t ir1 = MIN(ir0 + dr, nr); + + const float d_by_nr = ((const float *) opt0->data)[0] / (float) nr; + + for (int64_t i1 = ir0; i1 < ir1; i1++) { + float * ds0 = (float *)((char *) dst->data + i1*dst->nb[1]); + float * s0 = (float *)((char *) src0->data + i1*src0->nb[1]); + float * s1 = (float *)((char *) src1->data + i1*src1->nb[1]); + +#ifndef NDEBUG + for (int64_t i = 0; i < nc; ++i) { + //printf("p[%d] = %f\n", i, p[i]); + assert(!isnan(s0[i])); + assert(!isnan(s1[i])); + } +#endif + + // soft_max + float max = -INFINITY; + ggml_vec_max_f32(nc, &max, s0); + ggml_float sum = ggml_vec_soft_max_f32(nc, ds0, s0, max); + assert(sum > 0.0); + ggml_vec_scale_f32(nc, ds0, 1.0/sum); + + // grad(src0) = (softmax(src0) - src1) * grad(cross_entropy_loss(src0, src1)) / nr + ggml_vec_sub_f32(nc, ds0, ds0, s1); + ggml_vec_scale_f32(nc, ds0, d_by_nr); + +#ifndef NDEBUG + for (int64_t i = 0; i < nc; ++i) { + assert(!isnan(ds0[i])); + assert(!isinf(ds0[i])); + } +#endif + } +} + +static void ggml_compute_forward_cross_entropy_loss_back( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_cross_entropy_loss_back_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} + +static void ggml_compute_forward_opt_step_adamw_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src0_grad = dst->src[1]; + const struct ggml_tensor * src0_grad_m = dst->src[2]; + const struct ggml_tensor * src0_grad_v = dst->src[3]; + const struct ggml_tensor * adamw_params = dst->src[4]; + + GGML_ASSERT(ggml_are_same_shape(src0, src0_grad)); + GGML_ASSERT(ggml_are_same_shape(src0, src0_grad_m)); + GGML_ASSERT(ggml_are_same_shape(src0, src0_grad_v)); + GGML_ASSERT(ggml_nelements(adamw_params) == 7); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_UNARY_OP_LOCALS + GGML_ASSERT(nb00 == sizeof(float)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + const float * adamw_params_ptr = ggml_get_data_f32(adamw_params); + const float alpha = adamw_params_ptr[0]; + const float beta1 = adamw_params_ptr[1]; + const float beta2 = adamw_params_ptr[2]; + const float eps = adamw_params_ptr[3]; + const float wd = adamw_params_ptr[4]; + const float beta1h = adamw_params_ptr[5]; + const float beta2h = adamw_params_ptr[6]; + + for (int ir = ir0; ir < ir1; ++ir) { + const int64_t i03 = ir/(ne02*ne01); + const int64_t i02 = (ir - i03*ne02*ne01)/ne01; + const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); + + const size_t offset = i03*nb03 + i02*nb02 + i01*nb01; + + float * w = (float *) ((char *) src0->data + offset); // weight + const float * g = (const float *) ((const char *) src0_grad->data + offset); // grad + float * m = (float *) ((char *) src0_grad_m->data + offset); + float * v = (float *) ((char *) src0_grad_v->data + offset); + + for (int i00 = 0; i00 < ne00; ++i00) { + m[i00] = m[i00]*beta1 + g[i00]*(1.0f - beta1); + v[i00] = v[i00]*beta2 + g[i00]*g[i00]*(1.0f - beta2); + + const float mh = m[i00]*beta1h; + const float vh = sqrtf(v[i00]*beta2h) + eps; + + // The weight decay is applied independently of the Adam momenta m and v. + // This is NOT equivalent to l2 regularization that adds w[i00]*w[i00] to the loss. + // See: https://arxiv.org/pdf/1711.05101v3.pdf + w[i00] = w[i00]*(1.0f - alpha*wd) - alpha*mh/vh; + } + } +} + +static void ggml_compute_forward_opt_step_adamw( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_opt_step_adamw_f32(params, dst); + } break; + default: + { + GGML_ABORT("fatal error"); + } + } +} +///////////////////////////////// + +static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { + GGML_ASSERT(params); + + if (tensor->op == GGML_OP_NONE || ggml_is_empty(tensor)) { + return; + } + + switch (tensor->op) { + case GGML_OP_DUP: + { + ggml_compute_forward_dup(params, tensor); + } break; + case GGML_OP_ADD: + { + ggml_compute_forward_add(params, tensor); + } break; + case GGML_OP_ADD1: + { + ggml_compute_forward_add1(params, tensor); + } break; + case GGML_OP_ACC: + { + ggml_compute_forward_acc(params, tensor); + } break; + case GGML_OP_SUB: + { + ggml_compute_forward_sub(params, tensor); + } break; + case GGML_OP_MUL: + { + ggml_compute_forward_mul(params, tensor); + } break; + case GGML_OP_DIV: + { + ggml_compute_forward_div(params, tensor); + } break; + case GGML_OP_SQR: + { + ggml_compute_forward_sqr(params, tensor); + } break; + case GGML_OP_SQRT: + { + ggml_compute_forward_sqrt(params, tensor); + } break; + case GGML_OP_LOG: + { + ggml_compute_forward_log(params, tensor); + } break; + case GGML_OP_SIN: + { + ggml_compute_forward_sin(params, tensor); + } break; + case GGML_OP_COS: + { + ggml_compute_forward_cos(params, tensor); + } break; + case GGML_OP_SUM: + { + ggml_compute_forward_sum(params, tensor); + } break; + case GGML_OP_SUM_ROWS: + { + ggml_compute_forward_sum_rows(params, tensor); + } break; + case GGML_OP_MEAN: + { + ggml_compute_forward_mean(params, tensor); + } break; + case GGML_OP_ARGMAX: + { + ggml_compute_forward_argmax(params, tensor); + } break; + case GGML_OP_COUNT_EQUAL: + { + ggml_compute_forward_count_equal(params, tensor); + } break; + case GGML_OP_REPEAT: + { + ggml_compute_forward_repeat(params, tensor); + } break; + case GGML_OP_REPEAT_BACK: + { + ggml_compute_forward_repeat_back(params, tensor); + } break; + case GGML_OP_CONCAT: + { + ggml_compute_forward_concat(params, tensor); + } break; + case GGML_OP_SILU_BACK: + { + ggml_compute_forward_silu_back(params, tensor); + } break; + case GGML_OP_NORM: + { + ggml_compute_forward_norm(params, tensor); + } break; + case GGML_OP_RMS_NORM: + { + ggml_compute_forward_rms_norm(params, tensor); + } break; + case GGML_OP_RMS_NORM_BACK: + { + ggml_compute_forward_rms_norm_back(params, tensor); + } break; + case GGML_OP_GROUP_NORM: + { + ggml_compute_forward_group_norm(params, tensor); + } break; + case GGML_OP_MUL_MAT: + { + ggml_compute_forward_mul_mat(params, tensor); + } break; + case GGML_OP_MUL_MAT_ID: + { + ggml_compute_forward_mul_mat_id(params, tensor); + } break; + case GGML_OP_OUT_PROD: + { + ggml_compute_forward_out_prod(params, tensor); + } break; + case GGML_OP_SCALE: + { + ggml_compute_forward_scale(params, tensor); + } break; + case GGML_OP_SET: + { + ggml_compute_forward_set(params, tensor); + } break; + case GGML_OP_CPY: + { + ggml_compute_forward_cpy(params, tensor); + } break; + case GGML_OP_CONT: + { + ggml_compute_forward_cont(params, tensor); + } break; + case GGML_OP_RESHAPE: + { + ggml_compute_forward_reshape(params, tensor); + } break; + case GGML_OP_VIEW: + { + ggml_compute_forward_view(params, tensor); + } break; + case GGML_OP_PERMUTE: + { + ggml_compute_forward_permute(params, tensor); + } break; + case GGML_OP_TRANSPOSE: + { + ggml_compute_forward_transpose(params, tensor); + } break; + case GGML_OP_GET_ROWS: + { + ggml_compute_forward_get_rows(params, tensor); + } break; + case GGML_OP_GET_ROWS_BACK: + { + ggml_compute_forward_get_rows_back(params, tensor); + } break; + case GGML_OP_DIAG: + { + ggml_compute_forward_diag(params, tensor); + } break; + case GGML_OP_DIAG_MASK_INF: + { + ggml_compute_forward_diag_mask_inf(params, tensor); + } break; + case GGML_OP_DIAG_MASK_ZERO: + { + ggml_compute_forward_diag_mask_zero(params, tensor); + } break; + case GGML_OP_SOFT_MAX: + { + ggml_compute_forward_soft_max(params, tensor); + } break; + case GGML_OP_SOFT_MAX_BACK: + { + ggml_compute_forward_soft_max_back(params, tensor); + } break; + case GGML_OP_ROPE: + { + ggml_compute_forward_rope(params, tensor); + } break; + case GGML_OP_ROPE_BACK: + { + ggml_compute_forward_rope_back(params, tensor); + } break; + case GGML_OP_CLAMP: + { + ggml_compute_forward_clamp(params, tensor); + } break; + case GGML_OP_CONV_TRANSPOSE_1D: + { + ggml_compute_forward_conv_transpose_1d(params, tensor); + } break; + case GGML_OP_IM2COL: + { + ggml_compute_forward_im2col(params, tensor); + } break; + case GGML_OP_IM2COL_BACK: + { + ggml_compute_forward_im2col_back_f32(params, tensor); + } break; + case GGML_OP_CONV_TRANSPOSE_2D: + { + ggml_compute_forward_conv_transpose_2d(params, tensor); + } break; + case GGML_OP_POOL_1D: + { + ggml_compute_forward_pool_1d(params, tensor); + } break; + case GGML_OP_POOL_2D: + { + ggml_compute_forward_pool_2d(params, tensor); + } break; + case GGML_OP_POOL_2D_BACK: + { + ggml_compute_forward_pool_2d_back(params, tensor); + } break; + case GGML_OP_UPSCALE: + { + ggml_compute_forward_upscale(params, tensor); + } break; + case GGML_OP_PAD: + { + ggml_compute_forward_pad(params, tensor); + } break; + case GGML_OP_PAD_REFLECT_1D: + { + ggml_compute_forward_pad_reflect_1d(params, tensor); + } break; + case GGML_OP_ARANGE: + { + ggml_compute_forward_arange(params, tensor); + } break; + case GGML_OP_TIMESTEP_EMBEDDING: + { + ggml_compute_forward_timestep_embedding(params, tensor); + } break; + case GGML_OP_ARGSORT: + { + ggml_compute_forward_argsort(params, tensor); + } break; + case GGML_OP_LEAKY_RELU: + { + ggml_compute_forward_leaky_relu(params, tensor); + } break; + case GGML_OP_FLASH_ATTN_EXT: + { + ggml_compute_forward_flash_attn_ext(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], tensor); + } break; + case GGML_OP_FLASH_ATTN_BACK: + { + int32_t t = ggml_get_op_params_i32(tensor, 0); + GGML_ASSERT(t == 0 || t == 1); + bool masked = t != 0; + ggml_compute_forward_flash_attn_back(params, masked, tensor); + } break; + case GGML_OP_SSM_CONV: + { + ggml_compute_forward_ssm_conv(params, tensor); + } break; + case GGML_OP_SSM_SCAN: + { + ggml_compute_forward_ssm_scan(params, tensor); + } break; + case GGML_OP_WIN_PART: + { + ggml_compute_forward_win_part(params, tensor); + } break; + case GGML_OP_WIN_UNPART: + { + ggml_compute_forward_win_unpart(params, tensor); + } break; + case GGML_OP_UNARY: + { + ggml_compute_forward_unary(params, tensor); + } break; + case GGML_OP_GET_REL_POS: + { + ggml_compute_forward_get_rel_pos(params, tensor); + } break; + case GGML_OP_ADD_REL_POS: + { + ggml_compute_forward_add_rel_pos(params, tensor); + } break; + case GGML_OP_RWKV_WKV6: + { + ggml_compute_forward_rwkv_wkv6(params, tensor); + } break; + case GGML_OP_MAP_UNARY: + { + ggml_unary_op_f32_t fun; + memcpy(&fun, tensor->op_params, sizeof(fun)); + ggml_compute_forward_map_unary(params, tensor, fun); + } + break; + case GGML_OP_MAP_BINARY: + { + ggml_binary_op_f32_t fun; + memcpy(&fun, tensor->op_params, sizeof(fun)); + ggml_compute_forward_map_binary(params, tensor, fun); + } + break; + case GGML_OP_MAP_CUSTOM1_F32: + { + ggml_custom1_op_f32_t fun; + memcpy(&fun, tensor->op_params, sizeof(fun)); + ggml_compute_forward_map_custom1_f32(params, tensor, fun); + } + break; + case GGML_OP_MAP_CUSTOM2_F32: + { + ggml_custom2_op_f32_t fun; + memcpy(&fun, tensor->op_params, sizeof(fun)); + ggml_compute_forward_map_custom2_f32(params, tensor, fun); + } + break; + case GGML_OP_MAP_CUSTOM3_F32: + { + ggml_custom3_op_f32_t fun; + memcpy(&fun, tensor->op_params, sizeof(fun)); + ggml_compute_forward_map_custom3_f32(params, tensor, fun); + } + break; + case GGML_OP_MAP_CUSTOM1: + { + ggml_compute_forward_map_custom1(params, tensor); + } + break; + case GGML_OP_MAP_CUSTOM2: + { + ggml_compute_forward_map_custom2(params, tensor); + } + break; + case GGML_OP_MAP_CUSTOM3: + { + ggml_compute_forward_map_custom3(params, tensor); + } + break; + case GGML_OP_CROSS_ENTROPY_LOSS: + { + ggml_compute_forward_cross_entropy_loss(params, tensor); + } + break; + case GGML_OP_CROSS_ENTROPY_LOSS_BACK: + { + ggml_compute_forward_cross_entropy_loss_back(params, tensor); + } + break; + case GGML_OP_OPT_STEP_ADAMW: + { + ggml_compute_forward_opt_step_adamw(params, tensor); + } + break; + case GGML_OP_NONE: + { + // nop + } break; + case GGML_OP_COUNT: + { + GGML_ABORT("fatal error"); + } + } +} + +// Android's libc implementation "bionic" does not support setting affinity +#if defined(__gnu_linux__) +static void set_numa_thread_affinity(int thread_n) { + if (!ggml_is_numa()) { + return; + } + + int node_num; + int rv; + size_t setsize = CPU_ALLOC_SIZE(g_state.numa.total_cpus); + + switch(g_state.numa.numa_strategy) { + case GGML_NUMA_STRATEGY_DISTRIBUTE: + // run thread on node_num thread_n / (threads per node) + node_num = thread_n % g_state.numa.n_nodes; + break; + case GGML_NUMA_STRATEGY_ISOLATE: + // run thread on current_node + node_num = g_state.numa.current_node; + break; + case GGML_NUMA_STRATEGY_NUMACTL: + // use the cpuset that numactl gave us + rv = pthread_setaffinity_np(pthread_self(), setsize, &g_state.numa.cpuset); + if (rv) { + fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n",strerror(rv)); + } + return; + default: + return; + } + + struct ggml_numa_node * node = &g_state.numa.nodes[node_num]; + + cpu_set_t * cpus = CPU_ALLOC(g_state.numa.total_cpus); + CPU_ZERO_S(setsize, cpus); + for (size_t i = 0; i < node->n_cpus; ++i) { + CPU_SET_S(node->cpus[i], setsize, cpus); + } + + rv = pthread_setaffinity_np(pthread_self(), setsize, cpus); + if (rv) { + fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", strerror(rv)); + } + + CPU_FREE(cpus); +} + +static void clear_numa_thread_affinity(void) { + if (!ggml_is_numa()) { + return; + } + + size_t setsize = CPU_ALLOC_SIZE(g_state.numa.total_cpus); + + cpu_set_t * cpus = CPU_ALLOC(g_state.numa.total_cpus); + CPU_ZERO_S(setsize, cpus); + for (unsigned i = 0; i < g_state.numa.total_cpus; ++i) { + CPU_SET_S(i, setsize, cpus); + } + + int rv = pthread_setaffinity_np(pthread_self(), setsize, cpus); + if (rv) { + fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", strerror(rv)); + } + + CPU_FREE(cpus); +} +#else +// TODO: Windows etc. +// (the linux implementation may also work on BSD, someone should test) +static void set_numa_thread_affinity(int thread_n) { UNUSED(thread_n); } +static void clear_numa_thread_affinity(void) {} +#endif + +static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { + int n_tasks = 0; + + if (ggml_is_empty(node)) { + // no need to multi-thread a no-op + n_tasks = 1; + return n_tasks; + } + + switch (node->op) { + case GGML_OP_CPY: + case GGML_OP_DUP: + case GGML_OP_CONT: + case GGML_OP_ADD: + case GGML_OP_ADD1: + case GGML_OP_ACC: + { + n_tasks = n_threads; + } break; + case GGML_OP_SUB: + case GGML_OP_SQR: + case GGML_OP_SQRT: + case GGML_OP_LOG: + case GGML_OP_SIN: + case GGML_OP_COS: + case GGML_OP_SUM: + case GGML_OP_SUM_ROWS: + case GGML_OP_MEAN: + case GGML_OP_ARGMAX: + { + n_tasks = 1; + } break; + case GGML_OP_COUNT_EQUAL: + { + n_tasks = n_threads; + } break; + case GGML_OP_REPEAT: + case GGML_OP_REPEAT_BACK: + case GGML_OP_LEAKY_RELU: + { + n_tasks = 1; + } break; + case GGML_OP_UNARY: + switch (ggml_get_unary_op(node)) { + case GGML_UNARY_OP_ABS: + case GGML_UNARY_OP_SGN: + case GGML_UNARY_OP_NEG: + case GGML_UNARY_OP_STEP: + case GGML_UNARY_OP_TANH: + case GGML_UNARY_OP_ELU: + case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_SIGMOID: + case GGML_UNARY_OP_HARDSWISH: + case GGML_UNARY_OP_HARDSIGMOID: + case GGML_UNARY_OP_EXP: + { + n_tasks = 1; + } break; + + case GGML_UNARY_OP_GELU: + case GGML_UNARY_OP_GELU_QUICK: + case GGML_UNARY_OP_SILU: + { + n_tasks = n_threads; + } break; + default: + GGML_ABORT("fatal error"); + } + break; + case GGML_OP_SILU_BACK: + case GGML_OP_MUL: + case GGML_OP_DIV: + case GGML_OP_NORM: + case GGML_OP_RMS_NORM: + case GGML_OP_RMS_NORM_BACK: + case GGML_OP_GROUP_NORM: + case GGML_OP_CONCAT: + case GGML_OP_MUL_MAT: + case GGML_OP_MUL_MAT_ID: + case GGML_OP_OUT_PROD: + { + n_tasks = n_threads; + } break; + case GGML_OP_GET_ROWS: + { + // FIXME: get_rows can use additional threads, but the cost of launching additional threads + // decreases performance with GPU offloading + //n_tasks = n_threads; + n_tasks = 1; + } break; + case GGML_OP_SCALE: + case GGML_OP_SET: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + case GGML_OP_GET_ROWS_BACK: + case GGML_OP_DIAG: + { + n_tasks = 1; + } break; + case GGML_OP_DIAG_MASK_ZERO: + case GGML_OP_DIAG_MASK_INF: + case GGML_OP_SOFT_MAX_BACK: + case GGML_OP_ROPE: + case GGML_OP_ROPE_BACK: + case GGML_OP_ADD_REL_POS: + { + n_tasks = n_threads; + } break; + case GGML_OP_CLAMP: + { + n_tasks = 1; //TODO + } break; + case GGML_OP_SOFT_MAX: + { + n_tasks = MIN(n_threads, ggml_nrows(node->src[0])); + } break; + case GGML_OP_IM2COL: + case GGML_OP_IM2COL_BACK: + case GGML_OP_CONV_TRANSPOSE_1D: + case GGML_OP_CONV_TRANSPOSE_2D: + { + n_tasks = n_threads; + } break; + case GGML_OP_POOL_1D: + case GGML_OP_POOL_2D: + case GGML_OP_POOL_2D_BACK: + { + n_tasks = 1; + } break; + case GGML_OP_UPSCALE: + case GGML_OP_PAD: + case GGML_OP_PAD_REFLECT_1D: + case GGML_OP_ARANGE: + case GGML_OP_TIMESTEP_EMBEDDING: + case GGML_OP_ARGSORT: + case GGML_OP_FLASH_ATTN_EXT: + case GGML_OP_FLASH_ATTN_BACK: + case GGML_OP_SSM_CONV: + case GGML_OP_SSM_SCAN: + { + n_tasks = n_threads; + } break; + case GGML_OP_WIN_PART: + case GGML_OP_WIN_UNPART: + case GGML_OP_GET_REL_POS: + case GGML_OP_RWKV_WKV6: + case GGML_OP_MAP_UNARY: + case GGML_OP_MAP_BINARY: + case GGML_OP_MAP_CUSTOM1_F32: + case GGML_OP_MAP_CUSTOM2_F32: + case GGML_OP_MAP_CUSTOM3_F32: + { + n_tasks = 1; + } break; + case GGML_OP_MAP_CUSTOM1: + { + struct ggml_map_custom1_op_params p; + memcpy(&p, node->op_params, sizeof(p)); + if (p.n_tasks == GGML_N_TASKS_MAX) { + n_tasks = n_threads; + } else { + n_tasks = MIN(p.n_tasks, n_threads); + } + } break; + case GGML_OP_MAP_CUSTOM2: + { + struct ggml_map_custom2_op_params p; + memcpy(&p, node->op_params, sizeof(p)); + if (p.n_tasks == GGML_N_TASKS_MAX) { + n_tasks = n_threads; + } else { + n_tasks = MIN(p.n_tasks, n_threads); + } + } break; + case GGML_OP_MAP_CUSTOM3: + { + struct ggml_map_custom3_op_params p; + memcpy(&p, node->op_params, sizeof(p)); + if (p.n_tasks == GGML_N_TASKS_MAX) { + n_tasks = n_threads; + } else { + n_tasks = MIN(p.n_tasks, n_threads); + } + } break; + case GGML_OP_CROSS_ENTROPY_LOSS: + case GGML_OP_CROSS_ENTROPY_LOSS_BACK: + case GGML_OP_OPT_STEP_ADAMW: + { + n_tasks = n_threads; + } break; + case GGML_OP_NONE: + { + n_tasks = 1; + } break; + case GGML_OP_COUNT: + { + GGML_ABORT("fatal error"); + } + default: + { + fprintf(stderr, "%s: op not implemented: ", __func__); + if (node->op < GGML_OP_COUNT) { + fprintf(stderr, "%s\n", ggml_op_name(node->op)); + } else { + fprintf(stderr, "%d\n", node->op); + } + GGML_ABORT("fatal error"); + } + } + + assert(n_tasks > 0); + + return n_tasks; +} + +static thread_ret_t ggml_graph_compute_secondary_thread(void* data); + +#if defined(_WIN32) +#include "windows.h" + +// TODO: support > 64 CPUs +bool ggml_thread_apply_affinity(bool * mask) { + HANDLE h = GetCurrentThread(); + uint64_t bitmask = 0ULL; + + assert(GGML_MAX_N_THREADS >= 64); + + for (int32_t i = 0; i < 8; i++) { + int32_t idx = i * 8; + uint8_t val = 0; + val |= mask[idx + 0] << 0; + val |= mask[idx + 1] << 1; + val |= mask[idx + 2] << 2; + val |= mask[idx + 3] << 3; + val |= mask[idx + 4] << 4; + val |= mask[idx + 5] << 5; + val |= mask[idx + 6] << 6; + val |= mask[idx + 7] << 7; + bitmask |= (uint64_t)val << idx; + } + + for (int32_t i = 64; i < GGML_MAX_N_THREADS; i++) { + if (mask[i]) { + fprintf(stderr, "warn: setting thread-affinity for > 64 CPUs isn't supported on windows!\n"); + break; + } + } + + DWORD_PTR m = (DWORD_PTR)bitmask; + + m = SetThreadAffinityMask(h, m); + + return m != 0; +} + +static bool ggml_thread_apply_priority(int32_t prio) { + // Note that on Windows the Process Priority Class must be updated in order to set Thread priority. + // This is up to the applications. + DWORD p = THREAD_PRIORITY_NORMAL; + switch (prio) { + case GGML_SCHED_PRIO_NORMAL: p = THREAD_PRIORITY_NORMAL; break; + case GGML_SCHED_PRIO_MEDIUM: p = THREAD_PRIORITY_ABOVE_NORMAL; break; + case GGML_SCHED_PRIO_HIGH: p = THREAD_PRIORITY_HIGHEST; break; + case GGML_SCHED_PRIO_REALTIME: p = THREAD_PRIORITY_TIME_CRITICAL; break; + } + + if (prio == GGML_SCHED_PRIO_NORMAL) { + // Keep inherited policy/priority + return true; + } + + if (!SetThreadPriority(GetCurrentThread(), p)) { + fprintf(stderr, "warn: failed to set thread priority %d : (%d)\n", prio, (int) GetLastError()); + return false; + } + + return true; +} + +#elif defined(__APPLE__) +#include +#include + +static bool ggml_thread_apply_affinity(const bool * mask) { + // Not supported on Apple platforms + UNUSED(mask); + return true; +} + +static bool ggml_thread_apply_priority(int32_t prio) { + struct sched_param p; + int32_t policy = SCHED_OTHER; + switch (prio) { + case GGML_SCHED_PRIO_NORMAL: policy = SCHED_OTHER; p.sched_priority = 0; break; + case GGML_SCHED_PRIO_MEDIUM: policy = SCHED_FIFO; p.sched_priority = 40; break; + case GGML_SCHED_PRIO_HIGH: policy = SCHED_FIFO; p.sched_priority = 80; break; + case GGML_SCHED_PRIO_REALTIME: policy = SCHED_FIFO; p.sched_priority = 90; break; + } + + if (prio == GGML_SCHED_PRIO_NORMAL) { + // Keep inherited policy/priority + return true; + } + + int32_t err = pthread_setschedparam(pthread_self(), policy, &p); + if (err != 0) { + fprintf(stderr, "warn: failed to set thread priority %d : %s (%d)\n", prio, strerror(err), err); + return false; + } + + return true; +} + +#elif defined(__gnu_linux__) +// TODO: this may not work on BSD, to be verified + +static bool ggml_thread_apply_affinity(const bool * mask) { + cpu_set_t cpuset; + int err; + + CPU_ZERO(&cpuset); + + for (uint32_t i = 0; i < GGML_MAX_N_THREADS; i++) { + if (mask[i]) { + GGML_PRINT_DEBUG("Thread %lx: adding %d to cpuset\n", pthread_self(), i); + CPU_SET(i, &cpuset); + } + } + +#ifdef __ANDROID__ + err = sched_setaffinity(0, sizeof(cpuset), &cpuset); + if (err < 0) { + err = errno; + } +#else + err = pthread_setaffinity_np(pthread_self(), sizeof(cpuset), &cpuset); +#endif + if (err != 0) { + fprintf(stderr, "warn: failed to set affinity mask 0x%llx : %s (%d)\n", (unsigned long long)mask, strerror(err), err); + return false; + } + + return true; +} + +static bool ggml_thread_apply_priority(int32_t prio) { + struct sched_param p; + int32_t policy = SCHED_OTHER; + switch (prio) { + case GGML_SCHED_PRIO_NORMAL: policy = SCHED_OTHER; p.sched_priority = 0; break; + case GGML_SCHED_PRIO_MEDIUM: policy = SCHED_FIFO; p.sched_priority = 40; break; + case GGML_SCHED_PRIO_HIGH: policy = SCHED_FIFO; p.sched_priority = 80; break; + case GGML_SCHED_PRIO_REALTIME: policy = SCHED_FIFO; p.sched_priority = 90; break; + } + + if (prio == GGML_SCHED_PRIO_NORMAL) { + // Keep inherited policy/priority + return true; + } + + int32_t err = pthread_setschedparam(pthread_self(), policy, &p); + if (err != 0) { + fprintf(stderr, "warn: failed to set thread priority %d : %s (%d)\n", prio, strerror(err), err); + return false; + } + + return true; +} + +#else // unsupported platforms + +static bool ggml_thread_apply_affinity(const bool * mask) { + UNUSED(mask); + return true; +} + +static bool ggml_thread_apply_priority(int32_t prio) { + UNUSED(prio); + return true; +} + +#endif + +static bool ggml_thread_cpumask_is_valid(const bool * mask) { + for (int i = 0; i < GGML_MAX_N_THREADS; i++) { + if (mask[i]) { return true; } + } + return false; +} + +static void ggml_thread_cpumask_next(const bool * global_mask, bool * local_mask, bool strict, int32_t* iter) { + if (!strict) { + memcpy(local_mask, global_mask, GGML_MAX_N_THREADS); + return; + } else { + memset(local_mask, 0, GGML_MAX_N_THREADS); + int32_t base_idx = *iter; + for (int32_t i = 0; i < GGML_MAX_N_THREADS; i++) { + int32_t idx = base_idx + i; + if (idx >= GGML_MAX_N_THREADS) { + // Just a cheaper modulo + idx -= GGML_MAX_N_THREADS; + } + if (global_mask[idx]) { + local_mask[idx] = 1; + *iter = idx + 1; + return; + } + } + } +} + +void ggml_threadpool_free(struct ggml_threadpool* threadpool) { + if (!threadpool) return; + + const int n_threads = threadpool->n_threads_max; + +#ifndef GGML_USE_OPENMP + struct ggml_compute_state* workers = threadpool->workers; + + ggml_mutex_lock(&threadpool->mutex); + + threadpool->stop = true; + threadpool->pause = false; + + ggml_cond_broadcast(&threadpool->cond); + ggml_mutex_unlock(&threadpool->mutex); + + for (int j = 1; j < n_threads; j++) { + int32_t rc = ggml_thread_join(workers[j].thrd, NULL); + GGML_ASSERT(rc == GGML_EXIT_SUCCESS || rc == GGML_EXIT_ABORTED); + UNUSED(rc); + } + + ggml_mutex_destroy(&threadpool->mutex); + ggml_cond_destroy(&threadpool->cond); +#endif // GGML_USE_OPENMP + + const size_t workers_size = sizeof(struct ggml_compute_state) * n_threads; + ggml_aligned_free(threadpool->workers, workers_size); + ggml_aligned_free(threadpool, sizeof(struct ggml_threadpool)); +} + +#ifndef GGML_USE_OPENMP +// pause/resume must be called under mutex +static void ggml_threadpool_pause_locked(struct ggml_threadpool * threadpool) { + GGML_PRINT_DEBUG("Pausing threadpool\n"); + threadpool->pause = true; + ggml_cond_broadcast(&threadpool->cond); +} + +static void ggml_threadpool_resume_locked(struct ggml_threadpool * threadpool) { + GGML_PRINT_DEBUG("Resuming threadpool\n"); + threadpool->pause = false; + ggml_cond_broadcast(&threadpool->cond); +} +#endif + +void ggml_threadpool_pause(struct ggml_threadpool * threadpool) { +#ifndef GGML_USE_OPENMP + ggml_mutex_lock(&threadpool->mutex); + if (!threadpool->pause) { + ggml_threadpool_pause_locked(threadpool); + } + ggml_mutex_unlock(&threadpool->mutex); +#else + UNUSED(threadpool); +#endif +} + +void ggml_threadpool_resume(struct ggml_threadpool * threadpool) { +#ifndef GGML_USE_OPENMP + ggml_mutex_lock(&threadpool->mutex); + if (threadpool->pause) { + ggml_threadpool_resume_locked(threadpool); + } + ggml_mutex_unlock(&threadpool->mutex); +#else + UNUSED(threadpool); +#endif +} + +struct ggml_cplan ggml_graph_plan( + const struct ggml_cgraph * cgraph, + int n_threads, + struct ggml_threadpool * threadpool) { + + if (threadpool == NULL) { + //GGML_PRINT_DEBUG("Threadpool is not specified. Will create a disposable threadpool : n_threads %d\n", n_threads); + } + if (n_threads <= 0) { + n_threads = threadpool ? threadpool->n_threads_max : GGML_DEFAULT_N_THREADS; + } + + size_t work_size = 0; + + struct ggml_cplan cplan; + memset(&cplan, 0, sizeof(struct ggml_cplan)); + + int max_tasks = 1; + + // thread scheduling for the different operations + work buffer size estimation + for (int i = 0; i < cgraph->n_nodes; i++) { + struct ggml_tensor * node = cgraph->nodes[i]; + + const int n_tasks = ggml_get_n_tasks(node, n_threads); + + max_tasks = MAX(max_tasks, n_tasks); + + size_t cur = 0; + + switch (node->op) { + case GGML_OP_CPY: + case GGML_OP_DUP: + { + if (ggml_is_quantized(node->type) || + // F16 -> BF16 and BF16 -> F16 copies go through intermediate F32 + (node->src[0]->type == GGML_TYPE_F16 && node->src[1] && node->src[1]->type == GGML_TYPE_BF16) || + (node->src[0]->type == GGML_TYPE_BF16 && node->src[1] && node->src[1]->type == GGML_TYPE_F16)) { + cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks; + } + } break; + case GGML_OP_ADD: + case GGML_OP_ADD1: + { + if (ggml_is_quantized(node->src[0]->type)) { + cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks; + } + } break; + case GGML_OP_ACC: + { + if (ggml_is_quantized(node->src[0]->type)) { + cur = ggml_type_size(GGML_TYPE_F32) * node->src[1]->ne[0] * n_tasks; + } + } break; + case GGML_OP_COUNT_EQUAL: + { + cur = ggml_type_size(node->type)*n_tasks; + } break; + case GGML_OP_MUL_MAT: + { +#if defined(__AMX_INT8__) && defined(__AVX512VNNI__) + if (node->src[0]->buffer && ggml_backend_amx_buft_is_amx(node->src[0]->buffer->buft)) { + cur = ggml_backend_amx_desired_wsize(node); + } +#endif + const enum ggml_type vec_dot_type = type_traits_cpu[node->src[0]->type].vec_dot_type; + + if (node->src[1]->type != vec_dot_type) { + size_t cur2 = ggml_row_size(vec_dot_type, ggml_nelements(node->src[1])); + cur = MAX(cur, cur2); + } + } break; + case GGML_OP_MUL_MAT_ID: + { + cur = 0; + const struct ggml_tensor * src0 = node->src[0]; + const struct ggml_tensor * src1 = node->src[1]; + const enum ggml_type vec_dot_type = type_traits_cpu[src0->type].vec_dot_type; + if (src1->type != vec_dot_type) { + cur += ggml_row_size(vec_dot_type, ggml_nelements(src1)); + } + const int n_as = src0->ne[2]; + cur += GGML_PAD(cur, sizeof(int64_t)); // align + cur += n_as * sizeof(int64_t); // matrix_row_counts + cur += n_as * src1->ne[2] * sizeof(int64_t); // matrix_rows + } break; + case GGML_OP_OUT_PROD: + { + if (ggml_is_quantized(node->src[0]->type)) { + cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks; + } + } break; + case GGML_OP_SOFT_MAX: + case GGML_OP_ROPE: + { + cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks; + } break; + case GGML_OP_CONV_TRANSPOSE_1D: + { + GGML_ASSERT(node->src[0]->ne[3] == 1); + GGML_ASSERT(node->src[1]->ne[2] == 1); + GGML_ASSERT(node->src[1]->ne[3] == 1); + + const int64_t ne00 = node->src[0]->ne[0]; // K + const int64_t ne01 = node->src[0]->ne[1]; // Cout + const int64_t ne02 = node->src[0]->ne[2]; // Cin + + const int64_t ne10 = node->src[1]->ne[0]; // L + const int64_t ne11 = node->src[1]->ne[1]; // Cin + + if ((node->src[0]->type == GGML_TYPE_F16 || + node->src[0]->type == GGML_TYPE_BF16) && + node->src[1]->type == GGML_TYPE_F32) { + cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02; + cur += sizeof(ggml_fp16_t)*ne10*ne11; + } else if (node->src[0]->type == GGML_TYPE_F32 && + node->src[1]->type == GGML_TYPE_F32) { + cur += sizeof(float)*ne00*ne01*ne02; + cur += sizeof(float)*ne10*ne11; + } else { + GGML_ABORT("fatal error"); + } + } break; + case GGML_OP_CONV_TRANSPOSE_2D: + { + const int64_t ne00 = node->src[0]->ne[0]; // W + const int64_t ne01 = node->src[0]->ne[1]; // H + const int64_t ne02 = node->src[0]->ne[2]; // Channels Out + const int64_t ne03 = node->src[0]->ne[3]; // Channels In + + const int64_t ne10 = node->src[1]->ne[0]; // W + const int64_t ne11 = node->src[1]->ne[1]; // H + const int64_t ne12 = node->src[1]->ne[2]; // Channels In + + cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02*ne03; + cur += sizeof(ggml_fp16_t)*ne10*ne11*ne12; + } break; + case GGML_OP_FLASH_ATTN_EXT: + { + const int64_t ne00 = node->src[0]->ne[0]; // D + + cur = 3*sizeof(float)*ne00*n_tasks; // 3x head size/thread + } break; + case GGML_OP_FLASH_ATTN_BACK: + { + const int64_t D = node->src[0]->ne[0]; + const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL); + const int64_t mxDn = MAX(D, ne11) * 2; // *2 because of S and SM in ggml_compute_forward_flash_attn_back + if (node->src[1]->type == GGML_TYPE_F32) { + cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) + cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 + } else if (node->src[1]->type == GGML_TYPE_F16) { + cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) + cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 + } else if (node->src[1]->type == GGML_TYPE_BF16) { + cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) + cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 + } + } break; + + case GGML_OP_CROSS_ENTROPY_LOSS: + { + cur = ggml_type_size(node->type)*(n_tasks + node->src[0]->ne[0]*n_tasks); + } break; + case GGML_OP_COUNT: + { + GGML_ABORT("fatal error"); + } + default: + break; + } + + work_size = MAX(work_size, cur); + } + + if (work_size > 0) { + work_size += CACHE_LINE_SIZE*(n_threads); + } + + cplan.threadpool = threadpool; + cplan.n_threads = MIN(max_tasks, n_threads); + cplan.work_size = work_size; + cplan.work_data = NULL; + + return cplan; +} + +static thread_ret_t ggml_graph_compute_thread(void * data) { + struct ggml_compute_state * state = (struct ggml_compute_state *) data; + struct ggml_threadpool * tp = state->threadpool; + + const struct ggml_cgraph * cgraph = tp->cgraph; + const struct ggml_cplan * cplan = tp->cplan; + + set_numa_thread_affinity(state->ith); + + struct ggml_compute_params params = { + /*.ith =*/ state->ith, + /*.nth =*/ atomic_load_explicit(&tp->n_threads_cur, memory_order_relaxed), + /*.wsize =*/ cplan->work_size, + /*.wdata =*/ cplan->work_data, + /*.threadpool=*/ tp, + }; + + for (int node_n = 0; node_n < cgraph->n_nodes && !tp->abort; node_n++) { + struct ggml_tensor * node = cgraph->nodes[node_n]; + + ggml_compute_forward(¶ms, node); + + if (state->ith == 0 && cplan->abort_callback && + cplan->abort_callback(cplan->abort_callback_data)) { + tp->abort = true; + tp->ec = GGML_STATUS_ABORTED; + } + + ggml_barrier(state->threadpool); + } + + return 0; +} + +#ifndef GGML_USE_OPENMP + +// check if thread is active +static inline bool ggml_graph_compute_thread_active(struct ggml_compute_state * state) { + struct ggml_threadpool * threadpool = state->threadpool; + int n_threads = atomic_load_explicit(&threadpool->n_threads_cur, memory_order_relaxed); + return (state->ith < n_threads); +} + +// check if thread is ready to proceed (exit from polling or sleeping) +static inline bool ggml_graph_compute_thread_ready(struct ggml_compute_state * state) { + struct ggml_threadpool * threadpool = state->threadpool; + + if (state->pending || threadpool->stop || threadpool->pause) { return true; } + + // check for new graph/work + int new_graph = atomic_load_explicit(&threadpool->n_graph, memory_order_relaxed); + if (new_graph != state->last_graph) { + state->pending = ggml_graph_compute_thread_active(state); + state->last_graph = new_graph; + } + + return state->pending; +} + +// sync thread state after polling +static inline void ggml_graph_compute_thread_sync(struct ggml_compute_state * state) { + // TSAN doesn't support standalone fence yet, we use a dummy read-modify-write instead + #ifdef GGML_TSAN_ENABLED + atomic_fetch_add_explicit(&state->threadpool->n_graph, 0, memory_order_seq_cst); + #else + atomic_thread_fence(memory_order_seq_cst); + #endif + UNUSED(state); +} + +static inline bool ggml_graph_compute_poll_for_work(struct ggml_compute_state * state) { + struct ggml_threadpool * threadpool = state->threadpool; + + // Skip polling for unused threads + if (!ggml_graph_compute_thread_active(state)) { + return state->pending; + } + + // This seems to make 0 ... 100 a decent range for polling level across modern processors. + // Perhaps, we can adjust it dynamically based on load and things. + const uint64_t n_rounds = 1024UL * 128 * threadpool->poll; + + for (uint64_t i=0; !ggml_graph_compute_thread_ready(state) && i < n_rounds; i++) { + // No new work. Keep polling. + ggml_thread_cpu_relax(); + } + + return state->pending; +} + +static inline bool ggml_graph_compute_check_for_work(struct ggml_compute_state * state) { + struct ggml_threadpool * threadpool = state->threadpool; + + if (ggml_graph_compute_poll_for_work(state)) { + ggml_graph_compute_thread_sync(state); + return state->pending; + } + + ggml_mutex_lock_shared(&threadpool->mutex); + while (!ggml_graph_compute_thread_ready(state)) { + // No new work. Wait for the signal. + GGML_PRINT_DEBUG("thread #%d waiting for work (sleeping)\n", state->ith); + ggml_cond_wait(&threadpool->cond, &threadpool->mutex); + } + ggml_mutex_unlock_shared(&threadpool->mutex); + + return state->pending; +} + +static thread_ret_t ggml_graph_compute_secondary_thread(void* data) { + struct ggml_compute_state * state = (struct ggml_compute_state *) data; + struct ggml_threadpool * threadpool = state->threadpool; + + ggml_thread_apply_priority(threadpool->prio); + if (ggml_thread_cpumask_is_valid(state->cpumask)) { + ggml_thread_apply_affinity(state->cpumask); + } + + while (true) { + // Check if we need to sleep + while (threadpool->pause) { + GGML_PRINT_DEBUG("thread #%d inside pause loop\n", state->ith); + ggml_mutex_lock_shared(&threadpool->mutex); + if (threadpool->pause) { + ggml_cond_wait(&threadpool->cond, &threadpool->mutex); + } + GGML_PRINT_DEBUG("thread #%d resuming after wait\n", state->ith); + ggml_mutex_unlock_shared(&threadpool->mutex); + } + + // This needs to be checked for after the cond_wait + if (threadpool->stop) break; + + // Check if there is new work + // The main thread is the only one that can dispatch new work + + ggml_graph_compute_check_for_work(state); + if (state->pending) { + state->pending = false; + + ggml_graph_compute_thread(state); + } + } + + return (thread_ret_t) 0; +} + +// Start processing new graph +static void ggml_graph_compute_kickoff(struct ggml_threadpool * threadpool, int n_threads) +{ + // Always take the mutex here because the worker threads are doing hybrid poll/wait + + ggml_mutex_lock(&threadpool->mutex); + + GGML_PRINT_DEBUG("threadpool: n_threads_cur %d n_threads %d\n", threadpool->n_threads_cur, n_threads); + + // Update the number of active threads + atomic_store_explicit(&threadpool->n_threads_cur, n_threads, memory_order_relaxed); + + // Indicate the graph is ready to be processed + // We need the full seq-cst fence here because of the polling threads (used in thread_sync) + atomic_fetch_add_explicit(&threadpool->n_graph, 1, memory_order_seq_cst); + + if (threadpool->pause) { + // Update main thread prio and affinity to match the threadpool settings + ggml_thread_apply_priority(threadpool->prio); + if (ggml_thread_cpumask_is_valid(threadpool->workers[0].cpumask)) { + ggml_thread_apply_affinity(threadpool->workers[0].cpumask); + } + + // resume does cond broadcast + ggml_threadpool_resume_locked(threadpool); + } else { + ggml_cond_broadcast(&threadpool->cond); + } + + ggml_mutex_unlock(&threadpool->mutex); +} + +#endif // GGML_USE_OPENMP + +static struct ggml_threadpool * ggml_threadpool_new_impl( + struct ggml_threadpool_params * tpp, + struct ggml_cgraph * cgraph, + struct ggml_cplan * cplan) { + + struct ggml_threadpool * threadpool = + ggml_aligned_malloc(sizeof(struct ggml_threadpool)); + { + threadpool->cgraph = cgraph; + threadpool->cplan = cplan; + threadpool->n_graph = 0; + threadpool->n_barrier = 0; + threadpool->n_barrier_passed = 0; + threadpool->current_chunk = 0; + threadpool->stop = false; + threadpool->pause = tpp->paused; + threadpool->abort = false; + threadpool->workers = NULL; + threadpool->n_threads_max = tpp->n_threads; + threadpool->n_threads_cur = tpp->n_threads; + threadpool->poll = tpp->poll; + threadpool->prio = tpp->prio; + threadpool->ec = GGML_STATUS_SUCCESS; + } + + // Allocate and init workers state + const size_t workers_size = sizeof(struct ggml_compute_state) * tpp->n_threads; + struct ggml_compute_state * workers = ggml_aligned_malloc(workers_size); + + memset(workers, 0, workers_size); + for (int j = 0; j < tpp->n_threads; j++) { + workers[j].threadpool = threadpool; + workers[j].ith = j; + } + + threadpool->workers = workers; + +#ifndef GGML_USE_OPENMP + ggml_mutex_init(&threadpool->mutex); + ggml_cond_init(&threadpool->cond); + + // Spin the threads for all workers, and update CPU placements. + // Place the main thread last (towards the higher numbered CPU cores). + + int32_t cpumask_iter = 0; + + for (int j = 1; j < tpp->n_threads; j++) { + ggml_thread_cpumask_next(tpp->cpumask, workers[j].cpumask, tpp->strict_cpu, &cpumask_iter); + + int32_t rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_secondary_thread, &workers[j]); + GGML_ASSERT(rc == 0); + } + + ggml_thread_cpumask_next(tpp->cpumask, workers[0].cpumask, tpp->strict_cpu, &cpumask_iter); + + if (!threadpool->pause) { + // Update main thread prio and affinity at the start, otherwise we'll do it in resume + ggml_thread_apply_priority(threadpool->prio); + if (ggml_thread_cpumask_is_valid(threadpool->workers[0].cpumask)) { + ggml_thread_apply_affinity(threadpool->workers[0].cpumask); + } + } +#endif // GGML_USE_OPENMP + + return threadpool; +} + +struct ggml_threadpool * ggml_threadpool_new(struct ggml_threadpool_params * tpp) { + return ggml_threadpool_new_impl(tpp, NULL, NULL); +} + +enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { + ggml_cpu_init(); + + GGML_ASSERT(cplan); + GGML_ASSERT(cplan->n_threads > 0); + GGML_ASSERT(cplan->work_size == 0 || cplan->work_data != NULL); + + int n_threads = cplan->n_threads; + struct ggml_threadpool * threadpool = cplan->threadpool; + + bool disposable_threadpool = false; + + if (threadpool == NULL) { + //GGML_PRINT_DEBUG("Threadpool is not specified. Will create a disposable threadpool : n_threads %d\n", n_threads); + disposable_threadpool = true; + + struct ggml_threadpool_params ttp = ggml_threadpool_params_default(n_threads); + threadpool = ggml_threadpool_new_impl(&ttp, cgraph, cplan); + } else { + // Reset some of the parameters that need resetting + // No worker threads should be accessing the parameters below at this stage + threadpool->cgraph = cgraph; + threadpool->cplan = cplan; + threadpool->current_chunk = 0; + threadpool->abort = false; + threadpool->ec = GGML_STATUS_SUCCESS; + } + +#ifdef GGML_USE_OPENMP + if (n_threads > 1) { + #pragma omp parallel num_threads(n_threads) + { + #pragma omp single + { + // update the number of threads from the actual number of threads that we got from OpenMP + n_threads = omp_get_num_threads(); + atomic_store_explicit(&threadpool->n_threads_cur, n_threads, memory_order_relaxed); + } + + ggml_graph_compute_thread(&threadpool->workers[omp_get_thread_num()]); + } + } else { + atomic_store_explicit(&threadpool->n_threads_cur, 1, memory_order_relaxed); + ggml_graph_compute_thread(&threadpool->workers[0]); + } +#else + if (n_threads > threadpool->n_threads_max) { + GGML_LOG_WARN("cplan requested more threads (%d) than available (%d)\n", n_threads, threadpool->n_threads_max); + n_threads = threadpool->n_threads_max; + } + + // Kick all threads to start the new graph + ggml_graph_compute_kickoff(threadpool, n_threads); + + // This is a work thread too + ggml_graph_compute_thread(&threadpool->workers[0]); +#endif + + // don't leave affinity set on the main thread + clear_numa_thread_affinity(); + + enum ggml_status ret = threadpool->ec; + + if (disposable_threadpool) { + ggml_threadpool_free(threadpool); + } + + return ret; +} + +enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) { + struct ggml_cplan cplan = ggml_graph_plan(cgraph, n_threads, NULL); + + cplan.work_data = (uint8_t *)ggml_new_buffer(ctx, cplan.work_size); + + return ggml_graph_compute(cgraph, &cplan); +} + + +int ggml_cpu_has_avx(void) { +#if defined(__AVX__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_avx_vnni(void) { +#if defined(__AVXVNNI__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_avx2(void) { +#if defined(__AVX2__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_avx512(void) { +#if defined(__AVX512F__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_avx512_vbmi(void) { +#if defined(__AVX512VBMI__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_avx512_vnni(void) { +#if defined(__AVX512VNNI__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_avx512_bf16(void) { +#if defined(__AVX512BF16__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_amx_int8(void) { +#if defined(__AMX_INT8__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_fma(void) { +#if defined(__FMA__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_arm_fma(void) { +#if defined(__ARM_FEATURE_FMA) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_riscv_v(void) { +#if defined(__riscv_v_intrinsic) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_f16c(void) { +#if defined(__F16C__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_fp16_va(void) { +#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_wasm_simd(void) { +#if defined(__wasm_simd128__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_llamafile(void) { +#if defined(GGML_USE_LLAMAFILE) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_sse3(void) { +#if defined(__SSE3__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_ssse3(void) { +#if defined(__SSSE3__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_vsx(void) { +#if defined(__POWER9_VECTOR__) + return 1; +#else + return 0; +#endif +} + +int ggml_cpu_has_neon(void) { +#if defined(__ARM_ARCH) && defined(__ARM_NEON) + return ggml_arm_arch_features.has_neon; +#else + return 0; +#endif +} + +int ggml_cpu_has_dotprod(void) { +#if defined(__ARM_ARCH) && defined(__ARM_FEATURE_DOTPROD) + return ggml_arm_arch_features.has_dotprod; +#else + return 0; +#endif +} + +int ggml_cpu_has_sve(void) { +#if defined(__ARM_ARCH) && defined(__ARM_FEATURE_SVE) + return ggml_arm_arch_features.has_sve; +#else + return 0; +#endif +} + +int ggml_cpu_has_matmul_int8(void) { +#if defined(__ARM_ARCH) && defined(__ARM_FEATURE_MATMUL_INT8) + return ggml_arm_arch_features.has_i8mm; +#else + return 0; +#endif +} + +int ggml_cpu_get_sve_cnt(void) { +#if defined(__ARM_ARCH) && defined(__ARM_FEATURE_SVE) + return ggml_arm_arch_features.sve_cnt; +#else + return 0; +#endif +} + +void ggml_cpu_init(void) { + // needed to initialize f16 tables + { + struct ggml_init_params params = { 0, NULL, false }; + struct ggml_context * ctx = ggml_init(params); + ggml_free(ctx); + } + + ggml_critical_section_start(); + + static bool is_first_call = true; + + if (is_first_call) { + // initialize GELU, Quick GELU, SILU and EXP F32 tables + { + const uint64_t t_start = ggml_time_us(); UNUSED(t_start); + + for (int i = 0; i < (1 << 16); ++i) { + union { + uint16_t u16; + ggml_fp16_t fp16; + } u = {i}; + float f = GGML_FP16_TO_FP32(u.fp16); + ggml_table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f)); + ggml_table_gelu_quick_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_quick_f32(f)); + } + + const uint64_t t_end = ggml_time_us(); UNUSED(t_end); + + GGML_PRINT_DEBUG("%s: GELU, Quick GELU, SILU and EXP tables initialized in %f ms\n", __func__, (t_end - t_start)/1000.0); + } + +#if defined(__ARM_ARCH) + ggml_init_arm_arch_features(); +#endif + + is_first_call = false; + } + + ggml_critical_section_end(); +} diff --git a/ggml/src/ggml-cpu/ggml-cpu.cpp b/ggml/src/ggml-cpu/ggml-cpu.cpp new file mode 100644 index 000000000..d3b4bdb96 --- /dev/null +++ b/ggml/src/ggml-cpu/ggml-cpu.cpp @@ -0,0 +1,723 @@ +#include "ggml-backend.h" +#include "ggml-backend-impl.h" +#include "ggml-cpu.h" +#include "ggml-cpu-aarch64.h" +#include "ggml-impl.h" +#include "amx/amx.h" +#include +#include +#include + +#if defined(__APPLE__) +#include +#include +#endif + +#if defined(_WIN32) +#define WIN32_LEAN_AND_MEAN +#ifndef NOMINMAX + #define NOMINMAX +#endif +#include +#endif + +// ggml-backend interface + +#ifdef GGML_USE_CPU_HBM + +// buffer type HBM + +#include + +static const char * ggml_backend_cpu_hbm_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + return "CPU_HBM"; + + GGML_UNUSED(buft); +} + +static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) { + hbw_free(buffer->context); +} + +static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + void * ptr; + int result = hbw_posix_memalign(&ptr, ggml_backend_cpu_buffer_type_get_alignment(buft), size); + if (result != 0) { + GGML_LOG_ERROR("failed to allocate HBM buffer of size %zu\n", size); + return NULL; + } + + ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); + buffer->buft = buft; + buffer->iface.free_buffer = ggml_backend_cpu_hbm_buffer_free_buffer; + + return buffer; +} + +ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void) { + static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type_hbm = { + /* .iface = */ { + /* .get_name = */ ggml_backend_cpu_hbm_buffer_type_get_name, + /* .alloc_buffer = */ ggml_backend_cpu_hbm_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes + /* .is_host = */ ggml_backend_cpu_buffer_type_is_host, + }, + /* .context = */ NULL, + }; + + return &ggml_backend_cpu_buffer_type_hbm; +} +#endif + +// buffer type AARCH64 + +static void ggml_backend_cpu_aarch64_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { + tensor->extra = (void *)ggml_aarch64_get_optimal_repack_type(tensor); // NOLINT + + GGML_UNUSED(buffer); +} + +static void ggml_backend_cpu_aarch64_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + GGML_ASSERT(offset == 0); + GGML_ASSERT(size == ggml_nbytes(tensor)); + + enum ggml_type repack_type = (enum ggml_type)(intptr_t)tensor->extra; + + ggml_aarch64_repack_tensor(tensor, repack_type, data, size); + + GGML_UNUSED(buffer); +} + +static const char * ggml_backend_cpu_aarch64_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + return "CPU_AARCH64"; + + GGML_UNUSED(buft); +} + +static ggml_backend_buffer_t ggml_backend_cpu_aarch64_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + auto * buffer = ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size); + + if (buffer == NULL) { + return NULL; + } + + buffer->buft = buft; + buffer->iface.init_tensor = ggml_backend_cpu_aarch64_buffer_init_tensor; + buffer->iface.set_tensor = ggml_backend_cpu_aarch64_buffer_set_tensor; + + return buffer; +} + +ggml_backend_buffer_type_t ggml_backend_cpu_aarch64_buffer_type(void) { + static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type_aarch64 = { + /* .iface = */ { + /* .get_name = */ ggml_backend_cpu_aarch64_buffer_type_get_name, + /* .alloc_buffer = */ ggml_backend_cpu_aarch64_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes + /* .is_host = */ NULL, + }, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0), + /* .context = */ NULL, + }; + + return &ggml_backend_cpu_buffer_type_aarch64; +} + +bool ggml_backend_cpu_buft_is_aarch64(ggml_backend_buffer_type_t buft) { + return buft == ggml_backend_cpu_aarch64_buffer_type(); +} + +static ggml_backend_buffer_type_t * ggml_backend_cpu_get_extra_bufts(ggml_backend_dev_t device) { + static std::vector bufts = []() { + std::vector bufts; + +#if defined(__AMX_INT8__) && defined(__AVX512VNNI__) + if (ggml_backend_amx_buffer_type()) { + bufts.push_back(ggml_backend_amx_buffer_type()); + } +#endif + +#ifdef GGML_USE_CPU_AARCH64 + if (ggml_backend_cpu_aarch64_buffer_type()) { + bufts.push_back(ggml_backend_cpu_aarch64_buffer_type()); + } +#endif + + bufts.push_back(NULL); + + return bufts; + }(); + + return bufts.data(); + + GGML_UNUSED(device); +} + +// CPU backend - backend (stream) + +struct ggml_backend_cpu_context { + int n_threads; + ggml_threadpool_t threadpool; + + uint8_t * work_data; + size_t work_size; + + ggml_abort_callback abort_callback; + void * abort_callback_data; +}; + +static const char * ggml_backend_cpu_get_name(ggml_backend_t backend) { + return "CPU"; + + GGML_UNUSED(backend); +} + +static void ggml_backend_cpu_free(ggml_backend_t backend) { + struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; + delete[] cpu_ctx->work_data; + delete cpu_ctx; + delete backend; +} + +struct ggml_backend_plan_cpu { + struct ggml_cplan cplan; + struct ggml_cgraph cgraph; +}; + +static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, const struct ggml_cgraph * cgraph) { + struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; + + struct ggml_backend_plan_cpu * cpu_plan = new ggml_backend_plan_cpu; + + cpu_plan->cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads, cpu_ctx->threadpool); + cpu_plan->cgraph = *cgraph; // FIXME: deep copy + + if (cpu_plan->cplan.work_size > 0) { + cpu_plan->cplan.work_data = new uint8_t[cpu_plan->cplan.work_size]; + if (cpu_plan->cplan.work_data == NULL) { + delete cpu_plan; + return NULL; + } + } + + cpu_plan->cplan.abort_callback = cpu_ctx->abort_callback; + cpu_plan->cplan.abort_callback_data = cpu_ctx->abort_callback_data; + + return cpu_plan; +} + +static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { + struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan; + + delete[] cpu_plan->cplan.work_data; + delete cpu_plan; + + GGML_UNUSED(backend); +} + +static enum ggml_status ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { + struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan; + + return ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan); + + GGML_UNUSED(backend); +} + +static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { + struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; + + struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads, cpu_ctx->threadpool); + + if (cpu_ctx->work_size < cplan.work_size) { + delete[] cpu_ctx->work_data; + cpu_ctx->work_data = new uint8_t[cplan.work_size]; + if (cpu_ctx->work_data == NULL) { + cpu_ctx->work_size = 0; + return GGML_STATUS_ALLOC_FAILED; + } + cpu_ctx->work_size = cplan.work_size; + } + cplan.work_data = (uint8_t *)cpu_ctx->work_data; + + cplan.abort_callback = cpu_ctx->abort_callback; + cplan.abort_callback_data = cpu_ctx->abort_callback_data; + + return ggml_graph_compute(cgraph, &cplan); +} + +static const struct ggml_backend_i ggml_backend_cpu_i = { + /* .get_name = */ ggml_backend_cpu_get_name, + /* .free = */ ggml_backend_cpu_free, + /* .set_tensor_async = */ NULL, + /* .get_tensor_async = */ NULL, + /* .cpy_tensor_async = */ NULL, + /* .synchronize = */ NULL, + /* .graph_plan_create = */ ggml_backend_cpu_graph_plan_create, + /* .graph_plan_free = */ ggml_backend_cpu_graph_plan_free, + /* .graph_plan_update = */ NULL, + /* .graph_plan_compute = */ ggml_backend_cpu_graph_plan_compute, + /* .graph_compute = */ ggml_backend_cpu_graph_compute, + /* .event_record = */ NULL, + /* .event_wait = */ NULL, +}; + +static ggml_guid_t ggml_backend_cpu_guid(void) { + static ggml_guid guid = { 0xaa, 0x67, 0xc7, 0x43, 0x96, 0xe6, 0xa3, 0x8a, 0xe3, 0xaf, 0xea, 0x92, 0x36, 0xbc, 0xfc, 0x89 }; + return &guid; +} + +ggml_backend_t ggml_backend_cpu_init(void) { + // initialize CPU backend now to avoid slowing the first graph computation + ggml_cpu_init(); + + struct ggml_backend_cpu_context * ctx = new ggml_backend_cpu_context; + if (ctx == NULL) { + return NULL; + } + + ctx->n_threads = GGML_DEFAULT_N_THREADS; + ctx->threadpool = NULL; + ctx->work_data = NULL; + ctx->work_size = 0; + ctx->abort_callback = NULL; + ctx->abort_callback_data = NULL; + + ggml_backend_t cpu_backend = new ggml_backend { + /* .guid = */ ggml_backend_cpu_guid(), + /* .interface = */ ggml_backend_cpu_i, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0), + /* .context = */ ctx, + }; + + if (cpu_backend == NULL) { + delete ctx; + return NULL; + } + + return cpu_backend; +} + +bool ggml_backend_is_cpu(ggml_backend_t backend) { + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_cpu_guid()); +} + +void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) { + GGML_ASSERT(ggml_backend_is_cpu(backend_cpu)); + + struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context; + ctx->n_threads = n_threads; +} + +void ggml_backend_cpu_set_threadpool(ggml_backend_t backend_cpu, ggml_threadpool_t threadpool) { + GGML_ASSERT(ggml_backend_is_cpu(backend_cpu)); + + struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context; + + if (ctx->threadpool && ctx->threadpool != threadpool) { + // already had a different threadpool, pause/suspend it before switching + ggml_threadpool_pause(ctx->threadpool); + } + ctx->threadpool = threadpool; +} + +void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data) { + GGML_ASSERT(ggml_backend_is_cpu(backend_cpu)); + + struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context; + ctx->abort_callback = abort_callback; + ctx->abort_callback_data = abort_callback_data; +} + +// CPU backend - device + +struct ggml_backend_cpu_device_context { + std::string description = "CPU"; + + ggml_backend_cpu_device_context() { +#ifdef __APPLE__ + size_t len = 0; + if (!sysctlbyname("machdep.cpu.brand_string", NULL, &len, NULL, 0)) { + description.resize(len); + sysctlbyname("machdep.cpu.brand_string", &description[0], &len, NULL, 0); // NOLINT + } +#elif defined(__linux__) + FILE * f = fopen("/proc/cpuinfo", "r"); + if (f) { + char buf[1024]; + while (fgets(buf, sizeof(buf), f)) { + if (strncmp(buf, "model name", 10) == 0) { + char * p = strchr(buf, ':'); + if (p) { + p++; + while (std::isspace(*p)) { + p++; + } + while (std::isspace(p[strlen(p) - 1])) { + p[strlen(p) - 1] = '\0'; + } + description = p; + break; + } + } + } + fclose(f); + } +#elif defined(_WIN32) + HKEY hKey; + if (RegOpenKeyEx(HKEY_LOCAL_MACHINE, + TEXT("HARDWARE\\DESCRIPTION\\System\\CentralProcessor\\0"), + 0, + KEY_READ, + &hKey) == ERROR_SUCCESS) { + DWORD cpu_brand_size = 0; + if (RegQueryValueExA(hKey, + TEXT("ProcessorNameString"), + NULL, + NULL, + NULL, + &cpu_brand_size) == ERROR_SUCCESS) { + description.resize(cpu_brand_size); + if (RegQueryValueExA(hKey, + TEXT("ProcessorNameString"), + NULL, + NULL, + (LPBYTE)&description[0], // NOLINT + &cpu_brand_size) == ERROR_SUCCESS) { + if (description.find('\0') != std::string::npos) { + description.resize(description.find('\0')); + } + } + } + RegCloseKey(hKey); + } +#endif + } +}; + +static const char * ggml_backend_cpu_device_get_name(ggml_backend_dev_t dev) { + return "CPU"; + + GGML_UNUSED(dev); +} + +static const char * ggml_backend_cpu_device_get_description(ggml_backend_dev_t dev) { + struct ggml_backend_cpu_device_context * ctx = (struct ggml_backend_cpu_device_context *)dev->context; + + return ctx->description.c_str(); +} + +static void ggml_backend_cpu_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { + // TODO + *free = 0; + *total = 0; + + GGML_UNUSED(dev); +} + +static enum ggml_backend_dev_type ggml_backend_cpu_device_get_type(ggml_backend_dev_t dev) { + return GGML_BACKEND_DEVICE_TYPE_CPU; + + GGML_UNUSED(dev); +} + +static void ggml_backend_cpu_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) { + props->name = ggml_backend_cpu_device_get_name(dev); + props->description = ggml_backend_cpu_device_get_description(dev); + props->type = ggml_backend_cpu_device_get_type(dev); + ggml_backend_cpu_device_get_memory(dev, &props->memory_free, &props->memory_total); + props->caps = { + /* .async = */ false, + /* .host_buffer = */ false, + /* .buffer_from_host_ptr = */ true, + /* .events = */ false, + }; +} + +static ggml_backend_t ggml_backend_cpu_device_init_backend(ggml_backend_dev_t dev, const char * params) { + return ggml_backend_cpu_init(); + + GGML_UNUSED(dev); + GGML_UNUSED(params); +} + +static ggml_backend_buffer_type_t ggml_backend_cpu_device_get_buffer_type(ggml_backend_dev_t dev) { + return ggml_backend_cpu_buffer_type(); + + GGML_UNUSED(dev); +} + +static ggml_backend_buffer_t ggml_backend_cpu_device_buffer_from_host_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) { + return ggml_backend_cpu_buffer_from_ptr(ptr, size); + + GGML_UNUSED(dev); + GGML_UNUSED(max_tensor_size); +} + +static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { + const struct ggml_tensor * src0 = op->src[0]; + const struct ggml_tensor * src1 = op->src[1]; + + if (op->op == GGML_OP_NONE || op->op == GGML_OP_RESHAPE || op->op == GGML_OP_VIEW || op->op == GGML_OP_PERMUTE || op->op == GGML_OP_TRANSPOSE) { + return true; + } + + if (src0 && src0->buffer && ggml_backend_cpu_buft_is_aarch64(src0->buffer->buft)) { + if (op->op != GGML_OP_MUL_MAT || src0->type == ggml_aarch64_get_optimal_repack_type(src0)) { + return false; + } + } + +#if defined(__AMX_INT8__) && defined(__AVX512VNNI__) + if (src0 && src0->buffer && ggml_backend_amx_buft_is_amx(src0->buffer->buft)) { + return ggml_backend_amx_device_supports_op(op); + } + for (int i = 1; i < GGML_MAX_SRC; i++) { + if (op->src[i] && op->src[i]->buffer && ggml_backend_amx_buft_is_amx(op->src[i]->buffer->buft)) { + return false; + } + } +#endif + + for (int i = 1; i < GGML_MAX_SRC; i++) { + if (op->src[i] && op->src[i]->buffer && ggml_backend_cpu_buft_is_aarch64(op->src[i]->buffer->buft)) { + return false; + } + } + + switch (op->op) { + case GGML_OP_CPY: + return + op->type != GGML_TYPE_IQ2_XXS && + op->type != GGML_TYPE_IQ2_XS && + op->type != GGML_TYPE_IQ1_S && + op->type != GGML_TYPE_IQ1_M; // missing type_traits.from_float + case GGML_OP_MUL_MAT: + return src1->type == GGML_TYPE_F32 || src1->type == ggml_get_type_traits_cpu(src0->type)->vec_dot_type; + case GGML_OP_ROPE_BACK: + return op->src[2] == NULL && (op->op_params[2] & 4) == 0; + case GGML_OP_IM2COL_BACK: + return src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32; + case GGML_OP_OUT_PROD: + return (src0->type == GGML_TYPE_F32 || ggml_is_quantized(src0->type)) && src1->type == GGML_TYPE_F32; + default: + return true; + } + + GGML_UNUSED(dev); +} + +static bool ggml_backend_cpu_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { + bool supported = ggml_backend_buft_is_host(buft) || ggml_backend_cpu_buft_is_aarch64(buft); + +#if defined(__AMX_INT8__) && defined(__AVX512VNNI__) + supported = supported || ggml_backend_amx_buft_is_amx(buft); +#endif + + return supported; + + GGML_UNUSED(dev); +} + +static const struct ggml_backend_device_i ggml_backend_cpu_device_i = { + /* .get_name = */ ggml_backend_cpu_device_get_name, + /* .get_description = */ ggml_backend_cpu_device_get_description, + /* .get_memory = */ ggml_backend_cpu_device_get_memory, + /* .get_type = */ ggml_backend_cpu_device_get_type, + /* .get_props = */ ggml_backend_cpu_device_get_props, + /* .init_backend = */ ggml_backend_cpu_device_init_backend, + /* .get_buffer_type = */ ggml_backend_cpu_device_get_buffer_type, + /* .get_host_buffer_type = */ NULL, + /* .buffer_from_host_ptr = */ ggml_backend_cpu_device_buffer_from_host_ptr, + /* .supports_op = */ ggml_backend_cpu_device_supports_op, + /* .supports_buft = */ ggml_backend_cpu_device_supports_buft, + /* .offload_op = */ NULL, + /* .event_new = */ NULL, + /* .event_free = */ NULL, + /* .event_synchronize = */ NULL, +}; + +// CPU backend - backend (reg) + +static const char * ggml_backend_cpu_reg_get_name(ggml_backend_reg_t reg) { + return "CPU"; + + GGML_UNUSED(reg); +} + +static size_t ggml_backend_cpu_reg_get_device_count(ggml_backend_reg_t reg) { + return 1; + + GGML_UNUSED(reg); +} + +static ggml_backend_dev_t ggml_backend_cpu_reg_get_device(ggml_backend_reg_t reg, size_t index) { + GGML_ASSERT(index == 0); + + static ggml_backend_cpu_device_context ctx; + static ggml_backend_device ggml_backend_cpu_device = { + /* .iface = */ ggml_backend_cpu_device_i, + /* .reg = */ reg, + /* .context = */ &ctx, + }; + + return &ggml_backend_cpu_device; +} + +// This is intended to replace the the ggml_cpu_has_* functions when loading the CPU backend dynamically, +// and additionally to allow other backends to expose their own list of features that applications can query using the same API +static ggml_backend_feature * ggml_backend_cpu_get_features(ggml_backend_reg_t reg) { + static std::vector features = []() { + ggml_cpu_init(); + + std::vector features; + if (ggml_cpu_has_sse3()) { + features.push_back({ "SSE3", "1" }); + } + if (ggml_cpu_has_ssse3()) { + features.push_back({ "SSSE3", "1" }); + } + if (ggml_cpu_has_avx()) { + features.push_back({ "AVX", "1" }); + } + if (ggml_cpu_has_avx_vnni()) { + features.push_back({ "AVX_VNNI", "1" }); + } + if (ggml_cpu_has_avx2()) { + features.push_back({ "AVX2", "1" }); + } + if (ggml_cpu_has_f16c()) { + features.push_back({ "F16C", "1" }); + } + if (ggml_cpu_has_fma()) { + features.push_back({ "FMA", "1" }); + } + if (ggml_cpu_has_avx512()) { + features.push_back({ "AVX512", "1" }); + } + if (ggml_cpu_has_avx512_vbmi()) { + features.push_back({ "AVX512_VBMI", "1" }); + } + if (ggml_cpu_has_avx512_vnni()) { + features.push_back({ "AVX512_VNNI", "1" }); + } + if (ggml_cpu_has_avx512_bf16()) { + features.push_back({ "AVX512_BF16", "1" }); + } + if (ggml_cpu_has_amx_int8()) { + features.push_back({ "AMX_INT8", "1" }); + } + if (ggml_cpu_has_neon()) { + features.push_back({ "NEON", "1" }); + } + if (ggml_cpu_has_arm_fma()) { + features.push_back({ "ARM_FMA", "1" }); + } + if (ggml_cpu_has_fp16_va()) { + features.push_back({ "FP16_VA", "1" }); + } + if (ggml_cpu_has_matmul_int8()) { + features.push_back({ "MATMUL_INT8", "1" }); + } + if (ggml_cpu_has_sve()) { + features.push_back({ "SVE", "1" }); + } + if (ggml_cpu_get_sve_cnt() > 0) { + static std::string sve_cnt = std::to_string(ggml_cpu_get_sve_cnt()); + features.push_back({ "SVE_CNT", sve_cnt.c_str() }); + } + if (ggml_cpu_has_riscv_v()) { + features.push_back({ "RISCV_V", "1" }); + } + if (ggml_cpu_has_vsx()) { + features.push_back({ "VSX", "1" }); + } + if (ggml_cpu_has_wasm_simd()) { + features.push_back({ "WASM_SIMD", "1" }); + } + if (ggml_cpu_has_llamafile()) { + features.push_back({ "LLAMAFILE", "1" }); + } + #ifdef GGML_USE_ACCELERATE + features.push_back({ "ACCELERATE", "1" }); + #endif + #ifdef GGML_USE_CPU_HBM + features.push_back({ "CPU_HBM", "1" }); + #endif + #ifdef GGML_USE_OPENMP + features.push_back({ "OPENMP", "1" }); + #endif + #ifdef GGML_USE_CPU_AARCH64 + features.push_back({ "AARCH64_REPACK", "1" }); + #endif + + features.push_back({ nullptr, nullptr }); + + return features; + }(); + + return features.data(); + + GGML_UNUSED(reg); +} + +static void * ggml_backend_cpu_get_proc_address(ggml_backend_reg_t reg, const char * name) { + if (strcmp(name, "ggml_backend_set_n_threads") == 0) { + return (void *)ggml_backend_cpu_set_n_threads; + } + if (strcmp(name, "ggml_backend_dev_get_extra_bufts") == 0) { + return (void *)ggml_backend_cpu_get_extra_bufts; + } + if (strcmp(name, "ggml_backend_get_features") == 0) { + return (void *)ggml_backend_cpu_get_features; + } + if (strcmp(name, "ggml_backend_set_abort_callback") == 0) { + return (void *)ggml_backend_cpu_set_abort_callback; + } + if (strcmp(name, "ggml_backend_cpu_numa_init") == 0) { + return (void *)ggml_numa_init; + } + if (strcmp(name, "ggml_backend_cpu_is_numa") == 0) { + return (void *)ggml_is_numa; + } + + // threadpool - TODO: move to ggml-base + if (strcmp(name, "ggml_threadpool_new") == 0) { + return (void *)ggml_threadpool_new; + } + if (strcmp(name, "ggml_threadpool_free") == 0) { + return (void *)ggml_threadpool_free; + } + if (strcmp(name, "ggml_backend_cpu_set_threadpool") == 0) { + return (void *)ggml_backend_cpu_set_threadpool; + } + + return NULL; + + GGML_UNUSED(reg); +} + +static const struct ggml_backend_reg_i ggml_backend_cpu_reg_i = { + /* .get_name = */ ggml_backend_cpu_reg_get_name, + /* .get_device_count = */ ggml_backend_cpu_reg_get_device_count, + /* .get_device = */ ggml_backend_cpu_reg_get_device, + /* .get_proc_address = */ ggml_backend_cpu_get_proc_address, +}; + +ggml_backend_reg_t ggml_backend_cpu_reg(void) { + // init CPU feature detection + ggml_cpu_init(); + + static struct ggml_backend_reg ggml_backend_cpu_reg = { + /* .api_version = */ GGML_BACKEND_API_VERSION, + /* .iface = */ ggml_backend_cpu_reg_i, + /* .context = */ NULL, + }; + + return &ggml_backend_cpu_reg; +} + +GGML_BACKEND_DL_IMPL(ggml_backend_cpu_reg) diff --git a/ggml/src/llamafile/sgemm.cpp b/ggml/src/ggml-cpu/llamafile/sgemm.cpp similarity index 64% rename from ggml/src/llamafile/sgemm.cpp rename to ggml/src/ggml-cpu/llamafile/sgemm.cpp index 9eead3f61..da4146ec4 100644 --- a/ggml/src/llamafile/sgemm.cpp +++ b/ggml/src/ggml-cpu/llamafile/sgemm.cpp @@ -106,6 +106,10 @@ inline float16x8_t sub(float16x8_t x, float16x8_t y) { return vsubq_f16(x, y); } inline float16x8_t mul(float16x8_t x, float16x8_t y) { return vmulq_f16(x, y); } #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +#if defined(__MMA__) +typedef vector unsigned char vec_t; +typedef __vector_quad acc_t; +#endif //////////////////////////////////////////////////////////////////////////////////////////////////// // VECTORIZED FUSED MULTIPLY ADD @@ -1026,6 +1030,600 @@ class tinyBLAS_Q0_AVX { }; #endif // __AVX__ +//PPC Implementation +#if defined(__MMA__) + +#define SAVE_ACC(ACC, ii, jj) \ + __builtin_mma_disassemble_acc(vec_C, ACC); \ + for (int I = 0; I < 4; I++) { \ + for (int J = 0; J < 4; J++) { \ + *((float*)(C+ii+((jj+J)*ldc)+I)) = *((float*)&vec_C[I]+J); \ + } \ + } \ + +template +class tinyBLAS_PPC { + public: + tinyBLAS_PPC(int64_t k, + const TA *A, int64_t lda, + const TB *B, int64_t ldb, + TC *C, int64_t ldc, + int ith, int nth) + : A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) { + } + + void matmul(int64_t m, int64_t n) { + mnpack(0, m, 0, n); + } + + private: + + void (tinyBLAS_PPC::*kernel)(int64_t, int64_t); + + void READ_BLOCK(const float* a, int64_t lda, int rows, int cols, float* vec) { + int64_t i, j; + float *aoffset = NULL, *boffset = NULL; + float *aoffset1 = NULL, *aoffset2 = NULL, *aoffset3 = NULL, *aoffset4 = NULL; + float *aoffset5 = NULL, *aoffset6 = NULL, *aoffset7 = NULL, *aoffset8 = NULL; + + aoffset = const_cast(a); + boffset = vec; + j = (rows >> 3); + if (j > 0) { + do { + aoffset1 = aoffset; + aoffset2 = aoffset1 + lda; + aoffset3 = aoffset2 + lda; + aoffset4 = aoffset3 + lda; + aoffset5 = aoffset4 + lda; + aoffset6 = aoffset5 + lda; + aoffset7 = aoffset6 + lda; + aoffset8 = aoffset7 + lda; + aoffset += 8 * lda; + i = (cols >> 3); + if (i > 0) { + __vector_pair C1, C2, C3, C4, C5, C6, C7, C8; + vector float c1[2], c2[2], c3[2], c4[2], c5[2], c6[2], c7[2], c8[2]; + vector float t1, t2, t3, t4, t5, t6, t7, t8; + do { + C1 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset1); + C2 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset2); + C3 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset3); + C4 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset4); + C5 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset5); + C6 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset6); + C7 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset7); + C8 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset8); + __builtin_vsx_disassemble_pair(c1, &C1); + __builtin_vsx_disassemble_pair(c2, &C2); + __builtin_vsx_disassemble_pair(c3, &C3); + __builtin_vsx_disassemble_pair(c4, &C4); + __builtin_vsx_disassemble_pair(c5, &C5); + __builtin_vsx_disassemble_pair(c6, &C6); + __builtin_vsx_disassemble_pair(c7, &C7); + __builtin_vsx_disassemble_pair(c8, &C8); + + t1 = vec_mergeh(c1[0], c2[0]); + t2 = vec_mergeh(c3[0], c4[0]); + t3 = vec_mergeh(c5[0], c6[0]); + t4 = vec_mergeh(c7[0], c8[0]); + t5 = vec_xxpermdi(t1, t2, 0); + t6 = vec_xxpermdi(t3, t4, 0); + t7 = vec_xxpermdi(t1, t2, 3); + t8 = vec_xxpermdi(t3, t4, 3); + vec_xst(t5, 0, boffset); + vec_xst(t6, 0, boffset+4); + vec_xst(t7, 0, boffset+8); + vec_xst(t8, 0, boffset+12); + + t1 = vec_mergel(c1[0], c2[0]); + t2 = vec_mergel(c3[0], c4[0]); + t3 = vec_mergel(c5[0], c6[0]); + t4 = vec_mergel(c7[0], c8[0]); + t5 = vec_xxpermdi(t1, t2, 0); + t6 = vec_xxpermdi(t3, t4, 0); + t7 = vec_xxpermdi(t1, t2, 3); + t8 = vec_xxpermdi(t3, t4, 3); + vec_xst(t5, 0, boffset+16); + vec_xst(t6, 0, boffset+20); + vec_xst(t7, 0, boffset+24); + vec_xst(t8, 0, boffset+28); + + t1 = vec_mergeh(c1[1], c2[1]); + t2 = vec_mergeh(c3[1], c4[1]); + t3 = vec_mergeh(c5[1], c6[1]); + t4 = vec_mergeh(c7[1], c8[1]); + t5 = vec_xxpermdi(t1, t2, 0); + t6 = vec_xxpermdi(t3, t4, 0); + t7 = vec_xxpermdi(t1, t2, 3); + t8 = vec_xxpermdi(t3, t4, 3); + vec_xst(t5, 0, boffset+32); + vec_xst(t6, 0, boffset+36); + vec_xst(t7, 0, boffset+40); + vec_xst(t8, 0, boffset+44); + + t1 = vec_mergel(c1[1], c2[1]); + t2 = vec_mergel(c3[1], c4[1]); + t3 = vec_mergel(c5[1], c6[1]); + t4 = vec_mergel(c7[1], c8[1]); + t5 = vec_xxpermdi(t1, t2, 0); + t6 = vec_xxpermdi(t3, t4, 0); + t7 = vec_xxpermdi(t1, t2, 3); + t8 = vec_xxpermdi(t3, t4, 3); + vec_xst(t5, 0, boffset+48); + vec_xst(t6, 0, boffset+52); + vec_xst(t7, 0, boffset+56); + vec_xst(t8, 0, boffset+60); + + aoffset1 += 8*lda; + aoffset2 += 8*lda; + aoffset3 += 8*lda; + aoffset4 += 8*lda; + boffset += 64; + i--; + } while(i > 0); + } + if (cols & 4) { + vector float c1, c2, c3, c4, c5, c6, c7, c8; + vector float t1, t2, t3, t4, t5, t6, t7, t8; + c1 = vec_xl(0, aoffset1); + c2 = vec_xl(0, aoffset2); + c3 = vec_xl(0, aoffset3); + c4 = vec_xl(0, aoffset4); + c5 = vec_xl(0, aoffset5); + c6 = vec_xl(0, aoffset6); + c7 = vec_xl(0, aoffset7); + c8 = vec_xl(0, aoffset8); + + t1 = vec_mergeh(c1, c2); + t2 = vec_mergeh(c3, c4); + t3 = vec_mergeh(c5, c6); + t4 = vec_mergeh(c7, c8); + t5 = vec_xxpermdi(t1, t2, 0); + t6 = vec_xxpermdi(t3, t4, 0); + t7 = vec_xxpermdi(t1, t2, 3); + t8 = vec_xxpermdi(t3, t4, 3); + vec_xst(t5, 0, boffset); + vec_xst(t6, 0, boffset+4); + vec_xst(t7, 0, boffset+8); + vec_xst(t8, 0, boffset+12); + + t1 = vec_mergel(c1, c2); + t2 = vec_mergel(c3, c4); + t3 = vec_mergel(c5, c6); + t4 = vec_mergel(c7, c8); + t5 = vec_xxpermdi(t1, t2, 0); + t6 = vec_xxpermdi(t3, t4, 0); + t7 = vec_xxpermdi(t1, t2, 3); + t8 = vec_xxpermdi(t3, t4, 3); + vec_xst(t5, 0, boffset+16); + vec_xst(t6, 0, boffset+20); + vec_xst(t7, 0, boffset+24); + vec_xst(t8, 0, boffset+28); + } + j--; + } while(j > 0); + } + + if (rows & 4) { + aoffset1 = aoffset; + aoffset2 = aoffset1 + lda; + aoffset3 = aoffset2 + lda; + aoffset4 = aoffset3 + lda; + aoffset += 4 * lda; + i = (cols >> 3); + if (i > 0) { + __vector_pair C1, C2, C3, C4; + vector float c1[2], c2[2], c3[2], c4[2]; + vector float t1, t2, t3, t4, t5, t6, t7, t8; + do { + C1 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset1); + C2 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset2); + C3 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset3); + C4 = __builtin_vsx_lxvp(0, (__vector_pair*)aoffset4); + __builtin_vsx_disassemble_pair(c1, &C1); + __builtin_vsx_disassemble_pair(c2, &C2); + __builtin_vsx_disassemble_pair(c3, &C3); + __builtin_vsx_disassemble_pair(c4, &C4); + + t1 = vec_mergeh(c1[0], c2[0]); + t2 = vec_mergeh(c3[0], c4[0]); + t3 = vec_mergel(c1[0], c2[0]); + t4 = vec_mergel(c3[0], c4[0]); + t5 = vec_xxpermdi(t1, t2, 0); + t6 = vec_xxpermdi(t1, t2, 3); + t7 = vec_xxpermdi(t3, t4, 0); + t8 = vec_xxpermdi(t3, t4, 3); + vec_xst(t5, 0, boffset); + vec_xst(t6, 0, boffset+4); + vec_xst(t7, 0, boffset+8); + vec_xst(t8, 0, boffset+12); + + t1 = vec_mergeh(c1[1], c2[1]); + t2 = vec_mergeh(c3[1], c4[1]); + t3 = vec_mergel(c1[1], c2[1]); + t4 = vec_mergel(c3[1], c4[1]); + t5 = vec_xxpermdi(t1, t2, 0); + t6 = vec_xxpermdi(t1, t2, 3); + t7 = vec_xxpermdi(t3, t4, 0); + t8 = vec_xxpermdi(t3, t4, 3); + vec_xst(t5, 0, boffset+16); + vec_xst(t6, 0, boffset+20); + vec_xst(t7, 0, boffset+24); + vec_xst(t8, 0, boffset+28); + + aoffset1 += 8*lda; + aoffset2 += 8*lda; + aoffset3 += 8*lda; + aoffset4 += 8*lda; + boffset += 32; + i--; + } while(i > 0); + } + + if (cols & 4) { + vector float c1, c2, c3, c4; + vector float t1, t2, t3, t4; + c1 = vec_xl(0, aoffset1); + c2 = vec_xl(0, aoffset2); + c3 = vec_xl(0, aoffset3); + c4 = vec_xl(0, aoffset4); + + t1 = vec_mergeh(c1, c2); + t2 = vec_mergeh(c3, c4); + t3 = vec_xxpermdi(t1, t2, 0); + t4 = vec_xxpermdi(t1, t2, 3); + vec_xst(t3, 0, boffset); + vec_xst(t4, 0, boffset+4); + + t1 = vec_mergel(c1, c2); + t2 = vec_mergel(c3, c4); + t3 = vec_xxpermdi(t1, t2, 0); + t4 = vec_xxpermdi(t1, t2, 3); + vec_xst(t3, 0, boffset+8); + vec_xst(t4, 0, boffset+12); + } + } + if (rows & 3) { + aoffset1 = aoffset; + aoffset2 = aoffset1 + lda; + aoffset3 = aoffset2 + lda; + if (cols & 4) { + vector float c1, c2, c3, c4 = {0}; + vector float t1, t2, t3, t4; + c1 = vec_xl(0, aoffset1); + c2 = vec_xl(0, aoffset2); + c3 = vec_xl(0, aoffset3); + + t1 = vec_mergeh(c1, c2); + t2 = vec_mergeh(c3, c4); + t3 = vec_xxpermdi(t1, t2, 0); + t4 = vec_xxpermdi(t1, t2, 3); + vec_xst(t3, 0, boffset); + vec_xst(t4, 0, boffset+4); + + t1 = vec_mergel(c1, c2); + t2 = vec_mergel(c3, c4); + t3 = vec_xxpermdi(t1, t2, 0); + t4 = vec_xxpermdi(t1, t2, 3); + vec_xst(t3, 0, boffset+8); + vec_xst(t4, 0, boffset+12); + } + } + } + + void KERNEL_4x4(int64_t ii, int64_t jj) { + vec_t vec_A[4], vec_B[4], vec_C[4]; + acc_t acc_0; + __builtin_mma_xxsetaccz(&acc_0); + for (int l = 0; l < k; l+=4) { + READ_BLOCK(A+(ii*lda)+l, lda, 4, 4, (float*)vec_A); + READ_BLOCK(B+(jj*ldb)+l, ldb, 4, 4, (float*)vec_B); + __builtin_mma_xvf32gerpp(&acc_0, vec_A[0], vec_B[0]); + __builtin_mma_xvf32gerpp(&acc_0, vec_A[1], vec_B[1]); + __builtin_mma_xvf32gerpp(&acc_0, vec_A[2], vec_B[2]); + __builtin_mma_xvf32gerpp(&acc_0, vec_A[3], vec_B[3]); + } + SAVE_ACC(&acc_0, ii, jj); + } + + void KERNEL_4x8(int64_t ii, int64_t jj) { + vec_t vec_A[4], vec_B[8], vec_C[4]; + acc_t acc_0, acc_1; + __builtin_mma_xxsetaccz(&acc_0); + __builtin_mma_xxsetaccz(&acc_1); + for (int64_t l = 0; l < k; l+=4) { + READ_BLOCK(A+(ii*lda)+l, lda, 4, 4, (float*)vec_A); + READ_BLOCK(B+(jj*ldb)+l, ldb, 8, 4, (float*)vec_B); + __builtin_mma_xvf32gerpp(&acc_0, vec_A[0], (vec_t)vec_B[0]); + __builtin_mma_xvf32gerpp(&acc_1, vec_A[0], (vec_t)vec_B[1]); + __builtin_mma_xvf32gerpp(&acc_0, vec_A[1], (vec_t)vec_B[2]); + __builtin_mma_xvf32gerpp(&acc_1, vec_A[1], (vec_t)vec_B[3]); + __builtin_mma_xvf32gerpp(&acc_0, vec_A[2], (vec_t)vec_B[4]); + __builtin_mma_xvf32gerpp(&acc_1, vec_A[2], (vec_t)vec_B[5]); + __builtin_mma_xvf32gerpp(&acc_0, vec_A[3], (vec_t)vec_B[6]); + __builtin_mma_xvf32gerpp(&acc_1, vec_A[3], (vec_t)vec_B[7]); + } + SAVE_ACC(&acc_0, ii, jj); + SAVE_ACC(&acc_1, ii, jj+4); + } + + void KERNEL_8x4(int64_t ii, int64_t jj) { + vec_t vec_A[8], vec_B[4], vec_C[4]; + acc_t acc_0, acc_1; + __builtin_mma_xxsetaccz(&acc_0); + __builtin_mma_xxsetaccz(&acc_1); + for (int64_t l = 0; l < k; l+=4) { + READ_BLOCK(A+(ii*lda)+l, lda, 8, 4, (float*)vec_A); + READ_BLOCK(B+(jj*ldb)+l, ldb, 4, 4, (float*)vec_B); + __builtin_mma_xvf32gerpp(&acc_0, (vec_t)vec_A[0], vec_B[0]); + __builtin_mma_xvf32gerpp(&acc_1, (vec_t)vec_A[1], vec_B[0]); + __builtin_mma_xvf32gerpp(&acc_0, (vec_t)vec_A[2], vec_B[1]); + __builtin_mma_xvf32gerpp(&acc_1, (vec_t)vec_A[3], vec_B[1]); + __builtin_mma_xvf32gerpp(&acc_0, (vec_t)vec_A[4], vec_B[2]); + __builtin_mma_xvf32gerpp(&acc_1, (vec_t)vec_A[5], vec_B[2]); + __builtin_mma_xvf32gerpp(&acc_0, (vec_t)vec_A[6], vec_B[3]); + __builtin_mma_xvf32gerpp(&acc_1, (vec_t)vec_A[7], vec_B[3]); + } + SAVE_ACC(&acc_0, ii, jj); + SAVE_ACC(&acc_1, ii+4, jj); + } + + void KERNEL_8x8(int64_t ii, int64_t jj) { + vec_t vec_A[16], vec_B[16], vec_C[4]; + acc_t acc_0, acc_1, acc_2, acc_3; + __builtin_mma_xxsetaccz(&acc_0); + __builtin_mma_xxsetaccz(&acc_1); + __builtin_mma_xxsetaccz(&acc_2); + __builtin_mma_xxsetaccz(&acc_3); + for (int l = 0; l < k; l+=8) { + READ_BLOCK(A+(ii*lda)+l, lda, 8, 8, (float*)vec_A); + READ_BLOCK(B+(jj*ldb)+l, ldb, 8, 8, (float*)vec_B); + for(int x = 0; x < 16; x+=2) { + __builtin_mma_xvf32gerpp(&acc_0, (vec_t)vec_A[x], vec_B[x]); + __builtin_mma_xvf32gerpp(&acc_1, (vec_t)vec_A[x], vec_B[x+1]); + __builtin_mma_xvf32gerpp(&acc_2, (vec_t)vec_A[x+1], vec_B[x]); + __builtin_mma_xvf32gerpp(&acc_3, (vec_t)vec_A[x+1], vec_B[x+1]); + } + } + SAVE_ACC(&acc_0, ii, jj); + SAVE_ACC(&acc_1, ii, jj+4); + SAVE_ACC(&acc_2, ii+4, jj); + SAVE_ACC(&acc_3, ii+4, jj+4); + } + + void mnpack(int64_t m0, int64_t m, int64_t n0, int64_t n) { + int64_t mc, nc, mp, np; + int m_rem = MIN(m - m0, 16); + int n_rem = MIN(n - n0, 16); + if (m_rem >= 16 && n_rem >= 8) { + mc = 8; + nc = 8; + gemm<8,8>(m0, m, n0, n); + } else if(m_rem >= 8 && n_rem >= 16) { + mc = 8; + nc = 8; + gemm<8,8>(m0, m, n0, n); + } else if (m_rem >= 8 && n_rem >= 8) { + mc = 8; + nc = 8; + gemm<8,8>(m0, m, n0, n); + } else if (m_rem >= 4 && n_rem >= 8) { + mc = 4; + nc = 8; + gemm<4,8>(m0, m, n0, n); + } else if (m_rem >= 8 && n_rem >= 4) { + mc = 8; + nc = 4; + gemm<8,4>(m0, m, n0, n); + } else if (m_rem >= 4 && n_rem >= 4) { + mc = 4; + nc = 4; + gemm<4,4>(m0, m, n0, n); + } else if ((m_rem < 4) && (n_rem > 4)) { + nc = 4; + switch(m_rem) { + case 1: + mc = 1; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 2: + mc = 2; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 3: + mc = 3; + gemm_small(m0, m, n0, n, mc, nc); + break; + default: + return; + } + } else if ((m_rem > 4) && (n_rem < 4)) { + mc = 4; + switch(n_rem) { + case 1: + nc = 1; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 2: + nc = 2; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 3: + nc = 3; + gemm_small(m0, m, n0, n, mc, nc); + break; + default: + return; + } + } else { + switch((m_rem << 4) | n_rem) { + case 0x43: + mc = 4; + nc = 3; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x42: + mc = 4; + nc = 2; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x41: + mc = 4; + nc = 1; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x34: + mc = 3; + nc = 4; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x33: + mc = 3; + nc = 3; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x32: + mc = 3; + nc = 2; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x31: + mc = 3; + nc = 1; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x24: + mc = 2; + nc = 4; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x23: + mc = 2; + nc = 3; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x22: + mc = 2; + nc = 2; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x21: + mc = 2; + nc = 1; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x14: + mc = 1; + nc = 4; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x13: + mc = 1; + nc = 3; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x12: + mc = 1; + nc = 2; + gemm_small(m0, m, n0, n, mc, nc); + break; + case 0x11: + mc = 1; + nc = 1; + gemm_small(m0, m, n0, n, mc, nc); + break; + default: + return; + } + } + mp = m0 + (m - m0) / mc * mc; + np = n0 + (n - n0) / nc * nc; + mnpack(mp, m, n0, np); + mnpack(m0, m, np, n); + } + + void gemm_small(int64_t m0, int64_t m, int64_t n0, int64_t n, int RM, int RN) { + int64_t ytiles = (m - m0) / RM; + int64_t xtiles = (n - n0) / RN; + int64_t tiles = xtiles * ytiles; + int64_t duty = (tiles + nth - 1) / nth; + int64_t start = duty * ith; + int64_t end = start + duty; + if (end > tiles) + end = tiles; + for (int64_t job = start; job < end; ++job) { + int64_t ii = m0 + job / xtiles * RM; + int64_t jj = n0 + job % xtiles * RN; + vec_t vec_C[4]; + acc_t acc_0; + __builtin_mma_xxsetaccz(&acc_0); + vec_t vec_A[4], vec_B[4]; + for (int l=0; l= 4 && RM == 1) { + float* a = const_cast(A+(ii)*lda+l); + READ_BLOCK(B+(jj*ldb)+l, ldb, 4, 4, (float*)vec_B); + vec_A[0] = (vec_t)vec_xl(0,a); + vec_A[1] = (vec_t)vec_splats(*((float*)&vec_A+1)); + vec_A[2] = (vec_t)vec_splats(*((float*)&vec_A+2)); + vec_A[3] = (vec_t)vec_splats(*((float*)&vec_A+3)); + } else { + READ_BLOCK(A+(ii*lda)+l, lda, RM, 4, (float*)vec_A); + READ_BLOCK(B+(jj*ldb)+l, ldb, RN, 4, (float*)vec_B); + } + __builtin_mma_xvf32gerpp(&acc_0, vec_A[0], vec_B[0]); + __builtin_mma_xvf32gerpp(&acc_0, vec_A[1], vec_B[1]); + __builtin_mma_xvf32gerpp(&acc_0, vec_A[2], vec_B[2]); + __builtin_mma_xvf32gerpp(&acc_0, vec_A[3], vec_B[3]); + } + __builtin_mma_disassemble_acc(vec_C, &acc_0); + for (int I = 0; I < RM; I++) { + for (int J = 0; J < RN; J++) { + *((float*)(C+ii+((jj+J)*ldc)+I)) = *((float*)&vec_C[I]+J); + } + } + } + } + + template + NOINLINE void gemm(int64_t m0, int64_t m, int64_t n0, int64_t n) { + int64_t ytiles = (m - m0) / RM; + int64_t xtiles = (n - n0) / RN; + int64_t tiles = xtiles * ytiles; + int64_t duty = (tiles + nth - 1) / nth; + int64_t start = duty * ith; + int64_t end = start + duty; + if (RM == 4 && RN == 4) { + kernel = &tinyBLAS_PPC::KERNEL_4x4; + } else if (RM == 4 && RN == 8) { + kernel = &tinyBLAS_PPC::KERNEL_4x8; + } else if (RM == 8 && RN == 4) { + kernel = &tinyBLAS_PPC::KERNEL_8x4; + } else if (RM == 8 && RN == 8) { + kernel = &tinyBLAS_PPC::KERNEL_8x8; + } + if (end > tiles) + end = tiles; + for (int64_t job = start; job < end; ++job) { + int64_t ii = m0 + job / xtiles * RM; + int64_t jj = n0 + job % xtiles * RN; + (this->*kernel)(ii, jj); + } + } + + const TA *const A; + const TB *const B; + TC *C; + TA *At; + TB *Bt; + const int64_t k; + const int64_t lda; + const int64_t ldb; + const int64_t ldc; + const int ith; + const int nth; +}; +#endif } // namespace /** @@ -1114,6 +1712,16 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda ith, nth}; tb.matmul(m, n); return true; +#elif defined(__MMA__) + if (k % 8) + return false; + tinyBLAS_PPC tb{ + k, (const float *)A, lda, + (const float *)B, ldb, + (float *)C, ldc, + ith, nth}; + tb.matmul(m, n); + return true; #else return false; #endif diff --git a/ggml/src/llamafile/sgemm.h b/ggml/src/ggml-cpu/llamafile/sgemm.h similarity index 100% rename from ggml/src/llamafile/sgemm.h rename to ggml/src/ggml-cpu/llamafile/sgemm.h diff --git a/ggml/src/ggml-cuda/CMakeLists.txt b/ggml/src/ggml-cuda/CMakeLists.txt new file mode 100644 index 000000000..14761650f --- /dev/null +++ b/ggml/src/ggml-cuda/CMakeLists.txt @@ -0,0 +1,152 @@ +cmake_minimum_required(VERSION 3.18) # for CMAKE_CUDA_ARCHITECTURES + +find_package(CUDAToolkit) + +if (CUDAToolkit_FOUND) + message(STATUS "CUDA Toolkit found") + + if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES) + # native == GPUs available at build time + # 52 == Maxwell, lowest CUDA 12 standard + # 60 == P100, FP16 CUDA intrinsics + # 61 == Pascal, __dp4a instruction (per-byte integer dot product) + # 70 == V100, FP16 tensor cores + # 75 == Turing, int8 tensor cores + if (GGML_NATIVE AND CUDAToolkit_VERSION VERSION_GREATER_EQUAL "11.6" AND CMAKE_VERSION VERSION_GREATER_EQUAL "3.24") + set(CMAKE_CUDA_ARCHITECTURES "native") + elseif(GGML_CUDA_F16 OR GGML_CUDA_DMMV_F16) + set(CMAKE_CUDA_ARCHITECTURES "60;61;70;75") + else() + set(CMAKE_CUDA_ARCHITECTURES "52;61;70;75") + endif() + endif() + message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}") + + enable_language(CUDA) + + file(GLOB GGML_HEADERS_CUDA "*.cuh") + list(APPEND GGML_HEADERS_CUDA "../../include/ggml-cuda.h") + + file(GLOB GGML_SOURCES_CUDA "*.cu") + file(GLOB SRCS "template-instances/fattn-wmma*.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + file(GLOB SRCS "template-instances/mmq*.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + + if (GGML_CUDA_FA_ALL_QUANTS) + file(GLOB SRCS "template-instances/fattn-vec*.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS) + else() + file(GLOB SRCS "template-instances/fattn-vec*q4_0-q4_0.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + file(GLOB SRCS "template-instances/fattn-vec*q8_0-q8_0.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + file(GLOB SRCS "template-instances/fattn-vec*f16-f16.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + endif() + + ggml_add_backend_library(ggml-cuda + ${GGML_HEADERS_CUDA} + ${GGML_SOURCES_CUDA} + ) + + add_compile_definitions(GGML_CUDA_PEER_MAX_BATCH_SIZE=${GGML_CUDA_PEER_MAX_BATCH_SIZE}) + + if (GGML_CUDA_GRAPHS) + add_compile_definitions(GGML_CUDA_USE_GRAPHS) + endif() + + if (GGML_CUDA_FORCE_MMQ) + add_compile_definitions(GGML_CUDA_FORCE_MMQ) + endif() + + if (GGML_CUDA_FORCE_CUBLAS) + add_compile_definitions(GGML_CUDA_FORCE_CUBLAS) + endif() + + if (GGML_CUDA_NO_VMM) + add_compile_definitions(GGML_CUDA_NO_VMM) + endif() + + if (GGML_CUDA_F16 OR GGML_CUDA_DMMV_F16) + add_compile_definitions(GGML_CUDA_F16) + endif() + + if (GGML_CUDA_NO_PEER_COPY) + add_compile_definitions(GGML_CUDA_NO_PEER_COPY) + endif() + + if (GGML_STATIC) + if (WIN32) + # As of 12.3.1 CUDA Toolkit for Windows does not offer a static cublas library + target_link_libraries(ggml-cuda PRIVATE CUDA::cudart_static CUDA::cublas CUDA::cublasLt) + else () + target_link_libraries(ggml-cuda PRIVATE CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static) + endif() + else() + target_link_libraries(ggml-cuda PRIVATE CUDA::cudart CUDA::cublas CUDA::cublasLt) + endif() + + if (GGML_CUDA_NO_VMM) + # No VMM requested, no need to link directly with the cuda driver lib (libcuda.so) + else() + target_link_libraries(ggml-cuda PRIVATE CUDA::cuda_driver) + endif() + + set(CUDA_CXX_FLAGS "") + + set(CUDA_FLAGS -use_fast_math) + + if (GGML_FATAL_WARNINGS) + list(APPEND CUDA_FLAGS -Werror all-warnings) + endif() + + if (GGML_ALL_WARNINGS AND NOT MSVC) + set(NVCC_CMD ${CMAKE_CUDA_COMPILER} .c) + if (NOT CMAKE_CUDA_HOST_COMPILER STREQUAL "") + list(APPEND NVCC_CMD -ccbin ${CMAKE_CUDA_HOST_COMPILER}) + endif() + + execute_process( + COMMAND ${NVCC_CMD} -Xcompiler --version + OUTPUT_VARIABLE CUDA_CCFULLVER + ERROR_QUIET + ) + + if (NOT CUDA_CCFULLVER MATCHES clang) + set(CUDA_CCID "GNU") + execute_process( + COMMAND ${NVCC_CMD} -Xcompiler "-dumpfullversion -dumpversion" + OUTPUT_VARIABLE CUDA_CCVER + ERROR_QUIET + ) + else() + if (CUDA_CCFULLVER MATCHES Apple) + set(CUDA_CCID "AppleClang") + else() + set(CUDA_CCID "Clang") + endif() + string(REGEX REPLACE "^.* version ([0-9.]*).*$" "\\1" CUDA_CCVER ${CUDA_CCFULLVER}) + endif() + + message("-- CUDA host compiler is ${CUDA_CCID} ${CUDA_CCVER}") + + ggml_get_flags(${CUDA_CCID} ${CUDA_CCVER}) + list(APPEND CUDA_CXX_FLAGS ${CXX_FLAGS} ${GF_CXX_FLAGS}) # This is passed to -Xcompiler later + endif() + + if (NOT MSVC) + list(APPEND CUDA_CXX_FLAGS -Wno-pedantic) + endif() + + list(JOIN CUDA_CXX_FLAGS " " CUDA_CXX_FLAGS_JOINED) # pass host compiler flags as a single argument + + if (NOT CUDA_CXX_FLAGS_JOINED STREQUAL "") + list(APPEND CUDA_FLAGS -Xcompiler ${CUDA_CXX_FLAGS_JOINED}) + endif() + + target_compile_options(ggml-cuda PRIVATE "$<$:${CUDA_FLAGS}>") +else() + message(FATAL_ERROR "CUDA Toolkit not found") +endif() diff --git a/ggml/src/ggml-cuda/argmax.cu b/ggml/src/ggml-cuda/argmax.cu index aab04eca7..5340eedc0 100644 --- a/ggml/src/ggml-cuda/argmax.cu +++ b/ggml/src/ggml-cuda/argmax.cu @@ -1,57 +1,69 @@ -#include "common.cuh" -#include "argmax.cuh" -#include "sum.cuh" - +#include #include -static __global__ void argmax_f32( - const float * x, int32_t * dst, const int64_t ncols, const int64_t nrows) { +#include "argmax.cuh" +#include "common.cuh" +#include "sum.cuh" - int argmax_thread = 0; - const int64_t row0 = (int64_t)blockIdx.x*WARP_SIZE; +static __global__ void argmax_f32(const float * __restrict__ x, int32_t * __restrict__ dst, const int64_t ncols) { + const int64_t row = blockIdx.x; -#pragma unroll - for (int64_t row1 = 0; row1 < WARP_SIZE; ++row1) { - const int64_t row = row0 + row1; + float maxval = -FLT_MAX; + int argmax = -1; + const float * rowx = x + row * ncols; - if (row >= nrows) { - break; + for (int32_t col = threadIdx.x; col < ncols; col += blockDim.x) { + const float val = rowx[col]; + if (val > maxval) { + maxval = val; + argmax = col; } - - float maxval = -FLT_MAX; - int argmax = -1; - - for (int32_t col = threadIdx.x; col < ncols; col += WARP_SIZE) { - const float val = x[row*ncols + col]; - const int bigger = val > maxval; - const int not_bigger = bigger ^ 0x00000001; - - maxval = maxval*not_bigger + val*bigger; - argmax = argmax*not_bigger + col*bigger; - } - -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - const float val = __shfl_xor_sync(0xFFFFFFFF, maxval, mask, WARP_SIZE); - const int col = __shfl_xor_sync(0xFFFFFFFF, argmax, mask, WARP_SIZE); - const int bigger = val > maxval; - const int not_bigger = bigger ^ 0x00000001; - - maxval = maxval*not_bigger + val*bigger; - argmax = argmax*not_bigger + col*bigger; - } - - const int store = row1 == threadIdx.x; - argmax_thread += store*argmax; } - const int row = row0 + threadIdx.x; - - if (row >= nrows) { - return; +#pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) { + const float val = __shfl_xor_sync(0xFFFFFFFF, maxval, offset, WARP_SIZE); + const int col = __shfl_xor_sync(0xFFFFFFFF, argmax, offset, WARP_SIZE); + if (val > maxval) { + maxval = val; + argmax = col; + } } - dst[row] = argmax_thread; + const int n_warps = blockDim.x / WARP_SIZE; + const int lane_id = threadIdx.x % WARP_SIZE; + const int warp_id = threadIdx.x / WARP_SIZE; + if (n_warps > 1) { + constexpr int max_warps = 1024 / WARP_SIZE; + __shared__ float shared_maxval[max_warps]; + __shared__ int shared_argmax[max_warps]; + if (lane_id == 0) { + shared_maxval[warp_id] = maxval; + shared_argmax[warp_id] = argmax; + } + + __syncthreads(); + + if (warp_id == 0) { + if (lane_id < n_warps) { + maxval = shared_maxval[lane_id]; + argmax = shared_argmax[lane_id]; + } +#pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) { + const float val = __shfl_xor_sync(0xFFFFFFFF, maxval, offset, WARP_SIZE); + const int col = __shfl_xor_sync(0xFFFFFFFF, argmax, offset, WARP_SIZE); + if (val > maxval) { + maxval = val; + argmax = col; + } + } + } + } + + if (warp_id == 0 && lane_id == 0) { + dst[row] = argmax; + } } void ggml_cuda_argmax(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { @@ -70,10 +82,10 @@ void ggml_cuda_argmax(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { cudaStream_t stream = ctx.stream(); - const int64_t num_blocks = (nrows + WARP_SIZE - 1) / WARP_SIZE; - - const dim3 blocks_dim(WARP_SIZE, 1, 1); + const int64_t num_blocks = nrows; + const int64_t num_threads = std::min(1024, (ne00 + WARP_SIZE - 1) / WARP_SIZE * WARP_SIZE); + const dim3 blocks_dim(num_threads, 1, 1); const dim3 blocks_num(num_blocks, 1, 1); - argmax_f32<<>>(src0_d, dst_d, ne00, nrows); + argmax_f32<<>>(src0_d, dst_d, ne00); } diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh index dd203fcde..535118d87 100644 --- a/ggml/src/ggml-cuda/common.cuh +++ b/ggml/src/ggml-cuda/common.cuh @@ -6,7 +6,7 @@ #include #include -#if defined(GGML_USE_HIPBLAS) +#if defined(GGML_USE_HIP) #define GGML_COMMON_DECL_HIP #define GGML_COMMON_IMPL_HIP #else @@ -26,13 +26,13 @@ #include #include -#if defined(GGML_USE_HIPBLAS) +#if defined(GGML_USE_HIP) #include "vendors/hip.h" #elif defined(GGML_USE_MUSA) #include "vendors/musa.h" #else #include "vendors/cuda.h" -#endif // defined(GGML_USE_HIPBLAS) +#endif // defined(GGML_USE_HIP) #define STRINGIZE_IMPL(...) #__VA_ARGS__ #define STRINGIZE(...) STRINGIZE_IMPL(__VA_ARGS__) @@ -47,9 +47,20 @@ #define CC_TURING 750 #define CC_AMPERE 800 #define CC_OFFSET_AMD 1000000 -#define CC_RDNA1 (CC_OFFSET_AMD + 1010) -#define CC_RDNA2 (CC_OFFSET_AMD + 1030) -#define CC_RDNA3 (CC_OFFSET_AMD + 1100) + +// GCN/CNDA, wave size is 64 +#define CC_GCN4 (CC_OFFSET_AMD + 803) // Tonga, Fiji, Polaris, minimum for fast fp16 +#define CC_VEGA (CC_OFFSET_AMD + 900) // Vega56/64, minimum for fp16 dual issue +#define CC_VEGA20 (CC_OFFSET_AMD + 906) // MI50/Radeon VII, minimum for dp4a +#define CC_CDNA (CC_OFFSET_AMD + 908) // MI100, minimum for MFMA, acc registers +#define CC_CDNA2 (CC_OFFSET_AMD + 910) // MI210, minimum acc register renameing +#define CC_CDNA3 (CC_OFFSET_AMD + 942) // MI300 + +// RNDA removes MFMA, dp4a, xnack, acc registers, wave size is 32 +#define CC_RDNA1 (CC_OFFSET_AMD + 1010) // RX 5000 +#define CC_RDNA2 (CC_OFFSET_AMD + 1030) // RX 6000, minimum for dp4a +#define CC_RDNA3 (CC_OFFSET_AMD + 1100) // RX 7000, minimum for WMMA + #define CC_QY1 210 #define CC_QY2 220 @@ -97,7 +108,7 @@ void ggml_cuda_error(const char * stmt, const char * func, const char * file, in #define CUBLAS_CHECK(err) CUDA_CHECK_GEN(err, CUBLAS_STATUS_SUCCESS, cublas_get_error_str) -#if !defined(GGML_USE_HIPBLAS) +#if !defined(GGML_USE_HIP) static const char * cu_get_error_str(CUresult err) { const char * err_str; cuGetErrorString(err, &err_str); @@ -120,21 +131,21 @@ typedef float dfloat; // dequantize float typedef float2 dfloat2; #endif // GGML_CUDA_F16 -#if (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL +#if (defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL #define FP16_AVAILABLE -#endif // (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL +#endif // (defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL #if defined(FP16_AVAILABLE) && __CUDA_ARCH__ != 610 #define FAST_FP16_AVAILABLE #endif // defined(FP16_AVAILABLE) && __CUDA_ARCH__ != 610 -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA #define FP16_MMA_AVAILABLE -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_TURING +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_TURING #define INT8_MMA_AVAILABLE -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_TURING +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_TURING #if !(defined(GGML_USE_MUSA) && __MUSA_ARCH__ <= CC_QY1) #define FLASH_ATTN_AVAILABLE @@ -156,14 +167,14 @@ static constexpr bool int8_mma_available(const int cc) { static __device__ void no_device_code( const char * file_name, const int line, const char * function_name, const int arch, const char * arch_list) { -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) printf("%s:%d: ERROR: HIP kernel %s has no device code compatible with HIP arch %d.\n", file_name, line, function_name, arch); GGML_UNUSED(arch_list); #else printf("%s:%d: ERROR: CUDA kernel %s has no device code compatible with CUDA arch %d. ggml-cuda.cu was compiled for: %s\n", file_name, line, function_name, arch, arch_list); -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) __trap(); GGML_UNUSED(no_device_code); // suppress unused function warning @@ -176,30 +187,30 @@ static __device__ void no_device_code( #endif // __CUDA_ARCH__ static __device__ __forceinline__ int warp_reduce_sum(int x) { -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_AMPERE +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_AMPERE return __reduce_add_sync(0xffffffff, x); #else #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - x += __shfl_xor_sync(0xffffffff, x, mask, 32); + for (int offset = 16; offset > 0; offset >>= 1) { + x += __shfl_xor_sync(0xffffffff, x, offset, 32); } return x; -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_AMPERE +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_AMPERE } static __device__ __forceinline__ float warp_reduce_sum(float x) { #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - x += __shfl_xor_sync(0xffffffff, x, mask, 32); + for (int offset = 16; offset > 0; offset >>= 1) { + x += __shfl_xor_sync(0xffffffff, x, offset, 32); } return x; } static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) { #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - a.x += __shfl_xor_sync(0xffffffff, a.x, mask, 32); - a.y += __shfl_xor_sync(0xffffffff, a.y, mask, 32); + for (int offset = 16; offset > 0; offset >>= 1) { + a.x += __shfl_xor_sync(0xffffffff, a.x, offset, 32); + a.y += __shfl_xor_sync(0xffffffff, a.y, offset, 32); } return a; } @@ -207,21 +218,21 @@ static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) { static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) { #ifdef FP16_AVAILABLE -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - const half2 a_other = __shfl_xor_sync(0xffffffff, a, mask, 32); + for (int offset = 16; offset > 0; offset >>= 1) { + const half2 a_other = __shfl_xor_sync(0xffffffff, a, offset, 32); reinterpret_cast(a.x) += __low2half(a_other); reinterpret_cast(a.y) += __high2half(a_other); } return a; #else #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, mask, 32)); + for (int offset = 16; offset > 0; offset >>= 1) { + a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, offset, 32)); } return a; -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) #else NO_DEVICE_CODE; @@ -231,8 +242,8 @@ static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) { static __device__ __forceinline__ float warp_reduce_max(float x) { #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, mask, 32)); + for (int offset = 16; offset > 0; offset >>= 1) { + x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, offset, 32)); } return x; } @@ -240,11 +251,11 @@ static __device__ __forceinline__ float warp_reduce_max(float x) { static __device__ __forceinline__ half ggml_cuda_hmax(const half a, const half b) { #ifdef FP16_AVAILABLE -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX return __float2half(fmaxf(__half2float(a), __half2float(b))); #else return __hmax(a, b); -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX #else NO_DEVICE_CODE; @@ -254,7 +265,7 @@ static __device__ __forceinline__ half ggml_cuda_hmax(const half a, const half b } static __device__ __forceinline__ half2 ggml_cuda_hmax2(const half2 a, const half2 b) { -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) #if CUDART_VERSION >= CUDART_HMAX return __hmax2(a, b); @@ -269,20 +280,20 @@ static __device__ __forceinline__ half2 ggml_cuda_hmax2(const half2 a, const hal GGML_UNUSED(a); GGML_UNUSED(b); NO_DEVICE_CODE; -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) } static __device__ __forceinline__ half2 warp_reduce_max(half2 x) { -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL #pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - x = ggml_cuda_hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32)); + for (int offset = 16; offset > 0; offset >>= 1) { + x = ggml_cuda_hmax2(x, __shfl_xor_sync(0xffffffff, x, offset, 32)); } return x; #else GGML_UNUSED(x); NO_DEVICE_CODE; -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL } #if CUDART_VERSION < CUDART_HMASK @@ -294,7 +305,7 @@ static __device__ __forceinline__ uint32_t __hgt2_mask(const half2 a, const half #endif // CUDART_VERSION < CUDART_HMASK static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, int c) { -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) #if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(RDNA2) c = __builtin_amdgcn_sdot4(a, b, c, false); #elif defined(RDNA3) @@ -320,7 +331,7 @@ static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, i #endif return c; -#else // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#else // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) #if __CUDA_ARCH__ >= MIN_CC_DP4A return __dp4a(a, b, c); @@ -330,7 +341,7 @@ static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, i return c + a8[0]*b8[0] + a8[1]*b8[1] + a8[2]*b8[2] + a8[3]*b8[3]; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) } // TODO: move to ggml-common.h diff --git a/ggml/src/ggml-cuda/count-equal.cu b/ggml/src/ggml-cuda/count-equal.cu index ffb053b10..08898115d 100644 --- a/ggml/src/ggml-cuda/count-equal.cu +++ b/ggml/src/ggml-cuda/count-equal.cu @@ -44,7 +44,7 @@ void ggml_cuda_count_equal(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const int64_t ne = ggml_nelements(src0); GGML_ASSERT(ne < (1 << 30) && "atomicAdd implementation only supports int"); - const int64_t dne = GGML_PAD(ne / (4*nsm), CUDA_COUNT_EQUAL_CHUNK_SIZE); + const int64_t dne = GGML_PAD((ne + 4*nsm - 1) / (4*nsm), CUDA_COUNT_EQUAL_CHUNK_SIZE); CUDA_CHECK(cudaMemsetAsync(dst_d, 0, ggml_nbytes(dst), stream)); diff --git a/ggml/src/ggml-cuda/dmmv.cu b/ggml/src/ggml-cuda/dmmv.cu deleted file mode 100644 index 00e21b5d7..000000000 --- a/ggml/src/ggml-cuda/dmmv.cu +++ /dev/null @@ -1,699 +0,0 @@ -#include "dmmv.cuh" -#include "dequantize.cuh" -#include "convert.cuh" - -#ifndef K_QUANTS_PER_ITERATION -#define K_QUANTS_PER_ITERATION 2 -#else -static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2"); -#endif - -static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { - - static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION"); - - const int row = blockIdx.x*blockDim.y + threadIdx.y; - if (row > nrows) return; - - const int num_blocks_per_row = ncols / QK_K; - const int ib0 = row*num_blocks_per_row; - - const block_q2_K * x = (const block_q2_K *)vx + ib0; - - float tmp = 0; // partial sum for thread in warp - - const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...15 - const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1 - - const int step = 16/K_QUANTS_PER_ITERATION; - - const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const int in = tid - step*im; // 0...15 or 0...7 - - const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15 or 0...14 in steps of 2 - const int q_offset = 32*im + l0; - const int s_offset = 8*im; - const int y_offset = 128*im + l0; - - uint32_t aux[4]; - const uint8_t * d = (const uint8_t *)aux; - const uint8_t * m = (const uint8_t *)(aux + 2); - - for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - - const float * y = yy + i * QK_K + y_offset; - const uint8_t * q = x[i].qs + q_offset; - - const float dall = __low2half(x[i].dm); - const float dmin = __high2half(x[i].dm); - - const uint32_t * a = (const uint32_t *)(x[i].scales + s_offset); - aux[0] = a[0] & 0x0f0f0f0f; - aux[1] = a[1] & 0x0f0f0f0f; - aux[2] = (a[0] >> 4) & 0x0f0f0f0f; - aux[3] = (a[1] >> 4) & 0x0f0f0f0f; - - float sum1 = 0, sum2 = 0; - for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { - sum1 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3) - + y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3) - + y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3) - + y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3) - + y[l+16] * d[1] * ((q[l+16] >> 0) & 3) - + y[l+48] * d[3] * ((q[l+16] >> 2) & 3) - + y[l+80] * d[5] * ((q[l+16] >> 4) & 3) - +y[l+112] * d[7] * ((q[l+16] >> 6) & 3); - sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6] - + y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7]; - - } - tmp += dall * sum1 - dmin * sum2; - - } - - // sum up partial sums and write back result - tmp = warp_reduce_sum(tmp); - - if (threadIdx.x == 0) { - dst[row] = tmp; - } -} - -static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { - - const int row = blockIdx.x*blockDim.y + threadIdx.y; - if (row > nrows) return; - - const int num_blocks_per_row = ncols / QK_K; - const int ib0 = row*num_blocks_per_row; - - const block_q3_K * x = (const block_q3_K *)vx + ib0; - - float tmp = 0; // partial sum for thread in warp - - const uint16_t kmask1 = 0x0303; - const uint16_t kmask2 = 0x0f0f; - - const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1 - - const int n = K_QUANTS_PER_ITERATION; // iterations in the inner loop - const int step = 16/K_QUANTS_PER_ITERATION; - const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const int in = tid - step*im; // 0....15 or 0...7 - - const uint8_t m = 1 << (4*im); - - const int l0 = n*in; // 0...15 or 0...14 in steps of 2 - const int q_offset = 32*im + l0; - const int y_offset = 128*im + l0; - - uint16_t utmp[4]; - const int8_t * s = (const int8_t *)utmp; - - const uint16_t s_shift = 4*im; - - for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - - const float * y = yy + i * QK_K + y_offset; - const uint8_t * q = x[i].qs + q_offset; - const uint8_t * h = x[i].hmask + l0; - - const uint16_t * a = (const uint16_t *)x[i].scales; - utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4); - utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4); - utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4); - utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4); - - const float d = x[i].d; - - float sum = 0; - for (int l = 0; l < n; ++l) { - sum += y[l+ 0] * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4)) - + y[l+32] * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4)) - + y[l+64] * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4)) - + y[l+96] * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4)); - sum += y[l+16] * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4)) - + y[l+48] * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4)) - + y[l+80] * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4)) - + y[l+112] * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4)); - } - tmp += d * sum; - - } - - // sum up partial sums and write back result - tmp = warp_reduce_sum(tmp); - - if (threadIdx.x == 0) { - dst[row] = tmp; - } -} - -static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { - - const int row = blockIdx.x*blockDim.y + threadIdx.y; - if (row > nrows) return; - const int num_blocks_per_row = ncols / QK_K; - const int ib0 = row*num_blocks_per_row; - - const block_q4_K * x = (const block_q4_K *)vx + ib0; - - const uint16_t kmask1 = 0x3f3f; - const uint16_t kmask2 = 0x0f0f; - const uint16_t kmask3 = 0xc0c0; - - const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1 - - const int step = 8/K_QUANTS_PER_ITERATION; // 8 or 4 - - const int il = tid/step; // 0...3 - const int ir = tid - step*il; // 0...7 or 0...3 - const int n = 2 * K_QUANTS_PER_ITERATION; // 2 or 4 - - const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 - const int in = il%2; - - const int l0 = n*(2*ir + in); - const int q_offset = 32*im + l0; - const int y_offset = 64*im + l0; - - uint16_t aux[4]; - const uint8_t * sc = (const uint8_t *)aux; - -#if K_QUANTS_PER_ITERATION == 2 - uint32_t q32[4]; - const uint8_t * q4 = (const uint8_t *)q32; -#else - uint16_t q16[4]; - const uint8_t * q4 = (const uint8_t *)q16; -#endif - - float tmp = 0; // partial sum for thread in warp - - for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - - const float * y1 = yy + i*QK_K + y_offset; - const float * y2 = y1 + 128; - - const float dall = __low2half(x[i].dm); - const float dmin = __high2half(x[i].dm); - - const uint16_t * a = (const uint16_t *)x[i].scales; - aux[0] = a[im+0] & kmask1; - aux[1] = a[im+2] & kmask1; - aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2); - aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2); - -#if K_QUANTS_PER_ITERATION == 2 - const uint32_t * q1 = (const uint32_t *)(x[i].qs + q_offset); - const uint32_t * q2 = q1 + 16; - - q32[0] = q1[0] & 0x0f0f0f0f; - q32[1] = q1[0] & 0xf0f0f0f0; - q32[2] = q2[0] & 0x0f0f0f0f; - q32[3] = q2[0] & 0xf0f0f0f0; - - float4 s = {0.f, 0.f, 0.f, 0.f}; - float smin = 0; - for (int l = 0; l < 4; ++l) { - s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+ 4]; - s.z += y2[l] * q4[l+8]; s.w += y2[l+32] * q4[l+12]; - smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7]; - } - tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin; -#else - const uint16_t * q1 = (const uint16_t *)(x[i].qs + q_offset); - const uint16_t * q2 = q1 + 32; - - q16[0] = q1[0] & 0x0f0f; - q16[1] = q1[0] & 0xf0f0; - q16[2] = q2[0] & 0x0f0f; - q16[3] = q2[0] & 0xf0f0; - - float4 s = {0.f, 0.f, 0.f, 0.f}; - float smin = 0; - for (int l = 0; l < 2; ++l) { - s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+2]; - s.z += y2[l] * q4[l+4]; s.w += y2[l+32] * q4[l+6]; - smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7]; - } - tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin; -#endif - - } - - // sum up partial sums and write back result - tmp = warp_reduce_sum(tmp); - - if (tid == 0) { - dst[row] = tmp; - } -} - -static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols) { - - const int row = blockIdx.x; - const int num_blocks_per_row = ncols / QK_K; - const int ib0 = row*num_blocks_per_row; - - const block_q5_K * x = (const block_q5_K *)vx + ib0; - - float tmp = 0; // partial sum for thread in warp - - const uint16_t kmask1 = 0x3f3f; - const uint16_t kmask2 = 0x0f0f; - const uint16_t kmask3 = 0xc0c0; - - const int tid = threadIdx.x/2; // 0...15 - const int ix = threadIdx.x%2; - - const int il = tid/4; // 0...3 - const int ir = tid - 4*il;// 0...3 - const int n = 2; - - const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 - const int in = il%2; - - const int l0 = n*(2*ir + in); - const int q_offset = 32*im + l0; - const int y_offset = 64*im + l0; - - const uint8_t hm1 = 1 << (2*im); - const uint8_t hm2 = hm1 << 4; - - uint16_t aux[4]; - const uint8_t * sc = (const uint8_t *)aux; - - uint16_t q16[8]; - const uint8_t * q4 = (const uint8_t *)q16; - - for (int i = ix; i < num_blocks_per_row; i += 2) { - - const uint8_t * ql1 = x[i].qs + q_offset; - const uint8_t * qh = x[i].qh + l0; - const float * y1 = yy + i*QK_K + y_offset; - const float * y2 = y1 + 128; - - const float dall = __low2half(x[i].dm); - const float dmin = __high2half(x[i].dm); - - const uint16_t * a = (const uint16_t *)x[i].scales; - aux[0] = a[im+0] & kmask1; - aux[1] = a[im+2] & kmask1; - aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2); - aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2); - - float4 sum = {0.f, 0.f, 0.f, 0.f}; - float smin = 0; - const uint16_t * q1 = (const uint16_t *)ql1; - const uint16_t * q2 = q1 + 32; - q16[0] = q1[0] & 0x0f0f; - q16[1] = q1[8] & 0x0f0f; - q16[2] = (q1[0] >> 4) & 0x0f0f; - q16[3] = (q1[8] >> 4) & 0x0f0f; - q16[4] = q2[0] & 0x0f0f; - q16[5] = q2[8] & 0x0f0f; - q16[6] = (q2[0] >> 4) & 0x0f0f; - q16[7] = (q2[8] >> 4) & 0x0f0f; - for (int l = 0; l < n; ++l) { - sum.x += y1[l+ 0] * (q4[l +0] + (qh[l+ 0] & (hm1 << 0) ? 16 : 0)) - + y1[l+16] * (q4[l +2] + (qh[l+16] & (hm1 << 0) ? 16 : 0)); - sum.y += y1[l+32] * (q4[l +4] + (qh[l+ 0] & (hm1 << 1) ? 16 : 0)) - + y1[l+48] * (q4[l +6] + (qh[l+16] & (hm1 << 1) ? 16 : 0)); - sum.z += y2[l+ 0] * (q4[l +8] + (qh[l+ 0] & (hm2 << 0) ? 16 : 0)) - + y2[l+16] * (q4[l+10] + (qh[l+16] & (hm2 << 0) ? 16 : 0)); - sum.w += y2[l+32] * (q4[l+12] + (qh[l+ 0] & (hm2 << 1) ? 16 : 0)) - + y2[l+48] * (q4[l+14] + (qh[l+16] & (hm2 << 1) ? 16 : 0)); - smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3] - + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7]; - } - tmp += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin; - } - - // sum up partial sums and write back result - tmp = warp_reduce_sum(tmp); - - if (threadIdx.x == 0) { - dst[row] = tmp; - } -} - -static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { - - static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION"); - - const int row = blockIdx.x*blockDim.y + threadIdx.y; - if (row > nrows) return; - - const int num_blocks_per_row = ncols / QK_K; - const int ib0 = row*num_blocks_per_row; - - const block_q6_K * x = (const block_q6_K *)vx + ib0; - - const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - - const int step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 - - const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const int in = tid - step*im; // 0...15 or 0...7 - -#if K_QUANTS_PER_ITERATION == 1 - const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15 - const int is = 0; -#else - const int l0 = 4 * in; // 0, 4, 8, ..., 28 - const int is = in / 4; -#endif - const int ql_offset = 64*im + l0; - const int qh_offset = 32*im + l0; - const int s_offset = 8*im + is; - const int y_offset = 128*im + l0; - - float tmp = 0; // partial sum for thread in warp - - for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - - const float * y = yy + i * QK_K + y_offset; - const uint8_t * ql = x[i].ql + ql_offset; - const uint8_t * qh = x[i].qh + qh_offset; - const int8_t * s = x[i].scales + s_offset; - - const float d = x[i].d; - -#if K_QUANTS_PER_ITERATION == 1 - float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32) - + y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32) - + y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32) - + y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32) - + y[64] * s[4] * d * ((int8_t)((ql[ 0] >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32) - + y[80] * s[5] * d * ((int8_t)((ql[16] >> 4) | ((qh[16] & 0x30) >> 0)) - 32) - + y[96] * s[6] * d * ((int8_t)((ql[32] >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32) - +y[112] * s[7] * d * ((int8_t)((ql[48] >> 4) | ((qh[16] & 0xc0) >> 2)) - 32); - tmp += sum; -#else - float sum = 0; - for (int l = 0; l < 4; ++l) { - sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32) - + y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32) - + y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32) - + y[l+96] * s[6] * d * ((int8_t)((ql[l+32] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32); - } - tmp += sum; -#endif - - } - - // sum up partial sums and write back result - tmp = warp_reduce_sum(tmp); - - if (tid == 0) { - dst[row] = tmp; - } -} - -static __device__ void convert_f16(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){ - const half * x = (const half *) vx; - // load 2 halfs into register in a single instruction - const half2 x_reg = *((half2 *) &(x[ib + iqs])); - // automatic half -> float type cast if dfloat == float - v.x = __low2float(x_reg); - v.y = __high2float(x_reg); -} - -static constexpr __device__ dequantize_kernel_t get_dequantize_kernel(ggml_type type) { - return type == GGML_TYPE_Q4_0 ? dequantize_q4_0 : - type == GGML_TYPE_Q4_1 ? dequantize_q4_1 : - type == GGML_TYPE_Q5_0 ? dequantize_q5_0 : - type == GGML_TYPE_Q5_1 ? dequantize_q5_1 : - type == GGML_TYPE_Q8_0 ? dequantize_q8_0 : - type == GGML_TYPE_F16 ? convert_f16 : - nullptr; -} - -template -static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows) { - constexpr int qk = ggml_cuda_type_traits::qk; // quantized weights per x block - constexpr int qr = ggml_cuda_type_traits::qr; // number of quantized weights per data value in x block - constexpr dequantize_kernel_t dequantize_kernel = get_dequantize_kernel(type); - - const int64_t row = (int64_t)blockIdx.x*blockDim.y + threadIdx.y; - - if (row >= nrows) { - return; - } - - const int tid = threadIdx.x; - - const int iter_stride = 2*GGML_CUDA_DMMV_X; - const int vals_per_iter = iter_stride / WARP_SIZE; // num quantized vals per thread and i iter - const int y_offset = qr == 1 ? 1 : qk/2; - -// partial sum for each thread -#ifdef GGML_CUDA_F16 - half2 tmp = {0.0f, 0.0f}; // two sums for f16 to take advantage of half2 intrinsics -#else - float tmp = 0.0f; -#endif // GGML_CUDA_F16 - - for (int i = 0; i < ncols; i += iter_stride) { - const int col = i + vals_per_iter*tid; - const int64_t ib = ((int64_t)row*ncols + col)/qk; // x block index - const int iqs = (col%qk)/qr; // x quant index - const int iybs = col - col%qk; // y block start index - -// processing >2 values per i iter is faster for fast GPUs -#pragma unroll - for (int j = 0; j < vals_per_iter; j += 2) { - // process 2 vals per j iter - - // dequantize - // for qr = 2 the iqs needs to increase by 1 per j iter because 2 weights per data val - dfloat2 v; - dequantize_kernel(vx, ib, iqs + j/qr, v); - - // matrix multiplication - // for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2 -#ifdef GGML_CUDA_F16 - if ( y_offset == 1 ) { - // load 2 dfloats into register in a single instruction - const dfloat2 y_reg = *((dfloat2 *) &(y[iybs + iqs + j/qr])); - tmp += __hmul2(v, y_reg); - } - else { - tmp += __hmul2(v, { - y[iybs + iqs + j/qr + 0], - y[iybs + iqs + j/qr + y_offset] - }); - } -#else - if ( y_offset == 1 ) { - // load 2 dfloats into register in a single instruction - const dfloat2 y_reg = *((dfloat2 *) &(y[iybs + iqs + j/qr])); - tmp += v.x * y_reg.x; - tmp += v.y * y_reg.y; - } - else { - tmp += v.x * y[iybs + iqs + j/qr + 0]; - tmp += v.y * y[iybs + iqs + j/qr + y_offset]; - } -#endif // GGML_CUDA_F16 - } - } - - // sum up partial sums and write back result - tmp = warp_reduce_sum(tmp); - - if (tid == 0) { -#ifdef GGML_CUDA_F16 - dst[row] = tmp.x + tmp.y; -#else - dst[row] = tmp; -#endif // GGML_CUDA_F16 - } -} - -static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - // the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - dequantize_mul_mat_vec - <<>>(vx, y, dst, ncols, nrows); -} - -static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - dequantize_mul_mat_vec - <<>>(vx, y, dst, ncols, nrows); -} - -static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - dequantize_mul_mat_vec - <<>>(vx, y, dst, ncols, nrows); -} - -static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - dequantize_mul_mat_vec - <<>>(vx, y, dst, ncols, nrows); -} - -static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - dequantize_mul_mat_vec - <<>>(vx, y, dst, ncols, nrows); -} - -static void dequantize_mul_mat_vec_q2_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int ny = 2; // very slightly faster than 1 even when K_QUANTS_PER_ITERATION = 2 - const int block_num_y = (nrows + ny - 1) / ny; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(32, ny, 1); - dequantize_mul_mat_vec_q2_k<<>>(vx, y, dst, ncols, nrows); -} - -static void dequantize_mul_mat_vec_q3_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int ny = 2 / K_QUANTS_PER_ITERATION; - const int block_num_y = (nrows + ny - 1) / ny; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(32, ny, 1); - dequantize_mul_mat_vec_q3_k<<>>(vx, y, dst, ncols, nrows); -} - -static void dequantize_mul_mat_vec_q4_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int ny = 2 / K_QUANTS_PER_ITERATION; - const int block_num_y = (nrows + ny - 1) / ny; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(32, ny, 1); - dequantize_mul_mat_vec_q4_k<<>>(vx, y, dst, ncols, nrows); -} - -static void dequantize_mul_mat_vec_q5_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const dim3 block_dims(32, 1, 1); - dequantize_mul_mat_vec_q5_k<<>>(vx, y, dst, ncols); -} - -static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int ny = 2 / K_QUANTS_PER_ITERATION; - const int block_num_y = (nrows + ny - 1) / ny; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(32, ny, 1); - dequantize_mul_mat_vec_q6_k<<>>(vx, y, dst, ncols, nrows); -} - -static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % (GGML_CUDA_DMMV_X*2) == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - dequantize_mul_mat_vec - <<>>(vx, y, dst, ncols, nrows); -} - -void ggml_cuda_op_dequantize_mul_mat_vec( - ggml_backend_cuda_context & ctx, - const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, - const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, - const int64_t src1_padded_row_size, cudaStream_t stream) { - GGML_UNUSED(ctx); - const int64_t ne00 = src0->ne[0]; - const int64_t row_diff = row_high - row_low; - - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics -#ifdef GGML_CUDA_F16 - ggml_cuda_pool_alloc src1_dfloat_a(ctx.pool()); - half * src1_dfloat = nullptr; // dfloat == half - - bool src1_convert_f16 = - src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 || - src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 || - src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16; - - if (src1_convert_f16) { - src1_dfloat = src1_dfloat_a.alloc(ne00); - const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); - GGML_ASSERT(to_fp16_cuda != nullptr); - to_fp16_cuda(src1_ddf_i, src1_dfloat, ne00, stream); - } -#else - const dfloat * src1_dfloat = (const dfloat *) src1_ddf_i; // dfloat == float, no conversion -#endif // GGML_CUDA_F16 - - switch (src0->type) { - case GGML_TYPE_Q4_0: - dequantize_mul_mat_vec_q4_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q4_1: - dequantize_mul_mat_vec_q4_1_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q5_0: - dequantize_mul_mat_vec_q5_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q5_1: - dequantize_mul_mat_vec_q5_1_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q8_0: - dequantize_mul_mat_vec_q8_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q2_K: - dequantize_mul_mat_vec_q2_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q3_K: - dequantize_mul_mat_vec_q3_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q4_K: - dequantize_mul_mat_vec_q4_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q5_K: - dequantize_mul_mat_vec_q5_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_Q6_K: - dequantize_mul_mat_vec_q6_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream); - break; - case GGML_TYPE_F16: - convert_mul_mat_vec_f16_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); - break; - default: - GGML_ABORT("fatal error"); - break; - } - - GGML_UNUSED(src1); - GGML_UNUSED(dst); - GGML_UNUSED(src1_ddq_i); - GGML_UNUSED(src1_ncols); - GGML_UNUSED(src1_padded_row_size); -} - -bool ggml_cuda_dmmv_type_supported(ggml_type src0_type) { - return src0_type == GGML_TYPE_Q4_0 || src0_type == GGML_TYPE_Q4_1 || - src0_type == GGML_TYPE_Q5_0 || src0_type == GGML_TYPE_Q5_1 || - src0_type == GGML_TYPE_Q8_0 || src0_type == GGML_TYPE_Q2_K || - src0_type == GGML_TYPE_Q3_K || src0_type == GGML_TYPE_Q4_K || - src0_type == GGML_TYPE_Q5_K || src0_type == GGML_TYPE_Q6_K || - src0_type == GGML_TYPE_F16; -} diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh index 1fb5c09c3..ee9752da6 100644 --- a/ggml/src/ggml-cuda/fattn-common.cuh +++ b/ggml/src/ggml-cuda/fattn-common.cuh @@ -517,9 +517,9 @@ constexpr __device__ dequantize_1_f32_t get_dequantize_1_f32(ggml_type type_V) { } template // D == head size -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) __launch_bounds__(D, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) static __global__ void flash_attn_combine_results( const float * __restrict__ VKQ_parts, const float2 * __restrict__ VKQ_meta, diff --git a/ggml/src/ggml-cuda/fattn-tile-f16.cu b/ggml/src/ggml-cuda/fattn-tile-f16.cu index 5af02c7ec..4d314dacb 100644 --- a/ggml/src/ggml-cuda/fattn-tile-f16.cu +++ b/ggml/src/ggml-cuda/fattn-tile-f16.cu @@ -5,9 +5,9 @@ #define FATTN_KQ_STRIDE_TILE_F16 64 template // D == head size -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) __launch_bounds__(nwarps*WARP_SIZE, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) static __global__ void flash_attn_tile_ext_f16( const char * __restrict__ Q, const char * __restrict__ K, diff --git a/ggml/src/ggml-cuda/fattn-tile-f32.cu b/ggml/src/ggml-cuda/fattn-tile-f32.cu index f402195ce..bb3360447 100644 --- a/ggml/src/ggml-cuda/fattn-tile-f32.cu +++ b/ggml/src/ggml-cuda/fattn-tile-f32.cu @@ -5,9 +5,9 @@ #define FATTN_KQ_STRIDE_TILE_F32 32 template // D == head size -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) __launch_bounds__(nwarps*WARP_SIZE, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) static __global__ void flash_attn_tile_ext_f32( const char * __restrict__ Q, const char * __restrict__ K, diff --git a/ggml/src/ggml-cuda/fattn-vec-f16.cuh b/ggml/src/ggml-cuda/fattn-vec-f16.cuh index 2ed6509ac..34a2992c7 100644 --- a/ggml/src/ggml-cuda/fattn-vec-f16.cuh +++ b/ggml/src/ggml-cuda/fattn-vec-f16.cuh @@ -2,9 +2,9 @@ #include "fattn-common.cuh" template // D == head size -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) __launch_bounds__(D, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) static __global__ void flash_attn_vec_ext_f16( const char * __restrict__ Q, const char * __restrict__ K, @@ -220,7 +220,6 @@ static __global__ void flash_attn_vec_ext_f16( for (int j = 0; j < ncols; ++j) { half kqmax_new_j = ncols == 1 ? kqmax_new : kqmax_new_arr[j]; - kqmax_new_j = warp_reduce_max(kqmax_new_j); if (threadIdx.x == 0) { kqmax_shared[j][threadIdx.y] = kqmax_new_j; } diff --git a/ggml/src/ggml-cuda/fattn-vec-f32.cuh b/ggml/src/ggml-cuda/fattn-vec-f32.cuh index bf5125902..a28fc8b7f 100644 --- a/ggml/src/ggml-cuda/fattn-vec-f32.cuh +++ b/ggml/src/ggml-cuda/fattn-vec-f32.cuh @@ -2,9 +2,9 @@ #include "fattn-common.cuh" template // D == head size -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) __launch_bounds__(D, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) static __global__ void flash_attn_vec_ext_f32( const char * __restrict__ Q, const char * __restrict__ K, @@ -206,7 +206,6 @@ static __global__ void flash_attn_vec_ext_f32( for (int j = 0; j < ncols; ++j) { float kqmax_new_j = kqmax_new_arr[j]; - kqmax_new_j = warp_reduce_max(kqmax_new_j); if (threadIdx.x == 0) { kqmax_shared[j][threadIdx.y] = kqmax_new_j; } diff --git a/ggml/src/ggml-cuda/fattn-wmma-f16.cuh b/ggml/src/ggml-cuda/fattn-wmma-f16.cuh index b10d19d93..860d0e6dc 100644 --- a/ggml/src/ggml-cuda/fattn-wmma-f16.cuh +++ b/ggml/src/ggml-cuda/fattn-wmma-f16.cuh @@ -7,9 +7,9 @@ // D == head size, VKQ_stride == num VKQ rows calculated in parallel: template -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) __launch_bounds__(nwarps*WARP_SIZE, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) static __global__ void flash_attn_ext_f16( const char * __restrict__ Q, const char * __restrict__ K, diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu index 83e5589a1..0e7ebbc53 100644 --- a/ggml/src/ggml-cuda/fattn.cu +++ b/ggml/src/ggml-cuda/fattn.cu @@ -13,9 +13,9 @@ static void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, g const ggml_tensor * KQV = dst; const ggml_tensor * Q = dst->src[0]; - const int32_t precision = KQV->op_params[3]; + const enum ggml_prec prec = ggml_flash_attn_ext_get_prec(KQV); - if (precision != GGML_PREC_DEFAULT) { + if (prec != GGML_PREC_DEFAULT) { if (Q->ne[1] <= 32 || Q->ne[0] > 128) { constexpr int cols_per_block = 16; switch (Q->ne[0]) { @@ -301,11 +301,11 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst ggml_cuda_set_device(ctx.device); const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; - const int32_t precision = KQV->op_params[3]; + const enum ggml_prec prec = ggml_flash_attn_ext_get_prec(KQV); // On AMD the tile kernels perform poorly, use the vec kernel instead: if (cc >= CC_OFFSET_AMD) { - if (precision == GGML_PREC_DEFAULT && fast_fp16_available(cc)) { + if (prec == GGML_PREC_DEFAULT && fast_fp16_available(cc)) { ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); } else { ggml_cuda_flash_attn_ext_vec_f32(ctx, dst); @@ -332,7 +332,7 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst } if (Q->ne[1] == 1 && Q->ne[0] % (2*WARP_SIZE) == 0) { - if (precision == GGML_PREC_DEFAULT) { + if (prec == GGML_PREC_DEFAULT) { ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); return; } else if(Q->ne[0] <= 128) { diff --git a/ggml/src/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu similarity index 93% rename from ggml/src/ggml-cuda.cu rename to ggml/src/ggml-cuda/ggml-cuda.cu index 087091516..d6e4bfdd0 100644 --- a/ggml/src/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -16,11 +16,11 @@ #include "ggml-cuda/cpy.cuh" #include "ggml-cuda/cross-entropy-loss.cuh" #include "ggml-cuda/diagmask.cuh" -#include "ggml-cuda/dmmv.cuh" #include "ggml-cuda/fattn.cuh" #include "ggml-cuda/getrows.cuh" #include "ggml-cuda/im2col.cuh" #include "ggml-cuda/mmq.cuh" +#include "ggml-cuda/mmv.cuh" #include "ggml-cuda/mmvq.cuh" #include "ggml-cuda/norm.cuh" #include "ggml-cuda/opt-step-adamw.cuh" @@ -36,7 +36,7 @@ #include "ggml-cuda/tsembd.cuh" #include "ggml-cuda/unary.cuh" #include "ggml-cuda/upscale.cuh" -#include "ggml-cuda/rwkv-wkv.cuh" +#include "ggml-cuda/wkv6.cuh" #include #include @@ -91,7 +91,7 @@ int ggml_cuda_get_device() { static cudaError_t ggml_cuda_device_malloc(void ** ptr, size_t size, int device) { ggml_cuda_set_device(device); -#if defined(GGML_USE_HIPBLAS) && defined(GGML_HIP_UMA) +#if defined(GGML_USE_HIP) && defined(GGML_HIP_UMA) auto res = hipMallocManaged(ptr, size); if (res == hipSuccess) { // if error we "need" to know why... @@ -100,7 +100,7 @@ static cudaError_t ggml_cuda_device_malloc(void ** ptr, size_t size, int device) return res; #else -#if !defined(GGML_USE_HIPBLAS) +#if !defined(GGML_USE_HIP) cudaError_t err; if (getenv("GGML_CUDA_ENABLE_UNIFIED_MEMORY") != nullptr) { @@ -113,7 +113,7 @@ static cudaError_t ggml_cuda_device_malloc(void ** ptr, size_t size, int device) return err; #else return cudaMalloc(ptr, size); -#endif // !defined(GGML_USE_HIPBLAS) +#endif // !defined(GGML_USE_HIP) #endif } @@ -151,7 +151,7 @@ static ggml_cuda_device_info ggml_cuda_init() { for (int id = 0; id < info.device_count; ++id) { int device_vmm = 0; -#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) +#if !defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM) CUdevice device; CU_CHECK(cuDeviceGet(&device, id)); CU_CHECK(cuDeviceGetAttribute(&device_vmm, CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED, device)); @@ -163,7 +163,7 @@ static ggml_cuda_device_info ggml_cuda_init() { alloc_prop.location.id = id; CU_CHECK(cuMemGetAllocationGranularity(&info.devices[id].vmm_granularity, &alloc_prop, CU_MEM_ALLOC_GRANULARITY_RECOMMENDED)); } -#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) +#endif // !defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM) info.devices[id].vmm = !!device_vmm; cudaDeviceProp prop; @@ -175,13 +175,13 @@ static ggml_cuda_device_info ggml_cuda_init() { info.devices[id].nsm = prop.multiProcessorCount; info.devices[id].smpb = prop.sharedMemPerBlock; -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) info.devices[id].smpbo = prop.sharedMemPerBlock; info.devices[id].cc = 100*prop.major + 10*prop.minor + CC_OFFSET_AMD; #else info.devices[id].smpbo = prop.sharedMemPerBlockOptin; info.devices[id].cc = 100*prop.major + 10*prop.minor; -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) } for (int id = 0; id < info.device_count; ++id) { @@ -299,7 +299,7 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool { }; // pool with virtual memory -#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) +#if !defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM) struct ggml_cuda_pool_vmm : public ggml_cuda_pool { static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 35; // 32 GB @@ -393,14 +393,14 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool { GGML_ASSERT(ptr == (void *) (pool_addr + pool_used)); } }; -#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) +#endif // !defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM) std::unique_ptr ggml_backend_cuda_context::new_pool_for_device(int device) { -#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) +#if !defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM) if (ggml_cuda_info().devices[device].vmm) { return std::unique_ptr(new ggml_cuda_pool_vmm(device)); } -#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) +#endif // !defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM) return std::unique_ptr(new ggml_cuda_pool_leg(device)); } @@ -1020,114 +1020,6 @@ typedef void (*ggml_cuda_op_mul_mat_t)( #define MUL_MAT_SRC1_COL_STRIDE 128 -static __global__ void mul_mat_p021_f16_f32( - const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int nchannels_x, const int nchannels_y) { - - const half * x = (const half *) vx; - - const int row_x = blockDim.y*blockIdx.y + threadIdx.y; - const int channel = blockDim.z*blockIdx.z + threadIdx.z; - const int channel_x = channel / (nchannels_y / nchannels_x); - - const int nrows_y = ncols_x; - const int nrows_dst = nrows_x; - const int row_dst = row_x; - - float tmp = 0.0f; - - for (int col_x0 = 0; col_x0 < ncols_x; col_x0 += blockDim.x) { - const int col_x = col_x0 + threadIdx.x; - - if (col_x >= ncols_x) { - break; - } - - // x is transposed and permuted - const int ix = row_x*nchannels_x*ncols_x + channel_x*ncols_x + col_x; - const float xi = __half2float(x[ix]); - - const int row_y = col_x; - - // y is not transposed but permuted - const int iy = channel*nrows_y + row_y; - - tmp += xi * y[iy]; - } - - // dst is not transposed and not permuted - const int idst = channel*nrows_dst + row_dst; - - // sum up partial sums and write back result - tmp = warp_reduce_sum(tmp); - - if (threadIdx.x == 0) { - dst[idst] = tmp; - } -} - -static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous - const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst, const int ncols_x, const int nrows_x, - const int row_stride_x, const int channel_stride_x, const int channel_x_divisor) { - - const half * x = (const half *) vx; - - const int row_x = blockDim.y*blockIdx.y + threadIdx.y; - const int channel = blockDim.z*blockIdx.z + threadIdx.z; - const int channel_x = channel / channel_x_divisor; - - const int nrows_y = ncols_x; - const int nrows_dst = nrows_x; - const int row_dst = row_x; - - const int idst = channel*nrows_dst + row_dst; - - float tmp = 0.0f; - - for (int col_x0 = 0; col_x0 < ncols_x; col_x0 += blockDim.x) { - const int col_x = col_x0 + threadIdx.x; - - if (col_x >= ncols_x) { - break; - } - - const int row_y = col_x; - - const int ix = channel_x*channel_stride_x + row_x*row_stride_x + col_x; - const int iy = channel*nrows_y + row_y; - - const float xi = __half2float(x[ix]); - - tmp += xi * y[iy]; - } - - // sum up partial sums and write back result - tmp = warp_reduce_sum(tmp); - - if (threadIdx.x == 0) { - dst[idst] = tmp; - } -} - -static void ggml_mul_mat_p021_f16_f32_cuda( - const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x, - const int nchannels_x, const int nchannels_y, cudaStream_t stream) { - - const dim3 block_nums(1, nrows_x, nchannels_y); - const dim3 block_dims(WARP_SIZE, 1, 1); - mul_mat_p021_f16_f32<<>>(vx, y, dst, ncols_x, nrows_x, nchannels_x, nchannels_y); -} - -static void ggml_mul_mat_vec_nc_f16_f32_cuda( - const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x, const int row_stride_x, - const int nchannels_x, const int nchannels_y, const int channel_stride_x, cudaStream_t stream) { - - const dim3 block_nums(1, nrows_x, nchannels_y); - const dim3 block_dims(WARP_SIZE, 1, 1); - mul_mat_vec_nc_f16_f32<<>> - (vx, y, dst, ncols_x, nrows_x, row_stride_x, channel_stride_x, nchannels_y/nchannels_x); -} - static cudaError_t ggml_cuda_cpy_tensor_2d( void * dst, const struct ggml_tensor * src, int64_t i3, int64_t i2, int64_t i1_low, int64_t i1_high, cudaStream_t stream) { @@ -1215,6 +1107,11 @@ static void ggml_cuda_op_mul_mat_cublas( const half alpha_f16 = 1.0f; const half beta_f16 = 0.0f; + cublasComputeType_t cu_compute_type = CUBLAS_COMPUTE_16F; + if (ggml_cuda_info().devices[ctx.device].cc == CC_CDNA) { + cu_compute_type = CUBLAS_COMPUTE_32F; + } + CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(id), stream)); CUBLAS_CHECK( cublasGemmEx(ctx.cublas_handle(id), CUBLAS_OP_T, CUBLAS_OP_N, @@ -1222,7 +1119,7 @@ static void ggml_cuda_op_mul_mat_cublas( &alpha_f16, src0_ptr, CUDA_R_16F, ne00, src1_ptr, CUDA_R_16F, ne10, &beta_f16, dst_f16.get(), CUDA_R_16F, ldc, - CUBLAS_COMPUTE_16F, + cu_compute_type, CUBLAS_GEMM_DEFAULT_TENSOR_OP)); const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); @@ -1297,11 +1194,17 @@ static void ggml_cuda_set_peer_access(const int n_tokens, int main_device) { cudaError_t err = cudaDeviceEnablePeerAccess(id_other, 0); if (err != cudaErrorPeerAccessAlreadyEnabled) { CUDA_CHECK(err); + } else { + // reset the error + cudaGetLastError(); } } else { cudaError_t err = cudaDeviceDisablePeerAccess(id_other); if (err != cudaErrorPeerAccessNotEnabled) { CUDA_CHECK(err); + } else { + // reset the error + cudaGetLastError(); } } } @@ -1319,7 +1222,7 @@ static void ggml_cuda_set_peer_access(const int n_tokens, int main_device) { static cudaError_t ggml_cuda_Memcpy2DPeerAsync( void * dst, int dstDevice, size_t dpitch, void * src, int srcDevice, size_t spitch, size_t width, size_t height, cudaStream_t stream) { -#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_USE_MUSA) +#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) // cudaMemcpy2DAsync may fail with copies between vmm pools of different devices cudaMemcpy3DPeerParms p = {}; p.dstDevice = dstDevice; @@ -1333,7 +1236,7 @@ static cudaError_t ggml_cuda_Memcpy2DPeerAsync( GGML_UNUSED(dstDevice); GGML_UNUSED(srcDevice); return cudaMemcpy2DAsync(dst, dpitch, src, spitch, width, height, cudaMemcpyDeviceToDevice, stream); -#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_USE_MUSA) +#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) } static void ggml_cuda_op_mul_mat( @@ -1648,58 +1551,6 @@ static void ggml_cuda_op_mul_mat( } } -static void ggml_cuda_mul_mat_vec_p021(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1)); - GGML_ASSERT(ggml_backend_buffer_is_cuda(src0->buffer)); - GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation - GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // 0213 permutation - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - - const int64_t ne12 = src1->ne[2]; - - cudaStream_t main_stream = ctx.stream(); - - void * src0_ddq = src0->data; - float * src1_ddf = (float *) src1->data; - float * dst_ddf = (float *) dst->data; - - ggml_mul_mat_p021_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, ne12, main_stream); -} - -static void ggml_cuda_mul_mat_vec_nc(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_ASSERT(!ggml_is_transposed(src0)); - GGML_ASSERT(!ggml_is_transposed(src1)); - GGML_ASSERT(!ggml_is_permuted(src0)); - GGML_ASSERT(ggml_backend_buffer_is_cuda(src0->buffer)); - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - - const int64_t nb01 = src0->nb[1]; - const int64_t nb02 = src0->nb[2]; - - const int64_t ne12 = src1->ne[2]; - - cudaStream_t main_stream = ctx.stream(); - - void * src0_ddq = src0->data; - float * src1_ddf = (float *) src1->data; - float * dst_ddf = (float *) dst->data; - - const int64_t row_stride_x = nb01 / sizeof(half); - const int64_t channel_stride_x = nb02 / sizeof(half); - - ggml_mul_mat_vec_nc_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, row_stride_x, ne02, ne12, channel_stride_x, main_stream); -} - static __global__ void k_compute_batched_ptrs( const half * src0_as_f16, const half * src1_as_f16, char * dst, const void ** ptrs_src, void ** ptrs_dst, @@ -1761,6 +1612,10 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co cublasComputeType_t cu_compute_type = CUBLAS_COMPUTE_16F; cudaDataType_t cu_data_type = CUDA_R_16F; + if (ggml_cuda_info().devices[ctx.device].cc == CC_CDNA) { + cu_compute_type = CUBLAS_COMPUTE_32F; + } + // dst strides size_t nbd2 = dst->nb[2]; size_t nbd3 = dst->nb[3]; @@ -1873,21 +1728,17 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { const bool split = ggml_backend_buft_is_cuda_split(src0->buffer->buft); - bool use_dequantize_mul_mat_vec = ggml_cuda_dmmv_type_supported(src0->type) + bool use_mul_mat_vec = src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 - && src0->ne[0] % (GGML_CUDA_DMMV_X*2) == 0 && src1->ne[1] == 1; - bool use_mul_mat_vec_q = ggml_is_quantized(src0->type) + && src0->ne[0] % 2 == 0 && src1->ne[1] == 1; + bool use_mul_mat_vec_q = ggml_is_quantized(src0->type) && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 && src1->ne[1] <= MMVQ_MAX_BATCH_SIZE; - bool use_mul_mat_q = ggml_is_quantized(src0->type) + bool use_mul_mat_q = ggml_is_quantized(src0->type) && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32; - // if mmvq is available it's a better choice than dmmv: -#ifndef GGML_CUDA_FORCE_DMMV - use_dequantize_mul_mat_vec = use_dequantize_mul_mat_vec && !use_mul_mat_vec_q; -#endif // GGML_CUDA_FORCE_DMMV - - bool any_gpus_with_slow_fp16 = false; + bool any_gpus_with_slow_fp16 = false; + bool any_gpus_without_fp16_mma = false; if (split) { ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) src0->buffer->buft->context; @@ -1898,14 +1749,16 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor continue; } - const int cc = ggml_cuda_info().devices[id].cc; - use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]); - any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_available(cc); + const int cc = ggml_cuda_info().devices[id].cc; + use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]); + any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_available(cc); + any_gpus_without_fp16_mma = any_gpus_without_fp16_mma || !fp16_mma_available(cc); } } else { - const int cc = ggml_cuda_info().devices[ctx.device].cc; - use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]); - any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_available(cc); + const int cc = ggml_cuda_info().devices[ctx.device].cc; + use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]); + any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_available(cc); + any_gpus_without_fp16_mma = any_gpus_without_fp16_mma || !fp16_mma_available(cc); } // debug helpers @@ -1916,18 +1769,16 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor //printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name); //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name); - if (!split && any_gpus_with_slow_fp16 && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { - // FP32 precision KQ single-batch for batch size 1 without FlashAttention - ggml_cuda_mul_mat_vec_p021(ctx, src0, src1, dst); - } else if (!split && any_gpus_with_slow_fp16 && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { - // FP32 precision KQV single-batch for batch size 1 without FlashAttention - ggml_cuda_mul_mat_vec_nc(ctx, src0, src1, dst); + if (!split && use_mul_mat_vec && dst->ne[3] == 1 && (src0->ne[1] < MMV_MAX_ROWS || any_gpus_without_fp16_mma)) { + // the custom F16 vector kernel can be used over batched cuBLAS GEMM + // but this is only faster for GPUs without tensor cores or with a thin src0 matrix (particularly KQV in attention) + ggml_cuda_mul_mat_vec(ctx, src0, src1, dst); } else if (!split && src0->type == GGML_TYPE_F16 && (src1->type == GGML_TYPE_F16 || !any_gpus_with_slow_fp16) && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) { - // KQ + KQV multi-batch without FlashAttention + // general KQ + KQV multi-batch without FlashAttention ggml_cuda_mul_mat_batched_cublas(ctx, src0, src1, dst); - } else if (use_dequantize_mul_mat_vec) { - ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, nullptr); + } else if (use_mul_mat_vec) { + ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_vec, nullptr); } else if (use_mul_mat_vec_q) { ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, quantize_row_q8_1_cuda); } else if (use_mul_mat_q) { @@ -2313,8 +2164,8 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg case GGML_OP_CROSS_ENTROPY_LOSS: ggml_cuda_cross_entropy_loss(ctx, dst); break; - case GGML_OP_RWKV_WKV: - ggml_cuda_op_rwkv_wkv(ctx, dst); + case GGML_OP_RWKV_WKV6: + ggml_cuda_op_rwkv_wkv6(ctx, dst); break; case GGML_OP_CROSS_ENTROPY_LOSS_BACK: ggml_cuda_cross_entropy_loss_back(ctx, dst); @@ -2972,6 +2823,17 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g { struct ggml_tensor * a = op->src[0]; struct ggml_tensor * b = op->src[1]; + // for small weight matrices the active device can end up without any rows, don't use row split in those cases + // this avoids some edge cases (and the performance would not be good anyways) + if (a->buffer && ggml_backend_buft_is_cuda_split(a->buffer->buft)) { + ggml_backend_cuda_split_buffer_type_context * buft_ctx = (ggml_backend_cuda_split_buffer_type_context *) a->buffer->buft->context; + int64_t row_low; + int64_t row_high; + get_row_split(&row_low, &row_high, a, buft_ctx->tensor_split, dev_ctx->device); + if (row_low == row_high) { + return false; + } + } if (b->type == GGML_TYPE_F16 && a->type != GGML_TYPE_F16) { return false; } @@ -3107,18 +2969,20 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g } return false; } break; + case GGML_OP_NORM: + case GGML_OP_RMS_NORM: + return ggml_is_contiguous(op->src[0]) && op->ne[0] % WARP_SIZE == 0; + break; case GGML_OP_NONE: case GGML_OP_RESHAPE: case GGML_OP_VIEW: case GGML_OP_PERMUTE: case GGML_OP_TRANSPOSE: - case GGML_OP_NORM: case GGML_OP_ADD: case GGML_OP_ADD1: case GGML_OP_SUB: case GGML_OP_MUL: case GGML_OP_DIV: - case GGML_OP_RMS_NORM: case GGML_OP_SCALE: case GGML_OP_SQR: case GGML_OP_SQRT: @@ -3145,12 +3009,15 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g case GGML_OP_ARANGE: case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_LEAKY_RELU: - case GGML_OP_RWKV_WKV: + case GGML_OP_RWKV_WKV6: return true; case GGML_OP_FLASH_ATTN_EXT: { #ifndef FLASH_ATTN_AVAILABLE return false; #endif + if (op->src[1]->type == GGML_TYPE_BF16 || op->src[2]->type == GGML_TYPE_BF16) { + return false; + } if (op->src[0]->ne[0] == 64 && op->src[1]->type == GGML_TYPE_F16) { return true; } @@ -3268,6 +3135,61 @@ static ggml_backend_dev_t ggml_backend_cuda_reg_get_device(ggml_backend_reg_t re return ctx->devices[index]; } +static ggml_backend_feature * ggml_backend_cuda_get_features(ggml_backend_reg_t reg) { + static std::vector features = []() { + std::vector features; + #define _STRINGIFY(...) #__VA_ARGS__ + #define STRINGIFY(...) _STRINGIFY(__VA_ARGS__) + + #ifdef __CUDA_ARCH_LIST__ + features.push_back({ "ARCHS", STRINGIFY(__CUDA_ARCH_LIST__) }); + #endif + + #ifdef GGML_CUDA_FORCE_MMQ + features.push_back({ "FORCE_MMQ", "1" }); + #endif + + #ifdef GGML_CUDA_FORCE_CUBLAS + features.push_back({ "FORCE_CUBLAS", "1" }); + #endif + + #ifdef GGML_CUDA_NO_VMM + features.push_back({ "NO_VMM", "1" }); + #endif + + #ifdef GGML_CUDA_NO_PEER_COPY + features.push_back({ "NO_PEER_COPY", "1" }); + #endif + + #ifdef GGML_CUDA_F16 + features.push_back({ "F16", "1" }); + #endif + + #ifdef GGML_CUDA_USE_GRAPHS + features.push_back({ "USE_GRAPHS", "1" }); + #endif + + #ifdef GGML_CUDA_PEER_MAX_BATCH_SIZE + features.push_back({ "PEER_MAX_BATCH_SIZE", STRINGIFY(GGML_CUDA_PEER_MAX_BATCH_SIZE) }); + #endif + + #ifdef GGML_CUDA_FA_ALL_QUANTS + features.push_back({ "FA_ALL_QUANTS", "1" }); + #endif + + #undef _STRINGIFY + #undef STRINGIFY + + features.push_back({ nullptr, nullptr }); + + return features; + }(); + + return features.data(); + + GGML_UNUSED(reg); +} + static void * ggml_backend_cuda_reg_get_proc_address(ggml_backend_reg_t reg, const char * name) { GGML_UNUSED(reg); if (strcmp(name, "ggml_backend_split_buffer_type") == 0) { @@ -3279,6 +3201,9 @@ static void * ggml_backend_cuda_reg_get_proc_address(ggml_backend_reg_t reg, con if (strcmp(name, "ggml_backend_unregister_host_buffer") == 0) { return (void *)ggml_backend_cuda_unregister_host_buffer; } + if (strcmp(name, "ggml_backend_get_features") == 0) { + return (void *)ggml_backend_cuda_get_features; + } return nullptr; } @@ -3311,16 +3236,17 @@ ggml_backend_reg_t ggml_backend_cuda_reg() { dev_ctx->description = prop.name; ggml_backend_dev_t dev = new ggml_backend_device { - /* .interface = */ ggml_backend_cuda_device_interface, - /* .reg = */ ®, - /* .context = */ dev_ctx + /* .iface = */ ggml_backend_cuda_device_interface, + /* .reg = */ ®, + /* .context = */ dev_ctx }; ctx->devices.push_back(dev); } reg = ggml_backend_reg { - /* .interface = */ ggml_backend_cuda_reg_interface, - /* .context = */ ctx + /* .api_version = */ GGML_BACKEND_API_VERSION, + /* .iface = */ ggml_backend_cuda_reg_interface, + /* .context = */ ctx }; } @@ -3351,3 +3277,5 @@ ggml_backend_t ggml_backend_cuda_init(int device) { return cuda_backend; } + +GGML_BACKEND_DL_IMPL(ggml_backend_cuda_reg) diff --git a/ggml/src/ggml-cuda/mmq.cu b/ggml/src/ggml-cuda/mmq.cu index ae5c68ab3..7f7c8c90b 100644 --- a/ggml/src/ggml-cuda/mmq.cu +++ b/ggml/src/ggml-cuda/mmq.cu @@ -148,5 +148,5 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11) { return cc < CC_VOLTA || ne11 < MMQ_DP4A_MAX_BATCH_SIZE; } - return cc < CC_RDNA3 || ne11 < MMQ_DP4A_MAX_BATCH_SIZE; + return (cc < CC_RDNA3 && cc != CC_CDNA && cc != CC_VEGA20) || ne11 < MMQ_DP4A_MAX_BATCH_SIZE; } diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh index 021a25682..8d8867121 100644 --- a/ggml/src/ggml-cuda/mmq.cuh +++ b/ggml/src/ggml-cuda/mmq.cuh @@ -100,9 +100,9 @@ static constexpr __device__ int get_mmq_x_max_device() { return 128; #else // INT8_MMA_AVAILABLE -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) return 128; -#else // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#else // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) #if __CUDA_ARCH__ >= CC_VOLTA #ifdef GGML_CUDA_FORCE_MMQ @@ -115,7 +115,7 @@ static constexpr __device__ int get_mmq_x_max_device() { return 64; #endif // __CUDA_ARCH__ >= CC_VOLTA -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) #endif // INT8_MMA_AVAILABLE } @@ -124,7 +124,7 @@ static constexpr int get_mmq_y_host(const int cc) { } static constexpr __device__ int get_mmq_y_device() { -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) #if defined(RDNA1) return 64; #else @@ -136,7 +136,7 @@ static constexpr __device__ int get_mmq_y_device() { #else return 64; #endif // __CUDA_ARCH__ >= CC_VOLTA -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) } #define MMQ_DP4A_TXS_Q4_0 tile_x_sizes{mmq_y*WARP_SIZE + mmq_y, mmq_y*WARP_SIZE/QI4_0 + mmq_y/QI4_0, 0} @@ -2569,17 +2569,17 @@ static __device__ void mul_mat_q_process_tile( // The mul_mat_q kernel implements "stream-k" work partitioning as described in https://arxiv.org/abs/2301.03598 template -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) +#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) +#if defined(RDNA3) || defined(RDNA2) || defined(CDNA) || defined(GCN) __launch_bounds__(WARP_SIZE*nwarps, 2) -#endif // defined(RDNA3) || defined(RDNA2) +#endif // defined(RDNA3) || defined(RDNA2) || defined(CDNA) || defined(GCN) #else #if __CUDA_ARCH__ >= CC_VOLTA __launch_bounds__(WARP_SIZE*nwarps, 1) #else __launch_bounds__(WARP_SIZE*nwarps, 2) #endif // __CUDA_ARCH__ >= CC_VOLTA -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) static __global__ void mul_mat_q( const char * __restrict__ x, const char * __restrict__ yc, float * __restrict__ dst, float * __restrict__ tmp_fixup, const int ne00, const int ne01, const int stride01, const int ne10, const int ne11, const int stride11, const int ne0) { @@ -2594,7 +2594,7 @@ static __global__ void mul_mat_q( constexpr int mmq_y = get_mmq_y_device(); // On AMD or old CUDA the performance with stream-k was worse, use conventional tiling instead: -#if (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ < CC_VOLTA +#if (defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ < CC_VOLTA { constexpr bool fixup = false; mul_mat_q_process_tile @@ -2602,7 +2602,7 @@ static __global__ void mul_mat_q( blockIdx.x, blockIdx.y, 0, ne00/qk); return; } -#endif // (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ < CC_VOLTA +#endif // (defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ < CC_VOLTA const int64_t blocks_per_ne00 = ne00 / qk; constexpr int blocks_per_iter = MMQ_ITER_K / qk; @@ -2765,14 +2765,14 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a const int shmem = mmq_get_shmem(mmq_x, mmq_y, cc); -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) static bool shmem_limit_raised[GGML_CUDA_MAX_DEVICES] = {false}; if (!shmem_limit_raised[id]) { CUDA_CHECK(cudaFuncSetAttribute(mul_mat_q, cudaFuncAttributeMaxDynamicSharedMemorySize, shmem)); CUDA_CHECK(cudaFuncSetAttribute(mul_mat_q, cudaFuncAttributeMaxDynamicSharedMemorySize, shmem)); shmem_limit_raised[id] = true; } -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) const int nty = (args.ne01 + mmq_y - 1) / mmq_y; const int ntx = (args.ne11 + mmq_x - 1) / mmq_x; diff --git a/ggml/src/ggml-cuda/mmv.cu b/ggml/src/ggml-cuda/mmv.cu new file mode 100644 index 000000000..cfe91f428 --- /dev/null +++ b/ggml/src/ggml-cuda/mmv.cu @@ -0,0 +1,223 @@ +#include "common.cuh" +#include "mmv.cuh" + +template +static __global__ void mul_mat_vec( + const half * __restrict__ x, const float * __restrict__ y, float * __restrict__ dst, const int64_t ncols2, const int64_t stride_row, + const int64_t channel_ratio, const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst) { + const int64_t row = blockIdx.x; + const int64_t channel = blockIdx.z; + const int tid = threadIdx.x; + + x += (channel/channel_ratio)*stride_channel_x + row*stride_row; + y += channel *stride_channel_y; + dst += channel *stride_channel_dst; + + const half2 * x2 = (const half2 *) x; + const float2 * y2 = (const float2 *) y; + + extern __shared__ char data_mmv[]; + float * buf_iw = (float *) data_mmv; + + if (block_size > WARP_SIZE) { + if (tid < WARP_SIZE) { + buf_iw[tid] = 0.0f; + } + __syncthreads(); + } + + float sumf; + + if (std::is_same::value) { + sumf = 0.0f; + + for (int64_t col2 = tid; col2 < ncols2; col2 += block_size) { + const float2 tmpx = __half22float2(x2[col2]); + const float2 tmpy = y2[col2]; + sumf += tmpx.x * tmpy.x; + sumf += tmpx.y * tmpy.y; + } + } else { +#ifdef FP16_AVAILABLE + half2 sumh2 = make_half2(0.0f, 0.0f); + + for (int64_t col2 = tid; col2 < ncols2; col2 += block_size) { + const float2 tmp = y2[col2]; + sumh2 += x2[col2] * make_half2(tmp.x, tmp.y); + } + + sumf = __low2float(sumh2) + __high2float(sumh2); +#else + NO_DEVICE_CODE; +#endif // FP16_AVAILABLE + } + + sumf = warp_reduce_sum(sumf); + + if (block_size > WARP_SIZE) { + buf_iw[tid/WARP_SIZE] = sumf; + __syncthreads(); + if (tid > WARP_SIZE) { + return; + } + sumf = buf_iw[tid]; + sumf = warp_reduce_sum(sumf); + } + + if (tid != 0) { + return; + } + + dst[row] = sumf; +} + +template +static void launch_mul_mat_vec_cuda( + const half * x, const float * y, float * dst, + const int64_t ncols, const int64_t nrows, const int64_t stride_row, const int64_t nchannels_x, const int64_t nchannels_y, + const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, + cudaStream_t stream) { + GGML_ASSERT(ncols % 2 == 0); + GGML_ASSERT(stride_row % 2 == 0); + GGML_ASSERT(nchannels_y % nchannels_x == 0); + const int64_t channel_ratio = nchannels_y / nchannels_x; + + int64_t block_size_best = WARP_SIZE; + int64_t niter_best = (ncols + 2*WARP_SIZE - 1) / (2*WARP_SIZE); + for (int64_t block_size = 2*WARP_SIZE; block_size <= 256; block_size += WARP_SIZE) { + const int64_t niter = (ncols + 2*block_size - 1) / (2*block_size); + if (niter < niter_best) { + niter_best = niter; + block_size_best = block_size; + } + } + + const int smem = WARP_SIZE*sizeof(float); + const dim3 block_nums(nrows, 1, nchannels_y); + const dim3 block_dims(block_size_best, 1, 1); + switch (block_size_best) { + case 32: { + mul_mat_vec<<>> + (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst); + } break; + case 64: { + mul_mat_vec<<>> + (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst); + } break; + case 96: { + mul_mat_vec<<>> + (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst); + } break; + case 128: { + mul_mat_vec<<>> + (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst); + } break; + case 160: { + mul_mat_vec<<>> + (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst); + } break; + case 192: { + mul_mat_vec<<>> + (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst); + } break; + case 224: { + mul_mat_vec<<>> + (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst); + } break; + case 256: { + mul_mat_vec<<>> + (x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst); + } break; + default: { + GGML_ABORT("fatal error"); + } break; + } +} + +static void mul_mat_vec_cuda( + const half * x, const float * y, float * dst, + const int64_t ncols, const int64_t nrows, const int64_t stride_row, const int64_t nchannels_x, const int64_t nchannels_y, + const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, + enum ggml_prec prec, cudaStream_t stream) { + switch (prec) { + case GGML_PREC_DEFAULT: { + launch_mul_mat_vec_cuda(x, y, dst, ncols, nrows, stride_row, nchannels_x, nchannels_y, + stride_channel_x, stride_channel_y, stride_channel_dst, stream); + } break; + case GGML_PREC_F32: { + launch_mul_mat_vec_cuda(x, y, dst, ncols, nrows, stride_row, nchannels_x, nchannels_y, + stride_channel_x, stride_channel_y, stride_channel_dst, stream); + } break; + } +} + +void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + + GGML_ASSERT(src1->ne[1] == 1); + + const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; + const enum ggml_prec prec = fast_fp16_available(cc) ? ggml_prec(dst->op_params[0]) : GGML_PREC_F32; + + const half * src0_d = (const half *) src0->data; + const float * src1_d = (const float *) src1->data; + float * dst_d = (float *) dst->data; + + const int64_t ne02 = src0->ne[2]; + const int64_t ne12 = src1->ne[2]; + GGML_ASSERT(dst->ne[2] == ne12); + + GGML_ASSERT(src0->ne[3] == 1); + GGML_ASSERT(src1->ne[3] == 1); + GGML_ASSERT( dst->ne[3] == 1); + + const int64_t stride_row = src0->nb[1] / ggml_type_size(src0->type); + const int64_t channel_stride_x = src0->nb[2] / ggml_type_size(src0->type); + const int64_t channel_stride_y = src1->nb[2] / ggml_type_size(src1->type); + const int64_t channel_stride_dst = dst->nb[2] / ggml_type_size( dst->type); + + mul_mat_vec_cuda(src0_d, src1_d, dst_d, ne00, ne01, stride_row, ne02, ne12, channel_stride_x, channel_stride_y, channel_stride_dst, prec, ctx.stream()); +} + +void ggml_cuda_op_mul_mat_vec( + ggml_backend_cuda_context & ctx, + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, + const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, + const int64_t src1_padded_row_size, cudaStream_t stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + const int64_t ne00 = src0->ne[0]; + const int64_t row_diff = row_high - row_low; + + GGML_ASSERT(src1_ncols == 1); + + const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; + const enum ggml_prec prec = fast_fp16_available(cc) ? ggml_prec(dst->op_params[0]) : GGML_PREC_F32; + + + // ggml_cuda_op provides single, contiguous matrices + const int64_t stride_row = ne00; + const int64_t nchannels_x = 1; + const int64_t nchannels_y = 1; + const int64_t channel_stride_x = 0; + const int64_t channel_stride_y = 0; + const int64_t channel_stride_dst = 0; + + mul_mat_vec_cuda((const half *) src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stride_row, + nchannels_x, nchannels_y, channel_stride_x, channel_stride_y, channel_stride_dst, prec, stream); + + GGML_UNUSED(ctx); + GGML_UNUSED(src1); + GGML_UNUSED(dst); + GGML_UNUSED(src1_ddq_i); + GGML_UNUSED(src1_ncols); + GGML_UNUSED(src1_padded_row_size); +} diff --git a/ggml/src/ggml-cuda/dmmv.cuh b/ggml/src/ggml-cuda/mmv.cuh similarity index 55% rename from ggml/src/ggml-cuda/dmmv.cuh rename to ggml/src/ggml-cuda/mmv.cuh index e727eb97f..78a1cd4a6 100644 --- a/ggml/src/ggml-cuda/dmmv.cuh +++ b/ggml/src/ggml-cuda/mmv.cuh @@ -1,20 +1,12 @@ #include "common.cuh" -// dmmv = dequantize_mul_mat_vec +// maximum number of src0 rows with which to use mul_mat_vec over cuBLAS if FP16 tensor cores are available +#define MMV_MAX_ROWS 512 -// TODO: remove this? -#ifndef GGML_CUDA_DMMV_X -#define GGML_CUDA_DMMV_X 32 -#endif +void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); -#ifndef GGML_CUDA_MMV_Y -#define GGML_CUDA_MMV_Y 1 -#endif - -void ggml_cuda_op_dequantize_mul_mat_vec( +void ggml_cuda_op_mul_mat_vec( ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i, const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, const int64_t src1_padded_row_size, cudaStream_t stream); - -bool ggml_cuda_dmmv_type_supported(ggml_type src0_type); diff --git a/ggml/src/ggml-cuda/mmvq.cu b/ggml/src/ggml-cuda/mmvq.cu index 7dbbc9939..02d150983 100644 --- a/ggml/src/ggml-cuda/mmvq.cu +++ b/ggml/src/ggml-cuda/mmvq.cu @@ -48,10 +48,10 @@ static constexpr __device__ int get_vdr_mmvq(ggml_type type) { } template -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) // tell the compiler to use as many registers as it wants, see nwarps definition below __launch_bounds__((ncols_y <= 4 ? 4 : 2)*WARP_SIZE, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) static __global__ void mul_mat_vec_q( const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols_x, const int nrows_x, const int nrows_y, const int nrows_dst) { @@ -62,13 +62,13 @@ static __global__ void mul_mat_vec_q( constexpr vec_dot_q_cuda_t vec_dot_q_cuda = get_vec_dot_q_cuda(type); -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) && (defined(RDNA2) || defined(RDNA3)) +#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && (defined(RDNA2) || defined(RDNA3)) constexpr int nwarps = 1; constexpr int rows_per_cuda_block = 1; #else constexpr int nwarps = ncols_y <= 4 ? 4 : 2; constexpr int rows_per_cuda_block = ncols_y == 1 ? 1 : 2; -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) && !defined(RDNA2) && !defined(RDNA3) +#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && !defined(RDNA2) && !defined(RDNA3) const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; const int row0 = rows_per_cuda_block*blockIdx.x; @@ -142,7 +142,7 @@ static void mul_mat_vec_q_cuda( int64_t nwarps = 1; int64_t rows_per_cuda_block = 1; - if (ggml_cuda_info().devices[id].cc < CC_RDNA2) { // NVIDIA and AMD older than RDNA2 + if (ggml_cuda_info().devices[id].cc < CC_CDNA || ggml_cuda_info().devices[id].cc == CC_RDNA1) { // NVIDIA and AMD older than RDNA2 but not CDNA switch(ncols_y) { case 1: nwarps = 4; diff --git a/ggml/src/ggml-cuda/opt-step-adamw.cu b/ggml/src/ggml-cuda/opt-step-adamw.cu index d6f13a9c6..35154f299 100644 --- a/ggml/src/ggml-cuda/opt-step-adamw.cu +++ b/ggml/src/ggml-cuda/opt-step-adamw.cu @@ -1,11 +1,11 @@ +#include "ggml-impl.h" #include "opt-step-adamw.cuh" #include static __global__ void opt_step_adamw_f32( - float * __restrict__ x, const float * __restrict__ g, float * __restrict__ g_m, float * __restrict__ g_v, const int64_t k, - const float alpha, const float beta1, const float beta2, const float eps, const float wd, - const float beta1h, const float beta2h) { + float * __restrict__ x, const float * __restrict__ g, float * __restrict__ g_m, float * __restrict__ g_v, + const float * __restrict__ pars, const int64_t k) { const int64_t i = (int64_t) blockIdx.x*blockDim.x + threadIdx.x; @@ -13,6 +13,14 @@ static __global__ void opt_step_adamw_f32( return; } + const float alpha = pars[0]; + const float beta1 = pars[1]; + const float beta2 = pars[2]; + const float eps = pars[3]; + const float wd = pars[4]; + const float beta1h = pars[5]; + const float beta2h = pars[6]; + const float gi = g[i]; const float gmi = g_m[i]*beta1 + gi*(1.0f - beta1); const float gvi = g_v[i]*beta2 + gi*gi*(1.0f - beta2); @@ -23,58 +31,48 @@ static __global__ void opt_step_adamw_f32( const float mh = gmi*beta1h; const float vh = sqrtf(gvi*beta2h) + eps; - x[i] = x[i]*(1.0f - alpha*wd) - mh/vh; + x[i] = x[i]*(1.0f - alpha*wd) - alpha*mh/vh; } static void opt_step_adamw_f32_cuda( - float * x, const float * g, float * g_m, float * g_v, const int64_t k, - const float alpha, const float beta1, const float beta2, const float eps, const float wd, - const float beta1h, const float beta2h, cudaStream_t stream) { + float * x, const float * g, float * g_m, float * g_v, const float * pars, const int64_t k, cudaStream_t stream) { const dim3 block_dims(CUDA_OPT_STEP_ADAMW_BLOCK_SIZE, 1, 1); const dim3 block_nums((k + CUDA_OPT_STEP_ADAMW_BLOCK_SIZE - 1) / CUDA_OPT_STEP_ADAMW_BLOCK_SIZE, 1, 1); - opt_step_adamw_f32<<>>(x, g, g_m, g_v, k, alpha, beta1, beta2, eps, wd, beta1h, beta2h); + opt_step_adamw_f32<<>>(x, g, g_m, g_v, pars, k); } void ggml_cuda_opt_step_adamw(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * src0 = dst->src[0]; - const ggml_tensor * src0_grad = dst->src[1]; - const ggml_tensor * src0_grad_m = dst->src[2]; - const ggml_tensor * src0_grad_v = dst->src[3]; + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src0_grad = dst->src[1]; + const ggml_tensor * src0_grad_m = dst->src[2]; + const ggml_tensor * src0_grad_v = dst->src[3]; + const ggml_tensor * adamw_params = dst->src[4]; - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(src0_grad->type == GGML_TYPE_F32); - GGML_ASSERT(src0_grad_m->type == GGML_TYPE_F32); - GGML_ASSERT(src0_grad_v->type == GGML_TYPE_F32); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src0_grad->type == GGML_TYPE_F32); + GGML_ASSERT(src0_grad_m->type == GGML_TYPE_F32); + GGML_ASSERT(src0_grad_v->type == GGML_TYPE_F32); + GGML_ASSERT(adamw_params->type == GGML_TYPE_F32); GGML_ASSERT(ggml_is_contiguous(src0)); GGML_ASSERT(ggml_is_contiguous(src0_grad)); GGML_ASSERT(ggml_is_contiguous(src0_grad_m)); GGML_ASSERT(ggml_is_contiguous(src0_grad_v)); + GGML_ASSERT(ggml_is_contiguous(adamw_params)); GGML_ASSERT(ggml_are_same_shape(src0, src0_grad)); GGML_ASSERT(ggml_are_same_shape(src0, src0_grad_m)); GGML_ASSERT(ggml_are_same_shape(src0, src0_grad_v)); + GGML_ASSERT(ggml_nelements(adamw_params) == 7); - float * src0_d = (float *) src0->data; - const float * src0_grad_d = (const float *) src0_grad->data; - float * src0_grad_m_d = (float *) src0_grad_m->data; - float * src0_grad_v_d = (float *) src0_grad_v->data; + float * src0_d = (float *) src0->data; + const float * src0_grad_d = (const float *) src0_grad->data; + float * src0_grad_m_d = (float *) src0_grad_m->data; + float * src0_grad_v_d = (float *) src0_grad_v->data; + const float * adamw_params_d = (const float *) adamw_params->data; cudaStream_t stream = ctx.stream(); const int64_t ne = ggml_nelements(src0); - int64_t iter; memcpy(&iter, &dst->op_params[0], sizeof(int64_t)); - float alpha; memcpy(&alpha, &dst->op_params[2], sizeof(float)); - float beta1; memcpy(&beta1, &dst->op_params[3], sizeof(float)); - float beta2; memcpy(&beta2, &dst->op_params[4], sizeof(float)); - float eps; memcpy(&eps, &dst->op_params[5], sizeof(float)); - float wd; memcpy(&wd, &dst->op_params[6], sizeof(float)); - - const float beta1h = alpha/(1.0f - powf(beta1, iter)); - const float beta2h = 1.0f/(1.0f - powf(beta2, iter)); - - opt_step_adamw_f32_cuda(src0_d, src0_grad_d, src0_grad_m_d, src0_grad_v_d, ne, alpha, beta1, beta2, eps, wd, beta1h, beta2h, stream); - - iter++; - memcpy(&dst->op_params[0], &iter, sizeof(int64_t)); + opt_step_adamw_f32_cuda(src0_d, src0_grad_d, src0_grad_m_d, src0_grad_v_d, adamw_params_d, ne, stream); } diff --git a/ggml/src/ggml-cuda/quantize.cu b/ggml/src/ggml-cuda/quantize.cu index 45408ce86..1702e4ce2 100644 --- a/ggml/src/ggml-cuda/quantize.cu +++ b/ggml/src/ggml-cuda/quantize.cu @@ -69,8 +69,8 @@ static __global__ void quantize_mmq_q8_1( // Exchange max. abs. value between vals_per_scale/4 threads. #pragma unroll - for (int mask = vals_per_scale/8; mask > 0; mask >>= 1) { - amax = fmaxf(amax, __shfl_xor_sync(0xFFFFFFFF, amax, mask, WARP_SIZE)); + for (int offset = vals_per_scale/8; offset > 0; offset >>= 1) { + amax = fmaxf(amax, __shfl_xor_sync(0xFFFFFFFF, amax, offset, WARP_SIZE)); } float sum; @@ -79,8 +79,8 @@ static __global__ void quantize_mmq_q8_1( // Exchange calculate sum across vals_per_sum/4 threads. #pragma unroll - for (int mask = vals_per_sum/8; mask > 0; mask >>= 1) { - sum += __shfl_xor_sync(0xFFFFFFFF, sum, mask, WARP_SIZE); + for (int offset = vals_per_sum/8; offset > 0; offset >>= 1) { + sum += __shfl_xor_sync(0xFFFFFFFF, sum, offset, WARP_SIZE); } } diff --git a/ggml/src/ggml-cuda/rwkv-wkv.cuh b/ggml/src/ggml-cuda/rwkv-wkv.cuh deleted file mode 100644 index 13795247f..000000000 --- a/ggml/src/ggml-cuda/rwkv-wkv.cuh +++ /dev/null @@ -1,5 +0,0 @@ -#include "common.cuh" - -#define CUDA_WKV_BLOCK_SIZE 64 - -void ggml_cuda_op_rwkv_wkv(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml/src/ggml-cuda/sum.cu b/ggml/src/ggml-cuda/sum.cu index 0583e4fe0..31cfe5394 100644 --- a/ggml/src/ggml-cuda/sum.cu +++ b/ggml/src/ggml-cuda/sum.cu @@ -1,6 +1,6 @@ -#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_USE_MUSA) && CUDART_VERSION >= 11700 +#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) && CUDART_VERSION >= 11700 #define USE_CUB -#endif // !defined(GGML_USE_HIPBLAS) && !defined(GGML_USE_MUSA) && CUDART_VERSION >= 11700 +#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) && CUDART_VERSION >= 11700 #ifdef USE_CUB // On Windows CUB uses libraries with variables called CC_PASCAL which conflict with the define in common.cuh. diff --git a/ggml/src/ggml-cuda/vendors/hip.h b/ggml/src/ggml-cuda/vendors/hip.h index 1f3c70c2e..3205534d6 100644 --- a/ggml/src/ggml-cuda/vendors/hip.h +++ b/ggml/src/ggml-cuda/vendors/hip.h @@ -95,6 +95,14 @@ #define __CUDA_ARCH__ 1300 +#if defined(__gfx803__) || defined(__gfx900__) || defined(__gfx906__) +#define GCN +#endif + +#if defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx942__) +#define CDNA +#endif + #if defined(__gfx1100__) || defined(__gfx1101__) || defined(__gfx1102__) || defined(__gfx1103__) || \ defined(__gfx1150__) || defined(__gfx1151__) #define RDNA3 diff --git a/ggml/src/ggml-cuda/rwkv-wkv.cu b/ggml/src/ggml-cuda/wkv6.cu similarity index 93% rename from ggml/src/ggml-cuda/rwkv-wkv.cu rename to ggml/src/ggml-cuda/wkv6.cu index 098e92d35..42578341a 100644 --- a/ggml/src/ggml-cuda/rwkv-wkv.cu +++ b/ggml/src/ggml-cuda/wkv6.cu @@ -1,5 +1,5 @@ #include "common.cuh" -#include "rwkv-wkv.cuh" +#include "wkv6.cuh" static __global__ void rwkv_wkv_f32(const int B, const int T, const int C, const int H, const float * k, const float * v, const float * r, const float * tf, const float * td, const float * s, float * dst) { const int tid = threadIdx.x; @@ -64,7 +64,7 @@ static __global__ void rwkv_wkv_f32(const int B, const int T, const int C, const } } -void ggml_cuda_op_rwkv_wkv(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { +void ggml_cuda_op_rwkv_wkv6(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const float * k_d = (const float *)dst->src[0]->data; const float * v_d = (const float *)dst->src[1]->data; const float * r_d = (const float *)dst->src[2]->data; @@ -83,7 +83,7 @@ void ggml_cuda_op_rwkv_wkv(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { GGML_ASSERT(dst->src[5]->type == GGML_TYPE_F32); GGML_ASSERT(C % H == 0); - GGML_ASSERT(C / H == CUDA_WKV_BLOCK_SIZE); + GGML_ASSERT(C / H == CUDA_WKV_BLOCK_SIZE); // The current cuda kernel is designed for RWKV6, HEAD_SIZE == 64 rwkv_wkv_f32<<>>(B, T, C, H, k_d, v_d, r_d, tf_d, td_d, s_d, dst_d); } diff --git a/ggml/src/ggml-cuda/wkv6.cuh b/ggml/src/ggml-cuda/wkv6.cuh new file mode 100644 index 000000000..a7124ee51 --- /dev/null +++ b/ggml/src/ggml-cuda/wkv6.cuh @@ -0,0 +1,5 @@ +#include "common.cuh" + +#define CUDA_WKV_BLOCK_SIZE 64 + +void ggml_cuda_op_rwkv_wkv6(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml/src/ggml-hip/CMakeLists.txt b/ggml/src/ggml-hip/CMakeLists.txt new file mode 100644 index 000000000..b15fbd24d --- /dev/null +++ b/ggml/src/ggml-hip/CMakeLists.txt @@ -0,0 +1,104 @@ +if (NOT EXISTS $ENV{ROCM_PATH}) + if (NOT EXISTS /opt/rocm) + set(ROCM_PATH /usr) + else() + set(ROCM_PATH /opt/rocm) + endif() +else() + set(ROCM_PATH $ENV{ROCM_PATH}) +endif() + +list(APPEND CMAKE_PREFIX_PATH ${ROCM_PATH}) +list(APPEND CMAKE_PREFIX_PATH "${ROCM_PATH}/lib64/cmake") + +# CMake on Windows doesn't support the HIP language yet +if (WIN32) + set(CXX_IS_HIPCC TRUE) +else() + string(REGEX MATCH "hipcc(\.bat)?$" CXX_IS_HIPCC "${CMAKE_CXX_COMPILER}") +endif() + +if (CXX_IS_HIPCC) + if (LINUX) + if (NOT ${CMAKE_CXX_COMPILER_ID} MATCHES "Clang") + message(WARNING "Only LLVM is supported for HIP, hint: CXX=/opt/rocm/llvm/bin/clang++") + endif() + + message(WARNING "Setting hipcc as the C++ compiler is legacy behavior." + " Prefer setting the HIP compiler directly. See README for details.") + endif() +else() + # Forward AMDGPU_TARGETS to CMAKE_HIP_ARCHITECTURES. + if (AMDGPU_TARGETS AND NOT CMAKE_HIP_ARCHITECTURES) + set(CMAKE_HIP_ARCHITECTURES ${AMDGPU_TARGETS}) + endif() + cmake_minimum_required(VERSION 3.21) + enable_language(HIP) +endif() + +find_package(hip REQUIRED) +find_package(hipblas REQUIRED) +find_package(rocblas REQUIRED) + +message(STATUS "HIP and hipBLAS found") + +file(GLOB GGML_HEADERS_ROCM "../ggml-cuda/*.cuh") +list(APPEND GGML_HEADERS_ROCM "../../include/ggml-cuda.h") + +file(GLOB GGML_SOURCES_ROCM "../ggml-cuda/*.cu") +file(GLOB SRCS "../ggml-cuda/template-instances/fattn-wmma*.cu") +list(APPEND GGML_SOURCES_ROCM ${SRCS}) +file(GLOB SRCS "../ggml-cuda/template-instances/mmq*.cu") +list(APPEND GGML_SOURCES_ROCM ${SRCS}) + +if (GGML_CUDA_FA_ALL_QUANTS) + file(GLOB SRCS "../ggml-cuda/template-instances/fattn-vec*.cu") + list(APPEND GGML_SOURCES_ROCM ${SRCS}) + add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS) +else() + file(GLOB SRCS "../ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu") + list(APPEND GGML_SOURCES_ROCM ${SRCS}) + file(GLOB SRCS "../ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu") + list(APPEND GGML_SOURCES_ROCM ${SRCS}) + file(GLOB SRCS "../ggml-cuda/template-instances/fattn-vec*f16-f16.cu") + list(APPEND GGML_SOURCES_ROCM ${SRCS}) +endif() + +ggml_add_backend_library(ggml-hip + ${GGML_HEADERS_ROCM} + ${GGML_SOURCES_ROCM} + ) + +# TODO: do not use CUDA definitions for HIP +target_compile_definitions(ggml PUBLIC GGML_USE_CUDA) + +add_compile_definitions(GGML_USE_HIP) + +if (GGML_HIP_UMA) + add_compile_definitions(GGML_HIP_UMA) +endif() + +if (GGML_CUDA_FORCE_MMQ) + add_compile_definitions(GGML_CUDA_FORCE_MMQ) +endif() + +if (GGML_CUDA_FORCE_CUBLAS) + add_compile_definitions(GGML_CUDA_FORCE_CUBLAS) +endif() + +if (GGML_CUDA_NO_PEER_COPY) + add_compile_definitions(GGML_CUDA_NO_PEER_COPY) +endif() + +if (CXX_IS_HIPCC) + set_source_files_properties(${GGML_SOURCES_ROCM} PROPERTIES LANGUAGE CXX) + target_link_libraries(ggml-hip PRIVATE hip::device) +else() + set_source_files_properties(${GGML_SOURCES_ROCM} PROPERTIES LANGUAGE HIP) +endif() + +if (GGML_STATIC) + message(FATAL_ERROR "Static linking not supported for HIP/ROCm") +endif() + +target_link_libraries(ggml-hip PRIVATE ggml-base hip::host roc::rocblas roc::hipblas) diff --git a/ggml/src/ggml-impl.h b/ggml/src/ggml-impl.h index 65c4f8119..00a1546a7 100644 --- a/ggml/src/ggml-impl.h +++ b/ggml/src/ggml-impl.h @@ -3,21 +3,40 @@ // GGML internal header #include "ggml.h" - #include +#include #include // load `stdlib.h` before other headers to work around MinGW bug: https://sourceforge.net/p/mingw-w64/bugs/192/ #include #include +#include + +#ifdef __ARM_FEATURE_SVE +#include +#endif // __ARM_FEATURE_SVE + +#if defined(__ARM_NEON) && !defined(__CUDACC__) +// if YCM cannot find , make a symbolic link to it, for example: +// +// $ ln -sfn /Library/Developer/CommandLineTools/usr/lib/clang/13.1.6/include/arm_neon.h ./src/ +// +#include +#endif + +#if defined(__F16C__) +#include +#endif #ifdef __cplusplus extern "C" { #endif -#undef MIN -#undef MAX +#ifndef MIN +# define MIN(a, b) ((a) < (b) ? (a) : (b)) +#endif -#define MIN(a, b) ((a) < (b) ? (a) : (b)) -#define MAX(a, b) ((a) > (b) ? (a) : (b)) +#ifndef MAX +# define MAX(a, b) ((a) > (b) ? (a) : (b)) +#endif // required for mmap as gguf only guarantees 32-byte alignment #define TENSOR_ALIGNMENT 32 @@ -27,15 +46,29 @@ extern "C" { // if C99 - static_assert is noop // ref: https://stackoverflow.com/a/53923785/4039976 #ifndef __cplusplus -#ifndef static_assert -#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L) -#define static_assert(cond, msg) _Static_assert(cond, msg) -#else -#define static_assert(cond, msg) struct global_scope_noop_trick -#endif -#endif + #ifndef static_assert + #if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L) + #define static_assert(cond, msg) _Static_assert(cond, msg) + #else + #define static_assert(cond, msg) struct global_scope_noop_trick + #endif + #endif #endif +static inline int ggml_up32(int n) { + return (n + 31) & ~31; +} + +//static inline int ggml_up64(int n) { +// return (n + 63) & ~63; +//} + +static inline int ggml_up(int n, int m) { + // assert m is a power of 2 + GGML_ASSERT((m & (m - 1)) == 0); + return (n + m - 1) & ~(m - 1); +} + // // logging // @@ -51,6 +84,72 @@ void ggml_log_callback_default(enum ggml_log_level level, const char * text, voi #define GGML_LOG_DEBUG(...) ggml_log_internal(GGML_LOG_LEVEL_DEBUG, __VA_ARGS__) #define GGML_LOG_CONT(...) ggml_log_internal(GGML_LOG_LEVEL_CONT , __VA_ARGS__) +#define GGML_DEBUG 0 + +#if (GGML_DEBUG >= 1) +#define GGML_PRINT_DEBUG(...) GGML_LOG_DEBUG(__VA_ARGS__) +#else +#define GGML_PRINT_DEBUG(...) +#endif + +#if (GGML_DEBUG >= 5) +#define GGML_PRINT_DEBUG_5(...) GGML_LOG_DEBUG(__VA_ARGS__) +#else +#define GGML_PRINT_DEBUG_5(...) +#endif + +#if (GGML_DEBUG >= 10) +#define GGML_PRINT_DEBUG_10(...) GGML_LOG_DEBUG(__VA_ARGS__) +#else +#define GGML_PRINT_DEBUG_10(...) +#endif + +// tensor params + +static void ggml_set_op_params(struct ggml_tensor * tensor, const void * params, size_t params_size) { + GGML_ASSERT(tensor != NULL); // silence -Warray-bounds warnings + assert(params_size <= GGML_MAX_OP_PARAMS); + memcpy(tensor->op_params, params, params_size); +} + +static int32_t ggml_get_op_params_i32(const struct ggml_tensor * tensor, uint32_t i) { + assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t)); + return ((const int32_t *)(tensor->op_params))[i]; +} + +static float ggml_get_op_params_f32(const struct ggml_tensor * tensor, uint32_t i) { + assert(i < GGML_MAX_OP_PARAMS / sizeof(float)); + return ((const float *)(tensor->op_params))[i]; +} + +static void ggml_set_op_params_i32(struct ggml_tensor * tensor, uint32_t i, int32_t value) { + assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t)); + ((int32_t *)(tensor->op_params))[i] = value; +} + +static void ggml_set_op_params_f32(struct ggml_tensor * tensor, uint32_t i, float value) { + assert(i < GGML_MAX_OP_PARAMS / sizeof(float)); + ((float *)(tensor->op_params))[i] = value; +} + +struct ggml_map_custom1_op_params { + ggml_custom1_op_t fun; + int n_tasks; + void * userdata; +}; + +struct ggml_map_custom2_op_params { + ggml_custom2_op_t fun; + int n_tasks; + void * userdata; +}; + +struct ggml_map_custom3_op_params { + ggml_custom3_op_t fun; + int n_tasks; + void * userdata; +}; + // bitset typedef uint32_t ggml_bitset_t; @@ -99,7 +198,7 @@ void ggml_hash_set_reset(struct ggml_hash_set * hash_set); static bool ggml_hash_contains(const struct ggml_hash_set * hash_set, struct ggml_tensor * key); // returns GGML_HASHSET_FULL if table is full, otherwise the current index of the key or where it should be inserted -static size_t ggml_hash_find(const struct ggml_hash_set * hash_set, struct ggml_tensor * key); +static size_t ggml_hash_find(const struct ggml_hash_set * hash_set, const struct ggml_tensor * key); // returns GGML_HASHSET_ALREADY_EXISTS if key already exists, index otherwise, asserts if table is full static size_t ggml_hash_insert(struct ggml_hash_set * hash_set, struct ggml_tensor * key); @@ -113,7 +212,7 @@ static inline size_t ggml_hash(const struct ggml_tensor * p) { return (size_t)(uintptr_t)p >> 4; } -static size_t ggml_hash_find(const struct ggml_hash_set * hash_set, struct ggml_tensor * key) { +static size_t ggml_hash_find(const struct ggml_hash_set * hash_set, const struct ggml_tensor * key) { size_t h = ggml_hash(key) % hash_set->size; // linear probing @@ -184,19 +283,23 @@ enum ggml_cgraph_eval_order { }; struct ggml_cgraph { - int size; - int n_nodes; - int n_leafs; + int size; // maximum number of nodes/leafs/grads/grad_accs + int n_nodes; // number of nodes currently in use + int n_leafs; // number of leafs currently in use - struct ggml_tensor ** nodes; - struct ggml_tensor ** grads; - struct ggml_tensor ** leafs; + struct ggml_tensor ** nodes; // tensors with data that can change if the graph is evaluated + struct ggml_tensor ** grads; // the outputs of these tensors are the gradients of the nodes + struct ggml_tensor ** grad_accs; // accumulators for node gradients + struct ggml_tensor ** leafs; // tensors with constant data struct ggml_hash_set visited_hash_set; enum ggml_cgraph_eval_order order; }; +// returns a slice of cgraph with nodes [i0, i1) +// the slice does not have leafs or gradients +// if you need the gradients, get them from the original graph struct ggml_cgraph ggml_graph_view(struct ggml_cgraph * cgraph, int i0, int i1); // Memory allocation @@ -204,6 +307,250 @@ struct ggml_cgraph ggml_graph_view(struct ggml_cgraph * cgraph, int i0, int i1); void * ggml_aligned_malloc(size_t size); void ggml_aligned_free(void * ptr, size_t size); +// FP16 to FP32 conversion + +#if defined(__ARM_NEON) + #if defined(_MSC_VER) || (defined(__CUDACC__) && __CUDACC_VER_MAJOR__ <= 11) + typedef uint16_t ggml_fp16_internal_t; + #else + typedef __fp16 ggml_fp16_internal_t; + #endif +#endif + +#if defined(__ARM_NEON) && !defined(_MSC_VER) && !(defined(__CUDACC__) && __CUDACC_VER_MAJOR__ <= 11) + #define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) + #define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) + + #define GGML_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) + + static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) { + ggml_fp16_internal_t tmp; + memcpy(&tmp, &h, sizeof(ggml_fp16_t)); + return (float)tmp; + } + + static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { + ggml_fp16_t res; + ggml_fp16_internal_t tmp = f; + memcpy(&res, &tmp, sizeof(ggml_fp16_t)); + return res; + } + +#elif defined(__F16C__) + + #ifdef _MSC_VER + #define GGML_COMPUTE_FP16_TO_FP32(x) _mm_cvtss_f32(_mm_cvtph_ps(_mm_cvtsi32_si128(x))) + #define GGML_COMPUTE_FP32_TO_FP16(x) _mm_extract_epi16(_mm_cvtps_ph(_mm_set_ss(x), 0), 0) + #else + #define GGML_COMPUTE_FP16_TO_FP32(x) _cvtsh_ss(x) + #define GGML_COMPUTE_FP32_TO_FP16(x) _cvtss_sh(x, 0) + #endif + +#elif defined(__POWER9_VECTOR__) + + #define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) + #define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) + /* the inline asm below is about 12% faster than the lookup method */ + #define GGML_FP16_TO_FP32(x) GGML_COMPUTE_FP16_TO_FP32(x) + #define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x) + + static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) { + register float f; + register double d; + __asm__( + "mtfprd %0,%2\n" + "xscvhpdp %0,%0\n" + "frsp %1,%0\n" : + /* temp */ "=d"(d), + /* out */ "=f"(f): + /* in */ "r"(h)); + return f; + } + + static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { + register double d; + register ggml_fp16_t r; + __asm__( /* xscvdphp can work on double or single precision */ + "xscvdphp %0,%2\n" + "mffprd %1,%0\n" : + /* temp */ "=d"(d), + /* out */ "=r"(r): + /* in */ "f"(f)); + return r; + } + +#else + + // FP16 <-> FP32 + // ref: https://github.com/Maratyszcza/FP16 + + static inline float fp32_from_bits(uint32_t w) { + union { + uint32_t as_bits; + float as_value; + } fp32; + fp32.as_bits = w; + return fp32.as_value; + } + + static inline uint32_t fp32_to_bits(float f) { + union { + float as_value; + uint32_t as_bits; + } fp32; + fp32.as_value = f; + return fp32.as_bits; + } + + static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) { + const uint32_t w = (uint32_t) h << 16; + const uint32_t sign = w & UINT32_C(0x80000000); + const uint32_t two_w = w + w; + + const uint32_t exp_offset = UINT32_C(0xE0) << 23; + #if (defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__)) && (!defined(__cplusplus) || __cplusplus >= 201703L) + const float exp_scale = 0x1.0p-112f; + #else + const float exp_scale = fp32_from_bits(UINT32_C(0x7800000)); + #endif + const float normalized_value = fp32_from_bits((two_w >> 4) + exp_offset) * exp_scale; + + const uint32_t magic_mask = UINT32_C(126) << 23; + const float magic_bias = 0.5f; + const float denormalized_value = fp32_from_bits((two_w >> 17) | magic_mask) - magic_bias; + + const uint32_t denormalized_cutoff = UINT32_C(1) << 27; + const uint32_t result = sign | + (two_w < denormalized_cutoff ? fp32_to_bits(denormalized_value) : fp32_to_bits(normalized_value)); + return fp32_from_bits(result); + } + + static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { + #if (defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901L) || defined(__GNUC__) && !defined(__STRICT_ANSI__)) && (!defined(__cplusplus) || __cplusplus >= 201703L) + const float scale_to_inf = 0x1.0p+112f; + const float scale_to_zero = 0x1.0p-110f; + #else + const float scale_to_inf = fp32_from_bits(UINT32_C(0x77800000)); + const float scale_to_zero = fp32_from_bits(UINT32_C(0x08800000)); + #endif + float base = (fabsf(f) * scale_to_inf) * scale_to_zero; + + const uint32_t w = fp32_to_bits(f); + const uint32_t shl1_w = w + w; + const uint32_t sign = w & UINT32_C(0x80000000); + uint32_t bias = shl1_w & UINT32_C(0xFF000000); + if (bias < UINT32_C(0x71000000)) { + bias = UINT32_C(0x71000000); + } + + base = fp32_from_bits((bias >> 1) + UINT32_C(0x07800000)) + base; + const uint32_t bits = fp32_to_bits(base); + const uint32_t exp_bits = (bits >> 13) & UINT32_C(0x00007C00); + const uint32_t mantissa_bits = bits & UINT32_C(0x00000FFF); + const uint32_t nonsign = exp_bits + mantissa_bits; + return (sign >> 16) | (shl1_w > UINT32_C(0xFF000000) ? UINT16_C(0x7E00) : nonsign); + } + + #define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) + #define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) + +#endif // defined(__ARM_NEON) && (!defined(__MSC_VER) + +// precomputed f32 table for f16 (256 KB) +// defined in ggml.c, initialized in ggml_init() +GGML_API float ggml_table_f32_f16[1 << 16]; + +// On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32, +// so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON. +// This is also true for POWER9. +#if !defined(GGML_FP16_TO_FP32) +inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) { + uint16_t s; + memcpy(&s, &f, sizeof(uint16_t)); + return ggml_table_f32_f16[s]; +} + +#define GGML_FP16_TO_FP32(x) ggml_lookup_fp16_to_fp32(x) +#endif + +#if !defined(GGML_FP32_TO_FP16) +#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x) +#endif + +/** + * Converts brain16 to float32. + * + * The bfloat16 floating point format has the following structure: + * + * ┌sign + * │ + * │ ┌exponent + * │ │ + * │ │ ┌mantissa + * │ │ │ + * │┌──┴───┐┌─┴───┐ + * 0b0000000000000000 brain16 + * + * Since bf16 has the same number of exponent bits as a 32bit float, + * encoding and decoding numbers becomes relatively straightforward. + * + * ┌sign + * │ + * │ ┌exponent + * │ │ + * │ │ ┌mantissa + * │ │ │ + * │┌──┴───┐┌─┴───────────────────┐ + * 0b00000000000000000000000000000000 IEEE binary32 + * + * For comparison, the standard fp16 format has fewer exponent bits. + * + * ┌sign + * │ + * │ ┌exponent + * │ │ + * │ │ ┌mantissa + * │ │ │ + * │┌─┴─┐┌─┴──────┐ + * 0b0000000000000000 IEEE binary16 + * + * @see IEEE 754-2008 + */ +static inline float ggml_compute_bf16_to_fp32(ggml_bf16_t h) { + union { + float f; + uint32_t i; + } u; + u.i = (uint32_t)h.bits << 16; + return u.f; +} + +/** + * Converts float32 to brain16. + * + * This is binary identical with Google Brain float conversion. + * Floats shall round to nearest even, and NANs shall be quiet. + * Subnormals aren't flushed to zero, except perhaps when used. + * This code should vectorize nicely if using modern compilers. + */ +static inline ggml_bf16_t ggml_compute_fp32_to_bf16(float s) { + ggml_bf16_t h; + union { + float f; + uint32_t i; + } u; + u.f = s; + if ((u.i & 0x7fffffff) > 0x7f800000) { /* nan */ + h.bits = (u.i >> 16) | 64; /* force to quiet */ + return h; + } + h.bits = (u.i + (0x7fff + ((u.i >> 16) & 1))) >> 16; + return h; +} + +#define GGML_FP32_TO_BF16(x) ggml_compute_fp32_to_bf16(x) +#define GGML_BF16_TO_FP32(x) ggml_compute_bf16_to_fp32(x) + #ifdef __cplusplus } #endif diff --git a/ggml/src/ggml-kompute/CMakeLists.txt b/ggml/src/ggml-kompute/CMakeLists.txt new file mode 100644 index 000000000..c9109d5e8 --- /dev/null +++ b/ggml/src/ggml-kompute/CMakeLists.txt @@ -0,0 +1,166 @@ + +find_package(Vulkan COMPONENTS glslc REQUIRED) +find_program(glslc_executable NAMES glslc HINTS Vulkan::glslc) + +if (NOT glslc_executable) + message(FATAL_ERROR "glslc not found") +endif() + +ggml_add_backend_library(ggml-kompute + ggml-kompute.cpp + ../../include/ggml-kompute.h + ) + +target_link_libraries(ggml-kompute PRIVATE ggml-base kompute) +target_include_directories(ggml-kompute PRIVATE ${CMAKE_CURRENT_BINARY_DIR}) + +add_compile_definitions(VULKAN_HPP_DISPATCH_LOADER_DYNAMIC=1) + +function(compile_shader) + set(options) + set(oneValueArgs) + set(multiValueArgs SOURCES) + cmake_parse_arguments(compile_shader "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) + foreach(source ${compile_shader_SOURCES}) + get_filename_component(filename ${source} NAME) + set(spv_file ${filename}.spv) + add_custom_command( + OUTPUT ${spv_file} + DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/${source} + ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/common.comp + ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_getrows.comp + ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_mul_mv_q_n_pre.comp + ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_mul_mv_q_n.comp + COMMAND ${glslc_executable} --target-env=vulkan1.2 -o ${spv_file} ${CMAKE_CURRENT_SOURCE_DIR}/${source} + COMMENT "Compiling ${source} to ${spv_file}" + ) + + get_filename_component(RAW_FILE_NAME ${spv_file} NAME) + set(FILE_NAME "shader${RAW_FILE_NAME}") + string(REPLACE ".comp.spv" ".h" HEADER_FILE ${FILE_NAME}) + string(TOUPPER ${HEADER_FILE} HEADER_FILE_DEFINE) + string(REPLACE "." "_" HEADER_FILE_DEFINE "${HEADER_FILE_DEFINE}") + set(OUTPUT_HEADER_FILE "${HEADER_FILE}") + message(STATUS "${HEADER_FILE} generating ${HEADER_FILE_DEFINE}") + if(CMAKE_GENERATOR MATCHES "Visual Studio") + add_custom_command( + OUTPUT ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "/*THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT*/" > ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#ifndef ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_BINARY_DIR}/bin/$/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + DEPENDS ${spv_file} xxd + COMMENT "Converting to hpp: ${FILE_NAME} ${CMAKE_BINARY_DIR}/bin/$/xxd" + ) + else() + add_custom_command( + OUTPUT ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "/*THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT*/" > ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#ifndef ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_BINARY_DIR}/bin/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + DEPENDS ${spv_file} xxd + COMMENT "Converting to hpp: ${FILE_NAME} ${CMAKE_BINARY_DIR}/bin/xxd" + ) + endif() + endforeach() +endfunction() + +if (EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/kompute/CMakeLists.txt") + message(STATUS "Kompute found") + set(KOMPUTE_OPT_LOG_LEVEL Error CACHE STRING "Kompute log level") + add_subdirectory(kompute) + + # Compile our shaders + compile_shader(SOURCES + kompute-shaders/op_scale.comp + kompute-shaders/op_scale_8.comp + kompute-shaders/op_add.comp + kompute-shaders/op_addrow.comp + kompute-shaders/op_mul.comp + kompute-shaders/op_silu.comp + kompute-shaders/op_relu.comp + kompute-shaders/op_gelu.comp + kompute-shaders/op_softmax.comp + kompute-shaders/op_norm.comp + kompute-shaders/op_rmsnorm.comp + kompute-shaders/op_diagmask.comp + kompute-shaders/op_mul_mat_mat_f32.comp + kompute-shaders/op_mul_mat_f16.comp + kompute-shaders/op_mul_mat_q8_0.comp + kompute-shaders/op_mul_mat_q4_0.comp + kompute-shaders/op_mul_mat_q4_1.comp + kompute-shaders/op_mul_mat_q4_k.comp + kompute-shaders/op_mul_mat_q6_k.comp + kompute-shaders/op_getrows_f32.comp + kompute-shaders/op_getrows_f16.comp + kompute-shaders/op_getrows_q4_0.comp + kompute-shaders/op_getrows_q4_1.comp + kompute-shaders/op_getrows_q6_k.comp + kompute-shaders/op_rope_norm_f16.comp + kompute-shaders/op_rope_norm_f32.comp + kompute-shaders/op_rope_neox_f16.comp + kompute-shaders/op_rope_neox_f32.comp + kompute-shaders/op_cpy_f16_f16.comp + kompute-shaders/op_cpy_f16_f32.comp + kompute-shaders/op_cpy_f32_f16.comp + kompute-shaders/op_cpy_f32_f32.comp + ) + + # Create a custom target for our generated shaders + add_custom_target(generated_shaders DEPENDS + shaderop_scale.h + shaderop_scale_8.h + shaderop_add.h + shaderop_addrow.h + shaderop_mul.h + shaderop_silu.h + shaderop_relu.h + shaderop_gelu.h + shaderop_softmax.h + shaderop_norm.h + shaderop_rmsnorm.h + shaderop_diagmask.h + shaderop_mul_mat_mat_f32.h + shaderop_mul_mat_f16.h + shaderop_mul_mat_q8_0.h + shaderop_mul_mat_q4_0.h + shaderop_mul_mat_q4_1.h + shaderop_mul_mat_q4_k.h + shaderop_mul_mat_q6_k.h + shaderop_getrows_f32.h + shaderop_getrows_f16.h + shaderop_getrows_q4_0.h + shaderop_getrows_q4_1.h + shaderop_getrows_q6_k.h + shaderop_rope_norm_f16.h + shaderop_rope_norm_f32.h + shaderop_rope_neox_f16.h + shaderop_rope_neox_f32.h + shaderop_cpy_f16_f16.h + shaderop_cpy_f16_f32.h + shaderop_cpy_f32_f16.h + shaderop_cpy_f32_f32.h + ) + + # Create a custom command that depends on the generated_shaders + add_custom_command( + OUTPUT ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp + COMMAND ${CMAKE_COMMAND} -E touch ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp + DEPENDS generated_shaders + COMMENT "Ensuring shaders are generated before compiling ggml-kompute.cpp" + ) + + # Add the stamp to the main sources to ensure dependency tracking + target_sources(ggml-kompute PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp) +else() + message(WARNING "Kompute not found") +endif() diff --git a/ggml/src/ggml-kompute.cpp b/ggml/src/ggml-kompute/ggml-kompute.cpp similarity index 91% rename from ggml/src/ggml-kompute.cpp rename to ggml/src/ggml-kompute/ggml-kompute.cpp index fea69fb04..28ceecfc4 100644 --- a/ggml/src/ggml-kompute.cpp +++ b/ggml/src/ggml-kompute/ggml-kompute.cpp @@ -20,6 +20,7 @@ #include "shaderop_mul_mat_q8_0.h" #include "shaderop_mul_mat_q4_0.h" #include "shaderop_mul_mat_q4_1.h" +#include "shaderop_mul_mat_q4_k.h" #include "shaderop_mul_mat_q6_k.h" #include "shaderop_mul_mat_mat_f32.h" #include "shaderop_getrows_f32.h" @@ -27,8 +28,10 @@ #include "shaderop_getrows_q4_0.h" #include "shaderop_getrows_q4_1.h" #include "shaderop_getrows_q6_k.h" -#include "shaderop_rope_f16.h" -#include "shaderop_rope_f32.h" +#include "shaderop_rope_norm_f16.h" +#include "shaderop_rope_norm_f32.h" +#include "shaderop_rope_neox_f16.h" +#include "shaderop_rope_neox_f32.h" #include "shaderop_cpy_f16_f16.h" #include "shaderop_cpy_f16_f32.h" #include "shaderop_cpy_f32_f16.h" @@ -344,7 +347,7 @@ void ggml_vk_allocate_descriptor_pool(struct ggml_kompute_context * ctx, size_t std::vector descriptorPoolSizes = { vk::DescriptorPoolSize( vk::DescriptorType::eStorageBuffer, - 3 * size // Descriptor count is number of possible tensors to pass into an algorithm + 4 * size // Descriptor count is number of possible tensors to pass into an algorithm ) }; @@ -787,7 +790,8 @@ static void ggml_vk_soft_max( const std::shared_ptr& out, uint32_t inAOff, uint32_t inBOff, uint32_t outOff, int32_t ne00, int32_t ne01, int32_t ne02, uint32_t ne03, - float scale + float scale, float max_bias, float m0, float m1, + uint32_t n_head_log2 ) { const static auto spirv = getSpirvShader(kp::shader_data::op_softmax_comp_spv, kp::shader_data::op_softmax_comp_spv_len); @@ -795,12 +799,14 @@ static void ggml_vk_soft_max( struct PushConstants { uint32_t inAOff, inBOff, outOff; int32_t ne00, ne01, ne02; - float scale; + float scale, max_bias, m0, m1; + uint32_t n_head_log2; int32_t mask; } pushConsts { safe_divide(inAOff, 4), safe_divide(inBOff, 4), safe_divide(outOff, 4), ne00, ne01, ne02, - scale, + scale, max_bias, m0, m1, + n_head_log2, bool(inB) }; @@ -910,9 +916,9 @@ static void ggml_vk_mul_mat_f16( const std::shared_ptr& out, uint32_t inAOff, uint32_t inBOff, uint32_t outOff, int32_t ne00, int32_t ne01, int32_t ne02, - uint32_t nb00, uint32_t nb01, uint32_t nb02, + uint32_t nb00, uint32_t nb01, uint32_t nb02, uint32_t nb03, int32_t ne10, int32_t ne11, int32_t ne12, int32_t ne13, - uint32_t nb10, uint32_t nb11, uint32_t nb12, + uint32_t nb10, uint32_t nb11, uint32_t nb12, uint32_t nb13, int32_t ne0, int32_t ne1, uint32_t r2, uint32_t r3 ) { @@ -922,17 +928,17 @@ static void ggml_vk_mul_mat_f16( struct PushConstants { uint32_t inAOff, inBOff, outOff; int32_t ne00, ne01, ne02; - uint32_t nb00, nb01, nb02; + uint32_t nb00, nb01, nb02, nb03; int32_t ne10, ne11, ne12; - uint32_t nb10, nb11, nb12; + uint32_t nb10, nb11, nb12, nb13; int32_t ne0, ne1; uint32_t r2, r3; } pushConsts { safe_divide(inAOff, 2), safe_divide(inBOff, 4), safe_divide(outOff, 4), ne00, ne01, ne02, - nb00, nb01, nb02, + nb00, nb01, nb02, nb03, ne10, ne11, ne12, - nb10, nb11, nb12, + nb10, nb11, nb12, nb13, ne0, ne1, r2, r3 }; @@ -1012,6 +1018,8 @@ static void ggml_vk_mul_mat_impl( int32_t ne00, int32_t ne01, int32_t ne02, int32_t ne10, int32_t ne11, int32_t ne12, int32_t ne13, int32_t ne0, int32_t ne1, + uint32_t nb01, uint32_t nb02, uint32_t nb03, + uint32_t nb11, uint32_t nb12, uint32_t nb13, uint32_t r2, uint32_t r3 ) { struct PushConstants { @@ -1019,19 +1027,23 @@ static void ggml_vk_mul_mat_impl( int32_t ne00, ne01, ne02; int32_t ne10, ne12; int32_t ne0, ne1; + uint32_t nb01, nb02, nb03; + uint32_t nb11, nb12, nb13; uint32_t r2, r3; } pushConsts { safe_divide(inAOff, block_size), safe_divide(inBOff, 4), safe_divide(outOff, 4), ne00, ne01, ne02, ne10, ne12, ne0, ne1, + nb01, nb02, nb03, + nb11, nb12, nb13, r2, r3 }; auto name = std::string(__func__) + "_" + suffix; std::shared_ptr s_algo = nullptr; if (!komputeManager()->hasAlgorithm(name)) { - const uint32_t local_x = ggml_vk_current_device().subgroupSize * 2; + const uint32_t local_x = (ggml_vk_current_device().subgroupSize * 2) / 8; s_algo = komputeManager()->algorithm(name, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned((ne01 + 7)/8), unsigned(ne11), unsigned(ne12*ne13)}, {local_x}, {pushConsts}); } else { s_algo = komputeManager()->getAlgorithm(name); @@ -1067,34 +1079,84 @@ static void ggml_vk_mul_mat_q8_0(Args&&... args) { ggml_vk_mul_mat_impl(spirv, "q8_0", 1/*We access blocks unaligned*/, std::forward(args)...); } +static void ggml_vk_mul_mat_q4_k( + kp::Sequence& seq, + const std::shared_ptr& inA, + const std::shared_ptr& inB, + const std::shared_ptr& out, + uint32_t inAOff, uint32_t inBOff, uint32_t outOff, + int32_t ne00, int32_t ne01, int32_t ne02, + int32_t ne10, int32_t ne11, int32_t ne12, int32_t ne13, + int32_t ne0, int32_t ne1, + uint32_t nb01, uint32_t nb02, uint32_t nb03, + uint32_t nb11, uint32_t nb12, uint32_t nb13, + uint32_t r2, uint32_t r3 +) { + const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_q4_k_comp_spv, + kp::shader_data::op_mul_mat_q4_k_comp_spv_len); + + struct PushConstants { + uint32_t inAOff, inBOff, outOff; + int32_t ne00, ne10, ne0, ne1, ne01, ne02, ne12; + uint32_t nb01, nb02, nb03, nb11, nb12, nb13; + uint32_t r2, r3; + } pushConsts { + inAOff, safe_divide(inBOff, 4), safe_divide(outOff, 4), + ne00, ne10, ne0, ne1, ne01, ne02, ne12, + nb01, nb02, nb03, nb11, nb12, nb13, + r2, r3 + }; + + std::shared_ptr s_algo = nullptr; + if (!komputeManager()->hasAlgorithm(__func__)) { + s_algo = komputeManager()->algorithm(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned((ne01 + 3)/4), unsigned(ne11), unsigned(ne12) * unsigned(ne13)}, {}, {pushConsts}); + } else { + s_algo = komputeManager()->getAlgorithm(__func__); + s_algo->setTensors({inA, inB, out}); + s_algo->setWorkgroup({unsigned((ne01 + 3)/4), unsigned(ne11), unsigned(ne12) * unsigned(ne13)}); + s_algo->setPushConstants({pushConsts}); + s_algo->updateDescriptors(s_kompute_context->pool.get()); + } + seq.record(s_algo); +} + static void ggml_vk_mul_mat_q6_k( kp::Sequence& seq, const std::shared_ptr& inA, const std::shared_ptr& inB, const std::shared_ptr& out, uint32_t inAOff, uint32_t inBOff, uint32_t outOff, - int32_t ne00, int32_t ne10, int32_t ne0, int32_t ne1, - int32_t ne01, int32_t ne11, int32_t ne12, int32_t ne02 + int32_t ne00, int32_t ne01, int32_t ne02, + int32_t ne10, int32_t ne11, int32_t ne12, int32_t ne13, + int32_t ne0, int32_t ne1, + uint32_t nb01, uint32_t nb02, uint32_t nb03, + uint32_t nb11, uint32_t nb12, uint32_t nb13, + uint32_t r2, uint32_t r3 ) { const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_q6_k_comp_spv, kp::shader_data::op_mul_mat_q6_k_comp_spv_len); struct PushConstants { uint32_t inAOff, inBOff, outOff; - int32_t ne00, ne10, ne0, ne1, ne01, gqa; + int32_t ne00, ne10, ne0, ne1, ne01, ne02, ne12; + uint32_t nb01, nb02, nb03, nb11, nb12, nb13; + uint32_t r2, r3; } pushConsts { inAOff, safe_divide(inBOff, 4), safe_divide(outOff, 4), - ne00, ne10, ne0, ne1, ne01, ne12/ne02 + ne00, ne10, ne0, ne1, ne01, ne02, ne12, + nb01, nb02, nb03, nb11, nb12, nb13, + r2, r3 }; std::shared_ptr s_algo = nullptr; if (!komputeManager()->hasAlgorithm(__func__)) { - const uint32_t local_x = ggml_vk_current_device().subgroupSize * 2; - s_algo = komputeManager()->algorithm(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned((ne01 + 1)/2), unsigned(ne11), unsigned(ne12)}, {local_x}, {pushConsts}); + const uint32_t local_x = 2; + const uint32_t local_y = ggml_vk_current_device().subgroupSize; + s_algo = komputeManager()->algorithm(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned((ne01 + 1)/2), unsigned(ne11), unsigned(ne12)*unsigned(ne13)}, {local_x, local_y}, {pushConsts}); } else { s_algo = komputeManager()->getAlgorithm(__func__); s_algo->setTensors({inA, inB, out}); - s_algo->setWorkgroup({unsigned((ne01 + 1)/2), unsigned(ne11), unsigned(ne12)}); + s_algo->setWorkgroup({unsigned((ne01 + 1)/2), unsigned(ne11), unsigned(ne12)*unsigned(ne13)}); s_algo->setPushConstants({pushConsts}); s_algo->updateDescriptors(s_kompute_context->pool.get()); } @@ -1182,10 +1244,11 @@ static void ggml_vk_rope( kp::Sequence& seq, const std::shared_ptr& inA, const std::shared_ptr& inB, + const std::shared_ptr& inC, const std::shared_ptr& out, - uint32_t inAOff, uint32_t inBOff, uint32_t outOff, + uint32_t inAOff, uint32_t inBOff, uint32_t inCOff, uint32_t outOff, ggml_type src0t, int32_t n_dims, int32_t mode, int32_t n_ctx_orig, - float freq_base, float freq_scale, float ext_factor, float attn_factor, float beta_fast, float beta_slow, + float freq_base, float freq_scale, bool has_freq_factors, float ext_factor, float attn_factor, float beta_fast, float beta_slow, int32_t ne01, int32_t ne02, int32_t ne03, uint32_t nb00, uint32_t nb01, uint32_t nb02, uint32_t nb03, int32_t ne0, @@ -1193,11 +1256,17 @@ static void ggml_vk_rope( ) { GGML_ASSERT(src0t == GGML_TYPE_F16 || src0t == GGML_TYPE_F32); - static const auto spirv_f16 = getSpirvShader( - kp::shader_data::op_rope_f16_comp_spv, kp::shader_data::op_rope_f16_comp_spv_len + static const auto spirv_norm_f16 = getSpirvShader( + kp::shader_data::op_rope_norm_f16_comp_spv, kp::shader_data::op_rope_norm_f16_comp_spv_len ); - static const auto spirv_f32 = getSpirvShader( - kp::shader_data::op_rope_f32_comp_spv, kp::shader_data::op_rope_f32_comp_spv_len + static const auto spirv_norm_f32 = getSpirvShader( + kp::shader_data::op_rope_norm_f32_comp_spv, kp::shader_data::op_rope_norm_f32_comp_spv_len + ); + static const auto spirv_neox_f16 = getSpirvShader( + kp::shader_data::op_rope_neox_f16_comp_spv, kp::shader_data::op_rope_neox_f16_comp_spv_len + ); + static const auto spirv_neox_f32 = getSpirvShader( + kp::shader_data::op_rope_neox_f32_comp_spv, kp::shader_data::op_rope_neox_f32_comp_spv_len ); int type_size = src0t == GGML_TYPE_F16 ? 2 : 4; @@ -1212,32 +1281,40 @@ static void ggml_vk_rope( GGML_ASSERT(nb0 % type_size == 0); struct PushConstants { - uint32_t inAOff, inBOff, outOff; + uint32_t inAOff, inBOff, inCOff, outOff; int32_t n_dims, mode, n_ctx_orig; - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + float freq_base, freq_scale; + bool has_freq_factors; + float ext_factor, attn_factor, beta_fast, beta_slow; uint32_t nb00, nb01, nb02, nb03; int32_t ne0; uint32_t nb0, nb1, nb2, nb3; } pushConsts { - safe_divide(inAOff, type_size), safe_divide(inBOff, 4), safe_divide(outOff, type_size), + safe_divide(inAOff, type_size), safe_divide(inBOff, 4), safe_divide(inCOff, type_size), safe_divide(outOff, type_size), n_dims, mode, n_ctx_orig, - freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, + freq_base, freq_scale, + has_freq_factors, + ext_factor, attn_factor, beta_fast, beta_slow, nb00, nb01, nb02, nb03, ne0, nb0, nb1, nb2, nb3 }; - auto name = std::string(__func__) + (src0t == GGML_TYPE_F16 ? "_f16" : "_f32"); + auto & inC_ = inC ? inC : inA; + const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; + const bool is_f16 = src0t == GGML_TYPE_F16; + + auto name = std::string(__func__) + (is_neox ? "_neox" : "_norm") + (src0t == GGML_TYPE_F16 ? "_f16" : "_f32"); std::shared_ptr s_algo = nullptr; if (!komputeManager()->hasAlgorithm(name)) { + auto & spirv = is_neox ? is_f16 ? spirv_neox_f16 : spirv_neox_f32 : is_f16 ? spirv_norm_f16 : spirv_norm_f32; s_algo = komputeManager()->algorithm( - name, s_kompute_context->pool.get(), {inA, inB, out}, - src0t == GGML_TYPE_F16 ? spirv_f16 : spirv_f32, + name, s_kompute_context->pool.get(), {inA, inB, inC_, out}, spirv, {unsigned(ne01), unsigned(ne02), unsigned(ne03)}, {}, {pushConsts} ); } else { s_algo = komputeManager()->getAlgorithm(name); - s_algo->setTensors({inA, inB, out}); + s_algo->setTensors({inA, inB, inC_, out}); s_algo->setWorkgroup({unsigned(ne01), unsigned(ne02), unsigned(ne03)}); s_algo->setPushConstants({pushConsts}); s_algo->updateDescriptors(s_kompute_context->pool.get()); @@ -1316,11 +1393,15 @@ static void ggml_vk_cpy_f16_f32(Args&&... args) { } static bool ggml_backend_kompute_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { + int64_t n = ggml_nelements(op); switch (op->op) { case GGML_OP_UNARY: + if (n % 4 != 0) return false; switch (ggml_get_unary_op(op)) { - case GGML_UNARY_OP_RELU: case GGML_UNARY_OP_GELU: + if (n % 8 != 0) return false; + // fall through + case GGML_UNARY_OP_RELU: case GGML_UNARY_OP_SILU: return ggml_is_contiguous(op->src[0]); default: @@ -1378,12 +1459,13 @@ static bool ggml_backend_kompute_device_supports_op(ggml_backend_dev_t dev, cons switch (op->src[0]->type) { case GGML_TYPE_F32: - case GGML_TYPE_Q6_K: return op->ne[3] == 1; + case GGML_TYPE_Q6_K: case GGML_TYPE_F16: case GGML_TYPE_Q8_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: + case GGML_TYPE_Q4_K: return true; default: ; @@ -1479,9 +1561,11 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml const static std::shared_ptr nullTensor = nullptr; uint32_t off_src0 = 0; uint32_t off_src1 = 0; + uint32_t off_src2 = 0; uint32_t off_dst = 0; const std::shared_ptr& id_src0 = src0 ? ggml_vk_get_tensor(src0, &off_src0) : nullTensor; const std::shared_ptr& id_src1 = src1 ? ggml_vk_get_tensor(src1, &off_src1) : nullTensor; + const std::shared_ptr& id_src2 = src2 ? ggml_vk_get_tensor(src2, &off_src2) : nullTensor; const std::shared_ptr& id_dst = dst ? ggml_vk_get_tensor(dst, &off_dst) : nullTensor; switch (dst->op) { @@ -1557,11 +1641,16 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml #pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5021") GGML_ASSERT(!src1 || src1t == GGML_TYPE_F32); -#pragma message("TODO: add ALiBi support") -#pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/7192") - GGML_ASSERT(max_bias == 0.0f); + const int64_t nrows_x = ggml_nrows(src0); + const int64_t nrows_y = src0->ne[1]; - ggml_vk_soft_max(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, ne01, ne02, ne03, scale); + const uint32_t n_head = nrows_x/nrows_y; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + ggml_vk_soft_max(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, ne01, ne02, ne03, scale, max_bias, m0, m1, n_head_log2); } break; case GGML_OP_DIAG_MASK_INF: { @@ -1613,32 +1702,44 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml case GGML_TYPE_F16: ggml_vk_mul_mat_f16( seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, - ne00, ne01, ne02, nb00, nb01, nb02, ne10, ne11, ne12, ne13, nb10, nb11, nb12, + ne00, ne01, ne02, nb00, nb01, nb02, nb03, + ne10, ne11, ne12, ne13, nb10, nb11, nb12, nb13, ne0, ne1, r2, r3 ); break; case GGML_TYPE_Q8_0: ggml_vk_mul_mat_q8_0( seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, - ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, r2, r3 + ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, + nb01, nb02, nb03, nb11, nb12, nb13, r2, r3 ); break; case GGML_TYPE_Q4_0: ggml_vk_mul_mat_q4_0( seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, - ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, r2, r3 + ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, + nb01, nb02, nb03, nb11, nb12, nb13, r2, r3 ); break; case GGML_TYPE_Q4_1: ggml_vk_mul_mat_q4_1( seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, - ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, r2, r3 + ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, + nb01, nb02, nb03, nb11, nb12, nb13, r2, r3 + ); + break; + case GGML_TYPE_Q4_K: + ggml_vk_mul_mat_q4_k( + seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, + ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, + nb01, nb02, nb03, nb11, nb12, nb13, r2, r3 ); break; case GGML_TYPE_Q6_K: ggml_vk_mul_mat_q6_k( seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, - ne00, ne10, ne0, ne1, ne01, ne11, ne12, ne02 + ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, + nb01, nb02, nb03, nb11, nb12, nb13, r2, r3 ); break; default: { @@ -1667,13 +1768,6 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml } break; case GGML_OP_ROPE: { -#pragma message("TODO: implement phi3 frequency factors support") -#pragma message(" https://github.com/ggerganov/llama.cpp/pull/7225") - GGML_ASSERT(dst->src[2] == nullptr && "phi3 frequency factors not implemented yet"); - -#pragma message("TODO: update rope NORM mode to match NEOX mode") -#pragma message(" https://github.com/ggerganov/llama.cpp/pull/7634") - GGML_ASSERT(ne10 == ne02); GGML_ASSERT(src0t == dstt); // const int n_past = ((int32_t *) dst->op_params)[0]; @@ -1682,6 +1776,8 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml // skip 3, n_ctx used in GLM RoPE, unimplemented in Vulkan const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; + const bool has_freq_factors = dst->src[2] != nullptr; + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); @@ -1690,8 +1786,8 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); ggml_vk_rope( - seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, src0t, n_dims, mode, n_ctx_orig, - freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, + seq, id_src0, id_src1, id_src2, id_dst, off_src0, off_src1, off_src2, off_dst, src0t, n_dims, mode, n_ctx_orig, + freq_base, freq_scale, has_freq_factors, ext_factor, attn_factor, beta_fast, beta_slow, ne01, ne02, ne03, nb00, nb01, nb02, nb03, ne0, nb0, nb1, nb2, nb3 ); } break; @@ -2134,9 +2230,12 @@ static const struct ggml_backend_reg_i ggml_backend_kompute_reg_i = { ggml_backend_reg_t ggml_backend_kompute_reg() { static ggml_backend_reg reg = { - /* .iface = */ ggml_backend_kompute_reg_i, - /* .context = */ nullptr, + /* .api_version = */ GGML_BACKEND_API_VERSION, + /* .iface = */ ggml_backend_kompute_reg_i, + /* .context = */ nullptr, }; return ® } + +GGML_BACKEND_DL_IMPL(ggml_backend_kompute_reg) diff --git a/ggml/src/kompute b/ggml/src/ggml-kompute/kompute similarity index 100% rename from ggml/src/kompute rename to ggml/src/ggml-kompute/kompute diff --git a/ggml/src/kompute-shaders/common.comp b/ggml/src/ggml-kompute/kompute-shaders/common.comp similarity index 93% rename from ggml/src/kompute-shaders/common.comp rename to ggml/src/ggml-kompute/kompute-shaders/common.comp index 62d62b025..dbe4cf804 100644 --- a/ggml/src/kompute-shaders/common.comp +++ b/ggml/src/ggml-kompute/kompute-shaders/common.comp @@ -3,6 +3,7 @@ #extension GL_EXT_shader_explicit_arithmetic_types_float16: require #extension GL_EXT_shader_explicit_arithmetic_types_int8: require #extension GL_EXT_shader_explicit_arithmetic_types_int16: require +#extension GL_EXT_shader_explicit_arithmetic_types_int64: require #extension GL_EXT_control_flow_attributes: enable #extension GL_KHR_shader_subgroup_arithmetic : require #extension GL_EXT_debug_printf : enable @@ -15,6 +16,7 @@ #define TWOPI_F 6.283185307179586f #define QK_K 256 +#define K_SCALE_SIZE 12 #define u8BufToU16(buf, idx) (((uint16_t(buf[idx + 1]) << 8)) | buf[idx]) #define u8BufToFloat16(buf, idx) uint16BitsToHalf u8BufToU16(buf, idx) @@ -64,6 +66,14 @@ mat4 dequantize_q4_1(const block_q4_1 xb, uint il) { return reg; } +#define sizeof_block_q4_k 144 +struct block_q4_k { + float16_t d; + float16_t dmin; + uint8_t scales[K_SCALE_SIZE]; + uint8_t qs[QK_K/2]; +}; + #define sizeof_block_q6_k 210 struct block_q6_k { uint8_t ql[QK_K/2]; // quants, lower 4 bits diff --git a/ggml/src/kompute-shaders/op_add.comp b/ggml/src/ggml-kompute/kompute-shaders/op_add.comp similarity index 100% rename from ggml/src/kompute-shaders/op_add.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_add.comp diff --git a/ggml/src/kompute-shaders/op_addrow.comp b/ggml/src/ggml-kompute/kompute-shaders/op_addrow.comp similarity index 100% rename from ggml/src/kompute-shaders/op_addrow.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_addrow.comp diff --git a/ggml/src/kompute-shaders/op_cpy_f16_f16.comp b/ggml/src/ggml-kompute/kompute-shaders/op_cpy_f16_f16.comp similarity index 100% rename from ggml/src/kompute-shaders/op_cpy_f16_f16.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_cpy_f16_f16.comp diff --git a/ggml/src/kompute-shaders/op_cpy_f16_f32.comp b/ggml/src/ggml-kompute/kompute-shaders/op_cpy_f16_f32.comp similarity index 100% rename from ggml/src/kompute-shaders/op_cpy_f16_f32.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_cpy_f16_f32.comp diff --git a/ggml/src/kompute-shaders/op_cpy_f32_f16.comp b/ggml/src/ggml-kompute/kompute-shaders/op_cpy_f32_f16.comp similarity index 100% rename from ggml/src/kompute-shaders/op_cpy_f32_f16.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_cpy_f32_f16.comp diff --git a/ggml/src/kompute-shaders/op_cpy_f32_f32.comp b/ggml/src/ggml-kompute/kompute-shaders/op_cpy_f32_f32.comp similarity index 100% rename from ggml/src/kompute-shaders/op_cpy_f32_f32.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_cpy_f32_f32.comp diff --git a/ggml/src/kompute-shaders/op_diagmask.comp b/ggml/src/ggml-kompute/kompute-shaders/op_diagmask.comp similarity index 100% rename from ggml/src/kompute-shaders/op_diagmask.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_diagmask.comp diff --git a/ggml/src/kompute-shaders/op_gelu.comp b/ggml/src/ggml-kompute/kompute-shaders/op_gelu.comp similarity index 100% rename from ggml/src/kompute-shaders/op_gelu.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_gelu.comp diff --git a/ggml/src/kompute-shaders/op_getrows.comp b/ggml/src/ggml-kompute/kompute-shaders/op_getrows.comp similarity index 100% rename from ggml/src/kompute-shaders/op_getrows.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_getrows.comp diff --git a/ggml/src/kompute-shaders/op_getrows_f16.comp b/ggml/src/ggml-kompute/kompute-shaders/op_getrows_f16.comp similarity index 100% rename from ggml/src/kompute-shaders/op_getrows_f16.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_getrows_f16.comp diff --git a/ggml/src/kompute-shaders/op_getrows_f32.comp b/ggml/src/ggml-kompute/kompute-shaders/op_getrows_f32.comp similarity index 100% rename from ggml/src/kompute-shaders/op_getrows_f32.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_getrows_f32.comp diff --git a/ggml/src/kompute-shaders/op_getrows_q4_0.comp b/ggml/src/ggml-kompute/kompute-shaders/op_getrows_q4_0.comp similarity index 100% rename from ggml/src/kompute-shaders/op_getrows_q4_0.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_getrows_q4_0.comp diff --git a/ggml/src/kompute-shaders/op_getrows_q4_1.comp b/ggml/src/ggml-kompute/kompute-shaders/op_getrows_q4_1.comp similarity index 100% rename from ggml/src/kompute-shaders/op_getrows_q4_1.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_getrows_q4_1.comp diff --git a/ggml/src/kompute-shaders/op_getrows_q6_k.comp b/ggml/src/ggml-kompute/kompute-shaders/op_getrows_q6_k.comp similarity index 100% rename from ggml/src/kompute-shaders/op_getrows_q6_k.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_getrows_q6_k.comp diff --git a/ggml/src/kompute-shaders/op_mul.comp b/ggml/src/ggml-kompute/kompute-shaders/op_mul.comp similarity index 100% rename from ggml/src/kompute-shaders/op_mul.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_mul.comp diff --git a/ggml/src/kompute-shaders/op_mul_mat_f16.comp b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_f16.comp similarity index 91% rename from ggml/src/kompute-shaders/op_mul_mat_f16.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_f16.comp index 8f0a9031f..0ab1b2fc2 100644 --- a/ggml/src/kompute-shaders/op_mul_mat_f16.comp +++ b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_f16.comp @@ -20,12 +20,14 @@ layout (push_constant) uniform parameter { uint nb00; uint nb01; uint nb02; + uint nb03; int ne10; int ne11; int ne12; uint nb10; uint nb11; uint nb12; + uint nb13; int ne0; int ne1; uint r2; @@ -42,7 +44,7 @@ void main() { const uint i12 = im%pcs.ne12; const uint i13 = im/pcs.ne12; - const uint offset0 = r0*pcs.nb01 + (i12/pcs.r2)*pcs.nb02 + (i13/pcs.r3)*pcs.nb02*pcs.ne02; + const uint offset0 = r0*pcs.nb01 + (i12/pcs.r2)*pcs.nb02 + (i13/pcs.r3)*pcs.nb03; const uint x = offset0 / 2 + pcs.inAOff; // Based from inA @@ -52,7 +54,7 @@ void main() { break; } - const uint y = (r1*pcs.nb11 + im*pcs.nb12) / 4 + pcs.inBOff; // Based from inB + const uint y = (r1*pcs.nb11 + i12*pcs.nb12 + i13*pcs.nb13) / 4 + pcs.inBOff; float sumf = 0; for (uint i = gl_SubgroupInvocationID.x; i < pcs.ne00; i += gl_SubgroupSize) { diff --git a/ggml/src/kompute-shaders/op_mul_mat_mat_f32.comp b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_mat_f32.comp similarity index 100% rename from ggml/src/kompute-shaders/op_mul_mat_mat_f32.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_mat_f32.comp diff --git a/ggml/src/kompute-shaders/op_mul_mat_q4_0.comp b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q4_0.comp similarity index 100% rename from ggml/src/kompute-shaders/op_mul_mat_q4_0.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q4_0.comp diff --git a/ggml/src/kompute-shaders/op_mul_mat_q4_1.comp b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q4_1.comp similarity index 100% rename from ggml/src/kompute-shaders/op_mul_mat_q4_1.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q4_1.comp diff --git a/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q4_k.comp b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q4_k.comp new file mode 100644 index 000000000..a5752a3a0 --- /dev/null +++ b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q4_k.comp @@ -0,0 +1,140 @@ +#version 450 + +#include "common.comp" + +#define N_DST 4 +#define SIZE_OF_BLOCK sizeof_block_q4_k + +layout(local_size_x = 4) in; +layout(local_size_y = 8) in; +layout(local_size_z = 1) in; + +layout (binding = 0) readonly buffer tensorInA { block_q4_k inA[]; }; +layout (binding = 1) readonly buffer tensorInB { float inB[]; }; +layout (binding = 2) writeonly buffer tensorOut { float out_[]; }; + +layout (push_constant) uniform parameter { + uint inAOff; + uint inBOff; + uint outOff; + int ne00; + int ne10; + int ne0; + int ne1; + int ne01; + int ne02; + int ne12; + uint nb01; + uint nb02; + uint nb03; + uint nb11; + uint nb12; + uint nb13; + uint r2; + uint r3; +} pcs; + +void main() { + const uint16_t kmask1 = uint16_t(0x3f3f); + const uint16_t kmask2 = uint16_t(0x0f0f); + const uint16_t kmask3 = uint16_t(0xc0c0); + + const uint ix = gl_SubgroupInvocationID/8; // 0...3 + const uint it = gl_SubgroupInvocationID%8; // 0...7 + const uint iq = it/4; // 0 or 1 + const uint ir = it%4; // 0...3 + + const uint nb = pcs.ne00/QK_K; + + const uint r0 = gl_WorkGroupID.x; + const uint r1 = gl_WorkGroupID.y; + const uint im = gl_WorkGroupID.z; + + const uint first_row = r0 * N_DST; + const uint ib_row = first_row * nb; + + const uint i12 = im%pcs.ne12; + const uint i13 = im/pcs.ne12; + + const uint offset0 = first_row*(pcs.nb01/SIZE_OF_BLOCK) + (i12/pcs.r2)*(pcs.nb02/SIZE_OF_BLOCK) + (i13/pcs.r3)*(pcs.nb03/SIZE_OF_BLOCK); + const uint offset1 = r1*pcs.nb11 + (i12 )*pcs.nb12 + (i13 )*pcs.nb13; + + const uint xblk = offset0 + pcs.inAOff; + const uint y = (offset1 / 4) + pcs.inBOff; + + float yl[16]; + float yh[16]; + float sumf[N_DST] = {0.f, 0.f, 0.f, 0.f}; + float all_sum = 0.f; + + uint y4 = y + ix * QK_K + 64 * iq + 8 * ir; + + for (uint ib = ix; ib < nb; ib += 4) { + const uint blk_idx = ib + xblk; + + float sumy[4] = {0.f, 0.f, 0.f, 0.f}; + for (int i = 0; i < 8; ++i) { + yl[i+0] = inB[y4+i+ 0]; sumy[0] += yl[i+0]; + yl[i+8] = inB[y4+i+ 32]; sumy[1] += yl[i+8]; + yh[i+0] = inB[y4+i+128]; sumy[2] += yh[i+0]; + yh[i+8] = inB[y4+i+160]; sumy[3] += yh[i+8]; + } + + for (int row = 0; row < N_DST; row++) { + uint row_idx = row * (pcs.nb01 / SIZE_OF_BLOCK); + + uint16_t sc_0 = u8BufToU16(inA[blk_idx + row_idx].scales, iq * 2 + 0); + uint16_t sc_1 = u8BufToU16(inA[blk_idx + row_idx].scales, iq * 2 + 2); + uint16_t sc_2 = u8BufToU16(inA[blk_idx + row_idx].scales, iq * 2 + 4); + uint16_t sc_3 = u8BufToU16(inA[blk_idx + row_idx].scales, iq * 2 + 6); + uint16_t sc_4 = u8BufToU16(inA[blk_idx + row_idx].scales, iq * 2 + 8); + + uint16_t sc16[4]; + sc16[0] = sc_0 & kmask1; + sc16[1] = sc_2 & kmask1; + sc16[2] = ((sc_4 >> 0) & kmask2) | ((sc_0 & kmask3) >> 2); + sc16[3] = ((sc_4 >> 4) & kmask2) | ((sc_2 & kmask3) >> 2); + + float acc1[4] = {0.f, 0.f, 0.f, 0.f}; + float acc2[4] = {0.f, 0.f, 0.f, 0.f}; + for (int i = 0; i < 8; i += 2) { + uint16_t q1 = u8BufToU16(inA[blk_idx + row_idx].qs, 32 * iq + 8 * ir + i); + uint16_t q2 = u8BufToU16(inA[blk_idx + row_idx].qs, 64 + 32 * iq + 8 * ir + i); + acc1[0] += yl[i+0] * (q1 & 0x000F); + acc1[1] += yl[i+1] * (q1 & 0x0F00); + acc1[2] += yl[i+8] * (q1 & 0x00F0); + acc1[3] += yl[i+9] * (q1 & 0xF000); + acc2[0] += yh[i+0] * (q2 & 0x000F); + acc2[1] += yh[i+1] * (q2 & 0x0F00); + acc2[2] += yh[i+8] * (q2 & 0x00F0); + acc2[3] += yh[i+9] * (q2 & 0xF000); + } + + uint8_t sc8_0 = uint8_t(sc16[0] & 0xFF); + uint8_t sc8_1 = uint8_t(sc16[0] >> 8 ); + uint8_t sc8_2 = uint8_t(sc16[1] & 0xFF); + uint8_t sc8_3 = uint8_t(sc16[1] >> 8 ); + uint8_t sc8_4 = uint8_t(sc16[2] & 0xFF); + uint8_t sc8_5 = uint8_t(sc16[2] >> 8 ); + uint8_t sc8_6 = uint8_t(sc16[3] & 0xFF); + uint8_t sc8_7 = uint8_t(sc16[3] >> 8 ); + + float dall = float(inA[blk_idx + row_idx].d); + float dmin = float(inA[blk_idx + row_idx].dmin); + sumf[row] += dall * ((acc1[0] + 1.f/256.f * acc1[1]) * sc8_0 + + (acc1[2] + 1.f/256.f * acc1[3]) * sc8_1 * 1.f/16.f + + (acc2[0] + 1.f/256.f * acc2[1]) * sc8_4 + + (acc2[2] + 1.f/256.f * acc2[3]) * sc8_5 * 1.f/16.f) - + dmin * (sumy[0] * sc8_2 + sumy[1] * sc8_3 + sumy[2] * sc8_6 + sumy[3] * sc8_7); + } + + y4 += 4 * QK_K; + } + + for (int row = 0; row < N_DST; ++row) { + all_sum = subgroupAdd(sumf[row]); + if (subgroupElect()) { + out_[r1*pcs.ne0 + im*pcs.ne0*pcs.ne1 + first_row + row + pcs.outOff] = all_sum; + } + } +} diff --git a/ggml/src/kompute-shaders/op_mul_mat_q6_k.comp b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q6_k.comp similarity index 86% rename from ggml/src/kompute-shaders/op_mul_mat_q6_k.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q6_k.comp index c9baebdf4..d331d1a70 100644 --- a/ggml/src/kompute-shaders/op_mul_mat_q6_k.comp +++ b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q6_k.comp @@ -21,7 +21,16 @@ layout (push_constant) uniform parameter { int ne0; int ne1; int ne01; - int gqa; + int ne02; + int ne12; + uint nb01; + uint nb02; + uint nb03; + uint nb11; + uint nb12; + uint nb13; + uint r2; + uint r3; } pcs; void main() { @@ -34,12 +43,15 @@ void main() { const uint r0 = gl_WorkGroupID.x; const uint r1 = gl_WorkGroupID.y; - const uint r2 = gl_WorkGroupID.z; + const uint im = gl_WorkGroupID.z; const uint row = (r0 * gl_NumSubgroups + gl_SubgroupID); - const uint offset0 = r2/pcs.gqa*(nb*pcs.ne0); - const uint x = row * nb + offset0; // Based from inA without base offset - const uint yy = r1*pcs.ne10 + r2*pcs.ne00*pcs.ne1+pcs.inBOff; // Based from inB + + const uint i12 = im%pcs.ne12; + const uint i13 = im/pcs.ne12; + + const uint x = row*(pcs.nb01/SIZE_OF_BLOCK) + (i12/pcs.r2)*(pcs.nb02/SIZE_OF_BLOCK) + (i13/pcs.r3)*(pcs.nb03/SIZE_OF_BLOCK); + const uint yy = (r1*pcs.nb11 + i12*pcs.nb12 + i13*pcs.nb13) / 4 + pcs.inBOff; float sumf = 0; @@ -89,6 +101,6 @@ void main() { const float tot = subgroupAdd(sumf); if (subgroupElect()) { - out_[r1*pcs.ne0 + r2*pcs.ne0*pcs.ne1 + row + pcs.outOff] = tot; + out_[r1*pcs.ne0 + im*pcs.ne0*pcs.ne1 + row + pcs.outOff] = tot; } } diff --git a/ggml/src/kompute-shaders/op_mul_mat_q8_0.comp b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q8_0.comp similarity index 100% rename from ggml/src/kompute-shaders/op_mul_mat_q8_0.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q8_0.comp diff --git a/ggml/src/kompute-shaders/op_mul_mv_q_n.comp b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mv_q_n.comp similarity index 76% rename from ggml/src/kompute-shaders/op_mul_mv_q_n.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_mul_mv_q_n.comp index 440b5ab2c..a6517cc1f 100644 --- a/ggml/src/kompute-shaders/op_mul_mv_q_n.comp +++ b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mv_q_n.comp @@ -14,10 +14,15 @@ void main() { const uint i12 = im%pcs.ne12; const uint i13 = im/pcs.ne12; - const uint offset0 = first_row * nb + (i12/pcs.r2)*(nb*pcs.ne01) + (i13/pcs.r3)*(nb*pcs.ne01*pcs.ne02); + // pointers to src0 rows + uint ax[N_ROWS]; + for (int row = 0; row < N_ROWS; ++row) { + const uint offset0 = (first_row + row)*(pcs.nb01/SIZE_OF_BLOCK) + (i12/pcs.r2)*(pcs.nb02/SIZE_OF_BLOCK) + (i13/pcs.r3)*(pcs.nb03/SIZE_OF_BLOCK); - const uint x = offset0; // Based from inA without base offset - const uint y = r1*uint(pcs.ne10)+im*pcs.ne00*pcs.ne1+pcs.inBOff; // Based from inB + ax[row] = offset0 + pcs.inAOff; + } + + const uint y = (r1*pcs.nb11 + i12*pcs.nb12 + i13*pcs.nb13) / 4 + pcs.inBOff; float sumf[N_ROWS] = {0.0f, 0.0f, 0.0f, 0.0f}; @@ -32,8 +37,7 @@ void main() { for (uint ib = ix; ib < nb; ib += 16) { for (int row = 0; row < N_ROWS; row++) { - const uint block_index = x + ib + row * nb; - sumf[row] += block_q_n_dot_y(block_index, yb, il); + sumf[row] += block_q_n_dot_y(ax[row] + ib, yb, il); } yb += BLOCKS_IN_QUANT * 16; diff --git a/ggml/src/kompute-shaders/op_mul_mv_q_n_pre.comp b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mv_q_n_pre.comp similarity index 80% rename from ggml/src/kompute-shaders/op_mul_mv_q_n_pre.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_mul_mv_q_n_pre.comp index 7912b09ac..a9a2f2218 100644 --- a/ggml/src/kompute-shaders/op_mul_mv_q_n_pre.comp +++ b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mv_q_n_pre.comp @@ -1,5 +1,5 @@ layout(local_size_x_id = 0) in; -layout(local_size_y = 1) in; +layout(local_size_y = 8) in; layout(local_size_z = 1) in; layout (binding = 0) readonly buffer tensorInA { uint8_t inA[]; }; @@ -17,6 +17,12 @@ layout (push_constant) uniform parameter { int ne12; int ne0; int ne1; + uint nb01; + uint nb02; + uint nb03; + uint nb11; + uint nb12; + uint nb13; uint r2; uint r3; } pcs; diff --git a/ggml/src/kompute-shaders/op_norm.comp b/ggml/src/ggml-kompute/kompute-shaders/op_norm.comp similarity index 100% rename from ggml/src/kompute-shaders/op_norm.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_norm.comp diff --git a/ggml/src/kompute-shaders/op_relu.comp b/ggml/src/ggml-kompute/kompute-shaders/op_relu.comp similarity index 100% rename from ggml/src/kompute-shaders/op_relu.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_relu.comp diff --git a/ggml/src/kompute-shaders/op_rmsnorm.comp b/ggml/src/ggml-kompute/kompute-shaders/op_rmsnorm.comp similarity index 100% rename from ggml/src/kompute-shaders/op_rmsnorm.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_rmsnorm.comp diff --git a/ggml/src/ggml-kompute/kompute-shaders/op_rope_neox_f16.comp b/ggml/src/ggml-kompute/kompute-shaders/op_rope_neox_f16.comp new file mode 100644 index 000000000..63659cbfe --- /dev/null +++ b/ggml/src/ggml-kompute/kompute-shaders/op_rope_neox_f16.comp @@ -0,0 +1,52 @@ +#version 450 + +#include "rope_common.comp" + +layout(binding = 0) buffer restrict readonly tensorInA { float16_t inA[]; }; +layout(binding = 1) buffer restrict readonly tensorInB { int inB[]; }; +layout(binding = 2) buffer restrict readonly tensorInC { float inC[]; }; +layout(binding = 3) buffer restrict writeonly tensorOut { float16_t out_[]; }; + +void main() { + const uint i3 = gl_WorkGroupID.z; + const uint i2 = gl_WorkGroupID.y; + const uint i1 = gl_WorkGroupID.x; + + float corr_dims[2]; + rope_yarn_corr_dims(pcs.n_dims, pcs.n_ctx_orig, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims); + + const float theta_scale = pow(pcs.freq_base, -2.0/pcs.n_dims); + + float theta_base = float(inB[pcs.inBOff + i2]); + float inv_ndims = -1.f/pcs.n_dims; + + float cos_theta; + float sin_theta; + + for (uint i0 = 2*gl_LocalInvocationIndex; i0 < pcs.ne0; i0 += 2*gl_WorkGroupSize.x) { + if (i0 < pcs.n_dims) { + uint ic = i0/2; + + float theta = theta_base * pow(pcs.freq_base, inv_ndims*i0); + + const float freq_factor = pcs.has_freq_factors ? inC[pcs.inCOff + ic] : 1.0f; + + rope_yarn(theta/freq_factor, pcs.freq_scale, corr_dims, i0, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta); + + const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + ic*pcs.nb00) / 2) + pcs.inAOff; // Based from in + const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + ic*pcs.nb0) / 2) + pcs.outOff; // Based from out_ + + const float x0 = float(inA[src]); + const float x1 = float(inA[src+pcs.n_dims/2]); + + out_[dst_data] = float16_t(x0*cos_theta - x1*sin_theta); + out_[dst_data+pcs.n_dims/2] = float16_t(x0*sin_theta + x1*cos_theta); + } else { + const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 2) + pcs.inAOff; // Based from in + const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 2) + pcs.outOff; // Based from out_ + + out_[dst_data] = inA[src]; + out_[dst_data+1] = inA[src+1]; + } + } +} diff --git a/ggml/src/ggml-kompute/kompute-shaders/op_rope_neox_f32.comp b/ggml/src/ggml-kompute/kompute-shaders/op_rope_neox_f32.comp new file mode 100644 index 000000000..4df56204d --- /dev/null +++ b/ggml/src/ggml-kompute/kompute-shaders/op_rope_neox_f32.comp @@ -0,0 +1,52 @@ +#version 450 + +#include "rope_common.comp" + +layout(binding = 0) buffer restrict readonly tensorInA { float inA[]; }; +layout(binding = 1) buffer restrict readonly tensorInB { int inB[]; }; +layout(binding = 2) buffer restrict readonly tensorInC { float inC[]; }; +layout(binding = 3) buffer restrict writeonly tensorOut { float out_[]; }; + +void main() { + const uint i3 = gl_WorkGroupID.z; + const uint i2 = gl_WorkGroupID.y; + const uint i1 = gl_WorkGroupID.x; + + float corr_dims[2]; + rope_yarn_corr_dims(pcs.n_dims, pcs.n_ctx_orig, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims); + + const float theta_scale = pow(pcs.freq_base, -2.0/pcs.n_dims); + + float theta_base = float(inB[pcs.inBOff + i2]); + float inv_ndims = -1.f/pcs.n_dims; + + float cos_theta; + float sin_theta; + + for (uint i0 = 2*gl_LocalInvocationIndex; i0 < pcs.ne0; i0 += 2*gl_WorkGroupSize.x) { + if (i0 < pcs.n_dims) { + uint ic = i0/2; + + float theta = theta_base * pow(pcs.freq_base, inv_ndims*i0); + + const float freq_factor = pcs.has_freq_factors ? inC[pcs.inCOff + ic] : 1.0f; + + rope_yarn(theta/freq_factor, pcs.freq_scale, corr_dims, i0, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta); + + const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + ic*pcs.nb00) / 4) + pcs.inAOff; // Based from in + const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + ic*pcs.nb0) / 4) + pcs.outOff; // Based from out_ + + const float x0 = inA[src]; + const float x1 = inA[src+pcs.n_dims/2]; + + out_[dst_data] = x0*cos_theta - x1*sin_theta; + out_[dst_data+pcs.n_dims/2] = x0*sin_theta + x1*cos_theta; + } else { + const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 4) + pcs.inAOff; // Based from in + const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 4) + pcs.outOff; // Based from out_ + + out_[dst_data] = inA[src]; + out_[dst_data+1] = inA[src+1]; + } + } +} diff --git a/ggml/src/ggml-kompute/kompute-shaders/op_rope_norm_f16.comp b/ggml/src/ggml-kompute/kompute-shaders/op_rope_norm_f16.comp new file mode 100644 index 000000000..a3c0eda8b --- /dev/null +++ b/ggml/src/ggml-kompute/kompute-shaders/op_rope_norm_f16.comp @@ -0,0 +1,52 @@ +#version 450 + +#include "rope_common.comp" + +layout(binding = 0) buffer restrict readonly tensorInA { float16_t inA[]; }; +layout(binding = 1) buffer restrict readonly tensorInB { int inB[]; }; +layout(binding = 2) buffer restrict readonly tensorInC { float inC[]; }; +layout(binding = 3) buffer restrict writeonly tensorOut { float16_t out_[]; }; + +void main() { + const uint i3 = gl_WorkGroupID.z; + const uint i2 = gl_WorkGroupID.y; + const uint i1 = gl_WorkGroupID.x; + + float corr_dims[2]; + rope_yarn_corr_dims(pcs.n_dims, pcs.n_ctx_orig, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims); + + const float theta_scale = pow(pcs.freq_base, -2.0/pcs.n_dims); + + float theta_base = float(inB[pcs.inBOff + i2]); + float inv_ndims = -1.f/pcs.n_dims; + + float cos_theta; + float sin_theta; + + for (uint i0 = 2*gl_LocalInvocationIndex; i0 < pcs.ne0; i0 += 2*gl_WorkGroupSize.x) { + if (i0 < pcs.n_dims) { + uint ic = i0/2; + + float theta = theta_base * pow(pcs.freq_base, inv_ndims*i0); + + const float freq_factor = pcs.has_freq_factors ? inC[pcs.inCOff + ic] : 1.0f; + + rope_yarn(theta/freq_factor, pcs.freq_scale, corr_dims, i0, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta); + + const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 2) + pcs.inAOff; // Based from in + const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 2) + pcs.outOff; // Based from out_ + + const float x0 = float(inA[src]); + const float x1 = float(inA[src+1]); + + out_[dst_data] = float16_t(x0*cos_theta - x1*sin_theta); + out_[dst_data+1] = float16_t(x0*sin_theta + x1*cos_theta); + } else { + const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 2) + pcs.inAOff; // Based from in + const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 2) + pcs.outOff; // Based from out_ + + out_[dst_data] = inA[src]; + out_[dst_data+1] = inA[src+1]; + } + } +} diff --git a/ggml/src/ggml-kompute/kompute-shaders/op_rope_norm_f32.comp b/ggml/src/ggml-kompute/kompute-shaders/op_rope_norm_f32.comp new file mode 100644 index 000000000..b7963ae72 --- /dev/null +++ b/ggml/src/ggml-kompute/kompute-shaders/op_rope_norm_f32.comp @@ -0,0 +1,52 @@ +#version 450 + +#include "rope_common.comp" + +layout(binding = 0) buffer restrict readonly tensorInA { float inA[]; }; +layout(binding = 1) buffer restrict readonly tensorInB { int inB[]; }; +layout(binding = 2) buffer restrict readonly tensorInC { float inC[]; }; +layout(binding = 3) buffer restrict writeonly tensorOut { float out_[]; }; + +void main() { + const uint i3 = gl_WorkGroupID.z; + const uint i2 = gl_WorkGroupID.y; + const uint i1 = gl_WorkGroupID.x; + + float corr_dims[2]; + rope_yarn_corr_dims(pcs.n_dims, pcs.n_ctx_orig, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims); + + const float theta_scale = pow(pcs.freq_base, -2.0/pcs.n_dims); + + float theta_base = float(inB[pcs.inBOff + i2]); + float inv_ndims = -1.f/pcs.n_dims; + + float cos_theta; + float sin_theta; + + for (uint i0 = 2*gl_LocalInvocationIndex; i0 < pcs.ne0; i0 += 2*gl_WorkGroupSize.x) { + if (i0 < pcs.n_dims) { + uint ic = i0/2; + + float theta = theta_base * pow(pcs.freq_base, inv_ndims*i0); + + const float freq_factor = pcs.has_freq_factors ? inC[pcs.inCOff + ic] : 1.0f; + + rope_yarn(theta/freq_factor, pcs.freq_scale, corr_dims, i0, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta); + + const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 4) + pcs.inAOff; // Based from in + const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 4) + pcs.outOff; // Based from out_ + + const float x0 = inA[src]; + const float x1 = inA[src+1]; + + out_[dst_data] = x0*cos_theta - x1*sin_theta; + out_[dst_data+1] = x0*sin_theta + x1*cos_theta; + } else { + const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 4) + pcs.inAOff; // Based from in + const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 4) + pcs.outOff; // Based from out_ + + out_[dst_data] = inA[src]; + out_[dst_data+1] = inA[src+1]; + } + } +} diff --git a/ggml/src/kompute-shaders/op_scale.comp b/ggml/src/ggml-kompute/kompute-shaders/op_scale.comp similarity index 100% rename from ggml/src/kompute-shaders/op_scale.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_scale.comp diff --git a/ggml/src/kompute-shaders/op_scale_8.comp b/ggml/src/ggml-kompute/kompute-shaders/op_scale_8.comp similarity index 100% rename from ggml/src/kompute-shaders/op_scale_8.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_scale_8.comp diff --git a/ggml/src/kompute-shaders/op_silu.comp b/ggml/src/ggml-kompute/kompute-shaders/op_silu.comp similarity index 100% rename from ggml/src/kompute-shaders/op_silu.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_silu.comp diff --git a/ggml/src/kompute-shaders/op_softmax.comp b/ggml/src/ggml-kompute/kompute-shaders/op_softmax.comp similarity index 78% rename from ggml/src/kompute-shaders/op_softmax.comp rename to ggml/src/ggml-kompute/kompute-shaders/op_softmax.comp index 7bc9176ca..4165295bf 100644 --- a/ggml/src/kompute-shaders/op_softmax.comp +++ b/ggml/src/ggml-kompute/kompute-shaders/op_softmax.comp @@ -18,6 +18,10 @@ layout(push_constant) uniform PushConstants { int ne01; int ne02; float scale; + float max_bias; + float m0; + float m1; + uint n_head_log2; int mask; } pcs; @@ -34,17 +38,29 @@ void main() { const uint pmask = i01*pcs.ne00 + pcs.inBOff; // Based from inB const uint pdst = extra_off + pcs.outOff; // Based from out_ + float slope = 1.0f; + + // ALiBi + if (pcs.max_bias > 0.0f) { + int64_t h = i02; + + float base = h < pcs.n_head_log2 ? pcs.m0 : pcs.m1; + int64_t exp = h < pcs.n_head_log2 ? h + 1 : 2*(h - pcs.n_head_log2) + 1; + + slope = pow(base, float(exp)); + } + // parallel max float localMax = uintBitsToFloat(0xFF800000); for (uint i00 = gl_SubgroupInvocationID.x; i00 < pcs.ne00; i00 += 32) { - localMax = max(localMax, inA[psrc0 + i00]*pcs.scale + (pcs.mask!=0 ? inB[pmask + i00] : 0.0f)); + localMax = max(localMax, inA[psrc0 + i00]*pcs.scale + (pcs.mask!=0 ? slope*inB[pmask + i00] : 0.0f)); } float max_ = subgroupMax(localMax); // parallel sum float localSum = 0.0f; for (uint i00 = gl_SubgroupInvocationID.x; i00 < pcs.ne00; i00 += 32) { - const float exp_psrc0 = exp(inA[psrc0 + i00]*pcs.scale + (pcs.mask!=0 ? inB[pmask + i00] : 0.0f) - max_); + const float exp_psrc0 = exp(inA[psrc0 + i00]*pcs.scale + (pcs.mask!=0 ? slope*inB[pmask + i00] : 0.0f) - max_); localSum += exp_psrc0; out_[pdst + i00] = exp_psrc0; } diff --git a/ggml/src/kompute-shaders/rope_common.comp b/ggml/src/ggml-kompute/kompute-shaders/rope_common.comp similarity index 98% rename from ggml/src/kompute-shaders/rope_common.comp rename to ggml/src/ggml-kompute/kompute-shaders/rope_common.comp index df4702896..0fca640dc 100644 --- a/ggml/src/kompute-shaders/rope_common.comp +++ b/ggml/src/ggml-kompute/kompute-shaders/rope_common.comp @@ -8,12 +8,14 @@ layout(local_size_x = 1) in; layout (push_constant) uniform parameter { uint inAOff; uint inBOff; + uint inCOff; uint outOff; int n_dims; int mode; int n_ctx_orig; float freq_base; float freq_scale; + bool has_freq_factors; float ext_factor; float attn_factor; float beta_fast; diff --git a/ggml/src/ggml-metal/CMakeLists.txt b/ggml/src/ggml-metal/CMakeLists.txt new file mode 100644 index 000000000..1bad27206 --- /dev/null +++ b/ggml/src/ggml-metal/CMakeLists.txt @@ -0,0 +1,105 @@ +find_library(FOUNDATION_LIBRARY Foundation REQUIRED) +find_library(METAL_FRAMEWORK Metal REQUIRED) +find_library(METALKIT_FRAMEWORK MetalKit REQUIRED) + +message(STATUS "Metal framework found") + +ggml_add_backend_library(ggml-metal + ggml-metal.m + ) + +target_link_libraries(ggml-metal PRIVATE + ${FOUNDATION_LIBRARY} + ${METAL_FRAMEWORK} + ${METALKIT_FRAMEWORK} + ) + +if (GGML_METAL_NDEBUG) + add_compile_definitions(GGML_METAL_NDEBUG) +endif() + +if (GGML_METAL_USE_BF16) + add_compile_definitions(GGML_METAL_USE_BF16) +endif() + +# copy metal files to bin directory +configure_file(../ggml-common.h ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h COPYONLY) +configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY) +configure_file(ggml-metal-impl.h ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal-impl.h COPYONLY) + +if (GGML_METAL_EMBED_LIBRARY) + enable_language(ASM) + + add_compile_definitions(GGML_METAL_EMBED_LIBRARY) + + set(METALLIB_COMMON "${CMAKE_CURRENT_SOURCE_DIR}/../ggml-common.h") + set(METALLIB_SOURCE "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal") + set(METALLIB_IMPL "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal-impl.h") + + file(MAKE_DIRECTORY "${CMAKE_BINARY_DIR}/autogenerated") + + # merge ggml-common.h and ggml-metal.metal into a single file + set(METALLIB_EMBED_ASM "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.s") + set(METALLIB_SOURCE_EMBED "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.metal") + set(METALLIB_SOURCE_EMBED_TMP "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.metal.tmp") + + add_custom_command( + OUTPUT ${METALLIB_EMBED_ASM} + COMMAND echo "Embedding Metal library" + COMMAND sed -e '/__embed_ggml-common.h__/r ${METALLIB_COMMON}' -e '/__embed_ggml-common.h__/d' < ${METALLIB_SOURCE} > ${METALLIB_SOURCE_EMBED_TMP} + COMMAND sed -e '/\#include \"ggml-metal-impl.h\"/r ${METALLIB_IMPL}' -e '/\#include \"ggml-metal-impl.h\"/d' < ${METALLIB_SOURCE_EMBED_TMP} > ${METALLIB_SOURCE_EMBED} + COMMAND echo ".section __DATA,__ggml_metallib" > ${METALLIB_EMBED_ASM} + COMMAND echo ".globl _ggml_metallib_start" >> ${METALLIB_EMBED_ASM} + COMMAND echo "_ggml_metallib_start:" >> ${METALLIB_EMBED_ASM} + COMMAND echo ".incbin \\\"${METALLIB_SOURCE_EMBED}\\\"" >> ${METALLIB_EMBED_ASM} + COMMAND echo ".globl _ggml_metallib_end" >> ${METALLIB_EMBED_ASM} + COMMAND echo "_ggml_metallib_end:" >> ${METALLIB_EMBED_ASM} + DEPENDS ../ggml-common.h ggml-metal.metal ggml-metal-impl.h + COMMENT "Generate assembly for embedded Metal library" + ) + + target_sources(ggml-metal PRIVATE ${METALLIB_EMBED_ASM}) +else() + if (GGML_METAL_SHADER_DEBUG) + # custom command to do the following: + # xcrun -sdk macosx metal -fno-fast-math -c ggml-metal.metal -o ggml-metal.air + # xcrun -sdk macosx metallib ggml-metal.air -o default.metallib + # + # note: this is the only way I found to disable fast-math in Metal. it's ugly, but at least it works + # disabling fast math is needed in order to pass tests/test-backend-ops + # note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1 + # note: unfortunately, we have to call it default.metallib instead of ggml.metallib + # ref: https://github.com/ggerganov/whisper.cpp/issues/1720 + set(XC_FLAGS -fno-fast-math -fno-inline -g) + else() + set(XC_FLAGS -O3) + endif() + + # Append macOS metal versioning flags + if (GGML_METAL_MACOSX_VERSION_MIN) + message(STATUS "Adding -mmacosx-version-min=${GGML_METAL_MACOSX_VERSION_MIN} flag to metal compilation") + list (APPEND XC_FLAGS -mmacosx-version-min=${GGML_METAL_MACOSX_VERSION_MIN}) + endif() + + if (GGML_METAL_STD) + message(STATUS "Adding -std=${GGML_METAL_STD} flag to metal compilation") + list (APPEND XC_FLAGS -std=${GGML_METAL_STD}) + endif() + + add_custom_command( + OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib + COMMAND xcrun -sdk macosx metal ${XC_FLAGS} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air + COMMAND xcrun -sdk macosx metallib ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib + COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air + COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h + COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal + DEPENDS ggml-metal.metal ggml-common.h + COMMENT "Compiling Metal kernels" + ) + + # FIXME: only add to the ggml-metal target? + add_custom_target( + ggml-metal-lib ALL + DEPENDS ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib + ) +endif() # GGML_METAL_EMBED_LIBRARY diff --git a/ggml/src/ggml-metal/ggml-metal-impl.h b/ggml/src/ggml-metal/ggml-metal-impl.h new file mode 100644 index 000000000..e3dc25f16 --- /dev/null +++ b/ggml/src/ggml-metal/ggml-metal-impl.h @@ -0,0 +1,288 @@ +#ifndef GGML_METAL_IMPL +#define GGML_METAL_IMPL + +// kernel argument structs +// +// - element counters (e.g. ne00) typically use int32_t to reduce register usage +// however, be careful from int overflows when using those in the kernel implementation +// +// - strides (e.g. nb00) use uint64_t + +typedef struct { + int32_t ne00; + int32_t ne01; + int32_t ne02; + int32_t ne03; + uint64_t nb00; + uint64_t nb01; + uint64_t nb02; + uint64_t nb03; + int32_t ne10; + int32_t ne11; + int32_t ne12; + int32_t ne13; + uint64_t nb10; + uint64_t nb11; + uint64_t nb12; + uint64_t nb13; + int32_t ne0; + int32_t ne1; + int32_t ne2; + int32_t ne3; + uint64_t nb0; + uint64_t nb1; + uint64_t nb2; + uint64_t nb3; + int32_t dim; +} ggml_metal_kargs_concat; + +typedef struct { + int32_t ne00; + int32_t ne01; + int32_t ne02; + int32_t ne03; + uint64_t nb00; + uint64_t nb01; + uint64_t nb02; + uint64_t nb03; + int32_t ne10; + int32_t ne11; + int32_t ne12; + int32_t ne13; + uint64_t nb10; + uint64_t nb11; + uint64_t nb12; + uint64_t nb13; + int32_t ne0; + int32_t ne1; + int32_t ne2; + int32_t ne3; + uint64_t nb0; + uint64_t nb1; + uint64_t nb2; + uint64_t nb3; + uint64_t offs; +} ggml_metal_kargs_bin; + +typedef struct { + int32_t ne00; + int32_t ne01; + int32_t ne02; + int32_t ne03; + uint64_t nb00; + uint64_t nb01; + uint64_t nb02; + uint64_t nb03; + int32_t ne0; + int32_t ne1; + int32_t ne2; + int32_t ne3; + uint64_t nb0; + uint64_t nb1; + uint64_t nb2; + uint64_t nb3; +} ggml_metal_kargs_repeat; + +typedef struct { + int64_t ne00; + int64_t ne01; + int64_t ne02; + int64_t ne03; + uint64_t nb00; + uint64_t nb01; + uint64_t nb02; + uint64_t nb03; + int64_t ne0; + int64_t ne1; + int64_t ne2; + int64_t ne3; + uint64_t nb0; + uint64_t nb1; + uint64_t nb2; + uint64_t nb3; +} ggml_metal_kargs_cpy; + +typedef struct { + int64_t ne10; + int64_t ne11; + int64_t ne12; + uint64_t nb10; + uint64_t nb11; + uint64_t nb12; + uint64_t nb13; + uint64_t nb1; + uint64_t nb2; + uint64_t nb3; + uint64_t offs; + bool inplace; +} ggml_metal_kargs_set; + +typedef struct { + int32_t ne00; + int32_t ne01; + int32_t ne02; + int32_t ne03; + uint64_t nb00; + uint64_t nb01; + uint64_t nb02; + uint64_t nb03; + int32_t ne0; + int32_t ne1; + int32_t ne2; + int32_t ne3; + uint64_t nb0; + uint64_t nb1; + uint64_t nb2; + uint64_t nb3; + int32_t n_past; + int32_t n_dims; + int32_t n_ctx_orig; + float freq_base; + float freq_scale; + float ext_factor; + float attn_factor; + float beta_fast; + float beta_slow; +} ggml_metal_kargs_rope; + +typedef struct { + int32_t ne01; + int32_t ne02; + int32_t ne03; + uint64_t nb01; + uint64_t nb02; + uint64_t nb03; + int32_t ne11; + int32_t ne_12_2; // assume K and V are same shape + int32_t ne_12_3; + uint64_t nb_12_1; + uint64_t nb_12_2; + uint64_t nb_12_3; + uint64_t nb31; + int32_t ne1; + int32_t ne2; + float scale; + float max_bias; + float m0; + float m1; + uint16_t n_head_log2; + float logit_softcap; +} ggml_metal_kargs_flash_attn_ext; + +typedef struct { + int32_t ne00; + int32_t ne02; + uint64_t nb01; + uint64_t nb02; + uint64_t nb03; + int32_t ne12; + uint64_t nb10; + uint64_t nb11; + uint64_t nb12; + uint64_t nb13; + int32_t ne0; + int32_t ne1; + int16_t r2; + int16_t r3; +} ggml_metal_kargs_mul_mm; + +typedef struct { + int32_t ne00; + int32_t ne01; + int32_t ne02; + uint64_t nb00; + uint64_t nb01; + uint64_t nb02; + uint64_t nb03; + int32_t ne10; + int32_t ne11; + int32_t ne12; + uint64_t nb10; + uint64_t nb11; + uint64_t nb12; + uint64_t nb13; + int32_t ne0; + int32_t ne1; + int16_t r2; + int16_t r3; +} ggml_metal_kargs_mul_mv; + +typedef struct { + int32_t ne00; + int32_t ne01; + int32_t ne02; + uint64_t nb00; + uint64_t nb01; + uint64_t nb02; + uint64_t nb03; + int32_t ne10; + int32_t ne11; + int32_t ne12; + uint64_t nb10; + uint64_t nb11; + uint64_t nb12; + uint64_t nb13; + int32_t ne0; + int32_t ne1; + int16_t r2; + int16_t r3; + int16_t nsg; + int16_t nxpsg; + int16_t r1ptg; +} ggml_metal_kargs_mul_mv_ext; + +typedef struct { + int32_t nei0; + int32_t nei1; + uint64_t nbi1; + int32_t ne00; + int32_t ne02; + uint64_t nb01; + uint64_t nb02; + int32_t ne11; + int32_t ne12; + int32_t ne13; + uint64_t nb10; + uint64_t nb11; + uint64_t nb12; + int32_t ne0; + int32_t ne1; +} ggml_metal_kargs_mul_mm_id; + +typedef struct { + int32_t nei0; + int32_t nei1; + uint64_t nbi1; + int32_t ne00; + int32_t ne01; + int32_t ne02; + uint64_t nb00; + uint64_t nb01; + uint64_t nb02; + int32_t ne10; + int32_t ne11; + int32_t ne12; + int32_t ne13; + uint64_t nb10; + uint64_t nb11; + uint64_t nb12; + int32_t ne0; + int32_t ne1; + uint64_t nb1; +} ggml_metal_kargs_mul_mv_id; + +typedef struct { + int32_t ne00; + int32_t ne00_4; + uint64_t nb01; + float eps; +} ggml_metal_kargs_norm; + +typedef struct { + int32_t ne00; + int32_t ne00_4; + uint64_t nb01; + float eps; +} ggml_metal_kargs_rms_norm; + +#endif // GGML_METAL_IMPL diff --git a/ggml/src/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m similarity index 61% rename from ggml/src/ggml-metal.m rename to ggml/src/ggml-metal/ggml-metal.m index a2b4d49d5..f80fda7a4 100644 --- a/ggml/src/ggml-metal.m +++ b/ggml/src/ggml-metal/ggml-metal.m @@ -2,6 +2,7 @@ #import "ggml-impl.h" #import "ggml-backend-impl.h" +#import "ggml-metal-impl.h" #import @@ -36,16 +37,20 @@ static struct ggml_backend_metal_device_context { id mtl_device; int mtl_device_ref_count; - bool support_simdgroup_reduction; - bool support_simdgroup_mm; + bool has_simdgroup_reduction; + bool has_simdgroup_mm; + bool has_bfloat; + bool use_bfloat; char name[128]; } g_ggml_ctx_dev_main = { - /*.mtl_device =*/ nil, - /*.mtl_device_ref_count =*/ 0, - /*.support_simdgroup_reduction =*/ false, - /*.support_simdgroup_mm =*/ false, - /*.name =*/ "", + /*.mtl_device =*/ nil, + /*.mtl_device_ref_count =*/ 0, + /*.has_simdgroup_reduction =*/ false, + /*.has_simdgroup_mm =*/ false, + /*.has_bfloat =*/ false, + /*.use_bfloat =*/ false, + /*.name =*/ "", }; // acquire @@ -55,10 +60,19 @@ static id ggml_backend_metal_device_acq(struct ggml_backend_metal_dev if (ctx->mtl_device == nil) { ctx->mtl_device = MTLCreateSystemDefaultDevice(); - ctx->support_simdgroup_reduction = [ctx->mtl_device supportsFamily:MTLGPUFamilyApple7]; - ctx->support_simdgroup_reduction |= [ctx->mtl_device supportsFamily:MTLGPUFamilyMetal3_GGML]; + ctx->has_simdgroup_reduction = [ctx->mtl_device supportsFamily:MTLGPUFamilyApple7]; + ctx->has_simdgroup_reduction |= [ctx->mtl_device supportsFamily:MTLGPUFamilyMetal3_GGML]; - ctx->support_simdgroup_mm = [ctx->mtl_device supportsFamily:MTLGPUFamilyApple7]; + ctx->has_simdgroup_mm = [ctx->mtl_device supportsFamily:MTLGPUFamilyApple7]; + + ctx->has_bfloat = [ctx->mtl_device supportsFamily:MTLGPUFamilyMetal3_GGML]; + ctx->has_bfloat |= [ctx->mtl_device supportsFamily:MTLGPUFamilyApple6]; + +#if defined(GGML_METAL_USE_BF16) + ctx->use_bfloat = ctx->has_bfloat; +#else + ctx->use_bfloat = false; +#endif strncpy(ctx->name, [[ctx->mtl_device name] UTF8String], sizeof(ctx->name) - 1); } @@ -112,6 +126,7 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_GELU_QUICK_4, GGML_METAL_KERNEL_TYPE_SILU, GGML_METAL_KERNEL_TYPE_SILU_4, + GGML_METAL_KERNEL_TYPE_ELU, GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16, GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4, GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32, @@ -120,6 +135,7 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8, GGML_METAL_KERNEL_TYPE_GET_ROWS_F32, GGML_METAL_KERNEL_TYPE_GET_ROWS_F16, + GGML_METAL_KERNEL_TYPE_GET_ROWS_BF16, GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0, GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1, GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0, @@ -146,15 +162,59 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_SSM_CONV_F32, GGML_METAL_KERNEL_TYPE_SSM_SCAN_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32, - GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16, GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW, GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4, + GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16, + GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_1ROW, + GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_L4, + GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_BF16, GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_2, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_3, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_4, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_5, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_0_F32_R1_2, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_0_F32_R1_3, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_0_F32_R1_4, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_0_F32_R1_5, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_1_F32_R1_2, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_1_F32_R1_3, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_1_F32_R1_4, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_1_F32_R1_5, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_0_F32_R1_2, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_0_F32_R1_3, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_0_F32_R1_4, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_0_F32_R1_5, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_1_F32_R1_2, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_1_F32_R1_3, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_1_F32_R1_4, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_1_F32_R1_5, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_2, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_3, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_4, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_5, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_2, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_3, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_4, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_5, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_K_F32_R1_2, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_K_F32_R1_3, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_K_F32_R1_4, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_K_F32_R1_5, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q6_K_F32_R1_2, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q6_K_F32_R1_3, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q6_K_F32_R1_4, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q6_K_F32_R1_5, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_IQ4_NL_F32_R1_2, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_IQ4_NL_F32_R1_3, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_IQ4_NL_F32_R1_4, + GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_IQ4_NL_F32_R1_5, GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32, @@ -170,10 +230,11 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, - //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW, //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4, + //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_BF16_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32, @@ -195,6 +256,7 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_BF16_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32, @@ -216,6 +278,7 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_BF16_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32, @@ -243,8 +306,11 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_IM2COL_F32, GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F16, GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F32, + GGML_METAL_KERNEL_TYPE_CONV_TRANSPOSE_1D_F32_F32, + GGML_METAL_KERNEL_TYPE_CONV_TRANSPOSE_1D_F16_F32, GGML_METAL_KERNEL_TYPE_UPSCALE_F32, GGML_METAL_KERNEL_TYPE_PAD_F32, + GGML_METAL_KERNEL_TYPE_PAD_REFLECT_1D_F32, GGML_METAL_KERNEL_TYPE_ARANGE_F32, GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, @@ -255,13 +321,66 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96, GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, - //GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, // https://github.com/ggerganov/llama.cpp/issues/7261 + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H64, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H80, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H96, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H112, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H256, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H64, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H80, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H96, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H112, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H256, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H64, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H80, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H96, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H112, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H256, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H64, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H80, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H96, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H112, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H256, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H64, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H80, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H96, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H112, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H256, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H64, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H80, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H96, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H112, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H256, GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, - //GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, // https://github.com/ggerganov/llama.cpp/issues/7261 + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H256, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H256, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H256, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H256, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H256, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H256, + GGML_METAL_KERNEL_TYPE_SET_I32, + GGML_METAL_KERNEL_TYPE_SET_F32, GGML_METAL_KERNEL_TYPE_CPY_F32_F32, GGML_METAL_KERNEL_TYPE_CPY_F32_F16, + GGML_METAL_KERNEL_TYPE_CPY_F32_BF16, GGML_METAL_KERNEL_TYPE_CPY_F16_F16, GGML_METAL_KERNEL_TYPE_CPY_F16_F32, + GGML_METAL_KERNEL_TYPE_CPY_BF16_F32, + GGML_METAL_KERNEL_TYPE_CPY_BF16_BF16, GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0, GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1, @@ -276,6 +395,7 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_SUM_ROWS, GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32, GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32, + GGML_METAL_KERNEL_TYPE_ARGMAX, GGML_METAL_KERNEL_TYPE_COUNT }; @@ -440,7 +560,15 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de // dictionary of preprocessor macros NSMutableDictionary * prep = [NSMutableDictionary dictionary]; - MTLCompileOptions* options = [MTLCompileOptions new]; + if (ctx_dev->use_bfloat) { + [prep setObject:@"1" forKey:@"GGML_METAL_USE_BF16"]; + } + +#if GGML_METAL_EMBED_LIBRARY + [prep setObject:@"1" forKey:@"GGML_METAL_EMBED_LIBRARY"]; +#endif + + MTLCompileOptions * options = [MTLCompileOptions new]; options.preprocessorMacros = prep; //[options setFastMathEnabled:false]; @@ -450,7 +578,14 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]); return NULL; } + +#if !__has_feature(objc_arc) + [options release]; +#endif } +#if GGML_METAL_EMBED_LIBRARY + [src release]; +#endif // GGML_METAL_EMBED_LIBRARY } } @@ -483,9 +618,11 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de } } - GGML_LOG_INFO("%s: simdgroup reduction support = %s\n", __func__, ctx_dev->support_simdgroup_reduction ? "true" : "false"); - GGML_LOG_INFO("%s: simdgroup matrix mul. support = %s\n", __func__, ctx_dev->support_simdgroup_mm ? "true" : "false"); - GGML_LOG_INFO("%s: hasUnifiedMemory = %s\n", __func__, ctx_dev->mtl_device.hasUnifiedMemory ? "true" : "false"); + GGML_LOG_INFO("%s: simdgroup reduction = %s\n", __func__, ctx_dev->has_simdgroup_reduction ? "true" : "false"); + GGML_LOG_INFO("%s: simdgroup matrix mul. = %s\n", __func__, ctx_dev->has_simdgroup_mm ? "true" : "false"); + GGML_LOG_INFO("%s: has bfloat = %s\n", __func__, ctx_dev->has_bfloat ? "true" : "false"); + GGML_LOG_INFO("%s: use bfloat = %s\n", __func__, ctx_dev->use_bfloat ? "true" : "false"); + GGML_LOG_INFO("%s: hasUnifiedMemory = %s\n", __func__, ctx_dev->mtl_device.hasUnifiedMemory ? "true" : "false"); ctx->capture_next_compute = false; ctx->capture_started = false; @@ -511,16 +648,14 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de ctx->kernels[i].pipeline = nil; } - /* - GGML_LOG_INFO("%s: loaded %-40s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \ - (int) kernel->pipeline.maxTotalThreadsPerThreadgroup, \ - (int) kernel->pipeline.threadExecutionWidth); \ - */ #define GGML_METAL_ADD_KERNEL(e, name, supported) \ if (supported) { \ struct ggml_metal_kernel * kernel = &ctx->kernels[e]; \ id metal_function = [metal_library newFunctionWithName:@"kernel_"#name]; \ kernel->pipeline = [device newComputePipelineStateWithFunction:metal_function error:&error]; \ + GGML_LOG_DEBUG("%s: loaded %-40s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \ + (int) kernel->pipeline.maxTotalThreadsPerThreadgroup, \ + (int) kernel->pipeline.threadExecutionWidth); \ [metal_function release]; \ if (error) { \ GGML_LOG_ERROR("%s: error: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \ @@ -531,8 +666,9 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de GGML_LOG_WARN("%s: skipping %-40s (not supported)\n", __func__, "kernel_"#name); \ } - const bool support_simdgroup_mm = ctx_dev->support_simdgroup_mm; - const bool support_simdgroup_reduction = ctx_dev->support_simdgroup_reduction; + const bool has_simdgroup_mm = ctx_dev->has_simdgroup_mm; + const bool has_simdgroup_reduction = ctx_dev->has_simdgroup_reduction; + const bool use_bfloat = ctx_dev->use_bfloat; // simd_sum and simd_max requires MTLGPUFamilyApple7 @@ -560,14 +696,16 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK_4, gelu_quick_4, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU, silu, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU_4, silu_4, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16, soft_max_f16, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4, soft_max_f16_4, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32, soft_max_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4, soft_max_f32_4, support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ELU, elu, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16, soft_max_f16, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4, soft_max_f16_4, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32, soft_max_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4, soft_max_f32_4, has_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF, diag_mask_inf, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8, diag_mask_inf_8, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F32, get_rows_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F16, get_rows_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_BF16, get_rows_bf16, use_bfloat); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0, get_rows_q4_0, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1, get_rows_q4_1, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0, get_rows_q5_0, true); @@ -588,101 +726,148 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, get_rows_iq4_nl, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS, get_rows_iq4_xs, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, get_rows_i32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM, rms_norm, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM, group_norm, support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM, rms_norm, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM, group_norm, has_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NORM, norm, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SSM_CONV_F32, ssm_conv_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SSM_SCAN_F32, ssm_scan_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32, mul_mv_f32_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16, mul_mv_f16_f16, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32, mul_mv_f16_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW, mul_mv_f16_f32_1row, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4, mul_mv_f16_f32_l4, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32, mul_mv_q4_0_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32, mul_mv_q4_1_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32, mul_mv_q5_0_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32, mul_mv_q5_1_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32, mul_mv_q8_0_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32, mul_mv_q2_K_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32, mul_mv_q3_K_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32, mul_mv_q4_K_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32, mul_mv_q5_K_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32, mul_mv_q6_K_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, mul_mv_iq2_xxs_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, mul_mv_iq2_xs_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, mul_mv_iq3_xxs_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32, mul_mv_iq3_s_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32, mul_mv_iq2_s_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, mul_mv_iq1_s_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32, mul_mv_iq1_m_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, mul_mv_iq4_nl_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32, mul_mv_iq4_xs_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, support_simdgroup_reduction); - //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, mul_mv_id_f16_f16, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, mul_mv_id_f16_f32, support_simdgroup_reduction); - //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW, mul_mv_id_f16_f32_1row, support_simdgroup_reduction); - //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4, mul_mv_id_f16_f32_l4, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32, mul_mv_id_q4_0_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32, mul_mv_id_q4_1_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32, mul_mv_id_q5_0_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32, mul_mv_id_q5_1_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32, mul_mv_id_q8_0_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32, mul_mv_id_q2_K_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32, mul_mv_id_q3_K_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32, mul_mv_id_q4_K_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32, mul_mv_id_q5_K_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32, mul_mv_id_q6_K_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, mul_mv_id_iq2_xxs_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, mul_mv_id_iq2_xs_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, mul_mv_id_iq3_xxs_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32, mul_mv_id_iq3_s_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32, mul_mv_id_iq2_s_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, mul_mv_id_iq1_s_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32, mul_mv_id_iq1_m_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, mul_mv_id_iq4_nl_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32, mul_mv_id_iq4_xs_f32, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, mul_mm_f16_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, mul_mm_q4_0_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32, mul_mm_q4_1_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32, mul_mm_q5_0_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32, mul_mm_q5_1_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32, mul_mm_q8_0_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32, mul_mm_q2_K_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32, mul_mm_q3_K_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32, mul_mm_q4_K_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32, mul_mm_q5_K_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32, mul_mm_q6_K_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, mul_mm_iq2_xxs_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, mul_mm_iq2_xs_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, mul_mm_iq3_xxs_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32, mul_mm_iq3_s_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32, mul_mm_iq2_s_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, mul_mm_iq1_s_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32, mul_mm_iq1_m_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, mul_mm_iq4_nl_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32, mul_mm_iq4_xs_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, mul_mm_id_f16_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, mul_mm_id_q4_0_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32, mul_mm_id_q4_1_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32, mul_mm_id_q5_0_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32, mul_mm_id_q5_1_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32, mul_mm_id_q8_0_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32, mul_mm_id_q2_K_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32, mul_mm_id_q3_K_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32, mul_mm_id_q4_K_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32, mul_mm_id_q5_K_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32, mul_mm_id_q6_K_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, mul_mm_id_iq2_xxs_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, mul_mm_id_iq2_xs_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, mul_mm_id_iq3_xxs_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32, mul_mm_id_iq3_s_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32, mul_mm_id_iq2_s_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, mul_mm_id_iq1_s_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32, mul_mm_id_iq1_m_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, mul_mm_id_iq4_nl_f32, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, mul_mm_id_iq4_xs_f32, support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32, mul_mv_f32_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32, mul_mv_bf16_f32, has_simdgroup_reduction && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_1ROW, mul_mv_bf16_f32_1row, has_simdgroup_reduction && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_L4, mul_mv_bf16_f32_l4, has_simdgroup_reduction && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_BF16, mul_mv_bf16_bf16, has_simdgroup_reduction && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32, mul_mv_f16_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW, mul_mv_f16_f32_1row, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4, mul_mv_f16_f32_l4, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16, mul_mv_f16_f16, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32, mul_mv_q4_0_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32, mul_mv_q4_1_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32, mul_mv_q5_0_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32, mul_mv_q5_1_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32, mul_mv_q8_0_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_2, mul_mv_ext_f16_f32_r1_2, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_3, mul_mv_ext_f16_f32_r1_3, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_4, mul_mv_ext_f16_f32_r1_4, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_5, mul_mv_ext_f16_f32_r1_5, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_0_F32_R1_2, mul_mv_ext_q4_0_f32_r1_2, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_0_F32_R1_3, mul_mv_ext_q4_0_f32_r1_3, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_0_F32_R1_4, mul_mv_ext_q4_0_f32_r1_4, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_0_F32_R1_5, mul_mv_ext_q4_0_f32_r1_5, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_1_F32_R1_2, mul_mv_ext_q4_1_f32_r1_2, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_1_F32_R1_3, mul_mv_ext_q4_1_f32_r1_3, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_1_F32_R1_4, mul_mv_ext_q4_1_f32_r1_4, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_1_F32_R1_5, mul_mv_ext_q4_1_f32_r1_5, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_0_F32_R1_2, mul_mv_ext_q5_0_f32_r1_2, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_0_F32_R1_3, mul_mv_ext_q5_0_f32_r1_3, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_0_F32_R1_4, mul_mv_ext_q5_0_f32_r1_4, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_0_F32_R1_5, mul_mv_ext_q5_0_f32_r1_5, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_1_F32_R1_2, mul_mv_ext_q5_1_f32_r1_2, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_1_F32_R1_3, mul_mv_ext_q5_1_f32_r1_3, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_1_F32_R1_4, mul_mv_ext_q5_1_f32_r1_4, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_1_F32_R1_5, mul_mv_ext_q5_1_f32_r1_5, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_2, mul_mv_ext_q8_0_f32_r1_2, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_3, mul_mv_ext_q8_0_f32_r1_3, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_4, mul_mv_ext_q8_0_f32_r1_4, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_5, mul_mv_ext_q8_0_f32_r1_5, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_2, mul_mv_ext_q4_K_f32_r1_2, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_3, mul_mv_ext_q4_K_f32_r1_3, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_4, mul_mv_ext_q4_K_f32_r1_4, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_5, mul_mv_ext_q4_K_f32_r1_5, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_K_F32_R1_2, mul_mv_ext_q5_K_f32_r1_2, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_K_F32_R1_3, mul_mv_ext_q5_K_f32_r1_3, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_K_F32_R1_4, mul_mv_ext_q5_K_f32_r1_4, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_K_F32_R1_5, mul_mv_ext_q5_K_f32_r1_5, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q6_K_F32_R1_2, mul_mv_ext_q6_K_f32_r1_2, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q6_K_F32_R1_3, mul_mv_ext_q6_K_f32_r1_3, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q6_K_F32_R1_4, mul_mv_ext_q6_K_f32_r1_4, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q6_K_F32_R1_5, mul_mv_ext_q6_K_f32_r1_5, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_IQ4_NL_F32_R1_2, mul_mv_ext_iq4_nl_f32_r1_2, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_IQ4_NL_F32_R1_3, mul_mv_ext_iq4_nl_f32_r1_3, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_IQ4_NL_F32_R1_4, mul_mv_ext_iq4_nl_f32_r1_4, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_IQ4_NL_F32_R1_5, mul_mv_ext_iq4_nl_f32_r1_5, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32, mul_mv_q2_K_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32, mul_mv_q3_K_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32, mul_mv_q4_K_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32, mul_mv_q5_K_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32, mul_mv_q6_K_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, mul_mv_iq2_xxs_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, mul_mv_iq2_xs_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, mul_mv_iq3_xxs_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32, mul_mv_iq3_s_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32, mul_mv_iq2_s_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, mul_mv_iq1_s_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32, mul_mv_iq1_m_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, mul_mv_iq4_nl_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32, mul_mv_iq4_xs_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, mul_mv_id_f16_f32, has_simdgroup_reduction); + //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW, mul_mv_id_f16_f32_1row, has_simdgroup_reduction); + //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4, mul_mv_id_f16_f32_l4, has_simdgroup_reduction); + //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, mul_mv_id_f16_f16, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_BF16_F32, mul_mv_id_bf16_f32, has_simdgroup_reduction && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32, mul_mv_id_q4_0_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32, mul_mv_id_q4_1_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32, mul_mv_id_q5_0_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32, mul_mv_id_q5_1_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32, mul_mv_id_q8_0_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32, mul_mv_id_q2_K_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32, mul_mv_id_q3_K_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32, mul_mv_id_q4_K_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32, mul_mv_id_q5_K_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32, mul_mv_id_q6_K_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, mul_mv_id_iq2_xxs_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, mul_mv_id_iq2_xs_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, mul_mv_id_iq3_xxs_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32, mul_mv_id_iq3_s_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32, mul_mv_id_iq2_s_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, mul_mv_id_iq1_s_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32, mul_mv_id_iq1_m_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, mul_mv_id_iq4_nl_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32, mul_mv_id_iq4_xs_f32, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, mul_mm_f16_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_BF16_F32, mul_mm_bf16_f32, has_simdgroup_mm && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, mul_mm_q4_0_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32, mul_mm_q4_1_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32, mul_mm_q5_0_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32, mul_mm_q5_1_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32, mul_mm_q8_0_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32, mul_mm_q2_K_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32, mul_mm_q3_K_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32, mul_mm_q4_K_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32, mul_mm_q5_K_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32, mul_mm_q6_K_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, mul_mm_iq2_xxs_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, mul_mm_iq2_xs_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, mul_mm_iq3_xxs_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32, mul_mm_iq3_s_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32, mul_mm_iq2_s_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, mul_mm_iq1_s_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32, mul_mm_iq1_m_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, mul_mm_iq4_nl_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32, mul_mm_iq4_xs_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, mul_mm_id_f16_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_BF16_F32, mul_mm_id_bf16_f32, has_simdgroup_mm && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, mul_mm_id_q4_0_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32, mul_mm_id_q4_1_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32, mul_mm_id_q5_0_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32, mul_mm_id_q5_1_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32, mul_mm_id_q8_0_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32, mul_mm_id_q2_K_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32, mul_mm_id_q3_K_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32, mul_mm_id_q4_K_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32, mul_mm_id_q5_K_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32, mul_mm_id_q6_K_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, mul_mm_id_iq2_xxs_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, mul_mm_id_iq2_xs_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, mul_mm_id_iq3_xxs_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32, mul_mm_id_iq3_s_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32, mul_mm_id_iq2_s_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, mul_mm_id_iq1_s_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32, mul_mm_id_iq1_m_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, mul_mm_id_iq4_nl_f32, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, mul_mm_id_iq4_xs_f32, has_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NORM_F32, rope_norm_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NORM_F16, rope_norm_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F32, rope_neox_f32, true); @@ -691,25 +876,81 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32, im2col_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F16, im2col_ext_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_EXT_F32, im2col_ext_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONV_TRANSPOSE_1D_F32_F32, conv_transpose_1d_f32_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONV_TRANSPOSE_1D_F16_F32, conv_transpose_1d_f16_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32, pad_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_REFLECT_1D_F32, pad_reflect_1d_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, timestep_embedding_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARANGE_F32, arange_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, argsort_f32_i32_asc, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC, argsort_f32_i32_desc, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32, leaky_relu_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64, flash_attn_ext_f16_h64, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80, flash_attn_ext_f16_h80, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96, flash_attn_ext_f16_h96, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, flash_attn_ext_f16_h112, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, flash_attn_ext_f16_h128, support_simdgroup_mm); - //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, flash_attn_ext_f16_h256, support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, flash_attn_ext_vec_f16_h128, support_simdgroup_reduction); - //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, flash_attn_ext_vec_f16_h256, support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16, cpy_f32_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64, flash_attn_ext_f16_h64, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80, flash_attn_ext_f16_h80, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96, flash_attn_ext_f16_h96, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, flash_attn_ext_f16_h112, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, flash_attn_ext_f16_h128, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, flash_attn_ext_f16_h256, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H64, flash_attn_ext_bf16_h64, has_simdgroup_mm && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H80, flash_attn_ext_bf16_h80, has_simdgroup_mm && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H96, flash_attn_ext_bf16_h96, has_simdgroup_mm && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H112, flash_attn_ext_bf16_h112, has_simdgroup_mm && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H128, flash_attn_ext_bf16_h128, has_simdgroup_mm && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H256, flash_attn_ext_bf16_h256, has_simdgroup_mm && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H64, flash_attn_ext_q4_0_h64, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H80, flash_attn_ext_q4_0_h80, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H96, flash_attn_ext_q4_0_h96, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H112, flash_attn_ext_q4_0_h112, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H128, flash_attn_ext_q4_0_h128, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H256, flash_attn_ext_q4_0_h256, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H64, flash_attn_ext_q4_1_h64, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H80, flash_attn_ext_q4_1_h80, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H96, flash_attn_ext_q4_1_h96, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H112, flash_attn_ext_q4_1_h112, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H128, flash_attn_ext_q4_1_h128, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H256, flash_attn_ext_q4_1_h256, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H64, flash_attn_ext_q5_0_h64, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H80, flash_attn_ext_q5_0_h80, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H96, flash_attn_ext_q5_0_h96, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H112, flash_attn_ext_q5_0_h112, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H128, flash_attn_ext_q5_0_h128, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H256, flash_attn_ext_q5_0_h256, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H64, flash_attn_ext_q5_1_h64, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H80, flash_attn_ext_q5_1_h80, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H96, flash_attn_ext_q5_1_h96, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H112, flash_attn_ext_q5_1_h112, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H128, flash_attn_ext_q5_1_h128, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H256, flash_attn_ext_q5_1_h256, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H64, flash_attn_ext_q8_0_h64, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H80, flash_attn_ext_q8_0_h80, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H96, flash_attn_ext_q8_0_h96, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H112, flash_attn_ext_q8_0_h112, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H128, flash_attn_ext_q8_0_h128, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H256, flash_attn_ext_q8_0_h256, has_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, flash_attn_ext_vec_f16_h128, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H128, flash_attn_ext_vec_bf16_h128, has_simdgroup_reduction && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H128, flash_attn_ext_vec_q4_0_h128, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H128, flash_attn_ext_vec_q4_1_h128, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H128, flash_attn_ext_vec_q5_0_h128, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H128, flash_attn_ext_vec_q5_1_h128, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H128, flash_attn_ext_vec_q8_0_h128, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, flash_attn_ext_vec_f16_h256, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H256, flash_attn_ext_vec_bf16_h256, has_simdgroup_reduction && use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H256, flash_attn_ext_vec_q4_0_h256, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H256, flash_attn_ext_vec_q4_1_h256, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H256, flash_attn_ext_vec_q5_0_h256, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H256, flash_attn_ext_vec_q5_1_h256, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H256, flash_attn_ext_vec_q8_0_h256, has_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SET_F32, set_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SET_I32, set_i32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32, cpy_f32_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16, cpy_f16_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16, cpy_f32_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_BF16, cpy_f32_bf16, use_bfloat); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F32, cpy_f16_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16, cpy_f16_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_BF16_F32, cpy_bf16_f32, use_bfloat); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_BF16_BF16, cpy_bf16_bf16, use_bfloat); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, cpy_f32_q8_0, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0, cpy_f32_q4_0, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1, cpy_f32_q4_1, true); @@ -722,6 +963,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SIN, sin, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_COS, cos, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS, sum_rows, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGMAX, argmax, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32, pool_2d_avg_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32, pool_2d_max_f32, true); } @@ -799,15 +1041,18 @@ static id ggml_metal_get_buffer(struct ggml_tensor * t, size_t * offs } static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_context * ctx_dev, const struct ggml_tensor * op) { - for (size_t i = 0, n = 3; i < n; ++i) { - if (op->src[i] != NULL && op->src[i]->type == GGML_TYPE_BF16) { - return false; + const bool has_simdgroup_mm = ctx_dev->has_simdgroup_mm; + const bool has_simdgroup_reduction = ctx_dev->has_simdgroup_reduction; + const bool use_bfloat = ctx_dev->use_bfloat; + + if (!use_bfloat) { + for (size_t i = 0, n = 3; i < n; ++i) { + if (op->src[i] != NULL && op->src[i]->type == GGML_TYPE_BF16) { + return false; + } } } - const bool support_simdgroup_mm = ctx_dev->support_simdgroup_mm; - const bool support_simdgroup_reduction = ctx_dev->support_simdgroup_reduction; - switch (op->op) { case GGML_OP_UNARY: switch (ggml_get_unary_op(op)) { @@ -817,6 +1062,7 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_GELU_QUICK: case GGML_UNARY_OP_SILU: + case GGML_UNARY_OP_ELU: return ggml_is_contiguous(op->src[0]); default: return false; @@ -835,6 +1081,7 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex case GGML_OP_REPEAT: case GGML_OP_SCALE: case GGML_OP_CLAMP: + case GGML_OP_CONV_TRANSPOSE_1D: return true; case GGML_OP_SQR: case GGML_OP_SQRT: @@ -843,9 +1090,11 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex return ggml_is_contiguous(op->src[0]); case GGML_OP_SUM_ROWS: case GGML_OP_SOFT_MAX: - case GGML_OP_RMS_NORM: case GGML_OP_GROUP_NORM: - return support_simdgroup_reduction; + return has_simdgroup_reduction; + case GGML_OP_RMS_NORM: + return has_simdgroup_reduction && (op->ne[0] % 4 == 0); + case GGML_OP_ARGMAX: case GGML_OP_NORM: case GGML_OP_ROPE: return true; @@ -856,28 +1105,23 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex case GGML_OP_POOL_2D: case GGML_OP_UPSCALE: case GGML_OP_PAD: + case GGML_OP_PAD_REFLECT_1D: case GGML_OP_ARANGE: case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_ARGSORT: case GGML_OP_LEAKY_RELU: return true; case GGML_OP_FLASH_ATTN_EXT: - if (op->src[1]->type != GGML_TYPE_F16) { + if (op->src[1]->type != op->src[2]->type) { return false; } - if (op->src[2]->type != GGML_TYPE_F16) { - return false; - } - if (op->src[0]->ne[0] == 256) { - return false; - } - return support_simdgroup_mm; // TODO: over-restricted for vec-kernels + return has_simdgroup_mm; // TODO: over-restricted for vec-kernels case GGML_OP_SSM_CONV: case GGML_OP_SSM_SCAN: return true; case GGML_OP_MUL_MAT: case GGML_OP_MUL_MAT_ID: - return support_simdgroup_reduction && + return has_simdgroup_reduction && (op->src[0]->type != GGML_TYPE_F32 || op->src[1]->type == GGML_TYPE_F32); case GGML_OP_CPY: case GGML_OP_DUP: @@ -888,6 +1132,7 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex switch (op->type) { case GGML_TYPE_F32: case GGML_TYPE_F16: + case GGML_TYPE_BF16: case GGML_TYPE_Q8_0: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: @@ -900,12 +1145,30 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex } case GGML_TYPE_F16: switch (op->type) { - case GGML_TYPE_F32: - case GGML_TYPE_F16: + case GGML_TYPE_F32: + case GGML_TYPE_F16: return true; - default: + default: return false; } + case GGML_TYPE_BF16: + switch (op->type) { + case GGML_TYPE_F32: + case GGML_TYPE_BF16: + return true; + default: + return false; + } + default: + return false; + }; + } + case GGML_OP_SET: + { + switch (op->src[0]->type) { + case GGML_TYPE_F32: + case GGML_TYPE_I32: + return true; default: return false; }; @@ -989,7 +1252,7 @@ static void ggml_metal_encode_node( const uint64_t nb20 = src2 ? src2->nb[0] : 0; GGML_UNUSED(nb20); const uint64_t nb21 = src2 ? src2->nb[1] : 0; const uint64_t nb22 = src2 ? src2->nb[2] : 0; - const uint64_t nb23 = src2 ? src2->nb[3] : 0; + const uint64_t nb23 = src2 ? src2->nb[3] : 0; GGML_UNUSED(nb23); const int64_t ne0 = dst ? dst->ne[0] : 0; const int64_t ne1 = dst ? dst->ne[1] : 0; @@ -1040,35 +1303,39 @@ static void ggml_metal_encode_node( const int32_t dim = ((const int32_t *) dst->op_params)[0]; + ggml_metal_kargs_concat args = { + /*.ne00 =*/ ne00, + /*.ne01 =*/ ne01, + /*.ne02 =*/ ne02, + /*.ne03 =*/ ne03, + /*.nb00 =*/ nb00, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.nb03 =*/ nb03, + /*.ne10 =*/ ne10, + /*.ne11 =*/ ne11, + /*.ne12 =*/ ne12, + /*.ne13 =*/ ne13, + /*.nb10 =*/ nb10, + /*.nb11 =*/ nb11, + /*.nb12 =*/ nb12, + /*.nb13 =*/ nb13, + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + /*.ne2 =*/ ne2, + /*.ne3 =*/ ne3, + /*.nb0 =*/ nb0, + /*.nb1 =*/ nb1, + /*.nb2 =*/ nb2, + /*.nb3 =*/ nb3, + /*.dim =*/ dim, + }; + [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26]; - [encoder setBytes:&dim length:sizeof(dim) atIndex:27]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; const int nth = MIN(1024, ne0); @@ -1086,8 +1353,6 @@ static void ggml_metal_encode_node( bool bcast_row = false; - int64_t nb = ne00; // used by the "row" kernels - id pipeline = nil; if (ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0) { @@ -1096,7 +1361,6 @@ static void ggml_metal_encode_node( // src1 is a row GGML_ASSERT(ne11 == 1); - nb = ne00 / 4; switch (dst->op) { case GGML_OP_ADD: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ROW].pipeline; break; case GGML_OP_SUB: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUB_ROW].pipeline; break; @@ -1116,36 +1380,39 @@ static void ggml_metal_encode_node( } } + ggml_metal_kargs_bin args = { + /*.ne00 =*/ ne00, + /*.ne01 =*/ ne01, + /*.ne02 =*/ ne02, + /*.ne03 =*/ ne03, + /*.nb00 =*/ nb00, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.nb03 =*/ nb03, + /*.ne10 =*/ ne10, + /*.ne11 =*/ ne11, + /*.ne12 =*/ ne12, + /*.ne13 =*/ ne13, + /*.nb10 =*/ nb10, + /*.nb11 =*/ nb11, + /*.nb12 =*/ nb12, + /*.nb13 =*/ nb13, + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + /*.ne2 =*/ ne2, + /*.ne3 =*/ ne3, + /*.nb0 =*/ nb0, + /*.nb1 =*/ nb1, + /*.nb2 =*/ nb2, + /*.nb3 =*/ nb3, + /*.offs =*/ offs, + }; + [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26]; - [encoder setBytes:&offs length:sizeof(offs) atIndex:27]; - [encoder setBytes:&nb length:sizeof(nb) atIndex:28]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; if (bcast_row) { const int64_t n = ggml_nelements(dst)/4; @@ -1169,25 +1436,29 @@ static void ggml_metal_encode_node( default: GGML_ABORT("fatal error"); } + ggml_metal_kargs_repeat args = { + /*.ne00 =*/ ne00, + /*.ne01 =*/ ne01, + /*.ne02 =*/ ne02, + /*.ne03 =*/ ne03, + /*.nb00 =*/ nb00, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.nb03 =*/ nb03, + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + /*.ne2 =*/ ne2, + /*.ne3 =*/ ne3, + /*.nb0 =*/ nb0, + /*.nb1 =*/ nb1, + /*.nb2 =*/ nb2, + /*.nb3 =*/ nb3, + }; + [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); @@ -1216,25 +1487,29 @@ static void ggml_metal_encode_node( const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; + ggml_metal_kargs_cpy args = { + /*.ne00 =*/ ne00, + /*.ne01 =*/ ne01, + /*.ne02 =*/ ne02, + /*.ne03 =*/ ne03, + /*.nb00 =*/ nb00, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.nb03 =*/ nb03, + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + /*.ne2 =*/ ne2, + /*.ne3 =*/ ne3, + /*.nb0 =*/ nb0, + /*.nb1 =*/ nb1, + /*.nb2 =*/ nb2, + /*.nb3 =*/ nb3, + }; + [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00); @@ -1243,35 +1518,39 @@ static void ggml_metal_encode_node( const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD].pipeline; + ggml_metal_kargs_bin args = { + /*.ne00 =*/ ne00, + /*.ne01 =*/ ne01, + /*.ne02 =*/ ne02, + /*.ne03 =*/ ne03, + /*.nb00 =*/ nb00, + /*.nb01 =*/ pnb1, + /*.nb02 =*/ pnb2, + /*.nb03 =*/ pnb3, + /*.ne10 =*/ ne10, + /*.ne11 =*/ ne11, + /*.ne12 =*/ ne12, + /*.ne13 =*/ ne13, + /*.nb10 =*/ nb10, + /*.nb11 =*/ nb11, + /*.nb12 =*/ nb12, + /*.nb13 =*/ nb13, + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + /*.ne2 =*/ ne2, + /*.ne3 =*/ ne3, + /*.nb0 =*/ nb0, + /*.nb1 =*/ pnb1, + /*.nb2 =*/ pnb2, + /*.nb3 =*/ pnb3, + /*.offs =*/ offs, + }; + [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:6]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; - [encoder setBytes:&pnb1 length:sizeof(pnb1) atIndex:8]; - [encoder setBytes:&pnb2 length:sizeof(pnb2) atIndex:9]; - [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:10]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:12]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:18]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:19]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:20]; - [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:21]; - [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:22]; - [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:23]; - [encoder setBytes:&pnb1 length:sizeof(pnb1) atIndex:24]; - [encoder setBytes:&pnb2 length:sizeof(pnb2) atIndex:25]; - [encoder setBytes:&pnb3 length:sizeof(pnb3) atIndex:26]; - [encoder setBytes:&offs length:sizeof(offs) atIndex:27]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00); @@ -1312,10 +1591,10 @@ static void ggml_metal_encode_node( memcpy(&max, ((const int32_t *) dst->op_params) + 1, sizeof(float)); [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&min length:sizeof(min) atIndex:2]; - [encoder setBytes:&max length:sizeof(max) atIndex:3]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&min length:sizeof(min) atIndex:2]; + [encoder setBytes:&max length:sizeof(max) atIndex:3]; const int64_t n = ggml_nelements(dst); @@ -1419,6 +1698,18 @@ static void ggml_metal_encode_node( [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; + case GGML_UNARY_OP_ELU: + { + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ELU].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + + const int64_t n = ggml_nelements(dst); + + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; default: { GGML_LOG_WARN("%s: node %3d, op = %8s not implemented\n", __func__, idx, ggml_op_name(dst->op)); @@ -1487,6 +1778,7 @@ static void ggml_metal_encode_node( id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SUM_ROWS].pipeline; + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -1562,6 +1854,8 @@ static void ggml_metal_encode_node( const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + // TODO: add ggml_metal_kargs struct + // TODO: optimize (see https://github.com/ggerganov/llama.cpp/pull/10238/commits/7941b6b9ec29a2866fec6fa6c51612515ca509f6) [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; if (id_src1) { @@ -1578,6 +1872,7 @@ static void ggml_metal_encode_node( [encoder setBytes:&m0 length:sizeof(m0) atIndex:8]; [encoder setBytes:&m1 length:sizeof(m1) atIndex:9]; [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:10]; + [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; @@ -1594,6 +1889,7 @@ static void ggml_metal_encode_node( pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF].pipeline; } + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -1618,6 +1914,7 @@ static void ggml_metal_encode_node( id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SSM_CONV_F32].pipeline; + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; @@ -1688,6 +1985,7 @@ static void ggml_metal_encode_node( id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SSM_SCAN_F32].pipeline; + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; @@ -1735,310 +2033,490 @@ static void ggml_metal_encode_node( // find the break-even point where the matrix-matrix kernel becomes more efficient compared // to the matrix-vector kernel - int ne11_mm_min = 1; + const int ne11_mm_min = 4; -#if 0 - // the numbers below are measured on M2 Ultra for 7B and 13B models - // these numbers do not translate to other devices or model sizes - // TODO: need to find a better approach - if ([device.name isEqualToString:@"Apple M2 Ultra"]) { - switch (src0t) { - case GGML_TYPE_F16: ne11_mm_min = 2; break; - case GGML_TYPE_Q8_0: ne11_mm_min = 7; break; - case GGML_TYPE_Q2_K: ne11_mm_min = 15; break; - case GGML_TYPE_Q3_K: ne11_mm_min = 7; break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: ne11_mm_min = 15; break; - case GGML_TYPE_Q4_K: ne11_mm_min = 11; break; - case GGML_TYPE_Q5_0: // not tested yet - case GGML_TYPE_Q5_1: ne11_mm_min = 13; break; // not tested yet - case GGML_TYPE_Q5_K: ne11_mm_min = 7; break; - case GGML_TYPE_Q6_K: ne11_mm_min = 7; break; - default: ne11_mm_min = 1; break; - } - } -#endif + // first try to use small-batch mat-mv kernels + // these should be efficient for BS [2, ~8] + if (src1t == GGML_TYPE_F32 && (ne00%256 == 0) && + ( + ( + ( + src0t == GGML_TYPE_F16 || // TODO: helper function + src0t == GGML_TYPE_Q4_0 || + src0t == GGML_TYPE_Q4_1 || + src0t == GGML_TYPE_Q5_0 || + src0t == GGML_TYPE_Q5_1 || + src0t == GGML_TYPE_Q8_0 || + src0t == GGML_TYPE_IQ4_NL || + false) && (ne11 >= 2 && ne11 <= 8) + ) || + ( + ( + src0t == GGML_TYPE_Q4_K || + src0t == GGML_TYPE_Q5_K || + src0t == GGML_TYPE_Q6_K || + false) && (ne11 >= 4 && ne11 <= 8) + ) + ) + ) { + // TODO: determine the optimal parameters based on grid utilization + // I still don't know why we should not always use the maximum available threads: + // + // nsg = pipeline.maxTotalThreadsPerThreadgroup / 32 + // + // my current hypothesis is that the work grid is not evenly divisible for different nsg + // values and there can be some tail effects when nsg is high. need to confirm this + // + const int nsg = 2; // num simdgroups per threadgroup + const int nxpsg = ne11 < 3 ? 16 : 8; // num threads along row per simdgroup + const int nypsg = 32/nxpsg; // num threads along col per simdgroup (i.e. a simdgroup processes that many src0 rows at a time) + const int r0ptg = nypsg*nsg; // num src0 rows per threadgroup + int r1ptg = 4; // num src1 rows per threadgroup - // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs - // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel - if ([device supportsFamily:MTLGPUFamilyApple7] && - !ggml_is_transposed(src0) && - !ggml_is_transposed(src1) && - src1t == GGML_TYPE_F32 && - ne00 % 32 == 0 && ne00 >= 64 && - (ne11 > ne11_mm_min || (ggml_is_quantized(src0t) && ne12 > 1))) { - //printf("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); + // note: not sure how optimal are those across all different hardware. there might be someting cleverer + switch (ne11) { + case 2: + r1ptg = 2; break; + case 3: + case 6: + r1ptg = 3; break; + case 4: + case 7: + case 8: + r1ptg = 4; break; + case 5: + r1ptg = 5; break; + }; - // some Metal matrix data types require aligned pointers - // ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5) - switch (src0->type) { - case GGML_TYPE_F32: GGML_ASSERT(nb01 % 16 == 0); break; - case GGML_TYPE_F16: GGML_ASSERT(nb01 % 8 == 0); break; - default: break; - } + id pipeline = nil; - id pipeline = nil; + switch (src0->type) { + case GGML_TYPE_F16: + switch (r1ptg) { + case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_2].pipeline; break; + case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_3].pipeline; break; + case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_4].pipeline; break; + case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_5].pipeline; break; + default: GGML_ABORT("not implemented"); + } break; + case GGML_TYPE_Q4_0: + switch (r1ptg) { + case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_0_F32_R1_2].pipeline; break; + case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_0_F32_R1_3].pipeline; break; + case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_0_F32_R1_4].pipeline; break; + case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_0_F32_R1_5].pipeline; break; + default: GGML_ABORT("not implemented"); + } break; + case GGML_TYPE_Q4_1: + switch (r1ptg) { + case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_1_F32_R1_2].pipeline; break; + case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_1_F32_R1_3].pipeline; break; + case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_1_F32_R1_4].pipeline; break; + case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_1_F32_R1_5].pipeline; break; + default: GGML_ABORT("not implemented"); + } break; + case GGML_TYPE_Q5_0: + switch (r1ptg) { + case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_0_F32_R1_2].pipeline; break; + case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_0_F32_R1_3].pipeline; break; + case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_0_F32_R1_4].pipeline; break; + case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_0_F32_R1_5].pipeline; break; + default: GGML_ABORT("not implemented"); + } break; + case GGML_TYPE_Q5_1: + switch (r1ptg) { + case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_1_F32_R1_2].pipeline; break; + case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_1_F32_R1_3].pipeline; break; + case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_1_F32_R1_4].pipeline; break; + case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_1_F32_R1_5].pipeline; break; + default: GGML_ABORT("not implemented"); + } break; + case GGML_TYPE_Q8_0: + switch (r1ptg) { + case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_2].pipeline; break; + case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_3].pipeline; break; + case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_4].pipeline; break; + case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_5].pipeline; break; + default: GGML_ABORT("not implemented"); + } break; + case GGML_TYPE_Q4_K: + switch (r1ptg) { + case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_2].pipeline; break; + case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_3].pipeline; break; + case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_4].pipeline; break; + case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_5].pipeline; break; + default: GGML_ABORT("not implemented"); + } break; + case GGML_TYPE_Q5_K: + switch (r1ptg) { + case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_K_F32_R1_2].pipeline; break; + case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_K_F32_R1_3].pipeline; break; + case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_K_F32_R1_4].pipeline; break; + case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q5_K_F32_R1_5].pipeline; break; + default: GGML_ABORT("not implemented"); + } break; + case GGML_TYPE_Q6_K: + switch (r1ptg) { + case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q6_K_F32_R1_2].pipeline; break; + case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q6_K_F32_R1_3].pipeline; break; + case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q6_K_F32_R1_4].pipeline; break; + case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q6_K_F32_R1_5].pipeline; break; + default: GGML_ABORT("not implemented"); + } break; + case GGML_TYPE_IQ4_NL: + switch (r1ptg) { + case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_IQ4_NL_F32_R1_2].pipeline; break; + case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_IQ4_NL_F32_R1_3].pipeline; break; + case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_IQ4_NL_F32_R1_4].pipeline; break; + case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_IQ4_NL_F32_R1_5].pipeline; break; + default: GGML_ABORT("not implemented"); + } break; + default: GGML_ABORT("not implemented"); + } - switch (src0->type) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32 ].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32 ].pipeline; break; - case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32 ].pipeline; break; - case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32 ].pipeline; break; - case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32 ].pipeline; break; - case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32 ].pipeline; break; - case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32 ].pipeline; break; - case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32 ].pipeline; break; - case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32 ].pipeline; break; - case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32 ].pipeline; break; - case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32 ].pipeline; break; - case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32 ].pipeline; break; - case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32].pipeline; break; - case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32 ].pipeline; break; - case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32].pipeline; break; - case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32 ].pipeline; break; - case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32 ].pipeline; break; - case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32 ].pipeline; break; - case GGML_TYPE_IQ1_M: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32 ].pipeline; break; - case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32 ].pipeline; break; - case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32 ].pipeline; break; - default: GGML_ABORT("MUL MAT-MAT not implemented"); - } + ggml_metal_kargs_mul_mv_ext args = { + /*.ne00 =*/ ne00, + /*.ne01 =*/ ne01, + /*.ne02 =*/ ne02, + /*.nb00 =*/ nb00, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.nb03 =*/ nb03, + /*.ne10 =*/ ne10, + /*.ne11 =*/ ne11, + /*.ne12 =*/ ne12, + /*.nb10 =*/ nb10, + /*.nb11 =*/ nb11, + /*.nb12 =*/ nb12, + /*.nb13 =*/ nb13, + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + /*.r2 =*/ r2, + /*.r3 =*/ r3, + /*.nsg =*/ nsg, + /*.nxpsg =*/ nxpsg, + /*.r1ptg =*/ r1ptg, + }; - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:5]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:6]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:7]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:8]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:9]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:10]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:11]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:12]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:13]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:14]; - [encoder setBytes:&r2 length:sizeof(r2) atIndex:15]; - [encoder setBytes:&r3 length:sizeof(r3) atIndex:16]; - [encoder setThreadgroupMemoryLength:8192 atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake( (ne11 + 31)/32, (ne01 + 63)/64, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)]; - } else { - int nth0 = 32; - int nth1 = 1; - int nrows = 1; - //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); + [encoder setComputePipelineState:pipeline]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; - id pipeline = nil; + //printf("ne01 = %lld nr0ptg = %d\n", ne01, nr0ptg); + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + r0ptg - 1)/r0ptg, (ne11 + r1ptg - 1)/r1ptg, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)]; + } else + // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs + // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel + if ([device supportsFamily:MTLGPUFamilyApple7] && + !ggml_is_transposed(src0) && + !ggml_is_transposed(src1) && + src1t == GGML_TYPE_F32 && + ne00 % 32 == 0 && ne00 >= 64 && + (ne11 > ne11_mm_min || (ggml_is_quantized(src0t) && ne12 > 1))) { + //printf("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); - // use custom matrix x vector kernel - switch (src0t) { - case GGML_TYPE_F32: - { - GGML_ASSERT(src1t == GGML_TYPE_F32); - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32].pipeline; + // some Metal matrix data types require aligned pointers + // ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5) + switch (src0->type) { + case GGML_TYPE_F32: GGML_ASSERT(nb01 % 16 == 0); break; + case GGML_TYPE_F16: GGML_ASSERT(nb01 % 8 == 0); break; + case GGML_TYPE_BF16: GGML_ASSERT(nb01 % 8 == 0); break; + default: break; + } + + id pipeline = nil; + + switch (src0->type) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32 ].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32 ].pipeline; break; + case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_BF16_F32 ].pipeline; break; + case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32 ].pipeline; break; + case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32 ].pipeline; break; + case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32 ].pipeline; break; + case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32 ].pipeline; break; + case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32 ].pipeline; break; + case GGML_TYPE_Q2_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32 ].pipeline; break; + case GGML_TYPE_Q3_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32 ].pipeline; break; + case GGML_TYPE_Q4_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32 ].pipeline; break; + case GGML_TYPE_Q5_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32 ].pipeline; break; + case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32 ].pipeline; break; + case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32].pipeline; break; + case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32 ].pipeline; break; + case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32].pipeline; break; + case GGML_TYPE_IQ3_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32 ].pipeline; break; + case GGML_TYPE_IQ2_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32 ].pipeline; break; + case GGML_TYPE_IQ1_S: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32 ].pipeline; break; + case GGML_TYPE_IQ1_M: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32 ].pipeline; break; + case GGML_TYPE_IQ4_NL: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32 ].pipeline; break; + case GGML_TYPE_IQ4_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32 ].pipeline; break; + default: GGML_ABORT("MUL MAT-MAT not implemented"); + } + + ggml_metal_kargs_mul_mm args = { + /*.ne00 =*/ ne00, + /*.ne02 =*/ ne02, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.nb03 =*/ nb03, + /*.ne12 =*/ ne12, + /*.nb10 =*/ nb10, + /*.nb11 =*/ nb11, + /*.nb12 =*/ nb12, + /*.nb13 =*/ nb13, + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + /*.r2 =*/ r2, + /*.r3 =*/ r3, + }; + + [encoder setComputePipelineState:pipeline]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; + + [encoder setThreadgroupMemoryLength:8192 atIndex:0]; + [encoder dispatchThreadgroups:MTLSizeMake( (ne11 + 31)/32, (ne01 + 63)/64, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)]; + } else { + int nth0 = 32; + int nth1 = 1; + int nrows = 1; + //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12); + + id pipeline = nil; + + // use custom matrix x vector kernel + switch (src0t) { + case GGML_TYPE_F32: + { + GGML_ASSERT(src1t == GGML_TYPE_F32); + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32].pipeline; + nrows = 4; + } break; + case GGML_TYPE_F16: + { + nth0 = 32; + nth1 = 1; + if (src1t == GGML_TYPE_F32) { + if (ne11 * ne12 < 4) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW].pipeline; + } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4].pipeline; + nrows = ne11; + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32].pipeline; nrows = 4; - } break; - case GGML_TYPE_F16: - { - nth0 = 32; - nth1 = 1; - if (src1t == GGML_TYPE_F32) { - if (ne11 * ne12 < 4) { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW].pipeline; - } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4].pipeline; - nrows = ne11; - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32].pipeline; - nrows = 4; - } - } else { - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16].pipeline; - nrows = 4; - } - } break; - case GGML_TYPE_Q4_0: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32].pipeline; - } break; - case GGML_TYPE_Q4_1: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32].pipeline; - } break; - case GGML_TYPE_Q5_0: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32].pipeline; - } break; - case GGML_TYPE_Q5_1: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32].pipeline; - } break; - case GGML_TYPE_Q8_0: - { - nth0 = 8; - nth1 = 8; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32].pipeline; - } break; - case GGML_TYPE_Q2_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32].pipeline; - } break; - case GGML_TYPE_Q3_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32].pipeline; - } break; - case GGML_TYPE_Q4_K: - { - nth0 = 4; //1; - nth1 = 8; //32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32].pipeline; - } break; - case GGML_TYPE_Q5_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32].pipeline; - } break; - case GGML_TYPE_Q6_K: - { - nth0 = 2; - nth1 = 32; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32].pipeline; - } break; - case GGML_TYPE_IQ2_XXS: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32].pipeline; - } break; - case GGML_TYPE_IQ2_XS: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32].pipeline; - } break; - case GGML_TYPE_IQ3_XXS: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32].pipeline; - } break; - case GGML_TYPE_IQ3_S: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32].pipeline; - } break; - case GGML_TYPE_IQ2_S: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32].pipeline; - } break; - case GGML_TYPE_IQ1_S: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32].pipeline; - } break; - case GGML_TYPE_IQ1_M: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32].pipeline; - } break; - case GGML_TYPE_IQ4_NL: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32].pipeline; - } break; - case GGML_TYPE_IQ4_XS: - { - nth0 = 4; - nth1 = 16; - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32].pipeline; - } break; - default: - { - GGML_LOG_ERROR("Asserting on type %d\n", (int)src0t); - GGML_ABORT("not implemented"); } - }; + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16].pipeline; + nrows = 4; + } + } break; + case GGML_TYPE_BF16: + { + nth0 = 32; + nth1 = 1; + if (src1t == GGML_TYPE_F32) { + if (ne11 * ne12 < 4) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_1ROW].pipeline; + } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_L4].pipeline; + nrows = ne11; + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32].pipeline; + nrows = 4; + } + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_BF16].pipeline; + nrows = 4; + } + } break; + case GGML_TYPE_Q4_0: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32].pipeline; + } break; + case GGML_TYPE_Q4_1: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32].pipeline; + } break; + case GGML_TYPE_Q5_0: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32].pipeline; + } break; + case GGML_TYPE_Q5_1: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32].pipeline; + } break; + case GGML_TYPE_Q8_0: + { + nth0 = 8; + nth1 = 8; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32].pipeline; + } break; + case GGML_TYPE_Q2_K: + { + nth0 = 2; + nth1 = 32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32].pipeline; + } break; + case GGML_TYPE_Q3_K: + { + nth0 = 2; + nth1 = 32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32].pipeline; + } break; + case GGML_TYPE_Q4_K: + { + nth0 = 4; //1; + nth1 = 8; //32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32].pipeline; + } break; + case GGML_TYPE_Q5_K: + { + nth0 = 2; + nth1 = 32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32].pipeline; + } break; + case GGML_TYPE_Q6_K: + { + nth0 = 2; + nth1 = 32; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32].pipeline; + } break; + case GGML_TYPE_IQ2_XXS: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32].pipeline; + } break; + case GGML_TYPE_IQ2_XS: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32].pipeline; + } break; + case GGML_TYPE_IQ3_XXS: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32].pipeline; + } break; + case GGML_TYPE_IQ3_S: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32].pipeline; + } break; + case GGML_TYPE_IQ2_S: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32].pipeline; + } break; + case GGML_TYPE_IQ1_S: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32].pipeline; + } break; + case GGML_TYPE_IQ1_M: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32].pipeline; + } break; + case GGML_TYPE_IQ4_NL: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32].pipeline; + } break; + case GGML_TYPE_IQ4_XS: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32].pipeline; + } break; + default: + { + GGML_LOG_ERROR("Asserting on type %d\n", (int)src0t); + GGML_ABORT("not implemented"); + } + }; - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:10]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:11]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:13]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:14]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:15]; - [encoder setBytes:&nb13 length:sizeof(nb13) atIndex:16]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:17]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:18]; - [encoder setBytes:&r2 length:sizeof(r2) atIndex:19]; - [encoder setBytes:&r3 length:sizeof(r3) atIndex:20]; + ggml_metal_kargs_mul_mv args = { + /*.ne00 =*/ ne00, + /*.ne01 =*/ ne01, + /*.ne02 =*/ ne02, + /*.nb00 =*/ nb00, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.nb03 =*/ nb03, + /*.ne10 =*/ ne10, + /*.ne11 =*/ ne11, + /*.ne12 =*/ ne12, + /*.nb10 =*/ nb10, + /*.nb11 =*/ nb11, + /*.nb12 =*/ nb12, + /*.nb13 =*/ nb13, + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + /*.r2 =*/ r2, + /*.r3 =*/ r3, + }; - if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q5_0 || - src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || src0t == GGML_TYPE_Q2_K || - src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) { - const int mem_size = src0t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128; - [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_IQ3_XXS || src0t == GGML_TYPE_IQ3_S) { - const int mem_size = src0t == GGML_TYPE_IQ3_XXS ? 256*4+128 : 512*4; - [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_IQ4_NL || src0t == GGML_TYPE_IQ4_XS) { - const int mem_size = 32*sizeof(float); - [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_Q4_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_Q3_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_Q5_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - else if (src0t == GGML_TYPE_Q6_K) { - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } else { - const int64_t ny = (ne11 + nrows - 1)/nrows; - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; - } - } + [encoder setComputePipelineState:pipeline]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; + + if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q5_0 || + src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 || src0t == GGML_TYPE_Q2_K || + src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) { + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) { + const int mem_size = src0t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128; + [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src0t == GGML_TYPE_IQ3_XXS || src0t == GGML_TYPE_IQ3_S) { + const int mem_size = src0t == GGML_TYPE_IQ3_XXS ? 256*4+128 : 512*4; + [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src0t == GGML_TYPE_IQ4_NL || src0t == GGML_TYPE_IQ4_XS) { + const int mem_size = 32*sizeof(float); + [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src0t == GGML_TYPE_Q4_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src0t == GGML_TYPE_Q3_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src0t == GGML_TYPE_Q5_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src0t == GGML_TYPE_Q6_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } else { + const int64_t ny = (ne11 + nrows - 1)/nrows; + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + } } break; case GGML_OP_MUL_MAT_ID: { @@ -2077,12 +2555,12 @@ static void ggml_metal_encode_node( if ([device supportsFamily:MTLGPUFamilyApple7] && ne00 % 32 == 0 && ne00 >= 64 && dst_rows > dst_rows_min) { - // some Metal matrix data types require aligned pointers // ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5) switch (src0->type) { - case GGML_TYPE_F32: GGML_ASSERT(nb01 % 16 == 0); break; - case GGML_TYPE_F16: GGML_ASSERT(nb01 % 8 == 0); break; + case GGML_TYPE_F32: GGML_ASSERT(nb01 % 16 == 0); break; + case GGML_TYPE_F16: GGML_ASSERT(nb01 % 8 == 0); break; + case GGML_TYPE_BF16: GGML_ASSERT(nb01 % 8 == 0); break; default: break; } @@ -2091,6 +2569,7 @@ static void ggml_metal_encode_node( switch (src0->type) { case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32 ].pipeline; break; case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32 ].pipeline; break; + case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_BF16_F32 ].pipeline; break; case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32 ].pipeline; break; case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32 ].pipeline; break; case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32 ].pipeline; break; @@ -2113,27 +2592,30 @@ static void ggml_metal_encode_node( default: GGML_ABORT("MUL_MAT_ID not implemented"); } + ggml_metal_kargs_mul_mm_id args = { + /*.nei0 =*/ ne20, + /*.nei1 =*/ ne21, + /*.nbi1 =*/ nb21, + /*.ne00 =*/ ne00, + /*.ne02 =*/ ne02, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.ne11 =*/ ne11, + /*.ne12 =*/ ne12, + /*.ne13 =*/ ne13, + /*.nb10 =*/ nb10, + /*.nb11 =*/ nb11, + /*.nb12 =*/ nb12, + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + }; + [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBuffer:id_src2 offset:offs_src2 atIndex:3]; - [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4]; - [encoder setBytes:&ne21 length:sizeof(ne21) atIndex:5]; - [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:6]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:7]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:8]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:9]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:10]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:11]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:12]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:13]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:14]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:15]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:16]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:17]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:18]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:19]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; + [encoder setBuffer:id_src2 offset:offs_src2 atIndex:4]; [encoder setThreadgroupMemoryLength:GGML_PAD(8192 + dst_rows*4/*sizeof(ushort2)*/, 16) atIndex:0]; @@ -2160,6 +2642,13 @@ static void ggml_metal_encode_node( nth1 = 1; pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32].pipeline; } break; + case GGML_TYPE_BF16: + { + GGML_ASSERT(src1t == GGML_TYPE_F32); + nth0 = 32; + nth1 = 1; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_BF16_F32].pipeline; + } break; case GGML_TYPE_Q4_0: { nth0 = 8; @@ -2285,30 +2774,34 @@ static void ggml_metal_encode_node( GGML_ASSERT(ne00 >= nth0*nth1); } + ggml_metal_kargs_mul_mv_id args = { + /*.nei0 =*/ ne20, + /*.nei1 =*/ ne21, + /*.nbi1 =*/ nb21, + /*.ne00 =*/ ne00, + /*.ne01 =*/ ne01, + /*.ne02 =*/ ne02, + /*.nb00 =*/ nb00, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.ne10 =*/ ne10, + /*.ne11 =*/ ne11, + /*.ne12 =*/ ne12, + /*.ne13 =*/ ne13, + /*.nb10 =*/ nb10, + /*.nb11 =*/ nb11, + /*.nb12 =*/ nb12, + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + /*.nb1 =*/ nb1, + }; + [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBuffer:id_src2 offset:offs_src2 atIndex:3]; - [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4]; - [encoder setBytes:&ne21 length:sizeof(ne21) atIndex:5]; - [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:6]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:7]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:8]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:9]; - [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:10]; - [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:11]; - [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:12]; - [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:13]; - [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:14]; - [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:15]; - [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:16]; - [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:17]; - [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:18]; - [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:19]; - [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:20]; - [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:21]; - [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:22]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; + [encoder setBuffer:id_src2 offset:offs_src2 atIndex:4]; const int64_t _ne1 = 1; const int tgz = dst_rows; @@ -2357,6 +2850,7 @@ static void ggml_metal_encode_node( switch (src0->type) { case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F32 ].pipeline; break; case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_F16 ].pipeline; break; + case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_BF16 ].pipeline; break; case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0 ].pipeline; break; case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1 ].pipeline; break; case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0 ].pipeline; break; @@ -2380,6 +2874,7 @@ static void ggml_metal_encode_node( default: GGML_ABORT("not implemented"); } + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; @@ -2403,20 +2898,28 @@ static void ggml_metal_encode_node( float eps; memcpy(&eps, dst->op_params, sizeof(float)); + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RMS_NORM].pipeline; + int nth = 32; // SIMD width - while (nth < ne00/4 && nth < 1024) { + while (nth < ne00/4 && nth < (int) pipeline.maxTotalThreadsPerThreadgroup) { nth *= 2; } - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_RMS_NORM].pipeline; + nth = MIN(nth, ne00/4); + + ggml_metal_kargs_rms_norm args = { + /*.ne00 =*/ ne00, + /*.ne00_4 =*/ ne00/4, + /*.nb01 =*/ nb01, + /*.eps =*/ eps, + }; [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; - [encoder setBytes:&eps length:sizeof( float) atIndex:4]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; const int64_t nrows = ggml_nrows(src0); @@ -2425,7 +2928,6 @@ static void ggml_metal_encode_node( } break; case GGML_OP_GROUP_NORM: { - GGML_ASSERT(ne00 % 4 == 0); GGML_ASSERT(ggml_is_contiguous(src0)); float eps; @@ -2441,6 +2943,7 @@ static void ggml_metal_encode_node( id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GROUP_NORM].pipeline; + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -2458,22 +2961,35 @@ static void ggml_metal_encode_node( } break; case GGML_OP_NORM: { + GGML_ASSERT(ne00 % 4 == 0); GGML_ASSERT(ggml_is_contiguous_1(src0)); float eps; memcpy(&eps, dst->op_params, sizeof(float)); - const int nth = MIN(256, ne00); - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_NORM].pipeline; + int nth = 32; // SIMD width + + while (nth < ne00/4 && nth < (int) pipeline.maxTotalThreadsPerThreadgroup) { + nth *= 2; + } + + nth = MIN(nth, ne00/4); + + ggml_metal_kargs_norm args = { + /*.ne00 =*/ ne00, + /*.ne00_4 =*/ ne00/4, + /*.nb01 =*/ nb01, + /*.eps =*/ eps, + }; + [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; - [encoder setBytes:&eps length:sizeof( float) atIndex:4]; - [encoder setThreadgroupMemoryLength:GGML_PAD(nth*sizeof(float), 16) atIndex:0]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + + [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; const int64_t nrows = ggml_nrows(src0); @@ -2523,40 +3039,44 @@ static void ggml_metal_encode_node( }; } + ggml_metal_kargs_rope args = { + /*.ne00 =*/ ne00, + /*.ne01 =*/ ne01, + /*.ne02 =*/ ne02, + /*.ne03 =*/ ne03, + /*.nb00 =*/ nb00, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.nb03 =*/ nb03, + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + /*.ne2 =*/ ne2, + /*.ne3 =*/ ne3, + /*.nb0 =*/ nb0, + /*.nb1 =*/ nb1, + /*.nb2 =*/ nb2, + /*.nb3 =*/ nb3, + /*.n_past =*/ n_past, + /*.n_dims =*/ n_dims, + /*.n_ctx_orig =*/ n_ctx_orig, + /*.freq_base =*/ freq_base, + /*.freq_scale =*/ freq_scale, + /*.ext_factor =*/ ext_factor, + /*.attn_factor =*/ attn_factor, + /*.beta_fast =*/ beta_fast, + /*.beta_slow =*/ beta_slow, + }; + [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2]; if (id_src2 != nil) { - [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; + [encoder setBuffer:id_src2 offset:offs_src2 atIndex:3]; } else { - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:2]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:3]; } - [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:6]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:7]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:10]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:11]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:14]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:15]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:17]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:18]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:19]; - [encoder setBytes:&n_past length:sizeof( int) atIndex:20]; - [encoder setBytes:&n_dims length:sizeof( int) atIndex:21]; - [encoder setBytes:&n_ctx_orig length:sizeof( int) atIndex:22]; - [encoder setBytes:&freq_base length:sizeof( float) atIndex:23]; - [encoder setBytes:&freq_scale length:sizeof( float) atIndex:24]; - [encoder setBytes:&ext_factor length:sizeof( float) atIndex:25]; - [encoder setBytes:&attn_factor length:sizeof( float) atIndex:26]; - [encoder setBytes:&beta_fast length:sizeof( float) atIndex:27]; - [encoder setBytes:&beta_slow length:sizeof( float) atIndex:28]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:4]; [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; @@ -2613,6 +3133,7 @@ static void ggml_metal_encode_node( default: GGML_ABORT("fatal error"); }; + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -2642,6 +3163,49 @@ static void ggml_metal_encode_node( [encoder dispatchThreadgroups:MTLSizeMake(IC, OH, OW) threadsPerThreadgroup:MTLSizeMake(N, KH, KW)]; } } break; + case GGML_OP_CONV_TRANSPOSE_1D: + { + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguous(src1)); + GGML_ASSERT(src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; + + const int32_t IC = src1->ne[1]; + const int32_t IL = src1->ne[0]; + + const int32_t K = src0->ne[0]; + + const int32_t OL = dst->ne[0]; + const int32_t OC = dst->ne[1]; + + id pipeline; + + switch (src0->type) { + case GGML_TYPE_F32: { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CONV_TRANSPOSE_1D_F32_F32].pipeline; + } break; + case GGML_TYPE_F16: { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CONV_TRANSPOSE_1D_F16_F32].pipeline; + } break; + default: GGML_ABORT("fatal error"); + }; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&IC length:sizeof( int32_t) atIndex:3]; + [encoder setBytes:&IL length:sizeof( int32_t) atIndex:4]; + [encoder setBytes:&K length:sizeof( int32_t) atIndex:5]; + [encoder setBytes:&s0 length:sizeof( int32_t) atIndex:6]; + [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:7]; + [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:8]; + + [encoder dispatchThreadgroups:MTLSizeMake(OL, OC, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; case GGML_OP_UPSCALE: { GGML_ASSERT(src0->type == GGML_TYPE_F32); @@ -2653,6 +3217,7 @@ static void ggml_metal_encode_node( const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_UPSCALE_F32].pipeline; + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -2687,6 +3252,7 @@ static void ggml_metal_encode_node( id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_PAD_F32].pipeline; + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -2709,6 +3275,38 @@ static void ggml_metal_encode_node( const int nth = MIN(1024, ne0); + [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; + case GGML_OP_PAD_REFLECT_1D: + { + GGML_ASSERT(src0->type == GGML_TYPE_F32); + + const int32_t p0 = ((const int32_t *)(dst->op_params))[0]; + const int32_t p1 = ((const int32_t *)(dst->op_params))[1]; + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_PAD_REFLECT_1D_F32].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; + [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:6]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:7]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:8]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:9]; + [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:10]; + [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:11]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:12]; + [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:13]; + [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:14]; + [encoder setBytes:&p0 length:sizeof(p0) atIndex:15]; + [encoder setBytes:&p1 length:sizeof(p1) atIndex:16]; + + const int nth = MIN(1024, ne0); + [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; case GGML_OP_ARANGE: @@ -2723,6 +3321,7 @@ static void ggml_metal_encode_node( id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARANGE_F32].pipeline; + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_dst offset:offs_dst atIndex:0]; [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:1]; @@ -2744,6 +3343,7 @@ static void ggml_metal_encode_node( id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32].pipeline; + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -2782,6 +3382,7 @@ static void ggml_metal_encode_node( default: GGML_ABORT("fatal error"); }; + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -2800,6 +3401,7 @@ static void ggml_metal_encode_node( id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32].pipeline; + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -2815,6 +3417,7 @@ static void ggml_metal_encode_node( GGML_ASSERT(ne11 % 32 == 0); GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == src2->type); GGML_ASSERT(ggml_are_same_shape (src1, src2)); @@ -2861,27 +3464,176 @@ static void ggml_metal_encode_node( bool use_vec_kernel = false; + // TODO: add vec kernels for (ne00%64 == 0) and maybe also for (ne00%32 == 0) + // for now avoiding mainly to keep the number of templates/kernels a bit lower if (ne01 >= 4 || (ne00%128 != 0)) { - switch (ne00) { - case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64 ].pipeline; break; - case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80 ].pipeline; break; - case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96 ].pipeline; break; - case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112].pipeline; break; - case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128].pipeline; break; - //case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256].pipeline; break; + switch (src1->type) { + case GGML_TYPE_F16: + { + switch (ne00) { + case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64 ].pipeline; break; + case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80 ].pipeline; break; + case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96 ].pipeline; break; + case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112].pipeline; break; + case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128].pipeline; break; + case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256].pipeline; break; + default: + { + GGML_LOG_ERROR("unsupported size: %lld\n", ne00); + GGML_LOG_ERROR("add template specialization for this size\n"); + GGML_ABORT("add template specialization for this size"); + } + } + } break; + case GGML_TYPE_BF16: + { + switch (ne00) { + case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H64 ].pipeline; break; + case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H80 ].pipeline; break; + case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H96 ].pipeline; break; + case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H112].pipeline; break; + case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H128].pipeline; break; + case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_BF16_H256].pipeline; break; + default: + { + GGML_LOG_ERROR("unsupported size: %lld\n", ne00); + GGML_LOG_ERROR("add template specialization for this size\n"); + GGML_ABORT("add template specialization for this size"); + } + } + } break; + case GGML_TYPE_Q4_0: + { + switch (ne00) { + case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H64 ].pipeline; break; + case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H80 ].pipeline; break; + case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H96 ].pipeline; break; + case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H112].pipeline; break; + case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H128].pipeline; break; + case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_0_H256].pipeline; break; + default: + { + GGML_LOG_ERROR("unsupported size: %lld\n", ne00); + GGML_LOG_ERROR("add template specialization for this size\n"); + GGML_ABORT("add template specialization for this size"); + } + } + } break; + case GGML_TYPE_Q4_1: + { + switch (ne00) { + case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H64 ].pipeline; break; + case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H80 ].pipeline; break; + case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H96 ].pipeline; break; + case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H112].pipeline; break; + case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H128].pipeline; break; + case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q4_1_H256].pipeline; break; + default: + { + GGML_LOG_ERROR("unsupported size: %lld\n", ne00); + GGML_LOG_ERROR("add template specialization for this size\n"); + GGML_ABORT("add template specialization for this size"); + } + } + } break; + case GGML_TYPE_Q5_0: + { + switch (ne00) { + case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H64 ].pipeline; break; + case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H80 ].pipeline; break; + case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H96 ].pipeline; break; + case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H112].pipeline; break; + case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H128].pipeline; break; + case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_0_H256].pipeline; break; + default: + { + GGML_LOG_ERROR("unsupported size: %lld\n", ne00); + GGML_LOG_ERROR("add template specialization for this size\n"); + GGML_ABORT("add template specialization for this size"); + } + } + } break; + case GGML_TYPE_Q5_1: + { + switch (ne00) { + case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H64 ].pipeline; break; + case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H80 ].pipeline; break; + case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H96 ].pipeline; break; + case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H112].pipeline; break; + case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H128].pipeline; break; + case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q5_1_H256].pipeline; break; + default: + { + GGML_LOG_ERROR("unsupported size: %lld\n", ne00); + GGML_LOG_ERROR("add template specialization for this size\n"); + GGML_ABORT("add template specialization for this size"); + } + } + } break; + case GGML_TYPE_Q8_0: + { + switch (ne00) { + case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H64 ].pipeline; break; + case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H80 ].pipeline; break; + case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H96 ].pipeline; break; + case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H112].pipeline; break; + case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H128].pipeline; break; + case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H256].pipeline; break; + default: + { + GGML_LOG_ERROR("unsupported size: %lld\n", ne00); + GGML_LOG_ERROR("add template specialization for this size\n"); + GGML_ABORT("add template specialization for this size"); + } + } + } break; default: - { - GGML_LOG_ERROR("unsupported size: %lld\n", ne00); - GGML_LOG_ERROR("add template specialization for this size\n"); - GGML_ABORT("add template specialization for this size"); - } + { + GGML_LOG_ERROR("unsupported type: %d\n", src1->type); + GGML_LOG_ERROR("add template specialization for this type\n"); + GGML_ABORT("add template specialization for this type"); + } } } else { use_vec_kernel = true; switch (ne00) { - case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128].pipeline; break; - //case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256].pipeline; break; + case 128: + { + switch (src1->type) { + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128].pipeline; break; + case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H128].pipeline; break; + case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H128].pipeline; break; + case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H128].pipeline; break; + case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H128].pipeline; break; + case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H128].pipeline; break; + case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H128].pipeline; break; + default: + { + GGML_LOG_ERROR("unsupported type: %d\n", src1->type); + GGML_LOG_ERROR("add template specialization for this type\n"); + GGML_ABORT("add template specialization for this type"); + } + } + } break; + case 256: + { + switch (src1->type) { + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256].pipeline; break; + case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H256].pipeline; break; + case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H256].pipeline; break; + case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H256].pipeline; break; + case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H256].pipeline; break; + case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H256].pipeline; break; + case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H256].pipeline; break; + default: + { + GGML_LOG_ERROR("unsupported type: %d\n", src1->type); + GGML_LOG_ERROR("add template specialization for this type\n"); + GGML_ABORT("add template specialization for this type"); + } + } + } break; default: { GGML_LOG_ERROR("unsupported size: %lld\n", ne00); @@ -2891,40 +3643,41 @@ static void ggml_metal_encode_node( } } + ggml_metal_kargs_flash_attn_ext args = { + /*.ne01 =*/ ne01, + /*.ne02 =*/ ne02, + /*.ne03 =*/ ne03, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.nb03 =*/ nb03, + /*.ne11 =*/ ne11, + /*.ne_12_2 =*/ ne12, + /*.ne_12_3 =*/ ne13, + /*.nb_12_1 =*/ nb11, + /*.nb_12_2 =*/ nb12, + /*.nb_12_3 =*/ nb13, + /*.nb31 =*/ nb31, + /*.ne1 =*/ ne1, + /*.ne2 =*/ ne2, + /*.scale =*/ scale, + /*.max_bias =*/ max_bias, + /*.m0 =*/ m0, + /*.m1 =*/ m1, + /*.n_head_log2 =*/ n_head_log2, + /*.logit_softcap =*/ logit_softcap, + }; + [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2]; + [encoder setBuffer:id_src2 offset:offs_src2 atIndex:3]; if (id_src3) { - [encoder setBuffer:id_src3 offset:offs_src3 atIndex:3]; + [encoder setBuffer:id_src3 offset:offs_src3 atIndex:4]; } else { - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:3]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:4]; } - [encoder setBuffer:id_dst offset:offs_dst atIndex:4]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:6]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:7]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:10]; - [encoder setBytes:&ne11 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne12 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne13 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&nb11 length:sizeof(uint64_t) atIndex:14]; - [encoder setBytes:&nb12 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb13 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb21 length:sizeof(uint64_t) atIndex:17]; - [encoder setBytes:&nb22 length:sizeof(uint64_t) atIndex:18]; - [encoder setBytes:&nb23 length:sizeof(uint64_t) atIndex:19]; - [encoder setBytes:&nb31 length:sizeof(uint64_t) atIndex:20]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:21]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:22]; - [encoder setBytes:&scale length:sizeof( float) atIndex:23]; - [encoder setBytes:&max_bias length:sizeof( float) atIndex:24]; - [encoder setBytes:&m0 length:sizeof(m0) atIndex:25]; - [encoder setBytes:&m1 length:sizeof(m1) atIndex:26]; - [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:27]; - [encoder setBytes:&logit_softcap length:sizeof(logit_softcap) atIndex:28]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:5]; if (!use_vec_kernel) { // half8x8 kernel @@ -2935,10 +3688,19 @@ static void ggml_metal_encode_node( GGML_ASSERT(nqptg % 8 == 0); GGML_ASSERT(ncpsg % 32 == 0); + // 2*(2*ncpsg + nqptg)*(nsg) + // ncpsg soft_max values + ncpsg mask values + a diagonal scaling matrix (in float) + // + // 16*32*(nsg) + // the shared memory needed for the simdgroups to load the KV cache + // each thread loads (dequantizes) 16 head elements, there are 32 threads in th SG + // +#define FATTN_SMEM(nsg) (GGML_PAD((nqptg*(ne00 + 2*(2*ncpsg + nqptg)*(nsg)) + 16*32*(nsg))*(sizeof(float)/2), 16)) + int64_t nsgmax = 2; while (true) { - const size_t smem = nqptg*(ne00 + 2*nsgmax*(ncpsg + nqptg))*(sizeof(float)/2); + const size_t smem = FATTN_SMEM(nsgmax); if (smem > device.maxThreadgroupMemoryLength) { break; } @@ -2949,16 +3711,15 @@ static void ggml_metal_encode_node( // simdgroups per threadgroup (a.k.a. warps) const int64_t nsg = ne01 <= nqptg ? MAX(4, MIN(nsgmax, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32))) : 4; - const size_t smem = nqptg*(ne00 + 2*nsg*(ncpsg + nqptg))*(sizeof(float)/2); + const size_t smem = FATTN_SMEM(nsg); - //printf("smem: %zu, max: %zu\n", smem, device.maxThreadgroupMemoryLength); + //printf("smem: %zu, max: %zu, nsg = %d\n", smem, device.maxThreadgroupMemoryLength, (int) nsg); GGML_ASSERT(smem <= device.maxThreadgroupMemoryLength); - - [encoder setThreadgroupMemoryLength:GGML_PAD(smem, 16) atIndex:0]; - + [encoder setThreadgroupMemoryLength:smem atIndex:0]; +#undef FATTN_SMEM [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)]; } else { - // half1x4 kernel + // half4x4 kernel const int64_t nqptg = 1; // queries per threadgroup !! sync with kernel template arguments !! const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !! @@ -2966,8 +3727,28 @@ static void ggml_metal_encode_node( GGML_ASSERT(nqptg % 1 == 0); GGML_ASSERT(ncpsg % 32 == 0); + // ne00 + 2*ncpsg*(nsg) + // for each query, we load it as f16 in shared memory (ne00) + // and store the soft_max values and the mask + // + // ne00*(nsg) + // each simdgroup has a full f16 head vector in shared mem to accumulate results + // +#define FATTN_SMEM(nsg) (GGML_PAD((nqptg*(ne00 + 2*ncpsg*(nsg)) + ne00*(nsg))*(sizeof(float)/2), 16)) + + int64_t nsgmax = 2; + + while (true) { + const size_t smem = FATTN_SMEM(nsgmax); + if (smem > device.maxThreadgroupMemoryLength) { + break; + } + nsgmax *= 2; + } + nsgmax /= 2; + // simdgroups per threadgroup (a.k.a. warps) - const int64_t nsgt = MAX(2, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32)); + const int64_t nsgt = MAX(2, MIN(nsgmax, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32))); int64_t nsg = 1; while (nsg <= nsgt) { @@ -2975,12 +3756,12 @@ static void ggml_metal_encode_node( } nsg /= 2; - const size_t smem = (nqptg*(ne00 + 2*nsg*(ncpsg + nqptg)) + nsg*ne00)*(sizeof(float)/2); + const size_t smem = FATTN_SMEM(nsg); - //printf("smem: %zu, max: %zu\n", smem, device.maxThreadgroupMemoryLength); + //printf("smem: %zu, max: %zu, nsg = %d\n", smem, device.maxThreadgroupMemoryLength, (int) nsg); GGML_ASSERT(smem <= device.maxThreadgroupMemoryLength); - [encoder setThreadgroupMemoryLength:GGML_PAD(smem, 16) atIndex:0]; - + [encoder setThreadgroupMemoryLength:smem atIndex:0]; +#undef FATTN_SMEM [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)]; } } break; @@ -3002,6 +3783,7 @@ static void ggml_metal_encode_node( switch (dstt) { case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; break; case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline; break; + case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_BF16].pipeline; break; case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0].pipeline; break; case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0].pipeline; break; case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1].pipeline; break; @@ -3019,31 +3801,105 @@ static void ggml_metal_encode_node( default: GGML_ABORT("not implemented"); }; } break; + case GGML_TYPE_BF16: + { + switch (dstt) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_BF16_F32].pipeline; break; + case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_BF16_BF16].pipeline; break; + default: GGML_ASSERT(false && "not implemented"); + }; + } break; default: GGML_ABORT("not implemented"); } + ggml_metal_kargs_cpy args = { + /*.ne00 =*/ ne00, + /*.ne01 =*/ ne01, + /*.ne02 =*/ ne02, + /*.ne03 =*/ ne03, + /*.nb00 =*/ nb00, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb02, + /*.nb03 =*/ nb03, + /*.ne0 =*/ ne0, + /*.ne1 =*/ ne1, + /*.ne2 =*/ ne2, + /*.ne3 =*/ ne3, + /*.nb0 =*/ nb0, + /*.nb1 =*/ nb1, + /*.nb2 =*/ nb2, + /*.nb3 =*/ nb3, + }; + [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; + case GGML_OP_SET: + { + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); + + // src0 and dst as viewed during set + const size_t dst_nb0 = ggml_element_size(src0); + + const size_t dst_nb1 = ((int32_t *) dst->op_params)[0]; + const size_t dst_nb2 = ((int32_t *) dst->op_params)[1]; + const size_t dst_nb3 = ((int32_t *) dst->op_params)[2]; + const size_t offset = ((int32_t *) dst->op_params)[3]; + const bool inplace = (bool) ((int32_t *) dst->op_params)[4]; + + if (!inplace) { + memcpy(((char *) dst->data), ((char *) src0->data), ggml_nbytes(dst)); + } + + const int im0 = (ne10 == 0 ? 0 : ne10-1); + const int im1 = (ne11 == 0 ? 0 : ne11-1); + const int im2 = (ne12 == 0 ? 0 : ne12-1); + const int im3 = (ne13 == 0 ? 0 : ne13-1); + + GGML_ASSERT(offset + im0*dst_nb0 + im1*dst_nb1 + im2*dst_nb2 + im3*dst_nb3 <= ggml_nbytes(dst)); + + id pipeline = nil; + + switch (src0t) { + case GGML_TYPE_F32: + GGML_ASSERT(nb10 == sizeof(float)); + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SET_F32].pipeline; break; + case GGML_TYPE_I32: + GGML_ASSERT(nb10 == sizeof(int32_t)); + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SET_I32].pipeline; break; + default: GGML_ABORT("fatal error"); + } + + ggml_metal_kargs_set args = { + /*.ne10 =*/ ne10, + /*.ne11 =*/ ne11, + /*.ne12 =*/ ne12, + /*.nb10 =*/ nb10, + /*.nb11 =*/ nb11, + /*.nb12 =*/ nb12, + /*.nb13 =*/ nb13, + /*.nb1 =*/ dst_nb1, + /*.nb2 =*/ dst_nb2, + /*.nb3 =*/ dst_nb3, + /*.offs =*/ offset, + /*.inplace =*/ inplace, + }; + + const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne10); + + [encoder setComputePipelineState:pipeline]; + [encoder setBytes:&args length:sizeof(args) atIndex:0]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; + + [encoder dispatchThreadgroups:MTLSizeMake(ne11, ne12, ne13) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; case GGML_OP_POOL_2D: { GGML_ASSERT(ggml_is_contiguous(src0)); @@ -3085,6 +3941,7 @@ static void ggml_metal_encode_node( const int64_t n_threads = MIN((int64_t)[pipeline maxTotalThreadsPerThreadgroup], parallel_elements); const int64_t n_tg = (parallel_elements + n_threads - 1) / n_threads; + // TODO: add ggml_metal_kargs struct [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; @@ -3102,6 +3959,31 @@ static void ggml_metal_encode_node( [encoder dispatchThreadgroups:MTLSizeMake(n_tg, 1, 1) threadsPerThreadgroup:MTLSizeMake(n_threads, 1, 1)]; } break; + case GGML_OP_ARGMAX: + { + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(ggml_is_contiguous_1(src0)); + GGML_ASSERT(nb00 == ggml_type_size(src0->type)); + + const int64_t nrows = ggml_nrows(src0); + + int nth = 32; // SIMD width + while (nth < ne00 && nth*ne01*ne02*ne03 < 256) { + nth *= 2; + } + + id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARGMAX].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3]; + [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; + [encoder setThreadgroupMemoryLength:32*sizeof(int32_t) atIndex:1]; + + [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; default: { GGML_LOG_ERROR("%s: error: node %3d, op = %8s not implemented\n", __func__, idx, ggml_op_name(dst->op)); @@ -3272,6 +4154,12 @@ static void * ggml_backend_metal_buffer_get_base(ggml_backend_buffer_t buffer) { return ctx->all_data; } +static void ggml_backend_metal_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) { + memset((char *)tensor->data + offset, value, size); + + UNUSED(buffer); +} + static void ggml_backend_metal_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { memcpy((char *)tensor->data + offset, data, size); @@ -3304,7 +4192,7 @@ static struct ggml_backend_buffer_i ggml_backend_metal_buffer_i = { /* .free_buffer = */ ggml_backend_metal_buffer_free_buffer, /* .get_base = */ ggml_backend_metal_buffer_get_base, /* .init_tensor = */ NULL, - /* .memset_tensor = */ NULL, + /* .memset_tensor = */ ggml_backend_metal_buffer_memset_tensor, /* .set_tensor = */ ggml_backend_metal_buffer_set_tensor, /* .get_tensor = */ ggml_backend_metal_buffer_get_tensor, /* .cpy_tensor = */ ggml_backend_metal_buffer_cpy_tensor, @@ -3837,7 +4725,7 @@ static ggml_backend_buffer_t ggml_backend_metal_device_buffer_from_ptr(ggml_back } } - return ggml_backend_buffer_init(ggml_backend_metal_buffer_type(), ggml_backend_metal_buffer_i, ctx, size); + return ggml_backend_buffer_init(ggml_backend_metal_buffer_from_ptr_type(), ggml_backend_metal_buffer_i, ctx, size); } static bool ggml_backend_metal_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { @@ -3847,7 +4735,8 @@ static bool ggml_backend_metal_device_supports_op(ggml_backend_dev_t dev, const } static bool ggml_backend_metal_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { - return buft->iface.get_name == ggml_backend_metal_buffer_type_get_name; + return buft->iface.get_name == ggml_backend_metal_buffer_type_get_name || + buft->iface.get_name == ggml_backend_metal_buffer_from_ptr_type_get_name; UNUSED(dev); } @@ -3900,19 +4789,45 @@ static ggml_backend_dev_t ggml_backend_metal_reg_device_get(ggml_backend_reg_t r GGML_UNUSED(index); } +static struct ggml_backend_feature g_ggml_backend_metal_features[] = { +#if defined(GGML_METAL_EMBED_LIBRARY) + { "EMBED_LIBRARY", "1" }, +#endif +#if defined(GGML_METAL_USE_BF16) + { "BF16", "1" }, +#endif + { nil, nil }, +}; + +static struct ggml_backend_feature * ggml_backend_metal_get_features(ggml_backend_reg_t reg) { + return g_ggml_backend_metal_features; + + GGML_UNUSED(reg); +} + +static void * ggml_backend_metal_get_proc_address(ggml_backend_reg_t reg, const char * name) { + if (strcmp(name, "ggml_backend_get_features") == 0) { + return (void *)ggml_backend_metal_get_features; + } + + return NULL; + + GGML_UNUSED(reg); +} static struct ggml_backend_reg_i ggml_backend_metal_reg_i = { /* .get_name = */ ggml_backend_metal_reg_get_name, /* .device_count = */ ggml_backend_metal_reg_device_count, /* .device_get = */ ggml_backend_metal_reg_device_get, - /* .get_proc_address = */ NULL, + /* .get_proc_address = */ ggml_backend_metal_get_proc_address, }; ggml_backend_reg_t ggml_backend_metal_reg(void) { // TODO: make this thread-safe somehow? { g_ggml_backend_metal_reg = (struct ggml_backend_reg) { - /* .iface = */ ggml_backend_metal_reg_i, - /* .context = */ NULL, + /* .api_version = */ GGML_BACKEND_API_VERSION, + /* .iface = */ ggml_backend_metal_reg_i, + /* .context = */ NULL, }; g_ggml_backend_metal_device = (struct ggml_backend_device) { @@ -3924,3 +4839,5 @@ ggml_backend_reg_t ggml_backend_metal_reg(void) { return &g_ggml_backend_metal_reg; } + +GGML_BACKEND_DL_IMPL(ggml_backend_metal_reg) diff --git a/ggml/src/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal similarity index 52% rename from ggml/src/ggml-metal.metal rename to ggml/src/ggml-metal/ggml-metal.metal index defde6246..8ba43904d 100644 --- a/ggml/src/ggml-metal.metal +++ b/ggml/src/ggml-metal/ggml-metal.metal @@ -1,6 +1,12 @@ #define GGML_COMMON_DECL_METAL #define GGML_COMMON_IMPL_METAL -#include "ggml-common.h" +#if defined(GGML_METAL_EMBED_LIBRARY) +__embed_ggml-common.h__ +#else +// TODO: this should not be a relative path, but can't figure out how to set Metal include paths in Package.swift +#include "../ggml-common.h" +#endif +#include "ggml-metal-impl.h" #include @@ -12,6 +18,595 @@ using namespace metal; #define N_SIMDWIDTH 32 // assuming SIMD group size is 32 +// ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf +// +// cmd: +// .../usr/bin/metal -dM -E -c ggml/src/ggml-metal/ggml-metal.metal +// .../usr/bin/metal -dM -E -c -target air64-apple-ios14.0 ggml/src/ggml-metal/ggml-metal.metal +// +#if __METAL_VERSION__ < 310 && defined(GGML_METAL_USE_BF16) +#undef GGML_METAL_USE_BF16 +#endif + +#if defined(GGML_METAL_USE_BF16) +typedef matrix bfloat4x4; +#endif + +constexpr constant static float kvalues_iq4nl_f[16] = { + -127.f, -104.f, -83.f, -65.f, -49.f, -35.f, -22.f, -10.f, 1.f, 13.f, 25.f, 38.f, 53.f, 69.f, 89.f, 113.f +}; + +// NOTE: this is not dequantizing - we are simply fitting the template +template +void dequantize_f32(device const float4x4 * src, short il, thread type4x4 & reg) { + reg = (type4x4)(*src); +} + +template +void dequantize_f16(device const half4x4 * src, short il, thread type4x4 & reg) { + reg = (type4x4)(*src); +} + +template +void dequantize_f16_t4(device const half4 * src, short il, thread type4 & reg) { + reg = (type4)(*(src + il)); +} + +#if defined(GGML_METAL_USE_BF16) +template +void dequantize_bf16(device const bfloat4x4 * src, short il, thread type4x4 & reg) { + reg = (type4x4)(*src); +} +#endif + +template +void dequantize_q4_0(device const block_q4_0 * xb, short il, thread type4x4 & reg) { + device const uint16_t * qs = ((device const uint16_t *)xb + 1); + const float d1 = il ? (xb->d / 16.h) : xb->d; + const float d2 = d1 / 256.f; + const float md = -8.h * xb->d; + const ushort mask0 = il ? 0x00F0 : 0x000F; + const ushort mask1 = mask0 << 8; + + float4x4 reg_f; + + for (int i = 0; i < 8; i++) { + reg_f[i/2][2*(i%2) + 0] = d1 * (qs[i] & mask0) + md; + reg_f[i/2][2*(i%2) + 1] = d2 * (qs[i] & mask1) + md; + } + + reg = (type4x4) reg_f; +} + +template +void dequantize_q4_0_t4(device const block_q4_0 * xb, short il, thread type4 & reg) { + device const uint16_t * qs = ((device const uint16_t *)xb + 1); + const float d1 = (il/4) ? (xb->d / 16.h) : xb->d; + const float d2 = d1 / 256.f; + const float md = -8.h * xb->d; + const ushort mask0 = (il/4) ? 0x00F0 : 0x000F; + const ushort mask1 = mask0 << 8; + + for (int i = 0; i < 2; i++) { + reg[2*i + 0] = d1 * (qs[2*(il%4) + i] & mask0) + md; + reg[2*i + 1] = d2 * (qs[2*(il%4) + i] & mask1) + md; + } +} + +template +void dequantize_q4_1(device const block_q4_1 * xb, short il, thread type4x4 & reg) { + device const uint16_t * qs = ((device const uint16_t *)xb + 2); + const float d1 = il ? (xb->d / 16.h) : xb->d; + const float d2 = d1 / 256.f; + const float m = xb->m; + const ushort mask0 = il ? 0x00F0 : 0x000F; + const ushort mask1 = mask0 << 8; + + float4x4 reg_f; + + for (int i = 0; i < 8; i++) { + reg_f[i/2][2*(i%2) + 0] = ((qs[i] & mask0) * d1) + m; + reg_f[i/2][2*(i%2) + 1] = ((qs[i] & mask1) * d2) + m; + } + + reg = (type4x4) reg_f; +} + +template +void dequantize_q4_1_t4(device const block_q4_1 * xb, short il, thread type4 & reg) { + device const uint16_t * qs = ((device const uint16_t *)xb + 2); + const float d1 = (il/4) ? (xb->d / 16.h) : xb->d; + const float d2 = d1 / 256.f; + const float m = xb->m; + const ushort mask0 = (il/4) ? 0x00F0 : 0x000F; + const ushort mask1 = mask0 << 8; + + for (int i = 0; i < 2; i++) { + reg[2*i + 0] = d1 * (qs[2*(il%4) + i] & mask0) + m; + reg[2*i + 1] = d2 * (qs[2*(il%4) + i] & mask1) + m; + } +} + +template +void dequantize_q5_0(device const block_q5_0 * xb, short il, thread type4x4 & reg) { + device const uint16_t * qs = ((device const uint16_t *)xb + 3); + const float d = xb->d; + const float md = -16.h * xb->d; + const ushort mask = il ? 0x00F0 : 0x000F; + + const uint32_t qh = *((device const uint32_t *)xb->qh); + + const int x_mv = il ? 4 : 0; + + const int gh_mv = il ? 12 : 0; + const int gh_bk = il ? 0 : 4; + + float4x4 reg_f; + + for (int i = 0; i < 8; i++) { + // extract the 5-th bits for x0 and x1 + const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10; + const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10; + + // combine the 4-bits from qs with the 5th bit + const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0); + const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1); + + reg_f[i/2][2*(i%2) + 0] = d * x0 + md; + reg_f[i/2][2*(i%2) + 1] = d * x1 + md; + } + + reg = (type4x4) reg_f; +} + +template +void dequantize_q5_0_t4(device const block_q5_0 * xb, short il, thread type4 & reg) { + device const uint16_t * qs = ((device const uint16_t *)xb + 3); + const float d = xb->d; + const float md = -16.h * xb->d; + const ushort mask = (il/4) ? 0x00F0 : 0x000F; + + const uint32_t qh = *((device const uint32_t *)xb->qh); + + const int x_mv = (il/4) ? 4 : 0; + + const int gh_mv = (il/4) ? 12 : 0; + const int gh_bk = (il/4) ? 0 : 4; + + for (int ii = 0; ii < 2; ii++) { + int i = 2*(il%4) + ii; + + // extract the 5-th bits for x0 and x1 + const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10; + const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10; + + // combine the 4-bits from qs with the 5th bit + const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0); + const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1); + + reg[2*ii + 0] = d * x0 + md; + reg[2*ii + 1] = d * x1 + md; + } +} + +template +void dequantize_q5_1(device const block_q5_1 * xb, short il, thread type4x4 & reg) { + device const uint16_t * qs = ((device const uint16_t *)xb + 4); + const float d = xb->d; + const float m = xb->m; + const ushort mask = il ? 0x00F0 : 0x000F; + + const uint32_t qh = *((device const uint32_t *)xb->qh); + + const int x_mv = il ? 4 : 0; + + const int gh_mv = il ? 12 : 0; + const int gh_bk = il ? 0 : 4; + + float4x4 reg_f; + + for (int i = 0; i < 8; i++) { + // extract the 5-th bits for x0 and x1 + const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10; + const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10; + + // combine the 4-bits from qs with the 5th bit + const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0); + const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1); + + reg_f[i/2][2*(i%2) + 0] = d * x0 + m; + reg_f[i/2][2*(i%2) + 1] = d * x1 + m; + } + + reg = (type4x4) reg_f; +} + +template +void dequantize_q5_1_t4(device const block_q5_1 * xb, short il, thread type4 & reg) { + device const uint16_t * qs = ((device const uint16_t *)xb + 4); + const float d = xb->d; + const float m = xb->m; + const ushort mask = (il/4) ? 0x00F0 : 0x000F; + + const uint32_t qh = *((device const uint32_t *)xb->qh); + + const int x_mv = (il/4) ? 4 : 0; + + const int gh_mv = (il/4) ? 12 : 0; + const int gh_bk = (il/4) ? 0 : 4; + + for (int ii = 0; ii < 2; ii++) { + int i = 2*(il%4) + ii; + + // extract the 5-th bits for x0 and x1 + const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10; + const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10; + + // combine the 4-bits from qs with the 5th bit + const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0); + const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1); + + reg[2*ii + 0] = d * x0 + m; + reg[2*ii + 1] = d * x1 + m; + } +} + +template +void dequantize_q8_0(device const block_q8_0 *xb, short il, thread type4x4 & reg) { + device const int8_t * qs = ((device const int8_t *)xb->qs); + const float d = xb->d; + + float4x4 reg_f; + + for (int i = 0; i < 16; i++) { + reg_f[i/4][i%4] = (qs[i + 16*il] * d); + } + + reg = (type4x4) reg_f; +} + +template +void dequantize_q8_0_t4(device const block_q8_0 *xb, short il, thread type4 & reg) { + device const int8_t * qs = ((device const int8_t *)xb->qs); + const float d = xb->d; + + for (int i = 0; i < 4; i++) { + reg[i] = (qs[4*(il%4) + i + 16*(il/4)] * d); + } +} + +template +void dequantize_q2_K(device const block_q2_K *xb, short il, thread type4x4 & reg) { + const float d = xb->d; + const float min = xb->dmin; + device const uint8_t * q = (device const uint8_t *)xb->qs; + float dl, ml; + uint8_t sc = xb->scales[il]; + + q = q + 32*(il/8) + 16*(il&1); + il = (il/2)%4; + + half coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h); + uchar mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3); + dl = d * (sc & 0xF) * coef, ml = min * (sc >> 4); + for (int i = 0; i < 16; ++i) { + reg[i/4][i%4] = dl * (q[i] & mask) - ml; + } +} + +template +void dequantize_q3_K(device const block_q3_K *xb, short il, thread type4x4 & reg) { + const half d_all = xb->d; + device const uint8_t * q = (device const uint8_t *)xb->qs; + device const uint8_t * h = (device const uint8_t *)xb->hmask; + device const int8_t * scales = (device const int8_t *)xb->scales; + + q = q + 32 * (il/8) + 16 * (il&1); + h = h + 16 * (il&1); + uint8_t m = 1 << (il/2); + uint16_t kmask1 = (il/4)>1 ? ((il/4)>2 ? 192 : 48) : \ + ((il/4)>0 ? 12 : 3); + uint16_t kmask2 = il/8 ? 0xF0 : 0x0F; + uint16_t scale_2 = scales[il%8], scale_1 = scales[8 + il%4]; + int16_t dl_int = (il/4)&1 ? (scale_2&kmask2) | ((scale_1&kmask1) << 2) + : (scale_2&kmask2) | ((scale_1&kmask1) << 4); + float dl = il<8 ? d_all * (dl_int - 32.f) : d_all * (dl_int / 16.f - 32.f); + const float ml = 4.f * dl; + + il = (il/2) & 3; + const half coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h); + const uint8_t mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3); + dl *= coef; + + for (int i = 0; i < 16; ++i) { + reg[i/4][i%4] = dl * (q[i] & mask) - (h[i] & m ? 0 : ml); + } +} + +static inline uchar2 get_scale_min_k4_just2(int j, int k, device const uchar * q) { + return j < 4 ? uchar2{uchar(q[j+0+k] & 63), uchar(q[j+4+k] & 63)} + : uchar2{uchar((q[j+4+k] & 0xF) | ((q[j-4+k] & 0xc0) >> 2)), uchar((q[j+4+k] >> 4) | ((q[j-0+k] & 0xc0) >> 2))}; +} + +template +void dequantize_q4_K(device const block_q4_K * xb, short il, thread type4x4 & reg) { + device const uchar * q = xb->qs; + + short is = (il/4) * 2; + q = q + (il/4) * 32 + 16 * (il&1); + il = il & 3; + const uchar2 sc = get_scale_min_k4_just2(is, il/2, xb->scales); + const float d = il < 2 ? xb->d : xb->d / 16.h; + const float min = xb->dmin; + const float dl = d * sc[0]; + const float ml = min * sc[1]; + + const ushort mask = il < 2 ? 0x0F : 0xF0; + for (int i = 0; i < 16; ++i) { + reg[i/4][i%4] = dl * (q[i] & mask) - ml; + } +} + +template +void dequantize_q5_K(device const block_q5_K *xb, short il, thread type4x4 & reg) { + device const uint8_t * q = xb->qs; + device const uint8_t * qh = xb->qh; + + short is = (il/4) * 2; + q = q + 32 * (il/4) + 16 * (il&1); + qh = qh + 16 * (il&1); + uint8_t ul = 1 << (il/2); + il = il & 3; + const uchar2 sc = get_scale_min_k4_just2(is, il/2, xb->scales); + const float d = il < 2 ? xb->d : xb->d / 16.f; + const float min = xb->dmin; + const float dl = d * sc[0]; + const float ml = min * sc[1]; + + const ushort mask = il<2 ? 0x0F : 0xF0; + const float qh_val = il<2 ? 16.f : 256.f; + for (int i = 0; i < 16; ++i) { + reg[i/4][i%4] = dl * ((q[i] & mask) + (qh[i] & ul ? qh_val : 0)) - ml; + } +} + +template +void dequantize_q6_K(device const block_q6_K *xb, short il, thread type4x4 & reg) { + const half d_all = xb->d; + device const uint8_t * ql = (device const uint8_t *)xb->ql; + device const uint8_t * qh = (device const uint8_t *)xb->qh; + device const int8_t * scales = (device const int8_t *)xb->scales; + + ql = ql + 64*(il/8) + 32*((il/2)&1) + 16*(il&1); + qh = qh + 32*(il/8) + 16*(il&1); + float sc = scales[(il%2) + 2 * ((il/2))]; + il = (il/2) & 3; + + const uint16_t kmask1 = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3); + const uint16_t kmask2 = il>1 ? 0xF0 : 0x0F; + const float coef = il>1 ? 1.f/16.f : 1.f; + const float ml = d_all * sc * 32.f; + const float dl = d_all * sc * coef; + for (int i = 0; i < 16; ++i) { + const half q = il&1 ? ((ql[i] & kmask2) | ((qh[i] & kmask1) << 2)) + : ((ql[i] & kmask2) | ((qh[i] & kmask1) << 4)); + reg[i/4][i%4] = dl * q - ml; + } +} + +template +void dequantize_iq2_xxs(device const block_iq2_xxs * xb, short il, thread type4x4 & reg) { + // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const float d = xb->d; + const int ib32 = il/2; + il = il%2; + // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 + // each block of 32 needs 2 uint32_t's for the quants & scale, so 4 uint16_t's. + device const uint16_t * q2 = xb->qs + 4*ib32; + const uint32_t aux32_g = q2[0] | (q2[1] << 16); + const uint32_t aux32_s = q2[2] | (q2[3] << 16); + thread const uint8_t * aux8 = (thread const uint8_t *)&aux32_g; + const float dl = d * (0.5f + (aux32_s >> 28)) * 0.25f; + constant uint8_t * grid = (constant uint8_t *)(iq2xxs_grid + aux8[2*il+0]); + uint8_t signs = ksigns_iq2xs[(aux32_s >> 14*il) & 127]; + for (int i = 0; i < 8; ++i) { + reg[i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); + } + grid = (constant uint8_t *)(iq2xxs_grid + aux8[2*il+1]); + signs = ksigns_iq2xs[(aux32_s >> (14*il+7)) & 127]; + for (int i = 0; i < 8; ++i) { + reg[2+i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); + } +} + +template +void dequantize_iq2_xs(device const block_iq2_xs * xb, short il, thread type4x4 & reg) { + // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const float d = xb->d; + const int ib32 = il/2; + il = il%2; + // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 + device const uint16_t * q2 = xb->qs + 4*ib32; + const float dl = d * (0.5f + ((xb->scales[ib32] >> 4*il) & 0xf)) * 0.25f; + constant uint8_t * grid = (constant uint8_t *)(iq2xs_grid + (q2[2*il+0] & 511)); + uint8_t signs = ksigns_iq2xs[q2[2*il+0] >> 9]; + for (int i = 0; i < 8; ++i) { + reg[i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); + } + grid = (constant uint8_t *)(iq2xs_grid + (q2[2*il+1] & 511)); + signs = ksigns_iq2xs[q2[2*il+1] >> 9]; + for (int i = 0; i < 8; ++i) { + reg[2+i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); + } +} + +template +void dequantize_iq3_xxs(device const block_iq3_xxs * xb, short il, thread type4x4 & reg) { + // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const float d = xb->d; + const int ib32 = il/2; + il = il%2; + // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 + device const uint8_t * q3 = xb->qs + 8*ib32; + device const uint16_t * gas = (device const uint16_t *)(xb->qs + QK_K/4) + 2*ib32; + const uint32_t aux32 = gas[0] | (gas[1] << 16); + const float dl = d * (0.5f + (aux32 >> 28)) * 0.5f; + constant uint8_t * grid1 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+0]); + constant uint8_t * grid2 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+1]); + uint8_t signs = ksigns_iq2xs[(aux32 >> 14*il) & 127]; + for (int i = 0; i < 4; ++i) { + reg[0][i] = dl * grid1[i] * (signs & kmask_iq2xs[i+0] ? -1.f : 1.f); + reg[1][i] = dl * grid2[i] * (signs & kmask_iq2xs[i+4] ? -1.f : 1.f); + } + grid1 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+2]); + grid2 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+3]); + signs = ksigns_iq2xs[(aux32 >> (14*il+7)) & 127]; + for (int i = 0; i < 4; ++i) { + reg[2][i] = dl * grid1[i] * (signs & kmask_iq2xs[i+0] ? -1.f : 1.f); + reg[3][i] = dl * grid2[i] * (signs & kmask_iq2xs[i+4] ? -1.f : 1.f); + } +} + +template +void dequantize_iq3_s(device const block_iq3_s * xb, short il, thread type4x4 & reg) { + // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const float d = xb->d; + const int ib32 = il/2; + il = il%2; + // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 + device const uint8_t * qs = xb->qs + 8*ib32; + device const uint8_t * signs = xb->signs + 4*ib32 + 2*il; + const uint8_t qh = xb->qh[ib32] >> 4*il; + const float dl = d * (1 + 2*((xb->scales[ib32/2] >> 4*(ib32%2)) & 0xf)); + constant uint8_t * grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+0] | ((qh << 8) & 256))); + constant uint8_t * grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+1] | ((qh << 7) & 256))); + for (int i = 0; i < 4; ++i) { + reg[0][i] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i+0]); + reg[1][i] = dl * grid2[i] * select(1, -1, signs[0] & kmask_iq2xs[i+4]); + } + grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+2] | ((qh << 6) & 256))); + grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+3] | ((qh << 5) & 256))); + for (int i = 0; i < 4; ++i) { + reg[2][i] = dl * grid1[i] * select(1, -1, signs[1] & kmask_iq2xs[i+0]); + reg[3][i] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i+4]); + } +} + +template +void dequantize_iq2_s(device const block_iq2_s * xb, short il, thread type4x4 & reg) { + // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const float d = xb->d; + const int ib32 = il/2; + il = il%2; + // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 + device const uint8_t * qs = xb->qs + 4*ib32 + 2*il; + device const uint8_t * signs = qs + QK_K/8; + const uint8_t qh = xb->qh[ib32] >> 4*il; + const float dl = d * (0.5f + ((xb->scales[ib32] >> 4*il) & 0xf)) * 0.25f; + constant uint8_t * grid1 = (constant uint8_t *)(iq2s_grid + (qs[0] | ((qh << 8) & 0x300))); + constant uint8_t * grid2 = (constant uint8_t *)(iq2s_grid + (qs[1] | ((qh << 6) & 0x300))); + for (int i = 0; i < 8; ++i) { + reg[i/4+0][i%4] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i]); + reg[i/4+2][i%4] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i]); + } +} + +template +void dequantize_iq1_s(device const block_iq1_s * xb, short il, thread type4x4 & reg) { + // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const int ib32 = il/2; + il = il%2; + const float d = xb->d; + device const uint8_t * qs = xb->qs + 4*ib32 + 2*il; + device const uint16_t * qh = xb->qh; + const float dl = d * (2*((qh[ib32] >> 12) & 7) + 1); + const float ml = dl * (qh[ib32] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA); + const uint16_t h = qh[ib32] >> 6*il; + constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((h << 8) & 0x700))); + constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((h << 5) & 0x700))); + for (int i = 0; i < 4; ++i) { + reg[0][i] = dl * (grid1[i] & 0xf) + ml; + reg[1][i] = dl * (grid1[i] >> 4) + ml; + reg[2][i] = dl * (grid2[i] & 0xf) + ml; + reg[3][i] = dl * (grid2[i] >> 4) + ml; + } +} + +template +void dequantize_iq1_m(device const block_iq1_m * xb, short il, thread type4x4 & reg) { + // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const int ib32 = il/2; + il = il%2; + device const uint16_t * sc = (device const uint16_t *)xb->scales; + + iq1m_scale_t scale; + scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); + const float d = scale.f16; + + device const uint8_t * qs = xb->qs + 4*ib32 + 2*il; + device const uint8_t * qh = xb->qh + 2*ib32 + il; + + const float dl = d * (2*((sc[ib32/2] >> (6*(ib32%2)+3*il)) & 7) + 1); + const float ml1 = dl * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA); + const float ml2 = dl * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA); + constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700))); + constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 4) & 0x700))); + for (int i = 0; i < 4; ++i) { + reg[0][i] = dl * (grid1[i] & 0xf) + ml1; + reg[1][i] = dl * (grid1[i] >> 4) + ml1; + reg[2][i] = dl * (grid2[i] & 0xf) + ml2; + reg[3][i] = dl * (grid2[i] >> 4) + ml2; + } +} + +template +void dequantize_iq4_nl(device const block_iq4_nl * xb, short il, thread type4x4 & reg) { + device const uint16_t * q4 = (device const uint16_t *)xb->qs; + const float d = xb->d; + uint32_t aux32; + thread const uint8_t * q8 = (thread const uint8_t *)&aux32; + for (int i = 0; i < 4; ++i) { + aux32 = ((q4[2*i] | (q4[2*i+1] << 16)) >> 4*il) & 0x0f0f0f0f; + reg[i][0] = d * kvalues_iq4nl_f[q8[0]]; + reg[i][1] = d * kvalues_iq4nl_f[q8[1]]; + reg[i][2] = d * kvalues_iq4nl_f[q8[2]]; + reg[i][3] = d * kvalues_iq4nl_f[q8[3]]; + } +} + +template +void dequantize_iq4_nl_t4(device const block_iq4_nl * xb, short il, thread type4 & reg) { + device const uint16_t * q4 = (device const uint16_t *)xb->qs; + const float d = xb->d; + uint32_t aux32; + thread const uint8_t * q8 = (thread const uint8_t *)&aux32; + aux32 = ((q4[2*(il%4)] | (q4[2*(il%4)+1] << 16)) >> 4*(il/4)) & 0x0f0f0f0f; + reg[0] = d * kvalues_iq4nl_f[q8[0]]; + reg[1] = d * kvalues_iq4nl_f[q8[1]]; + reg[2] = d * kvalues_iq4nl_f[q8[2]]; + reg[3] = d * kvalues_iq4nl_f[q8[3]]; +} + +template +void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4 & reg) { + // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const int ib32 = il/2; + il = il%2; + // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 + device const uint32_t * q4 = (device const uint32_t *)xb->qs + 4*ib32; + const int ls = ((xb->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((xb->scales_h >> 2*ib32) & 3) << 4); + const float d = (float)xb->d * (ls - 32); + uint32_t aux32; + thread const uint8_t * q8 = (thread const uint8_t *)&aux32; + for (int i = 0; i < 4; ++i) { + aux32 = (q4[i] >> 4*il) & 0x0f0f0f0f; + reg[i][0] = d * kvalues_iq4nl_f[q8[0]]; + reg[i][1] = d * kvalues_iq4nl_f[q8[1]]; + reg[i][2] = d * kvalues_iq4nl_f[q8[2]]; + reg[i][3] = d * kvalues_iq4nl_f[q8[3]]; + } +} + enum ggml_sort_order { GGML_SORT_ORDER_ASC, GGML_SORT_ORDER_DESC, @@ -21,240 +616,131 @@ enum ggml_sort_order { // pros: works for non-contiguous tensors, supports broadcast across all dims // cons: not very efficient kernel void kernel_add( + constant ggml_metal_kargs_bin & args, device const char * src0, device const char * src1, device char * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - constant int64_t & offs, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig.z; - const int64_t i02 = tgpig.y; - const int64_t i01 = tgpig.x; + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + const int i03 = tgpig.z; + const int i02 = tgpig.y; + const int i01 = tgpig.x; - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; + const int i13 = i03%args.ne13; + const int i12 = i02%args.ne12; + const int i11 = i01%args.ne11; - device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01 + offs; - device const char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11; - device char * dst_ptr = dst + i03*nb3 + i02*nb2 + i01*nb1 + offs; + device const char * src0_ptr = src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + args.offs; + device const char * src1_ptr = src1 + i13*args.nb13 + i12*args.nb12 + i11*args.nb11; + device char * dst_ptr = dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1 + args.offs; - for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - const int i10 = i0 % ne10; - *((device float *)(dst_ptr + i0*nb0)) = *((device float *)(src0_ptr + i0*nb00)) + *((device float *)(src1_ptr + i10*nb10)); + for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) { + const int i10 = i0%args.ne10; + *((device float *)(dst_ptr + i0*args.nb0)) = *((device float *)(src0_ptr + i0*args.nb00)) + *((device float *)(src1_ptr + i10*args.nb10)); } } kernel void kernel_sub( + constant ggml_metal_kargs_bin & args, device const char * src0, device const char * src1, device char * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - constant int64_t & offs, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig.z; - const int64_t i02 = tgpig.y; - const int64_t i01 = tgpig.x; + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + const int i03 = tgpig.z; + const int i02 = tgpig.y; + const int i01 = tgpig.x; - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; + const int i13 = i03%args.ne13; + const int i12 = i02%args.ne12; + const int i11 = i01%args.ne11; - device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01 + offs; - device const char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11; - device char * dst_ptr = dst + i03*nb3 + i02*nb2 + i01*nb1 + offs; + device const char * src0_ptr = src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + args.offs; + device const char * src1_ptr = src1 + i13*args.nb13 + i12*args.nb12 + i11*args.nb11; + device char * dst_ptr = dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1 + args.offs; - for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - const int i10 = i0 % ne10; - *((device float *)(dst_ptr + i0*nb0)) = *((device float *)(src0_ptr + i0*nb00)) - *((device float *)(src1_ptr + i10*nb10)); + for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) { + const int i10 = i0%args.ne10; + *((device float *)(dst_ptr + i0*args.nb0)) = *((device float *)(src0_ptr + i0*args.nb00)) - *((device float *)(src1_ptr + i10*args.nb10)); } } kernel void kernel_mul( + constant ggml_metal_kargs_bin & args, device const char * src0, device const char * src1, device char * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig.z; - const int64_t i02 = tgpig.y; - const int64_t i01 = tgpig.x; + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + const int i03 = tgpig.z; + const int i02 = tgpig.y; + const int i01 = tgpig.x; - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; + const int i13 = i03%args.ne13; + const int i12 = i02%args.ne12; + const int i11 = i01%args.ne11; - device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01; - device const char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11; - device char * dst_ptr = dst + i03*nb3 + i02*nb2 + i01*nb1; + device const char * src0_ptr = src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01; + device const char * src1_ptr = src1 + i13*args.nb13 + i12*args.nb12 + i11*args.nb11; + device char * dst_ptr = dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1; - for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - const int i10 = i0 % ne10; - *((device float *)(dst_ptr + i0*nb0)) = *((device float *)(src0_ptr + i0*nb00)) * *((device float *)(src1_ptr + i10*nb10)); + for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) { + const int i10 = i0%args.ne10; + *((device float *)(dst_ptr + i0*args.nb0)) = *((device float *)(src0_ptr + i0*args.nb00)) * *((device float *)(src1_ptr + i10*args.nb10)); } } kernel void kernel_div( + constant ggml_metal_kargs_bin & args, device const char * src0, device const char * src1, device char * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig.z; - const int64_t i02 = tgpig.y; - const int64_t i01 = tgpig.x; + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + const int i03 = tgpig.z; + const int i02 = tgpig.y; + const int i01 = tgpig.x; - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; + const int i13 = i03%args.ne13; + const int i12 = i02%args.ne12; + const int i11 = i01%args.ne11; - device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01; - device const char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11; - device char * dst_ptr = dst + i03*nb3 + i02*nb2 + i01*nb1; + device const char * src0_ptr = src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01; + device const char * src1_ptr = src1 + i13*args.nb13 + i12*args.nb12 + i11*args.nb11; + device char * dst_ptr = dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1; - for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - const int i10 = i0 % ne10; - *((device float *)(dst_ptr + i0*nb0)) = *((device float *)(src0_ptr + i0*nb00)) / *((device float *)(src1_ptr + i10*nb10)); + for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) { + const int i10 = i0%args.ne10; + *((device float *)(dst_ptr + i0*args.nb0)) = *((device float *)(src0_ptr + i0*args.nb00)) / *((device float *)(src1_ptr + i10*args.nb10)); } } template kernel void kernel_repeat( + constant ggml_metal_kargs_repeat & args, device const char * src0, device char * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i3 = tgpig.z; - const int64_t i2 = tgpig.y; - const int64_t i1 = tgpig.x; + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + const int i3 = tgpig.z; + const int i2 = tgpig.y; + const int i1 = tgpig.x; - const int64_t i03 = i3 % ne03; - const int64_t i02 = i2 % ne02; - const int64_t i01 = i1 % ne01; + const int i03 = i3%args.ne03; + const int i02 = i2%args.ne02; + const int i01 = i1%args.ne01; - device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01; - device char * dst_ptr = dst + i3*nb3 + i2*nb2 + i1*nb1 ; + device const char * src0_ptr = src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01; + device char * dst_ptr = dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1; - for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - const int i00 = i0 % ne00; - *((device T *)(dst_ptr + i0*nb0)) = *((device T *)(src0_ptr + i00*nb00)); + for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) { + const int i00 = i0%args.ne00; + *((device T *)(dst_ptr + i0*args.nb0)) = *((device T *)(src0_ptr + i00*args.nb00)); } } @@ -268,38 +754,42 @@ template [[host_name("kernel_repeat_i16")]] kernel kernel_repeat_t kernel_repeat // assumption: src1 is a row // broadcast src1 into src0 kernel void kernel_add_row( + constant ggml_metal_kargs_bin & args, device const float4 * src0, device const float4 * src1, device float4 * dst, - constant uint64_t & nb [[buffer(28)]], uint tpig[[thread_position_in_grid]]) { + const uint nb = args.ne00/4; dst[tpig] = src0[tpig] + src1[tpig % nb]; } kernel void kernel_sub_row( + constant ggml_metal_kargs_bin & args, device const float4 * src0, device const float4 * src1, device float4 * dst, - constant uint64_t & nb [[buffer(28)]], uint tpig[[thread_position_in_grid]]) { + const uint nb = args.ne00/4; dst[tpig] = src0[tpig] - src1[tpig % nb]; } kernel void kernel_mul_row( + constant ggml_metal_kargs_bin & args, device const float4 * src0, device const float4 * src1, device float4 * dst, - constant uint64_t & nb [[buffer(28)]], uint tpig[[thread_position_in_grid]]) { + const uint nb = args.ne00/4; dst[tpig] = src0[tpig] * src1[tpig % nb]; } kernel void kernel_div_row( + constant ggml_metal_kargs_bin & args, device const float4 * src0, device const float4 * src1, device float4 * dst, - constant uint64_t & nb [[buffer(28)]], uint tpig[[thread_position_in_grid]]) { + const uint nb = args.ne00/4; dst[tpig] = src0[tpig] / src1[tpig % nb]; } @@ -410,6 +900,14 @@ kernel void kernel_silu_4( dst[tpig] = x / (1.0f + exp(-x)); } +kernel void kernel_elu( + device const float * src0, + device float * dst, + uint tpig[[thread_position_in_grid]]) { + device const float & x = src0[tpig]; + dst[tpig] = (x > 0.0f) ? x : (exp(x) - 1.0f); +} + kernel void kernel_sqr( device const float * src0, device float * dst, @@ -868,103 +1366,170 @@ kernel void kernel_ssm_scan_f32( } } -kernel void kernel_norm( - device const void * src0, - device float * dst, - constant int64_t & ne00, - constant uint64_t & nb01, - constant float & eps, - threadgroup float * sum [[threadgroup(0)]], - uint tgpig[[threadgroup_position_in_grid]], - uint tpitg[[thread_position_in_threadgroup]], - uint ntg[[threads_per_threadgroup]]) { - device const float * x = (device const float *) ((device const char *) src0 + tgpig*nb01); - // MEAN - // parallel sum - sum[tpitg] = 0.0f; - for (int i00 = tpitg; i00 < ne00; i00 += ntg) { - sum[tpitg] += x[i00]; - } - // reduce - threadgroup_barrier(mem_flags::mem_threadgroup); - for (uint i = ntg/2; i > 0; i /= 2) { - if (tpitg < i) { - sum[tpitg] += sum[tpitg + i]; +kernel void kernel_argmax( + device const void * x, + device int32_t * dst, + constant int64_t & ncols, + constant uint64_t & nb01, + threadgroup float * shared_maxval [[threadgroup(0)]], + threadgroup int32_t * shared_argmax [[threadgroup(1)]], + uint tgpig[[threadgroup_position_in_grid]], + uint tpitg[[thread_position_in_threadgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]], + uint tiisg[[thread_index_in_simdgroup]], + uint ntg[[threads_per_threadgroup]]) { + device const float * x_row = (device const float *) ((device const char *) x + tgpig * nb01); + + float lmax = -INFINITY; + int32_t larg = -1; + + for (int i00 = tpitg; i00 < ncols; i00 += ntg) { + if (x_row[i00] > lmax) { + lmax = x_row[i00]; + larg = i00; } - threadgroup_barrier(mem_flags::mem_threadgroup); - } - const float mean = sum[0] / ne00; - - // recenter and VARIANCE - threadgroup_barrier(mem_flags::mem_threadgroup); - device float * y = dst + tgpig*ne00; - sum[tpitg] = 0.0f; - for (int i00 = tpitg; i00 < ne00; i00 += ntg) { - y[i00] = x[i00] - mean; - sum[tpitg] += y[i00] * y[i00]; } - // reduce - threadgroup_barrier(mem_flags::mem_threadgroup); - for (uint i = ntg/2; i > 0; i /= 2) { - if (tpitg < i) { - sum[tpitg] += sum[tpitg + i]; - } - threadgroup_barrier(mem_flags::mem_threadgroup); - } - const float variance = sum[0] / ne00; + // find the argmax value in the block + float max_val = simd_max(lmax); + int32_t arg_val = simd_max(select(-1, larg, lmax == max_val)); - const float scale = 1.0f/sqrt(variance + eps); - for (int i00 = tpitg; i00 < ne00; i00 += ntg) { - y[i00] = y[i00] * scale; - } -} - -kernel void kernel_rms_norm( - device const void * src0, - device float * dst, - constant int64_t & ne00, - constant uint64_t & nb01, - constant float & eps, - threadgroup float * buf [[threadgroup(0)]], - uint tgpig[[threadgroup_position_in_grid]], - uint tpitg[[thread_position_in_threadgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint ntg[[threads_per_threadgroup]]) { - device const float4 * x = (device const float4 *) ((device const char *) src0 + tgpig*nb01); - - float4 sumf = 0; - float all_sum = 0; - - // parallel sum - for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) { - sumf += x[i00] * x[i00]; - } - all_sum = sumf[0] + sumf[1] + sumf[2] + sumf[3]; - all_sum = simd_sum(all_sum); if (ntg > N_SIMDWIDTH) { if (sgitg == 0) { - buf[tiisg] = 0.0f; + shared_maxval[tiisg] = -INFINITY; + shared_argmax[tiisg] = -1; } threadgroup_barrier(mem_flags::mem_threadgroup); if (tiisg == 0) { - buf[sgitg] = all_sum; + shared_maxval[sgitg] = max_val; + shared_argmax[sgitg] = arg_val; } threadgroup_barrier(mem_flags::mem_threadgroup); - all_sum = buf[tiisg]; - all_sum = simd_sum(all_sum); + max_val = shared_maxval[tiisg]; + arg_val = shared_argmax[tiisg]; + + float max_val_reduced = simd_max(max_val); + int32_t arg_val_reduced = simd_max(select(-1, arg_val, max_val == max_val_reduced)); + + dst[tgpig] = arg_val_reduced; + + return; } - const float mean = all_sum/ne00; - const float scale = 1.0f/sqrt(mean + eps); + dst[tgpig] = arg_val; +} - device float4 * y = (device float4 *) (dst + tgpig*ne00); - for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) { +kernel void kernel_norm( + constant ggml_metal_kargs_norm & args, + device const char * src0, + device char * dst, + threadgroup float * shmem_f32 [[threadgroup(0)]], + uint tgpig[[threadgroup_position_in_grid]], + ushort tpitg[[thread_position_in_threadgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort ntg[[threads_per_threadgroup]]) { + if (sgitg == 0) { + shmem_f32[tiisg] = 0.0f; + } + + device const float4 * x = (device const float4 *) (src0 + tgpig*args.nb01); + + float4 sumf4(0.0f); + + float sumf = 0.0f; + + for (int i00 = tpitg; i00 < args.ne00_4; i00 += ntg) { + sumf4 += x[i00]; + } + sumf = sumf4[0] + sumf4[1] + sumf4[2] + sumf4[3]; + sumf = simd_sum(sumf); + + threadgroup_barrier(mem_flags::mem_threadgroup); + + if (tiisg == 0) { + shmem_f32[sgitg] = sumf; + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + + sumf = shmem_f32[tiisg]; + sumf = simd_sum(sumf); + + const float mean = sumf/args.ne00; + + device float4 * y = (device float4 *) dst + tgpig*args.ne00_4; + + sumf = 0.0f; + for (int i00 = tpitg; i00 < args.ne00_4; i00 += ntg) { + y[i00] = x[i00] - mean; + sumf += dot(y[i00], y[i00]); + } + sumf = simd_sum(sumf); + + threadgroup_barrier(mem_flags::mem_threadgroup); + + if (tiisg == 0) { + shmem_f32[sgitg] = sumf; + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + + sumf = shmem_f32[tiisg]; + sumf = simd_sum(sumf); + + const float variance = sumf/args.ne00; + + const float scale = 1.0f/sqrt(variance + args.eps); + for (int i00 = tpitg; i00 < args.ne00_4; i00 += ntg) { + y[i00] = y[i00] * scale; + } +} + +kernel void kernel_rms_norm( + constant ggml_metal_kargs_rms_norm & args, + device const char * src0, + device char * dst, + threadgroup float * shmem_f32 [[threadgroup(0)]], + uint tgpig[[threadgroup_position_in_grid]], + ushort tpitg[[thread_position_in_threadgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort ntg[[threads_per_threadgroup]]) { + if (sgitg == 0) { + shmem_f32[tiisg] = 0.0f; + } + + device const float4 * x = (device const float4 *) (src0 + tgpig*args.nb01); + + float sumf = 0.0f; + + // parallel sum + for (int i00 = tpitg; i00 < args.ne00_4; i00 += ntg) { + sumf += dot(x[i00], x[i00]); + } + sumf = simd_sum(sumf); + + threadgroup_barrier(mem_flags::mem_threadgroup); + + if (tiisg == 0) { + shmem_f32[sgitg] = sumf; + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + + sumf = shmem_f32[tiisg]; + sumf = simd_sum(sumf); + + const float mean = sumf/args.ne00; + const float scale = 1.0f/sqrt(mean + args.eps); + + device float4 * y = (device float4 *) dst + tgpig*args.ne00_4; + for (int i00 = tpitg; i00 < args.ne00_4; i00 += ntg) { y[i00] = x[i00] * scale; } } @@ -1152,31 +1717,17 @@ inline float block_q_n_dot_y(device const block_q5_1 * qb_curr, float sumy, thre // quantizations where the block size is 32. It also does not // guard against the number of rows not being divisible by // N_DST, so this is another explicit assumption of the implementation. -template +template void mul_vec_q_n_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { - const int nb = ne00/QK4_0; + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { + const int nb = args.ne00/QK4_0; const int r0 = tgpig.x; const int r1 = tgpig.y; @@ -1184,19 +1735,19 @@ void mul_vec_q_n_f32_impl( const int first_row = (r0 * nsg + sgitg) * nr; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - //const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + //const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - //device const block_q_type * x = (device const block_q_type *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); + //device const block_q_type * x = (device const block_q_type *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); // pointers to src0 rows device const block_q_type * ax[nr]; for (int row = 0; row < nr; ++row) { - const uint offset0 = (first_row + row)*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; + const uint64_t offset0 = (first_row + row)*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; ax[row] = (device const block_q_type *) ((device char *) src0 + offset0); } @@ -1204,10 +1755,10 @@ void mul_vec_q_n_f32_impl( float yl[16]; // src1 vector cache float sumf[nr] = {0.f}; - const int ix = (tiisg/2); - const int il = (tiisg%2)*8; + const short ix = (tiisg/2); + const short il = (tiisg%2)*8; - device const float * yb = y + ix * QK4_0 + il; + device const float * yb = y + ix*QK4_0 + il; // each thread in a SIMD group deals with half a block. for (int ib = ix; ib < nb; ib += nw/2) { @@ -1232,324 +1783,511 @@ void mul_vec_q_n_f32_impl( yb += QK4_0 * 16; } + device float * dst_f32 = (device float *) dst + im*args.ne0*args.ne1 + r1*args.ne0; + for (int row = 0; row < nr; ++row) { const float tot = simd_sum(sumf[row]); - if (tiisg == 0 && first_row + row < ne01) { - dst[im*ne0*ne1 + r1*ne0 + first_row + row] = tot; + + if (tiisg == 0 && first_row + row < args.ne01) { + dst_f32[first_row + row] = tot; } } } kernel void kernel_mul_mv_q4_0_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,nb01,nb02,nb03,ne10,ne12,nb11,nb12,nb13,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { + mul_vec_q_n_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } kernel void kernel_mul_mv_q4_1_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,nb01,nb02,nb03,ne10,ne12,nb11,nb12,nb13,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { + mul_vec_q_n_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } kernel void kernel_mul_mv_q5_0_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,nb01,nb02,nb03,ne10,ne12,nb11,nb12,nb13,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { + mul_vec_q_n_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } kernel void kernel_mul_mv_q5_1_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - mul_vec_q_n_f32_impl(src0,src1,dst,ne00,ne01,ne02,nb01,nb02,nb03,ne10,ne12,nb11,nb12,nb13,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { + mul_vec_q_n_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } - #define NB_Q8_0 8 +template void kernel_mul_mv_q8_0_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { const int nr = N_DST; const int nsg = N_SIMDGROUP; const int nw = N_SIMDWIDTH; - const int nb = ne00/QK8_0; + const int nb = args.ne00/QK8_0; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; - const int first_row = (r0 * nsg + sgitg) * nr; + const int first_row = (r0*nsg + sgitg)*nr; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - //const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + //const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - //device const block_q8_0 * x = (device const block_q8_0 *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); + //device const block_q8_0 * x = (device const block_q8_0 *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); // pointers to src0 rows device const block_q8_0 * ax[nr]; for (int row = 0; row < nr; ++row) { - const uint offset0 = (first_row + row)*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; + const uint64_t offset0 = (first_row + row)*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; ax[row] = (device const block_q8_0 *) ((device char *) src0 + offset0); } float yl[NB_Q8_0]; - float sumf[nr]={0.f}; + float sumf[nr] = { 0.f }; - const int ix = tiisg/4; - const int il = tiisg%4; + const short ix = tiisg/4; + const short il = tiisg%4; - device const float * yb = y + ix * QK8_0 + NB_Q8_0*il; + device const float * yb = y + ix*QK8_0 + il*NB_Q8_0; // each thread in a SIMD group deals with NB_Q8_0 quants at a time for (int ib = ix; ib < nb; ib += nw/4) { - for (int i = 0; i < NB_Q8_0; ++i) { + for (short i = 0; i < NB_Q8_0; ++i) { yl[i] = yb[i]; } for (int row = 0; row < nr; row++) { - device const int8_t * qs = ax[row][ib].qs + NB_Q8_0*il; + device const int8_t * qs = ax[row][ib].qs + il*NB_Q8_0; float sumq = 0.f; - for (int iq = 0; iq < NB_Q8_0; ++iq) { + for (short iq = 0; iq < NB_Q8_0; ++iq) { sumq += qs[iq] * yl[iq]; } sumf[row] += sumq*ax[row][ib].d; } - yb += NB_Q8_0 * nw; + yb += nw*NB_Q8_0; } + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + for (int row = 0; row < nr; ++row) { const float tot = simd_sum(sumf[row]); - if (tiisg == 0 && first_row + row < ne01) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = tot; + + if (tiisg == 0 && first_row + row < args.ne01) { + dst_f32[first_row + row] = tot; } } } [[host_name("kernel_mul_mv_q8_0_f32")]] kernel void kernel_mul_mv_q8_0_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q8_0_f32_impl(src0,src1,dst,ne00,ne01,ne02,nb01,nb02,nb03,ne10,ne12,nb11,nb12,nb13,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg); + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { + kernel_mul_mv_q8_0_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } +// mat-vec kernel processing in chunks of float4 +// chpb - chunks per quantization block +template +void kernel_mul_mv_ext_q4_f32_impl( + constant ggml_metal_kargs_mul_mv_ext & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { + const short chpt = 4; // chunks per thread + + //const short nxpsg = (32); + const short nypsg = (32/nxpsg); + + const short tx = tiisg%nxpsg; + const short ty = tiisg/nxpsg; + + const int i01 = tgpig.x*(nypsg*args.nsg) + nypsg*sgitg + ty; + const int i11 = tgpig.y*r1ptg; + const int i1m = tgpig.z; + + const int i12 = i1m%args.ne12; + const int i13 = i1m/args.ne12; + + const uint64_t offset0 = i01*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = i11*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; + + device const q_t * xq = (i01 < args.ne01) ? (device const q_t *) (src0 + offset0) + tx/chpb : (device const q_t *) src0; + + device const float4 * y4[r1ptg]; + + for (int ir1 = 0; ir1 < r1ptg; ++ir1) { + y4[ir1] = (i11 + ir1 < args.ne11) ? (device const float4 *) (src1 + offset1 + ir1*args.nb11) + tx : (device const float4 *) src1; + } + + float sumf[r1ptg] = { [ 0 ... r1ptg - 1 ] = 0.0f }; + + short cch = tx%chpb; // current chunk index + + for (int ich = tx; 4*ich < args.ne00; ich += chpt*nxpsg) { + float4 lx[chpt]; + +#pragma unroll(chpt) + for (short ch = 0; ch < chpt; ++ch) { + deq_t4(xq, cch, lx[ch]); + + cch += nxpsg; + if (cch >= chpb) { + xq += cch/chpb; + cch %= chpb; + } + } + +#pragma unroll(chpt) + for (short ch = 0; ch < chpt; ++ch) { +#pragma unroll(r1ptg) + for (short ir1 = 0; ir1 < r1ptg; ++ir1) { + sumf[ir1] += dot(lx[ch], y4[ir1][ch*nxpsg]); + + } + } + +#pragma unroll(r1ptg) + for (short ir1 = 0; ir1 < r1ptg; ++ir1) { + y4[ir1] += chpt*nxpsg; + } + } + + // reduce only the threads in each row + for (short ir1 = 0; ir1 < r1ptg; ++ir1) { + if (nxpsg >= 32) { + sumf[ir1] += simd_shuffle_down(sumf[ir1], 16); + } + if (nxpsg >= 16) { + sumf[ir1] += simd_shuffle_down(sumf[ir1], 8); + } + if (nxpsg >= 8) { + sumf[ir1] += simd_shuffle_down(sumf[ir1], 4); + } + if (nxpsg >= 4) { + sumf[ir1] += simd_shuffle_down(sumf[ir1], 2); + } + if (nxpsg >= 2) { + sumf[ir1] += simd_shuffle_down(sumf[ir1], 1); + } + + //sumf[ir1] = simd_sum(sumf[ir1]); + } + + if (tx == 0) { + for (short ir1 = 0; ir1 < r1ptg && i11 + ir1 < args.ne11; ++ir1) { + device float * dst_f32 = (device float *) dst + (uint64_t)i1m*args.ne0*args.ne1 + (uint64_t)(i11 + ir1)*args.ne0; + + if (i01 < args.ne01) { + dst_f32[i01] = sumf[ir1]; + } + } + } +} + +// mat-vec kernel processing in chunks of float4x4 +template +void kernel_mul_mv_ext_q4x4_f32_impl( + constant ggml_metal_kargs_mul_mv_ext & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { + const short chpt = 1; + + //const short nxpsg = (32); + const short nypsg = (32/nxpsg); + + const short tx = tiisg%nxpsg; + const short ty = tiisg/nxpsg; + + const int i01 = tgpig.x*(nypsg*args.nsg) + nypsg*sgitg + ty; + const int i11 = tgpig.y*r1ptg; + const int i1m = tgpig.z; + + const int i12 = i1m%args.ne12; + const int i13 = i1m/args.ne12; + + const uint64_t offset0 = i01*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = i11*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; + + device const q_t * xq = (i01 < args.ne01) ? (device const q_t *) (src0 + offset0) + tx/chpb : (device const q_t *) src0; + + device const float4x4 * y4x4[r1ptg]; + + for (int ir1 = 0; ir1 < r1ptg; ++ir1) { + y4x4[ir1] = (i11 + ir1 < args.ne11) ? (device const float4x4 *) (src1 + offset1 + ir1*args.nb11) + tx : (device const float4x4 *) src1; + } + + float sumf[r1ptg] = { [ 0 ... r1ptg - 1 ] = 0.0f }; + + short cch = tx%chpb; + + for (int ich = tx; 16*ich < args.ne00; ich += chpt*nxpsg) { + float4x4 lx[chpt]; + +#pragma unroll(chpt) + for (short ch = 0; ch < chpt; ++ch) { + deq_t4x4(xq, cch, lx[ch]); + + cch += nxpsg; + if (cch >= chpb) { + xq += cch/chpb; + cch %= chpb; + } + } + +#pragma unroll(chpt) + for (short ch = 0; ch < chpt; ++ch) { +#pragma unroll(r1ptg) + for (short ir1 = 0; ir1 < r1ptg; ++ir1) { + sumf[ir1] += + dot(lx[ch][0], y4x4[ir1][ch*nxpsg][0]) + + dot(lx[ch][1], y4x4[ir1][ch*nxpsg][1]) + + dot(lx[ch][2], y4x4[ir1][ch*nxpsg][2]) + + dot(lx[ch][3], y4x4[ir1][ch*nxpsg][3]); + + } + } + +#pragma unroll(r1ptg) + for (short ir1 = 0; ir1 < r1ptg; ++ir1) { + y4x4[ir1] += chpt*nxpsg; + } + } + + for (short ir1 = 0; ir1 < r1ptg; ++ir1) { + if (nxpsg >= 32) { + sumf[ir1] += simd_shuffle_down(sumf[ir1], 16); + } + if (nxpsg >= 16) { + sumf[ir1] += simd_shuffle_down(sumf[ir1], 8); + } + if (nxpsg >= 8) { + sumf[ir1] += simd_shuffle_down(sumf[ir1], 4); + } + if (nxpsg >= 4) { + sumf[ir1] += simd_shuffle_down(sumf[ir1], 2); + } + if (nxpsg >= 2) { + sumf[ir1] += simd_shuffle_down(sumf[ir1], 1); + } + + //sumf[ir1] = simd_sum(sumf[ir1]); + } + + if (tx == 0) { + for (short ir1 = 0; ir1 < r1ptg && i11 + ir1 < args.ne11; ++ir1) { + device float * dst_f32 = (device float *) dst + (uint64_t)i1m*args.ne0*args.ne1 + (uint64_t)(i11 + ir1)*args.ne0; + + if (i01 < args.ne01) { + dst_f32[i01] = sumf[ir1]; + } + } + } +} + +// dispatchers needed for compile-time nxpsg +// epb - elements per quantization block +template +kernel void kernel_mul_mv_ext_q4_f32_disp( + constant ggml_metal_kargs_mul_mv_ext & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { + switch (args.nxpsg) { + case 4: kernel_mul_mv_ext_q4_f32_impl<4, r1ptg, q_t, epb/4, deq_t4>(args, src0, src1, dst, tgpig, tiisg, sgitg); break; + case 8: kernel_mul_mv_ext_q4_f32_impl<8, r1ptg, q_t, epb/4, deq_t4>(args, src0, src1, dst, tgpig, tiisg, sgitg); break; + case 16: kernel_mul_mv_ext_q4_f32_impl<16, r1ptg, q_t, epb/4, deq_t4>(args, src0, src1, dst, tgpig, tiisg, sgitg); break; + case 32: kernel_mul_mv_ext_q4_f32_impl<32, r1ptg, q_t, epb/4, deq_t4>(args, src0, src1, dst, tgpig, tiisg, sgitg); break; + } +} + +template +kernel void kernel_mul_mv_ext_q4x4_f32_disp( + constant ggml_metal_kargs_mul_mv_ext & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { + switch (args.nxpsg) { + case 4: kernel_mul_mv_ext_q4x4_f32_impl<4, r1ptg, q_t, epb/16, deq_t4x4>(args, src0, src1, dst, tgpig, tiisg, sgitg); break; + case 8: kernel_mul_mv_ext_q4x4_f32_impl<8, r1ptg, q_t, epb/16, deq_t4x4>(args, src0, src1, dst, tgpig, tiisg, sgitg); break; + case 16: kernel_mul_mv_ext_q4x4_f32_impl<16, r1ptg, q_t, epb/16, deq_t4x4>(args, src0, src1, dst, tgpig, tiisg, sgitg); break; + case 32: kernel_mul_mv_ext_q4x4_f32_impl<32, r1ptg, q_t, epb/16, deq_t4x4>(args, src0, src1, dst, tgpig, tiisg, sgitg); break; + } +} + +typedef decltype(kernel_mul_mv_ext_q4_f32_disp <2, block_q8_0, 32, dequantize_q8_0_t4>) mul_mv_ext_q4_f32_t; +typedef decltype(kernel_mul_mv_ext_q4x4_f32_disp<2, block_q4_K, 256, dequantize_q4_K>) mul_mv_ext_q4x4_f32_t; + +template [[host_name("kernel_mul_mv_ext_f16_f32_r1_2")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<2, half4, 4, dequantize_f16_t4>; +template [[host_name("kernel_mul_mv_ext_f16_f32_r1_3")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<3, half4, 4, dequantize_f16_t4>; +template [[host_name("kernel_mul_mv_ext_f16_f32_r1_4")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<4, half4, 4, dequantize_f16_t4>; +template [[host_name("kernel_mul_mv_ext_f16_f32_r1_5")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<5, half4, 4, dequantize_f16_t4>; + +template [[host_name("kernel_mul_mv_ext_q4_0_f32_r1_2")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<2, block_q4_0, 32, dequantize_q4_0_t4>; +template [[host_name("kernel_mul_mv_ext_q4_0_f32_r1_3")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<3, block_q4_0, 32, dequantize_q4_0_t4>; +template [[host_name("kernel_mul_mv_ext_q4_0_f32_r1_4")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<4, block_q4_0, 32, dequantize_q4_0_t4>; +template [[host_name("kernel_mul_mv_ext_q4_0_f32_r1_5")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<5, block_q4_0, 32, dequantize_q4_0_t4>; + +template [[host_name("kernel_mul_mv_ext_q4_1_f32_r1_2")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<2, block_q4_1, 32, dequantize_q4_1_t4>; +template [[host_name("kernel_mul_mv_ext_q4_1_f32_r1_3")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<3, block_q4_1, 32, dequantize_q4_1_t4>; +template [[host_name("kernel_mul_mv_ext_q4_1_f32_r1_4")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<4, block_q4_1, 32, dequantize_q4_1_t4>; +template [[host_name("kernel_mul_mv_ext_q4_1_f32_r1_5")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<5, block_q4_1, 32, dequantize_q4_1_t4>; + +template [[host_name("kernel_mul_mv_ext_q5_0_f32_r1_2")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<2, block_q5_0, 32, dequantize_q5_0_t4>; +template [[host_name("kernel_mul_mv_ext_q5_0_f32_r1_3")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<3, block_q5_0, 32, dequantize_q5_0_t4>; +template [[host_name("kernel_mul_mv_ext_q5_0_f32_r1_4")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<4, block_q5_0, 32, dequantize_q5_0_t4>; +template [[host_name("kernel_mul_mv_ext_q5_0_f32_r1_5")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<5, block_q5_0, 32, dequantize_q5_0_t4>; + +template [[host_name("kernel_mul_mv_ext_q5_1_f32_r1_2")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<2, block_q5_1, 32, dequantize_q5_1_t4>; +template [[host_name("kernel_mul_mv_ext_q5_1_f32_r1_3")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<3, block_q5_1, 32, dequantize_q5_1_t4>; +template [[host_name("kernel_mul_mv_ext_q5_1_f32_r1_4")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<4, block_q5_1, 32, dequantize_q5_1_t4>; +template [[host_name("kernel_mul_mv_ext_q5_1_f32_r1_5")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<5, block_q5_1, 32, dequantize_q5_1_t4>; + +template [[host_name("kernel_mul_mv_ext_q8_0_f32_r1_2")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<2, block_q8_0, 32, dequantize_q8_0_t4>; +template [[host_name("kernel_mul_mv_ext_q8_0_f32_r1_3")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<3, block_q8_0, 32, dequantize_q8_0_t4>; +template [[host_name("kernel_mul_mv_ext_q8_0_f32_r1_4")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<4, block_q8_0, 32, dequantize_q8_0_t4>; +template [[host_name("kernel_mul_mv_ext_q8_0_f32_r1_5")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<5, block_q8_0, 32, dequantize_q8_0_t4>; + +template [[host_name("kernel_mul_mv_ext_iq4_nl_f32_r1_2")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<2, block_iq4_nl, 32, dequantize_iq4_nl_t4>; +template [[host_name("kernel_mul_mv_ext_iq4_nl_f32_r1_3")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<3, block_iq4_nl, 32, dequantize_iq4_nl_t4>; +template [[host_name("kernel_mul_mv_ext_iq4_nl_f32_r1_4")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<4, block_iq4_nl, 32, dequantize_iq4_nl_t4>; +template [[host_name("kernel_mul_mv_ext_iq4_nl_f32_r1_5")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<5, block_iq4_nl, 32, dequantize_iq4_nl_t4>; + +template [[host_name("kernel_mul_mv_ext_q4_K_f32_r1_2")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<2, block_q4_K, 256, dequantize_q4_K>; +template [[host_name("kernel_mul_mv_ext_q4_K_f32_r1_3")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<3, block_q4_K, 256, dequantize_q4_K>; +template [[host_name("kernel_mul_mv_ext_q4_K_f32_r1_4")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<4, block_q4_K, 256, dequantize_q4_K>; +template [[host_name("kernel_mul_mv_ext_q4_K_f32_r1_5")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<5, block_q4_K, 256, dequantize_q4_K>; + +template [[host_name("kernel_mul_mv_ext_q5_K_f32_r1_2")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<2, block_q5_K, 256, dequantize_q5_K>; +template [[host_name("kernel_mul_mv_ext_q5_K_f32_r1_3")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<3, block_q5_K, 256, dequantize_q5_K>; +template [[host_name("kernel_mul_mv_ext_q5_K_f32_r1_4")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<4, block_q5_K, 256, dequantize_q5_K>; +template [[host_name("kernel_mul_mv_ext_q5_K_f32_r1_5")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<5, block_q5_K, 256, dequantize_q5_K>; + +template [[host_name("kernel_mul_mv_ext_q6_K_f32_r1_2")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<2, block_q6_K, 256, dequantize_q6_K>; +template [[host_name("kernel_mul_mv_ext_q6_K_f32_r1_3")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<3, block_q6_K, 256, dequantize_q6_K>; +template [[host_name("kernel_mul_mv_ext_q6_K_f32_r1_4")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<4, block_q6_K, 256, dequantize_q6_K>; +template [[host_name("kernel_mul_mv_ext_q6_K_f32_r1_5")]] kernel mul_mv_ext_q4x4_f32_t kernel_mul_mv_ext_q4x4_f32_disp<5, block_q6_K, 256, dequantize_q6_K>; + #define N_MV_T_T 4 -template +template void kernel_mul_mv_impl( - device const char * src0, - device const char * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb00, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne11, - int64_t ne12, - uint64_t nb10, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - uint3 tgpig, - uint tiisg) { - const int64_t r0 = tgpig.x; - const int64_t rb = tgpig.y*N_MV_T_T; - const int64_t im = tgpig.z; + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig, + ushort tiisg) { + const int r0 = tgpig.x; + const int rb = tgpig.y*N_MV_T_T; + const int im = tgpig.z; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; + const uint64_t offset0 = r0*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; device const T0 * x = (device const T0 *) (src0 + offset0); - if (ne00 < 128) { + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1; + + if (args.ne00 < 128) { for (int row = 0; row < N_MV_T_T; ++row) { int r1 = rb + row; - if (r1 >= ne11) { + if (r1 >= args.ne11) { break; } - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; device const T1 * y = (device const T1 *) (src1 + offset1); float sumf = 0; - for (int i = tiisg; i < ne00; i += 32) { + for (int i = tiisg; i < args.ne00; i += 32) { sumf += (T0) x[i] * (T1) y[i]; } float all_sum = simd_sum(sumf); if (tiisg == 0) { - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + dst_f32[(uint64_t)r1*args.ne0 + r0] = all_sum; } } } else { device const T04 * x4 = (device const T04 *) x; for (int row = 0; row < N_MV_T_T; ++row) { int r1 = rb + row; - if (r1 >= ne11) { + if (r1 >= args.ne11) { break; } - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; device const T1 * y = (device const T1 *) (src1 + offset1); device const T14 * y4 = (device const T14 *) y; float sumf = 0; - for (int i = tiisg; i < ne00/4; i += 32) { - for (int k = 0; k < 4; ++k) sumf += (float) (x4[i][k] * y4[i][k]); + for (int i = tiisg; i < args.ne00/4; i += 32) { + sumf += dot((float4) x4[i], (float4) y4[i]); } float all_sum = simd_sum(sumf); if (tiisg == 0) { - for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) (x[i] * y[i]); - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + for (int i = 4*(args.ne00/4); i < args.ne00; ++i) all_sum += (float) (x[i] * y[i]); + dst_f32[(uint64_t)r1*args.ne0 + r0] = all_sum; } } } @@ -1557,51 +2295,17 @@ void kernel_mul_mv_impl( template kernel void kernel_mul_mv( - device const char * src0, - device const char * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]]) { - kernel_mul_mv_impl( + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]]) { + kernel_mul_mv_impl( + args, src0, src1, dst, - ne00, - ne01, - ne02, - nb00, - nb01, - nb02, - nb03, - ne10, - ne11, - ne12, - nb10, - nb11, - nb12, - nb13, - ne0, - ne1, - r2, - r3, tgpig, tiisg); } @@ -1611,68 +2315,57 @@ typedef decltype(kernel_mul_mv) mul_mv_t; template [[host_name("kernel_mul_mv_f32_f32")]] kernel mul_mv_t kernel_mul_mv; template [[host_name("kernel_mul_mv_f16_f32")]] kernel mul_mv_t kernel_mul_mv; template [[host_name("kernel_mul_mv_f16_f16")]] kernel mul_mv_t kernel_mul_mv; +#if defined(GGML_METAL_USE_BF16) +template [[host_name("kernel_mul_mv_bf16_f32")]] kernel mul_mv_t kernel_mul_mv; +template [[host_name("kernel_mul_mv_bf16_bf16")]] kernel mul_mv_t kernel_mul_mv; +#endif template kernel void kernel_mul_mv_1row( - device const char * src0, - device const char * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]]) { - const int64_t r0 = tgpig.x; - const int64_t r1 = tgpig.y; - const int64_t im = tgpig.z; + const int r0 = tgpig.x; + const int r1 = tgpig.y; + const int im = tgpig.z; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + const uint64_t offset0 = r0*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; device const T * x = (device const T *) (src0 + offset0); device const float * y = (device const float *) (src1 + offset1); + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + float sumf = 0; - if (ne00 < 128) { - for (int i = tiisg; i < ne00; i += 32) { + if (args.ne00 < 128) { + for (int i = tiisg; i < args.ne00; i += 32) { sumf += (float) x[i] * (float) y[i]; } float all_sum = simd_sum(sumf); if (tiisg == 0) { - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + dst_f32[r0] = all_sum; } } else { device const T4 * x4 = (device const T4 *) x; device const float4 * y4 = (device const float4 *) y; - for (int i = tiisg; i < ne00/4; i += 32) { - for (int k = 0; k < 4; ++k) sumf += (float) (x4[i][k] * y4[i][k]); + for (int i = tiisg; i < args.ne00/4; i += 32) { + sumf += dot((float4) x4[i], y4[i]); } float all_sum = simd_sum(sumf); if (tiisg == 0) { - for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) (x[i] * y[i]); - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + for (int i = 4*(args.ne00/4); i < args.ne00; ++i) all_sum += (float) (x[i] * y[i]); + dst_f32[r0] = all_sum; } } } @@ -1680,58 +2373,46 @@ kernel void kernel_mul_mv_1row( typedef decltype(kernel_mul_mv_1row) mul_mv_1row_t; template [[host_name("kernel_mul_mv_f16_f32_1row")]] kernel mul_mv_1row_t kernel_mul_mv_1row; +#if defined(GGML_METAL_USE_BF16) +template [[host_name("kernel_mul_mv_bf16_f32_1row")]] kernel mul_mv_1row_t kernel_mul_mv_1row; +#endif // Assumes row size (ne00) is a multiple of 4 template kernel void kernel_mul_mv_l4( - device const char * src0, - device const char * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]]) { - const int nrows = ne11; - const int64_t r0 = tgpig.x; - const int64_t im = tgpig.z; + const int nrows = args.ne11; + const int r0 = tgpig.x; + const int im = tgpig.z; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; + const uint64_t offset0 = r0*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; device const T4 * x4 = (device const T4 *) (src0 + offset0); + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1; + for (int r1 = 0; r1 < nrows; ++r1) { - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; device const float4 * y4 = (device const float4 *) (src1 + offset1); float sumf = 0; - for (int i = tiisg; i < ne00/4; i += 32) { - for (int k = 0; k < 4; ++k) sumf += (float) (x4[i][k] * y4[i][k]); + for (int i = tiisg; i < args.ne00/4; i += 32) { + sumf += dot((float4) x4[i], y4[i]); } float all_sum = simd_sum(sumf); if (tiisg == 0) { - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + dst_f32[(uint64_t)r1*args.ne0 + r0] = all_sum; } } } @@ -1739,6 +2420,9 @@ kernel void kernel_mul_mv_l4( typedef decltype(kernel_mul_mv_l4) mul_mv_l4_t; template [[host_name("kernel_mul_mv_f16_f32_l4")]] kernel mul_mv_l4_t kernel_mul_mv_l4; +#if defined(GGML_METAL_USE_BF16) +template [[host_name("kernel_mul_mv_bf16_f32_l4")]] kernel mul_mv_l4_t kernel_mul_mv_l4; +#endif static float rope_yarn_ramp(const float low, const float high, const int i0) { const float y = (i0 / 2 - low) / max(0.001f, high - low); @@ -1748,7 +2432,7 @@ static float rope_yarn_ramp(const float low, const float high, const int i0) { // YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn // MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. static void rope_yarn( - float theta_extrap, float freq_scale, float corr_dims[2], int64_t i0, float ext_factor, float mscale, + float theta_extrap, float freq_scale, float corr_dims[2], int i0, float ext_factor, float mscale, thread float * cos_theta, thread float * sin_theta) { // Get n-d rotational scaling corrected for extrapolation float theta_interp = freq_scale * theta_extrap; @@ -1780,65 +2464,41 @@ static void rope_yarn_corr_dims( template kernel void kernel_rope_norm( - device const void * src0, - device const int32_t * src1, - device const float * src2, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - constant int & n_past, - constant int & n_dims, - constant int & n_ctx_orig, - constant float & freq_base, - constant float & freq_scale, - constant float & ext_factor, - constant float & attn_factor, - constant float & beta_fast, - constant float & beta_slow, - uint tiitg[[thread_index_in_threadgroup]], - uint3 tptg[[threads_per_threadgroup]], - uint3 tgpig[[threadgroup_position_in_grid]]) { - const int64_t i3 = tgpig[2]; - const int64_t i2 = tgpig[1]; - const int64_t i1 = tgpig[0]; + constant ggml_metal_kargs_rope & args, + device const char * src0, + device const char * src1, + device const char * src2, + device char * dst, + ushort tiitg[[thread_index_in_threadgroup]], + ushort3 tptg [[threads_per_threadgroup]], + uint3 tgpig[[threadgroup_position_in_grid]]) { + const int i3 = tgpig[2]; + const int i2 = tgpig[1]; + const int i1 = tgpig[0]; float corr_dims[2]; - rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); + rope_yarn_corr_dims(args.n_dims, args.n_ctx_orig, args.freq_base, args.beta_fast, args.beta_slow, corr_dims); - device const int32_t * pos = src1; + device const int32_t * pos = (device const int32_t *) src1; const float theta_base = (float) pos[i2]; - const float inv_ndims = -1.f/n_dims; + const float inv_ndims = -1.f/args.n_dims; float cos_theta; float sin_theta; - for (int64_t i0 = 2*tiitg; i0 < ne0; i0 += 2*tptg.x) { - if (i0 < n_dims) { - const int64_t ic = i0/2; + for (int i0 = 2*tiitg; i0 < args.ne0; i0 += 2*tptg.x) { + if (i0 < args.n_dims) { + const int ic = i0/2; - const float theta = theta_base * pow(freq_base, inv_ndims*i0); + const float theta = theta_base * pow(args.freq_base, inv_ndims*i0); - const float freq_factor = src2 != src0 ? src2[ic] : 1.0f; + const float freq_factor = src2 != src0 ? ((device const float *) src2)[ic] : 1.0f; - rope_yarn(theta/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta); + rope_yarn(theta/freq_factor, args.freq_scale, corr_dims, i0, args.ext_factor, args.attn_factor, &cos_theta, &sin_theta); - device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + i0*args.nb00); + device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0); const float x0 = src[0]; const float x1 = src[1]; @@ -1846,8 +2506,8 @@ kernel void kernel_rope_norm( dst_data[0] = x0*cos_theta - x1*sin_theta; dst_data[1] = x0*sin_theta + x1*cos_theta; } else { - device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + i0*args.nb00); + device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0); dst_data[0] = src[0]; dst_data[1] = src[1]; @@ -1857,74 +2517,50 @@ kernel void kernel_rope_norm( template kernel void kernel_rope_neox( - device const void * src0, - device const int32_t * src1, - device const float * src2, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - constant int & n_past, - constant int & n_dims, - constant int & n_ctx_orig, - constant float & freq_base, - constant float & freq_scale, - constant float & ext_factor, - constant float & attn_factor, - constant float & beta_fast, - constant float & beta_slow, - uint tiitg[[thread_index_in_threadgroup]], - uint3 tptg[[threads_per_threadgroup]], - uint3 tgpig[[threadgroup_position_in_grid]]) { - const int64_t i3 = tgpig[2]; - const int64_t i2 = tgpig[1]; - const int64_t i1 = tgpig[0]; + constant ggml_metal_kargs_rope & args, + device const char * src0, + device const char * src1, + device const char * src2, + device char * dst, + ushort tiitg[[thread_index_in_threadgroup]], + ushort3 tptg [[threads_per_threadgroup]], + uint3 tgpig[[threadgroup_position_in_grid]]) { + const int i3 = tgpig[2]; + const int i2 = tgpig[1]; + const int i1 = tgpig[0]; float corr_dims[2]; - rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); + rope_yarn_corr_dims(args.n_dims, args.n_ctx_orig, args.freq_base, args.beta_fast, args.beta_slow, corr_dims); - device const int32_t * pos = src1; + device const int32_t * pos = (device const int32_t *) src1; const float theta_base = (float) pos[i2]; - const float inv_ndims = -1.f/n_dims; + const float inv_ndims = -1.f/args.n_dims; float cos_theta; float sin_theta; - for (int64_t i0 = 2*tiitg; i0 < ne0; i0 += 2*tptg.x) { - if (i0 < n_dims) { - const int64_t ic = i0/2; + for (int i0 = 2*tiitg; i0 < args.ne0; i0 += 2*tptg.x) { + if (i0 < args.n_dims) { + const int ic = i0/2; - const float theta = theta_base * pow(freq_base, inv_ndims*i0); + const float theta = theta_base * pow(args.freq_base, inv_ndims*i0); - const float freq_factor = src2 != src0 ? src2[ic] : 1.0f; + const float freq_factor = src2 != src0 ? ((device const float *) src2)[ic] : 1.0f; - rope_yarn(theta/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta); + rope_yarn(theta/freq_factor, args.freq_scale, corr_dims, i0, args.ext_factor, args.attn_factor, &cos_theta, &sin_theta); - device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); - device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); + device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + ic*args.nb00); + device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + ic*args.nb0); const float x0 = src[0]; - const float x1 = src[n_dims/2]; + const float x1 = src[args.n_dims/2]; - dst_data[0] = x0*cos_theta - x1*sin_theta; - dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta; + dst_data[0] = x0*cos_theta - x1*sin_theta; + dst_data[args.n_dims/2] = x0*sin_theta + x1*cos_theta; } else { - device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + device const T * const src = (device T *)(src0 + i3*args.nb03 + i2*args.nb02 + i1*args.nb01 + i0*args.nb00); + device T * dst_data = (device T *)( dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0); dst_data[0] = src[0]; dst_data[1] = src[1]; @@ -1979,20 +2615,34 @@ kernel void kernel_im2col( uint3 tgpg[[threadgroups_per_grid]], uint3 tpitg[[thread_position_in_threadgroup]], uint3 ntg[[threads_per_threadgroup]]) { - const int32_t iiw = tgpig[2] * s0 + tpitg[2] * d0 - p0; - const int32_t iih = tgpig[1] * s1 + tpitg[1] * d1 - p1; +// const int64_t IC = tgpg[0]; + const int64_t OH = tgpg[1]; + const int64_t OW = tgpg[2]; - const int32_t offset_dst = - (tpitg[0] * tgpg[1] * tgpg[2] + tgpig[1] * tgpg[2] + tgpig[2]) * CHW + - (tgpig[0] * (ntg[1] * ntg[2]) + tpitg[1] * ntg[2] + tpitg[2]); +// const int64_t N = ntg[0]; + const int64_t KH = ntg[1]; + const int64_t KW = ntg[2]; + + const int64_t in = tpitg[0]; + const int64_t ikh = tpitg[1]; + const int64_t ikw = tpitg[2]; + + const int64_t iic = tgpig[0]; + const int64_t ioh = tgpig[1]; + const int64_t iow = tgpig[2]; + + const int64_t iiw = iow*s0 + ikw*d0 - p0; + const int64_t iih = ioh*s1 + ikh*d1 - p1; + + const int64_t offset_dst = (in*OH*OW + ioh*OW + iow)*CHW + (iic*(KH*KW) + ikh*KW + ikw); device T * pdst = (device T *) (dst); if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { pdst[offset_dst] = 0.0f; } else { - const int32_t offset_src = tpitg[0] * ofs0 + tgpig[0] * ofs1; - pdst[offset_dst] = x[offset_src + iih * IW + iiw]; + const int64_t offset_src = in*ofs0 + iic*ofs1 + iih*IW + iiw; + pdst[offset_dst] = x[offset_src]; } } @@ -2043,25 +2693,25 @@ kernel void kernel_im2col_ext( uint3 tgpg[[threadgroups_per_grid]], // tgpg[0] = D x IC x KH x KW, CHW = IC x KH x KW uint3 tpitg[[thread_position_in_threadgroup]], uint3 ntg[[threads_per_threadgroup]]) { // [M, 1, 1] - const int32_t KHW = KH * KW; // KHW == ntg[1] * ntg[2], KW == ntg[2] + const int64_t KHW = KH * KW; // KHW == ntg[1] * ntg[2], KW == ntg[2] - const int32_t d = tgpig[0] / CHW; - const int32_t chw = tgpig[0] % CHW; - const int32_t tgpig_0 = chw / KHW; // 0 ~ (IC - 1) - const int32_t HW = tgpig[0] % KHW; + const int64_t d = tgpig[0] / CHW; + const int64_t chw = tgpig[0] % CHW; + const int64_t tgpig_0 = chw / KHW; // 0 ~ (IC - 1) + const int64_t HW = tgpig[0] % KHW; - const int32_t tpitg_0 = (d * ntg[0]) + tpitg[0]; + const int64_t tpitg_0 = (d * ntg[0]) + tpitg[0]; if (tpitg_0 >= N) { return; } - const int32_t tpitg_1 = HW / KW; - const int32_t tpitg_2 = HW % KW; + const int64_t tpitg_1 = HW / KW; + const int64_t tpitg_2 = HW % KW; - const int32_t iiw = tgpig[2] * s0 + tpitg_2 * d0 - p0; - const int32_t iih = tgpig[1] * s1 + tpitg_1 * d1 - p1; + const int64_t iiw = tgpig[2] * s0 + tpitg_2 * d0 - p0; + const int64_t iih = tgpig[1] * s1 + tpitg_1 * d1 - p1; - const int32_t offset_dst = + const int64_t offset_dst = (tpitg_0 * tgpg[1] * tgpg[2] + tgpig[1] * tgpg[2] + tgpig[2]) * CHW + (tgpig_0 * KHW + tpitg_1 * KW + tpitg_2); @@ -2070,7 +2720,7 @@ kernel void kernel_im2col_ext( if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { pdst[offset_dst] = 0.0f; } else { - const int32_t offset_src = tpitg_0 * ofs0 + tgpig_0 * ofs1; + const int64_t offset_src = tpitg_0 * ofs0 + tgpig_0 * ofs1; pdst[offset_dst] = x[offset_src + iih * IW + iiw]; } } @@ -2078,6 +2728,79 @@ kernel void kernel_im2col_ext( template [[host_name("kernel_im2col_ext_f32")]] kernel im2col_ext_t kernel_im2col_ext; template [[host_name("kernel_im2col_ext_f16")]] kernel im2col_ext_t kernel_im2col_ext; +typedef void (conv_transpose_1d_t)( + device const float * src0, + device const float * src1, + device char * dst, + constant int32_t & IC, + constant int32_t & IL, + constant int32_t & K, + constant int32_t & s0, + constant uint64_t & nb0, + constant uint64_t & nb1, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tgpg[[threadgroups_per_grid]]); + +template +kernel void kernel_conv_transpose_1d( + device const T * src0, + device const float * src1, + device char * dst, + constant int32_t & IC, + constant int32_t & IL, + constant int32_t & K, + constant int32_t & s0, + constant uint64_t & nb0, + constant uint64_t & nb1, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tgpg[[threadgroups_per_grid]]) { + + float v = 0.0f; + + for (int64_t c = 0; c < IC; c++) { + const int32_t kernel_offset = c * tgpg[1] * K + K * tgpig[1]; + const int32_t input_offset = c * IL; + + for (int64_t i = 0; i < IL; i++) { + if (tgpig[0] >= i * s0 && tgpig[0] < i * s0 + K) { + v += src0[kernel_offset + tgpig[0] - i * s0] * src1[input_offset + i]; + } + } + } + + device float * dst_ptr = (device float *) (dst + tgpig[0] * nb0 + tgpig[1] * nb1); + + dst_ptr[0] = v; +} + +template [[host_name("kernel_conv_transpose_1d_f32_f32")]] +kernel void kernel_conv_transpose_1d( + device const float * src0, + device const float * src1, + device char * dst, + constant int32_t & IC, + constant int32_t & IL, + constant int32_t & K, + constant int32_t & s0, + constant uint64_t & nb0, + constant uint64_t & nb1, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tgpg[[threadgroups_per_grid]]); + +template [[host_name("kernel_conv_transpose_1d_f16_f32")]] +kernel void kernel_conv_transpose_1d( + device const half * src0, + device const float * src1, + device char * dst, + constant int32_t & IC, + constant int32_t & IL, + constant int32_t & K, + constant int32_t & s0, + constant uint64_t & nb0, + constant uint64_t & nb1, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tgpg[[threadgroups_per_grid]]); + kernel void kernel_upscale_f32( device const char * src0, device char * dst, @@ -2174,6 +2897,53 @@ kernel void kernel_pad_f32( } } +kernel void kernel_pad_reflect_1d_f32( + device const char * src0, + device char * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant int64_t & ne03, + constant int64_t & ne0, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant uint64_t & nb03, + constant uint64_t & nb0, + constant uint64_t & nb1, + constant uint64_t & nb2, + constant uint64_t & nb3, + constant int32_t & p0, + constant int32_t & p1, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tgpg[[threadgroups_per_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + + const int64_t i3 = tgpig.z; + const int64_t i2 = tgpig.y; + const int64_t i1 = tgpig.x; + + const int64_t i03 = i3; + const int64_t i02 = i2; + const int64_t i01 = i1; + + device const float * src0_ptr = (device const float *) (src0 + i03*nb03 + i02*nb02 + i01*nb01); + device float * dst_ptr = (device float *) (dst + i3*nb3 + i2*nb2 + i1*nb1); + + if (i1 < ne01 && i2 < ne02 && i3 < ne03) { + for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { + if (i0 < p0) { + dst_ptr[i0] = src0_ptr[p0 - i0]; + } else if (i0 < ne0 - p1) { + dst_ptr[i0] = src0_ptr[i0 - p0]; + } else { + dst_ptr[i0] = src0_ptr[(ne0 - p1 - p0) - (p1 + 1 - (ne0 - i0)) - 1]; + } + } + } +} + kernel void kernel_arange_f32( device char * dst, constant int64_t & ne0, @@ -2293,252 +3063,281 @@ kernel void kernel_leaky_relu_f32( dst[tpig] = src0[tpig] > 0.0f ? src0[tpig] : src0[tpig] * slope; } -typedef void (flash_attn_ext_f16_t)( - device const char * q, - device const char * k, - device const char * v, - device const char * mask, - device float * dst, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant uint64_t & nb21, - constant uint64_t & nb22, - constant uint64_t & nb23, - constant uint64_t & nb31, - constant int64_t & ne1, - constant int64_t & ne2, - constant float & scale, - constant float & max_bias, - constant float & m0, - constant float & m1, - constant uint32_t & n_head_log2, - constant float & logit_softcap, - threadgroup half * shared, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]], - ushort tiisg[[thread_index_in_simdgroup]], - ushort sgitg[[simdgroup_index_in_threadgroup]]); - // ref: https://arxiv.org/pdf/2307.08691.pdf -template // head size, queries per threadgroup, cache items per threadgroup -kernel void kernel_flash_attn_ext_f16( - device const char * q, - device const char * k, - device const char * v, - device const char * mask, - device float * dst, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant uint64_t & nb21, - constant uint64_t & nb22, - constant uint64_t & nb23, - constant uint64_t & nb31, - constant int64_t & ne1, - constant int64_t & ne2, - constant float & scale, - constant float & max_bias, - constant float & m0, - constant float & m1, - constant uint32_t & n_head_log2, - constant float & logit_softcap, - threadgroup half * shared [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]], - ushort tiisg[[thread_index_in_simdgroup]], - ushort sgitg[[simdgroup_index_in_threadgroup]]) { +template< + typename q_t, // query types in shared memory + typename q4_t, + typename q8x8_t, + typename k_t, // key types in shared memory + typename k4x4_t, + typename k8x8_t, + typename v_t, // value types in shared memory + typename v4x4_t, + typename v8x8_t, + typename qk_t, // Q*K types + typename qk8x8_t, + typename s_t, // soft-max types + typename s8x8_t, + typename o_t, // attention accumulation types + typename o4_t, + typename o8x8_t, + typename kd4x4_t, // key type in device memory + short nl_k, + void (*deq_k)(device const kd4x4_t *, short, thread k4x4_t &), + typename vd4x4_t, // key type in device memory + short nl_v, + void (*deq_v)(device const vd4x4_t *, short, thread v4x4_t &), + short D, // head size + short Q = 8, // queries per threadgroup + short KV = 8, // key/value processed per each simdgroup + short C = 32> // cache items per threadgroup +kernel void kernel_flash_attn_ext( + constant ggml_metal_kargs_flash_attn_ext & args, + device const char * q, + device const char * k, + device const char * v, + device const char * mask, + device char * dst, + threadgroup half * shmem_f16 [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 ntg[[threads_per_threadgroup]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { const short nsg = ntg.y; // number of simdgroups - const short iq3 = tgpig[2]; - const short iq2 = tgpig[1]; - const short iq1 = tgpig[0]*Q; + const int iq3 = tgpig[2]; + const int iq2 = tgpig[1]; + const int iq1 = tgpig[0]*Q; - const short D4 = D/4; - const short D8 = D/8; - //const short Q8 = Q/8; - const short NW = N_SIMDWIDTH; - const short SH = (C + Q); // shared memory per simdgroup in (half) + const short D4 = D/4; + const short D8 = D/8; + const short D16 = D/16; + const short NW = N_SIMDWIDTH; + const short SH = (2*C + Q); // shared memory per simdgroup (s_t == float) - const short T = D + 2*nsg*SH; // shared memory size per query in (half) - const short TF = T/2; // shared memory size per query in (float) - const short T4 = T/4; // shared memory size per query in (half4) + const short TS = nsg*SH; // shared memory size per query in (s_t == float) + const short T = D + 2*TS; // shared memory size per query in (half) - threadgroup half * sq = (threadgroup half *) (shared + 0*D); // holds the query data - threadgroup half4 * sq4 = (threadgroup half4 *) (shared + 0*D); // same as above but in half4 - threadgroup float * ss = (threadgroup float *) (shared + 2*sgitg*SH + 1*D); // scratch buffer for attention and diagonal matrix + threadgroup q_t * sq = (threadgroup q_t *) (shmem_f16 + 0*D); // holds the query data + threadgroup q4_t * sq4 = (threadgroup q4_t *) (shmem_f16 + 0*D); // same as above but in q4_t + threadgroup o_t * so = (threadgroup o_t *) (shmem_f16 + 0*D); // reuse query data for accumulation + threadgroup o4_t * so4 = (threadgroup o4_t *) (shmem_f16 + 0*D); // same as above but in o4_t + threadgroup s_t * ss = (threadgroup s_t *) (shmem_f16 + 2*sgitg*SH + Q*D); // scratch buffer for attention, mask and diagonal matrix + + threadgroup k_t * sk = (threadgroup k_t *) (shmem_f16 + sgitg*(4*16*KV) + Q*T); // scratch buffer to load K in shared memory + threadgroup k4x4_t * sk4x4 = (threadgroup k4x4_t *) (shmem_f16 + sgitg*(4*16*KV) + Q*T); // same as above but in k4x4_t + + threadgroup v_t * sv = (threadgroup v_t *) (shmem_f16 + sgitg*(4*16*KV) + Q*T); // scratch buffer to load V in shared memory + threadgroup v4x4_t * sv4x4 = (threadgroup v4x4_t *) (shmem_f16 + sgitg*(4*16*KV) + Q*T); // same as above but in v4x4_t // store the result for all queries in local memory in 8x8 matrices (the O matrix from the paper) - simdgroup_half8x8 lo[D8]; + o8x8_t lo[D8]; // load heads from Q to shared memory for (short j = sgitg; j < Q; j += nsg) { - device const float4 * q4 = (device const float4 *) ((device const char *) q + ((iq1 + j)*nb01 + iq2*nb02 + iq3*nb03)); + device const float4 * q4 = (device const float4 *) ((device const char *) q + ((iq1 + j)*args.nb01 + iq2*args.nb02 + iq3*args.nb03)); for (short i = tiisg; i < D4; i += NW) { - if (iq1 + j < ne01) { - sq4[j*T4 + i] = (half4) q4[i]; + if (iq1 + j < args.ne01) { + sq4[j*D4 + i] = (q4_t) q4[i]; } else { - sq4[j*T4 + i] = 0.0h; + sq4[j*D4 + i] = (q4_t) 0.0f; } } } // zero out lo for (short i = 0; i < D8; ++i) { - lo[i] = make_filled_simdgroup_matrix(0.0h); + lo[i] = make_filled_simdgroup_matrix((o_t) 0.0f); } // zero out shared memory SH for (short j = 0; j < Q; ++j) { for (short i = tiisg; i < SH; i += NW) { - ss[j*TF + i] = 0.0f; + ss[j*TS + i] = 0.0f; } } threadgroup_barrier(mem_flags::mem_threadgroup); { - float S[Q] = { [0 ... Q-1] = 0.0h }; - float M[Q] = { [0 ... Q-1] = -FLT_MAX/2 }; + half S[Q] = { [0 ... Q-1] = 0.0f }; + half M[Q] = { [0 ... Q-1] = -__FLT16_MAX__/2 }; - // assume K and V are same shape - const short ne22 = ne12; - const short ne23 = ne13; + // thread indices inside the simdgroup + // TODO: see if we can utilize quad-group functions for better performance + // https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (6.9.3) + const short tx = tiisg%4; + const short ty = tiisg/4; - // broadcast - const short rk2 = ne02/ne12; - const short rk3 = ne03/ne13; + // broadcast kv + //const short rk2 = args.ne02/args.ne12; + //const short rk3 = args.ne03/args.ne13; - const short rv2 = ne02/ne22; - const short rv3 = ne03/ne23; - - // k indices - const short ik2 = iq2/rk2; - const short ik3 = iq3/rk3; - - // v indices - const short iv2 = iq2/rv2; - const short iv3 = iq3/rv3; + const short ikv2 = iq2/(args.ne02/args.ne_12_2); + const short ikv3 = iq3/(args.ne03/args.ne_12_3); // load the queries from shared memory into local memory - simdgroup_half8x8 mq[D8]; + q8x8_t mq[D8]; for (short i = 0; i < D8; ++i) { - simdgroup_load(mq[i], sq + i*8, T); + simdgroup_load(mq[i], sq + i*8, D); } - // pointer to the mask - device const half * mp = (device const half *) (mask + iq1*nb31); + const bool has_mask = mask != q; - float slope = 1.0f; + half slope = 1.0f; // ALiBi - if (max_bias > 0.0f) { - const uint32_t h = iq2; + if (args.max_bias > 0.0f) { + const short h = iq2; - const float base = h < n_head_log2 ? m0 : m1; - const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; + const half base = h < args.n_head_log2 ? args.m0 : args.m1; + const short exph = h < args.n_head_log2 ? h + 1 : 2*(h - args.n_head_log2) + 1; slope = pow(base, exph); } // loop over the KV cache // each simdgroup handles blocks of Q rows and C columns - for (int ic0 = 0; ic0 < ne11; ic0 += C*nsg) { + for (int ic0 = 0; ic0 < args.ne11; ic0 += C*nsg) { const int ic = ic0 + C*sgitg; - if (ic >= ne11) { + if (ic >= args.ne11) { break; } + if (has_mask) { + // used to detect blocks full of -INF + half smax = -INFINITY; + + // load the mask in shared memory + #pragma unroll(Q) + for (short j = 0; j < Q; ++j) { + device const half * pm = (device const half *) ((device const char *) mask + (iq1 + j)*args.nb31); + + const half m = pm[ic + tiisg]; + + ss[j*TS + C + tiisg] = m; + smax = max(smax, m); + } + + smax = simd_max(smax); + + if (smax == -INFINITY) { + continue; + } + } + // Q*K^T { for (short cc = 0; cc < C/8; ++cc) { - simdgroup_float8x8 mqk = make_filled_simdgroup_matrix(0.h); + qk8x8_t mqk = make_filled_simdgroup_matrix((qk_t) 0.0f); - device const half * pk = (device const half *) ((device const char *) k + ((ic + 8*cc)*nb11 + ik2*nb12 + ik3*nb13)); + // this is compile-time check, so it does not have runtime overhead + if (is_same::value) { + // we can read directly from global memory + device const k_t * pk = (device const k_t *) ((device const char *) k + ((ic + 8*cc)*args.nb_12_1 + ikv2*args.nb_12_2 + ikv3*args.nb_12_3)); - for (short i = 0; i < D8; ++i) { - simdgroup_half8x8 mk; - simdgroup_load(mk, pk + i*8, nb11/sizeof(half), 0, true); // transpose + #pragma unroll(D8) + for (short i = 0; i < D8; ++i) { + k8x8_t mk; + simdgroup_load(mk, pk + i*8, args.nb_12_1/sizeof(k_t), 0, true); // transpose // TODO: use ne10 - simdgroup_multiply_accumulate(mqk, mq[i], mk, mqk); + simdgroup_multiply_accumulate(mqk, mq[i], mk, mqk); + } + } else { + for (short ii = 0; ii < D16; ii += 4) { + device const kd4x4_t * pk4x4 = (device const kd4x4_t *) ((device const char *) k + ((ic + 8*cc + ty)*args.nb_12_1 + ikv2*args.nb_12_2 + ikv3*args.nb_12_3)); + + if (D16%4 == 0) { + // the head is evenly divisible by 4*16 = 64, so no need for bound checks + { + k4x4_t tmp; + deq_k(pk4x4 + (ii + tx)/nl_k, (ii + tx)%nl_k, tmp); + sk4x4[4*ty + tx] = tmp; + } + + simdgroup_barrier(mem_flags::mem_threadgroup); + + #pragma unroll(4) + for (short k = 0; k < 4; ++k) { + k8x8_t mk; + + simdgroup_load(mk, sk + 16*k + 0*8, 4*16, 0, true); // transpose + simdgroup_multiply_accumulate(mqk, mq[2*(ii + k) + 0], mk, mqk); + + simdgroup_load(mk, sk + 16*k + 1*8, 4*16, 0, true); // transpose + simdgroup_multiply_accumulate(mqk, mq[2*(ii + k) + 1], mk, mqk); + } + } else { + if (ii + tx < D16) { + k4x4_t tmp; + deq_k(pk4x4 + (ii + tx)/nl_k, (ii + tx)%nl_k, tmp); + sk4x4[4*ty + tx] = tmp; + } + + simdgroup_barrier(mem_flags::mem_threadgroup); + + for (short k = 0; k < 4 && ii + k < D16; ++k) { + k8x8_t mk; + + simdgroup_load(mk, sk + 16*k + 0*8, 4*16, 0, true); // transpose + simdgroup_multiply_accumulate(mqk, mq[2*(ii + k) + 0], mk, mqk); + + simdgroup_load(mk, sk + 16*k + 1*8, 4*16, 0, true); // transpose + simdgroup_multiply_accumulate(mqk, mq[2*(ii + k) + 1], mk, mqk); + } + } + } } - simdgroup_store(mqk, ss + 8*cc, TF, 0, false); + // cast qk_t -> s_t + //s8x8_t mqks(1.0f); + //simdgroup_multiply(mqks, mqk, mqks); + //simdgroup_store(mqks, ss + 8*cc, TS, 0, false); + + simdgroup_store(mqk, ss + 8*cc, TS, 0, false); } } - // used to detect blocks full of -INF - float smax = -INFINITY; - // online softmax { - float ms[Q]; - - for (short j = 0; j < Q; ++j) { - const float m = M[j]; + for (ushort j = 0; j < Q; ++j) { + const half m = M[j]; // scale and apply the logitcap / mask - float s = ss[j*TF + tiisg]*scale; + half s = ss[j*TS + tiisg]*args.scale; - if (logit_softcap != 0.0f) { - s = logit_softcap*precise::tanh(s); + if (args.logit_softcap != 0.0f) { + s = args.logit_softcap*precise::tanh(s); } - if (mask != q) { - // mqk = mqk + mask*slope - s += slope*mp[ic + j*nb31/sizeof(half) + tiisg]; - } + // mqk = mqk + mask*slope + s += slope*ss[j*TS + C + tiisg]; - smax = simd_max(max(smax, s)); M[j] = simd_max(max(M[j], s)); - ms[j] = exp(m - M[j]); - const float vs = exp(s - M[j]); + const half ms = exp(m - M[j]); + const half vs = exp(s - M[j]); - S[j] = S[j]*ms[j] + simd_sum(vs); + S[j] = S[j]*ms + simd_sum(vs); // the P matrix from the paper (Q rows, C columns) - ss[j*TF + tiisg] = vs; - } + ss[j*TS + tiisg] = vs; - // create a QxQ diagonal matrix for rescaling the output - if (tiisg < Q) { - ss[tiisg*TF + C + tiisg] = ms[tiisg]; + // create a QxQ diagonal matrix for rescaling the output + if (tiisg == j) { + ss[j*TS + 2*C + j] = ms; + } } } - // skip -INF blocks - if (smax == -INFINITY) { - continue; - } - // O = diag(ms)*O { - simdgroup_float8x8 mm; - simdgroup_load(mm, ss + C, TF, 0, false); + s8x8_t mm; + simdgroup_load(mm, ss + 2*C, TS, 0, false); + #pragma unroll(D8) for (short i = 0; i < D8; ++i) { simdgroup_multiply(lo[i], mm, lo[i]); } @@ -2547,16 +3346,64 @@ kernel void kernel_flash_attn_ext_f16( // O = O + (Q*K^T)*V { for (short cc = 0; cc < C/8; ++cc) { - device const half * pv = (device const half *) ((device const char *) v + ((ic + 8*cc)*nb21 + iv2*nb22 + iv3*nb23)); + s8x8_t ms; + simdgroup_load(ms, ss + 8*cc, TS, 0, false); - for (short i = 0; i < D8; ++i) { - simdgroup_half8x8 mk; - simdgroup_load(mk, pv + i*8, nb21/sizeof(half), 0, false); + if (is_same::value) { + // we can read directly from global memory + device const v_t * pv = (device const v_t *) ((device const char *) v + ((ic + 8*cc)*args.nb_12_1 + ikv2*args.nb_12_2 + ikv3*args.nb_12_3)); - simdgroup_float8x8 mv; - simdgroup_load(mv, ss + 8*cc, TF, 0, false); + #pragma unroll(D8) + for (short i = 0; i < D8; ++i) { + v8x8_t mv; + simdgroup_load(mv, pv + i*8, args.nb_12_1/sizeof(v_t), 0, false); // TODO: use ne20 - simdgroup_multiply_accumulate(lo[i], mv, mk, lo[i]); + simdgroup_multiply_accumulate(lo[i], ms, mv, lo[i]); + } + } else { + for (short ii = 0; ii < D16; ii += 4) { + device const vd4x4_t * pv4x4 = (device const vd4x4_t *) ((device const char *) v + ((ic + 8*cc + ty)*args.nb_12_1 + ikv2*args.nb_12_2 + ikv3*args.nb_12_3)); + + if (D16%4 == 0) { + // no need for bound checks + { + v4x4_t tmp; + deq_v(pv4x4 + (ii + tx)/nl_v, (ii + tx)%nl_v, tmp); + sv4x4[4*ty + tx] = tmp; + } + + simdgroup_barrier(mem_flags::mem_threadgroup); + + #pragma unroll(4) + for (short k = 0; k < 4; ++k) { + v8x8_t mv; + + simdgroup_load(mv, sv + 16*k + 0*8, 4*16, 0, false); + simdgroup_multiply_accumulate(lo[2*(ii + k) + 0], ms, mv, lo[2*(ii + k) + 0]); + + simdgroup_load(mv, sv + 16*k + 1*8, 4*16, 0, false); + simdgroup_multiply_accumulate(lo[2*(ii + k) + 1], ms, mv, lo[2*(ii + k) + 1]); + } + } else { + if (ii + tx < D16) { + v4x4_t tmp; + deq_v(pv4x4 + (ii + tx)/nl_v, (ii + tx)%nl_v, tmp); + sv4x4[4*ty + tx] = tmp; + } + + simdgroup_barrier(mem_flags::mem_threadgroup); + + for (short k = 0; k < 4 && ii + k < D16; ++k) { + v8x8_t mv; + + simdgroup_load(mv, sv + 16*k + 0*8, 4*16, 0, false); + simdgroup_multiply_accumulate(lo[2*(ii + k) + 0], ms, mv, lo[2*(ii + k) + 0]); + + simdgroup_load(mv, sv + 16*k + 1*8, 4*16, 0, false); + simdgroup_multiply_accumulate(lo[2*(ii + k) + 1], ms, mv, lo[2*(ii + k) + 1]); + } + } + } } } } @@ -2565,23 +3412,23 @@ kernel void kernel_flash_attn_ext_f16( // these are needed for reducing the results from the simdgroups (reuse the ss buffer) for (short j = 0; j < Q; ++j) { if (tiisg == 0) { - ss[j*TF + 0] = S[j]; - ss[j*TF + 1] = M[j]; + ss[j*TS + 0] = S[j]; + ss[j*TS + 1] = M[j]; } } } // reduce the warps sequentially - for (short sg = 1; sg < nsg; ++sg) { - float S = { 0.0h }; - float M = { -FLT_MAX/2 }; + for (ushort sg = 1; sg < nsg; ++sg) { + half S = { 0.0f }; + half M = { -__FLT16_MAX__/2 }; threadgroup_barrier(mem_flags::mem_threadgroup); // each simdgroup stores its output to shared memory, reusing sq if (sgitg == sg) { for (short i = 0; i < D8; ++i) { - simdgroup_store(lo[i], sq + i*8, T, 0, false); + simdgroup_store(lo[i], so + i*8, D, 0, false); } } @@ -2590,39 +3437,41 @@ kernel void kernel_flash_attn_ext_f16( // the first simdgroup accumulates the results from the other simdgroups if (sgitg == 0) { for (short j = 0; j < Q; ++j) { - const float S0 = ss[j*TF + 0]; - const float S1 = ss[j*TF + sg*SH + 0]; + const half S0 = ss[j*TS + 0]; + const half S1 = ss[j*TS + sg*SH + 0]; - const float M0 = ss[j*TF + 1]; - const float M1 = ss[j*TF + sg*SH + 1]; + const half M0 = ss[j*TS + 1]; + const half M1 = ss[j*TS + sg*SH + 1]; M = max(M0, M1); - const float ms0 = exp(M0 - M); - const float ms1 = exp(M1 - M); + const half ms0 = exp(M0 - M); + const half ms1 = exp(M1 - M); S = S0*ms0 + S1*ms1; if (tiisg == 0) { - ss[j*TF + 0] = S; - ss[j*TF + 1] = M; + ss[j*TS + 0] = S; + ss[j*TS + 1] = M; - ss[j*TF + C + j ] = ms0; - ss[j*TF + C + j + sg*SH] = ms1; + ss[j*TS + 2*C + j ] = ms0; + ss[j*TS + 2*C + j + sg*SH] = ms1; } } // O_0 = diag(ms0)*O_0 + diag(ms1)*O_1 { - simdgroup_half8x8 t; - simdgroup_float8x8 ms0; - simdgroup_float8x8 ms1; + s8x8_t ms0; + s8x8_t ms1; - simdgroup_load(ms0, ss + C, TF, 0, false); - simdgroup_load(ms1, ss + C + sg*SH, TF, 0, false); + simdgroup_load(ms0, ss + 2*C, TS, 0, false); + simdgroup_load(ms1, ss + 2*C + sg*SH, TS, 0, false); + #pragma unroll(D8) for (short i = 0; i < D8; ++i) { - simdgroup_load (t, sq + i*8, T, 0, false); + o8x8_t t; + + simdgroup_load (t, so + i*8, D, 0, false); simdgroup_multiply(t, ms1, t); simdgroup_multiply_accumulate(lo[i], ms0, lo[i], t); @@ -2634,7 +3483,7 @@ kernel void kernel_flash_attn_ext_f16( // store result to shared memory (reuse sq) if (sgitg == 0) { for (short i = 0; i < D8; ++i) { - simdgroup_store(lo[i], sq + i*8, T, 0, false); + simdgroup_store(lo[i], so + i*8, D, 0, false); } } @@ -2642,257 +3491,361 @@ kernel void kernel_flash_attn_ext_f16( // final rescale with 1/S and store to global memory if (sgitg == 0) { - for (short j = 0; j < Q && iq1 + j < ne01; ++j) { - const float S = ss[j*TF + 0]; + for (short j = 0; j < Q && iq1 + j < args.ne01; ++j) { + const float S = ss[j*TS + 0]; for (short i = tiisg; i < D4; i += NW) { - dst4[(iq3*ne2*ne1 + iq2 + (iq1 + j)*ne1)*D4 + i] = (float4) sq4[j*T4 + i]/S; + dst4[((uint64_t)iq3*args.ne2*args.ne1 + iq2 + (uint64_t)(iq1 + j)*args.ne1)*D4 + i] = (float4) so4[j*D4 + i]/S; } } } } -template [[host_name("kernel_flash_attn_ext_f16_h64" )]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<64>; -template [[host_name("kernel_flash_attn_ext_f16_h80" )]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<80>; -template [[host_name("kernel_flash_attn_ext_f16_h96" )]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<96>; -template [[host_name("kernel_flash_attn_ext_f16_h112")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<112>; -template [[host_name("kernel_flash_attn_ext_f16_h128")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<128>; -//template [[host_name("kernel_flash_attn_ext_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<256>; +// TODO: this is quite ugly. in the future these types will be hardcoded in the kernel, but for now keep them as +// template to be able to explore different combinations +// +#define FA_TYPES \ + half, half4, simdgroup_half8x8, \ + half, half4x4, simdgroup_half8x8, \ + half, half4x4, simdgroup_half8x8, \ + float, simdgroup_float8x8, \ + float, simdgroup_float8x8, \ + half, half4, simdgroup_half8x8 -template // head size, queries per threadgroup, cache items per threadgroup -kernel void kernel_flash_attn_ext_vec_f16( - device const char * q, - device const char * k, - device const char * v, - device const char * mask, - device float * dst, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant uint64_t & nb21, - constant uint64_t & nb22, - constant uint64_t & nb23, - constant uint64_t & nb31, - constant int64_t & ne1, - constant int64_t & ne2, - constant float & scale, - constant float & max_bias, - constant float & m0, - constant float & m1, - constant uint32_t & n_head_log2, - constant float & logit_softcap, - threadgroup half * shared [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]], - ushort tiisg[[thread_index_in_simdgroup]], - ushort sgitg[[simdgroup_index_in_threadgroup]]) { +typedef decltype(kernel_flash_attn_ext) flash_attn_ext_t; + +template [[host_name("kernel_flash_attn_ext_f16_h64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_f16_h80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_f16_h96" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_f16_h112")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_f16_h128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_f16_h256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; + +#if defined(GGML_METAL_USE_BF16) +template [[host_name("kernel_flash_attn_ext_bf16_h64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_bf16_h80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_bf16_h96" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_bf16_h112")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_bf16_h128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_bf16_h256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +#endif + +template [[host_name("kernel_flash_attn_ext_q4_0_h64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q4_0_h80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q4_0_h96" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q4_0_h112")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q4_0_h128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q4_0_h256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; + +template [[host_name("kernel_flash_attn_ext_q4_1_h64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q4_1_h80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q4_1_h96" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q4_1_h112")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q4_1_h128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q4_1_h256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; + +template [[host_name("kernel_flash_attn_ext_q5_0_h64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q5_0_h80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q5_0_h96" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q5_0_h112")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q5_0_h128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q5_0_h256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; + +template [[host_name("kernel_flash_attn_ext_q5_1_h64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q5_1_h80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q5_1_h96" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q5_1_h112")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q5_1_h128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q5_1_h256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; + +template [[host_name("kernel_flash_attn_ext_q8_0_h64" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q8_0_h80" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q8_0_h96" )]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q8_0_h112")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q8_0_h128")]] kernel flash_attn_ext_t kernel_flash_attn_ext; +template [[host_name("kernel_flash_attn_ext_q8_0_h256")]] kernel flash_attn_ext_t kernel_flash_attn_ext; + +#undef FA_TYPES + +template< + typename q4_t, // query types in shared memory + typename q4x4_t, + typename k4x4_t, // key types in shared memory + typename v4x4_t, // value types in shared memory + typename qk_t, // Q*K types + typename s_t, // soft-max types + typename s4_t, + typename s4x4_t, + typename o4x4_t, // attention accumulation types + typename kd4x4_t, // key type in device memory + short nl_k, + void (*deq_k)(device const kd4x4_t *, short, thread k4x4_t &), + typename vd4x4_t, // key type in device memory + short nl_v, + void (*deq_v)(device const vd4x4_t *, short, thread v4x4_t &), + short D, // head size + short Q = 1, // queries per threadgroup + short C = 32> // cache items per threadgroup +kernel void kernel_flash_attn_ext_vec( + constant ggml_metal_kargs_flash_attn_ext & args, + device const char * q, + device const char * k, + device const char * v, + device const char * mask, + device char * dst, + threadgroup half * shmem_f16 [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 ntg[[threads_per_threadgroup]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { const short nsg = ntg.y; // number of simdgroups - const short iq3 = tgpig[2]; - const short iq2 = tgpig[1]; - const short iq1 = tgpig[0]; + const int iq3 = tgpig[2]; + const int iq2 = tgpig[1]; + const int iq1 = tgpig[0]; - const short D4 = D/4; - const short NW = N_SIMDWIDTH; - const short SH = (C + Q); // shared memory per simdgroup in (half) + const short D4 = D/4; + const short D16 = D/16; + const short NW = N_SIMDWIDTH; + const short NL = NW/4; // note: this can be adjusted to support D%64 == 0 and D%32 == 0 + const short SH = 2*C; // shared memory per simdgroup - const short T = D + 2*nsg*SH; // shared memory size per query in (half) + const short T = D + nsg*SH; // shared memory size per query in (half) - float slope = 1.0f; - - // ALiBi - if (max_bias > 0.0f) { - const uint32_t h = iq2; - - const float base = h < n_head_log2 ? m0 : m1; - const int exp = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; - - slope = pow(base, exp); - } - - //threadgroup half * sq = (threadgroup half *) (shared + 0*D); // holds the query data - threadgroup half4 * sq4 = (threadgroup half4 *) (shared + 0*D); // same as above but in half4 - threadgroup float * ss = (threadgroup float *) (shared + 2*sgitg*SH + 1*D); // scratch buffer for attention and diagonal matrix - threadgroup float4 * ss4 = (threadgroup float4 *) (shared + 2*sgitg*SH + 1*D); // same as above but in half4 - threadgroup half4 * sr4 = (threadgroup half4 *) (shared + sgitg*D + 1*T); // scratch buffer for the results + //threadgroup q_t * sq = (threadgroup q_t *) (shmem_f16 + 0*D); // holds the query data + threadgroup q4_t * sq4 = (threadgroup q4_t *) (shmem_f16 + 0*D); // same as above but in q4_t + threadgroup q4x4_t * sq4x4 = (threadgroup q4x4_t *) (shmem_f16 + 0*D); // same as above but in q4x4_t + threadgroup s_t * ss = (threadgroup s_t *) (shmem_f16 + sgitg*SH + Q*D); // scratch buffer for attention + threadgroup s4_t * ss4 = (threadgroup s4_t *) (shmem_f16 + sgitg*SH + Q*D); // same as above but in s4_t + threadgroup half * sm = (threadgroup half *) (shmem_f16 + sgitg*SH + C + Q*D); // scratch buffer for mask + threadgroup o4x4_t * sr4x4 = (threadgroup o4x4_t *) (shmem_f16 + sgitg*D + Q*T); // scratch buffer for the results // store the result for all queries in local memory in 8x8 matrices (the O matrix from the paper) - half4 lo[D4/NW]; + o4x4_t lo[D16/NL]; // load heads from Q to shared memory - device const float4 * q4 = (device const float4 *) ((device const char *) q + (iq1*nb01 + iq2*nb02 + iq3*nb03)); + device const float4 * q4 = (device const float4 *) ((device const char *) q + (iq1*args.nb01 + iq2*args.nb02 + iq3*args.nb03)); for (short i = tiisg; i < D4; i += NW) { - if (iq1 < ne01) { - sq4[i] = (half4) q4[i]; + if (iq1 < args.ne01) { + sq4[i] = (q4_t) q4[i]; } else { - sq4[i] = 0.0h; + sq4[i] = (q4_t) 0.0f; } } // zero out lo - for (short i = tiisg; i < D4; i += NW) { - lo[i/NW] = 0.0h; + for (short i = 0; i < D16/NL; ++i) { + lo[i] = (o4x4_t) 0.0f; } // zero out shared memory SH for (short i = tiisg; i < SH/4; i += NW) { - ss4[i] = 0.0h; + ss4[i] = (s4_t) 0.0f; } threadgroup_barrier(mem_flags::mem_threadgroup); { - float S = { 0.0h }; - float M = { -FLT_MAX/2 }; + half S = 0.0f; + half M = -__FLT16_MAX__/2; - // assume K and V are same shape - const short ne22 = ne12; - const short ne23 = ne13; + // thread indices inside the simdgroup + const short tx = tiisg%NL; + const short ty = tiisg/NL; - // broadcast - const short rk2 = ne02/ne12; - const short rk3 = ne03/ne13; + // broadcast kv + //const short rk2 = args.ne02/args.ne12; + //const short rk3 = args.ne03/args.ne13; - const short rv2 = ne02/ne22; - const short rv3 = ne03/ne23; - - // k indices - const short ik2 = iq2 / rk2; - const short ik3 = iq3 / rk3; - - // v indices - const short iv2 = iq2 / rv2; - const short iv3 = iq3 / rv3; + const short ikv2 = iq2/(args.ne02/args.ne_12_2); + const short ikv3 = iq3/(args.ne03/args.ne_12_3); // load the queries from shared memory into local memory - float4 mq[D4]; + q4x4_t mq[D16/NL]; - for (short ii = 0; ii < D4; ii += NW) { - short i = ii + tiisg; - mq[i] = (float4) sq4[i]; + #pragma unroll(D16/NL) + for (short ii = 0; ii < D16; ii += NL) { + mq[ii/NL] = sq4x4[ii + tx]; } + const bool has_mask = mask != q; + // pointer to the mask - device const half4 * mp4 = (device const half4 *) (mask + iq1*nb31); + device const half * pm = (device const half *) (mask + iq1*args.nb31); + + half slope = 1.0f; + + // ALiBi + if (args.max_bias > 0.0f) { + const short h = iq2; + + const half base = h < args.n_head_log2 ? args.m0 : args.m1; + const short exph = h < args.n_head_log2 ? h + 1 : 2*(h - args.n_head_log2) + 1; + + slope = pow(base, exph); + } // loop over the KV cache // each simdgroup handles blocks of Q rows and C columns - for (int ic0 = 0; ic0 < ne11; ic0 += C*nsg) { + for (int ic0 = 0; ic0 < args.ne11; ic0 += C*nsg) { const int ic = ic0 + C*sgitg; - if (ic >= ne11) { + if (ic >= args.ne11) { break; } + if (has_mask) { + sm[tiisg] = pm[ic + tiisg]; + } + // Q*K^T { -#pragma unroll + // each simdgroup processes 1 query and 4 (NW/NL) keys for (short cc = 0; cc < C/4; ++cc) { - float4 mqk = { 0.0h }; + qk_t mqka[4] = { 0.0, 0.0, 0.0, 0.0 }; - device const half4 * pk4 = (device const half4 *) ((device const char *) k + ((ic + 4*cc)*nb11 + ik2*nb12 + ik3*nb13)); + device const kd4x4_t * pk = (device const kd4x4_t *) ((device const char *) k + ((ic + 4*cc + ty)*args.nb_12_1 + ikv2*args.nb_12_2 + ikv3*args.nb_12_3)); -#pragma unroll - for (short ii = 0; ii < D4; ii += NW) { - const short i = ii + tiisg; + #pragma unroll(D16/NL) + for (short ii = 0; ii < D16; ii += NL) { + const short i = ii + tx; - float4x4 mk; - mk[0] = (float4) pk4[i + 0*(nb11/8)]; - mk[1] = (float4) pk4[i + 1*(nb11/8)]; - mk[2] = (float4) pk4[i + 2*(nb11/8)]; - mk[3] = (float4) pk4[i + 3*(nb11/8)]; + k4x4_t mk; + deq_k(pk + i/nl_k, i%nl_k, mk); - mqk += (float4) (mq[i] * mk); + // note: this is less precise than the version below + //mqka[0] += dot(mq[ii/NL][0], mk[0]); + //mqka[1] += dot(mq[ii/NL][1], mk[1]); + //mqka[2] += dot(mq[ii/NL][2], mk[2]); + //mqka[3] += dot(mq[ii/NL][3], mk[3]); + + mqka[0] += dot((float4) mq[ii/NL][0], (float4) mk[0]); + mqka[1] += dot((float4) mq[ii/NL][1], (float4) mk[1]); + mqka[2] += dot((float4) mq[ii/NL][2], (float4) mk[2]); + mqka[3] += dot((float4) mq[ii/NL][3], (float4) mk[3]); } - // reduce the results from the threads in the simdgroup - mqk += simd_shuffle_down(mqk, 16); - mqk += simd_shuffle_down(mqk, 8); + qk_t mqk = mqka[0] + mqka[1] + mqka[2] + mqka[3]; + + // simdgroup reduce + // [ 0 .. 7] -> [ 0] + // [ 8 .. 15] -> [ 8] + // [16 .. 23] -> [16] + // [24 .. 31] -> [24] + //mqk += simd_shuffle_down(mqk, 16); + //mqk += simd_shuffle_down(mqk, 8); mqk += simd_shuffle_down(mqk, 4); mqk += simd_shuffle_down(mqk, 2); mqk += simd_shuffle_down(mqk, 1); // mqk = mqk*scale + mask*slope - if (tiisg == 0) { - mqk *= scale; + if (tx == 0) { + mqk *= args.scale; - if (logit_softcap != 0.0f) { - mqk = logit_softcap*precise::tanh(mqk); + if (args.logit_softcap != 0.0f) { + mqk = args.logit_softcap*precise::tanh(mqk); } - mqk += (mask != q) ? ((float4) mp4[ic/4 + cc])*slope : (float4) 0.0f; + mqk += sm[4*cc + ty]*slope; - ss4[cc] = mqk; + ss[4*cc + ty] = mqk; } } } + simdgroup_barrier(mem_flags::mem_threadgroup); + // online softmax { - const short p = tiisg; - - const float m = M; - const float s = ss[p]; + const half m = M; + const half s = ss[tiisg]; M = simd_max(max(M, s)); - const float ms = exp(m - M); - const float vs = exp(s - M); + const half ms = exp(m - M); + const half vs = exp(s - M); S = S*ms + simd_sum(vs); // the P matrix from the paper (Q rows, C columns) - ss[p] = vs; + ss[tiisg] = vs; // O = diag(ms)*O -#pragma unroll - for (short ii = 0; ii < D4; ii += NW) { - const short i = ii + tiisg; - lo[i/NW] *= ms; + #pragma unroll(D16/NL) + for (short ii = 0; ii < D16; ii += NL) { + lo[ii/NL] *= ms; } } + simdgroup_barrier(mem_flags::mem_threadgroup); + // O = O + (Q*K^T)*V { -#pragma unroll for (short cc = 0; cc < C/4; ++cc) { - device const half4 * pv4 = (device const half4 *) ((device const char *) v + ((ic + 4*cc)*nb21 + iv2*nb22 + iv3*nb23)); + device const vd4x4_t * pv4 = (device const vd4x4_t *) ((device const char *) v + ((ic + 4*cc + ty)*args.nb_12_1 + ikv2*args.nb_12_2 + ikv3*args.nb_12_3)); -#pragma unroll - for (short ii = 0; ii < D4; ii += NW) { - const short i = ii + tiisg; + const s4x4_t ms(ss[4*cc + ty]); - lo[i/NW] += pv4[i + 0*(nb21/8)] * ss[4*cc + 0]; - lo[i/NW] += pv4[i + 1*(nb21/8)] * ss[4*cc + 1]; - lo[i/NW] += pv4[i + 2*(nb21/8)] * ss[4*cc + 2]; - lo[i/NW] += pv4[i + 3*(nb21/8)] * ss[4*cc + 3]; + #pragma unroll(D16/NL) + for (short ii = 0; ii < D16; ii += NL) { + const short i = ii + tx; + + v4x4_t mv; + deq_v(pv4 + i/nl_v, i%nl_v, mv); + + lo[ii/NL] += mv*ms; } } } - } // these are needed for reducing the results from the simdgroups (reuse the ss buffer) if (tiisg == 0) { - ss[0] = S; - ss[1] = M; + ss[0] = (s_t) S; + ss[1] = (s_t) M; } } + // simdgroup reduce + // [ 0, 8, 16, 24] -> [ 0] + // [ 1, 9, 17, 25] -> [ 1] + // [ 2, 10, 18, 26] -> [ 2] + // [ 3, 11, 19, 27] -> [ 3] + // [ 4, 12, 20, 28] -> [ 4] + // [ 5, 13, 21, 29] -> [ 5] + // [ 6, 14, 22, 30] -> [ 6] + // [ 7, 15, 23, 31] -> [ 7] + for (short ii = 0; ii < D16; ii += NL) { + lo[ii/NL][0] += simd_shuffle_down(lo[ii/NL][0], 16); + lo[ii/NL][0] += simd_shuffle_down(lo[ii/NL][0], 8); + //lo[ii/NL][0] += simd_shuffle_down(lo[ii/NL][0], 4); + //lo[ii/NL][0] += simd_shuffle_down(lo[ii/NL][0], 2); + //lo[ii/NL][0] += simd_shuffle_down(lo[ii/NL][0], 1); + + lo[ii/NL][1] += simd_shuffle_down(lo[ii/NL][1], 16); + lo[ii/NL][1] += simd_shuffle_down(lo[ii/NL][1], 8); + //lo[ii/NL][1] += simd_shuffle_down(lo[ii/NL][1], 4); + //lo[ii/NL][1] += simd_shuffle_down(lo[ii/NL][1], 2); + //lo[ii/NL][1] += simd_shuffle_down(lo[ii/NL][1], 1); + + lo[ii/NL][2] += simd_shuffle_down(lo[ii/NL][2], 16); + lo[ii/NL][2] += simd_shuffle_down(lo[ii/NL][2], 8); + //lo[ii/NL][2] += simd_shuffle_down(lo[ii/NL][2], 4); + //lo[ii/NL][2] += simd_shuffle_down(lo[ii/NL][2], 2); + //lo[ii/NL][2] += simd_shuffle_down(lo[ii/NL][2], 1); + + lo[ii/NL][3] += simd_shuffle_down(lo[ii/NL][3], 16); + lo[ii/NL][3] += simd_shuffle_down(lo[ii/NL][3], 8); + //lo[ii/NL][3] += simd_shuffle_down(lo[ii/NL][3], 4); + //lo[ii/NL][3] += simd_shuffle_down(lo[ii/NL][3], 2); + //lo[ii/NL][3] += simd_shuffle_down(lo[ii/NL][3], 1); + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + // store results to shared memory - for (short ii = 0; ii < D4; ii += NW) { - short i = ii + tiisg; - sr4[i] = lo[ii/NW]; + for (short i = tiisg; i < D16; i += NL) { + sr4x4[i] = lo[i/NL]; } threadgroup_barrier(mem_flags::mem_threadgroup); @@ -2900,18 +3853,18 @@ kernel void kernel_flash_attn_ext_vec_f16( // parallel reduce for (short r = nsg/2; r > 0; r >>= 1) { if (sgitg < r) { - const float S0 = ss[ 0]; - const float S1 = ss[r*SH + 0]; + const half S0 = ss[ 0]; + const half S1 = ss[r*SH + 0]; - const float M0 = ss[ 1]; - const float M1 = ss[r*SH + 1]; + const half M0 = ss[ 1]; + const half M1 = ss[r*SH + 1]; - const float M = max(M0, M1); + const half M = max(M0, M1); - const float ms0 = exp(M0 - M); - const float ms1 = exp(M1 - M); + const half ms0 = exp(M0 - M); + const half ms1 = exp(M1 - M); - const float S = S0*ms0 + S1*ms1; + const half S = S0*ms0 + S1*ms1; if (tiisg == 0) { ss[0] = S; @@ -2919,117 +3872,156 @@ kernel void kernel_flash_attn_ext_vec_f16( } // O_0 = diag(ms0)*O_0 + diag(ms1)*O_1 - for (short ii = 0; ii < D4; ii += NW) { - short i = ii + tiisg; - sr4[i] = sr4[i]*ms0 + sr4[i + r*D4]*ms1; + for (short i = tiisg; i < D16; i += NW) { + sr4x4[i] = sr4x4[i]*ms0 + sr4x4[i + r*D16]*ms1; } } threadgroup_barrier(mem_flags::mem_threadgroup); } - device float4 * dst4 = (device float4 *) dst; + device float4x4 * dst44 = (device float4x4 *) dst; // final rescale with 1/S and store to global memory if (sgitg == 0) { const float S = ss[0]; - for (short ii = 0; ii < D4; ii += NW) { - short i = ii + tiisg; - dst4[(iq3*ne2*ne1 + iq2 + (iq1)*ne1)*D4 + i] = (float4) sr4[i]/S; + for (short i = tiisg; i < D16; i += NW) { + dst44[((uint64_t)iq3*args.ne2*args.ne1 + iq2 + (uint64_t)iq1*args.ne1)*D16 + i] = (float4x4) sr4x4[i]/S; } } } -template [[host_name("kernel_flash_attn_ext_vec_f16_h128")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<128>; -//template [[host_name("kernel_flash_attn_ext_vec_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<256>; +// note: I think the s_t can be half instead of float, because the Q*K scaling is done before storing to shared mem +// in the other (non-vec) kernel, we need s_t to also be float because we scale during the soft_max +// +#define FA_TYPES \ + half4, half4x4, \ + half4x4, \ + half4x4, \ + float, \ + half, half4, half4x4, \ + half4x4 + +typedef decltype(kernel_flash_attn_ext_vec) flash_attn_ext_vec_t; + +template [[host_name("kernel_flash_attn_ext_vec_f16_h128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +#if defined(GGML_METAL_USE_BF16) +template [[host_name("kernel_flash_attn_ext_vec_bf16_h128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +#endif +template [[host_name("kernel_flash_attn_ext_vec_q4_0_h128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q4_1_h128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_0_h128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_1_h128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q8_0_h128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; + +template [[host_name("kernel_flash_attn_ext_vec_f16_h256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +#if defined(GGML_METAL_USE_BF16) +template [[host_name("kernel_flash_attn_ext_vec_bf16_h256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +#endif +template [[host_name("kernel_flash_attn_ext_vec_q4_0_h256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q4_1_h256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_0_h256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q5_1_h256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; +template [[host_name("kernel_flash_attn_ext_vec_q8_0_h256")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec; + +#undef FA_TYPES + +template +kernel void kernel_set( + constant ggml_metal_kargs_set & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + const int i13 = tgpig[2]; + const int i12 = tgpig[1]; + const int i11 = tgpig[0]; + + const int64_t n = i13*args.ne12*args.ne11*args.ne10 + i12*args.ne11*args.ne10 + i11*args.ne10; + + const int64_t i3 = n / (args.ne12*args.ne11*args.ne10); + const int64_t i2 = (n - i3*args.ne12*args.ne11*args.ne10) / (args.ne11*args.ne10); + const int64_t i1 = (n - i3*args.ne12*args.ne11*args.ne10 - i2*args.ne11*args.ne10) / args.ne10; + + device T * dst_data = (device T *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + args.offs); + + for (int64_t i10 = tpitg.x; i10 < args.ne10; i10 += ntg.x) { + device const T * src = (device T *) (src1 + i13*args.nb13 + i12*args.nb12 + i11*args.nb11 + i10*args.nb10); + dst_data[i10] = (T) src[0]; + } +} + +typedef decltype(kernel_set) kernel_set_t; + +template [[host_name("kernel_set_f32")]] kernel kernel_set_t kernel_set; +template [[host_name("kernel_set_i32")]] kernel kernel_set_t kernel_set; template kernel void kernel_cpy( - device const void * src0, - device void * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig[2]; - const int64_t i02 = tgpig[1]; - const int64_t i01 = tgpig[0]; + constant ggml_metal_kargs_cpy & args, + device const char * src0, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + const int i03 = tgpig[2]; + const int i02 = tgpig[1]; + const int i01 = tgpig[0]; - const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; + const int64_t n = i03*args.ne02*args.ne01*args.ne00 + i02*args.ne01*args.ne00 + i01*args.ne00; - const int64_t i3 = n / (ne2*ne1*ne0); - const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); - const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; - const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0); + const int64_t i3 = n/(args.ne2*args.ne1*args.ne0); + const int64_t i2 = (n - i3*args.ne2*args.ne1*args.ne0)/(args.ne1*args.ne0); + const int64_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0)/args.ne0; + const int64_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0); - device T1 * dst_data = (device T1 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + device T1 * dst_data = (device T1 *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0); - for (int64_t i00 = tpitg.x; i00 < ne00; i00 += ntg.x) { - device const T0 * src = (device T0 *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); + for (int64_t i00 = tpitg.x; i00 < args.ne00; i00 += ntg.x) { + device const T0 * src = (device T0 *)(src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + i00*args.nb00); dst_data[i00] = (T1) src[0]; } } typedef decltype(kernel_cpy) kernel_cpy_t; -template [[host_name("kernel_cpy_f32_f32")]] kernel kernel_cpy_t kernel_cpy; -template [[host_name("kernel_cpy_f32_f16")]] kernel kernel_cpy_t kernel_cpy; -template [[host_name("kernel_cpy_f16_f16")]] kernel kernel_cpy_t kernel_cpy; -template [[host_name("kernel_cpy_f16_f32")]] kernel kernel_cpy_t kernel_cpy; +template [[host_name("kernel_cpy_f32_f32")]] kernel kernel_cpy_t kernel_cpy; +template [[host_name("kernel_cpy_f32_f16")]] kernel kernel_cpy_t kernel_cpy; +#if defined(GGML_METAL_USE_BF16) +template [[host_name("kernel_cpy_f32_bf16")]] kernel kernel_cpy_t kernel_cpy; +#endif +template [[host_name("kernel_cpy_f16_f32")]] kernel kernel_cpy_t kernel_cpy; +template [[host_name("kernel_cpy_f16_f16")]] kernel kernel_cpy_t kernel_cpy; +#if defined(GGML_METAL_USE_BF16) +template [[host_name("kernel_cpy_bf16_f32")]] kernel kernel_cpy_t kernel_cpy; +template [[host_name("kernel_cpy_bf16_bf16")]] kernel kernel_cpy_t kernel_cpy; +#endif kernel void kernel_cpy_f32_q8_0( - device const float * src0, - device void * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig[2]; - const int64_t i02 = tgpig[1]; - const int64_t i01 = tgpig[0]; + constant ggml_metal_kargs_cpy & args, + device const char * src0, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + const int i03 = tgpig[2]; + const int i02 = tgpig[1]; + const int i01 = tgpig[0]; - const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; + const int64_t n = i03*args.ne02*args.ne01*args.ne00 + i02*args.ne01*args.ne00 + i01*args.ne00; - const int64_t i3 = n / (ne2*ne1*ne0); - const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); - const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; - const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK8_0; + const int64_t i3 = n / (args.ne2*args.ne1*args.ne0); + const int64_t i2 = (n - i3*args.ne2*args.ne1*args.ne0) / (args.ne1*args.ne0); + const int64_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0) / args.ne0; + const int64_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0)/QK8_0; - device block_q8_0 * dst_data = (device block_q8_0 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + device block_q8_0 * dst_data = (device block_q8_0 *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0); - for (int64_t i00 = tpitg.x*QK8_0; i00 < ne00; i00 += ntg.x*QK8_0) { - device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); + for (int64_t i00 = tpitg.x*QK8_0; i00 < args.ne00; i00 += ntg.x*QK8_0) { + device const float * src = (device float *)(src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + i00*args.nb00); float amax = 0.0f; // absolute max @@ -3052,42 +4044,27 @@ kernel void kernel_cpy_f32_q8_0( } kernel void kernel_cpy_f32_q4_0( - device const float * src0, - device void * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig[2]; - const int64_t i02 = tgpig[1]; - const int64_t i01 = tgpig[0]; + constant ggml_metal_kargs_cpy & args, + device const char * src0, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + const int i03 = tgpig[2]; + const int i02 = tgpig[1]; + const int i01 = tgpig[0]; - const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; + const int64_t n = i03*args.ne02*args.ne01*args.ne00 + i02*args.ne01*args.ne00 + i01*args.ne00; - const int64_t i3 = n / (ne2*ne1*ne0); - const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); - const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; - const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK4_0; + const int64_t i3 = n / (args.ne2*args.ne1*args.ne0); + const int64_t i2 = (n - i3*args.ne2*args.ne1*args.ne0) / (args.ne1*args.ne0); + const int64_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0) / args.ne0; + const int64_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0)/QK4_0; - device block_q4_0 * dst_data = (device block_q4_0 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + device block_q4_0 * dst_data = (device block_q4_0 *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0); - for (int64_t i00 = tpitg.x*QK4_0; i00 < ne00; i00 += ntg.x*QK4_0) { - device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); + for (int64_t i00 = tpitg.x*QK4_0; i00 < args.ne00; i00 += ntg.x*QK4_0) { + device const float * src = (device float *)(src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + i00*args.nb00); float amax = 0.0f; // absolute max float max = 0.0f; @@ -3119,42 +4096,27 @@ kernel void kernel_cpy_f32_q4_0( } kernel void kernel_cpy_f32_q4_1( - device const float * src0, - device void * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig[2]; - const int64_t i02 = tgpig[1]; - const int64_t i01 = tgpig[0]; + constant ggml_metal_kargs_cpy & args, + device const char * src0, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + const int i03 = tgpig[2]; + const int i02 = tgpig[1]; + const int i01 = tgpig[0]; - const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; + const int64_t n = i03*args.ne02*args.ne01*args.ne00 + i02*args.ne01*args.ne00 + i01*args.ne00; - const int64_t i3 = n / (ne2*ne1*ne0); - const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); - const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; - const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK4_1; + const int64_t i3 = n / (args.ne2*args.ne1*args.ne0); + const int64_t i2 = (n - i3*args.ne2*args.ne1*args.ne0) / (args.ne1*args.ne0); + const int64_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0) / args.ne0; + const int64_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0)/QK4_1; - device block_q4_1 * dst_data = (device block_q4_1 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + device block_q4_1 * dst_data = (device block_q4_1 *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0); - for (int64_t i00 = tpitg.x*QK4_1; i00 < ne00; i00 += ntg.x*QK4_1) { - device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); + for (int64_t i00 = tpitg.x*QK4_1; i00 < args.ne00; i00 += ntg.x*QK4_1) { + device const float * src = (device float *)(src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + i00*args.nb00); float min = FLT_MAX; float max = -FLT_MAX; @@ -3185,42 +4147,27 @@ kernel void kernel_cpy_f32_q4_1( } kernel void kernel_cpy_f32_q5_0( - device const float * src0, - device void * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig[2]; - const int64_t i02 = tgpig[1]; - const int64_t i01 = tgpig[0]; + constant ggml_metal_kargs_cpy & args, + device const char * src0, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + const int i03 = tgpig[2]; + const int i02 = tgpig[1]; + const int i01 = tgpig[0]; - const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; + const int64_t n = i03*args.ne02*args.ne01*args.ne00 + i02*args.ne01*args.ne00 + i01*args.ne00; - const int64_t i3 = n / (ne2*ne1*ne0); - const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); - const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; - const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK5_0; + const int64_t i3 = n / (args.ne2*args.ne1*args.ne0); + const int64_t i2 = (n - i3*args.ne2*args.ne1*args.ne0) / (args.ne1*args.ne0); + const int64_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0) / args.ne0; + const int64_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0)/QK5_0; - device block_q5_0 * dst_data = (device block_q5_0 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + device block_q5_0 * dst_data = (device block_q5_0 *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0); - for (int64_t i00 = tpitg.x*QK5_0; i00 < ne00; i00 += ntg.x*QK5_0) { - device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); + for (int64_t i00 = tpitg.x*QK5_0; i00 < args.ne00; i00 += ntg.x*QK5_0) { + device const float * src = (device float *)(src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + i00*args.nb00); float amax = 0.0f; // absolute max float max = 0.0f; @@ -3258,42 +4205,27 @@ kernel void kernel_cpy_f32_q5_0( } kernel void kernel_cpy_f32_q5_1( - device const float * src0, - device void * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig[2]; - const int64_t i02 = tgpig[1]; - const int64_t i01 = tgpig[0]; + constant ggml_metal_kargs_cpy & args, + device const char * src0, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + const int i03 = tgpig[2]; + const int i02 = tgpig[1]; + const int i01 = tgpig[0]; - const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; + const int64_t n = i03*args.ne02*args.ne01*args.ne00 + i02*args.ne01*args.ne00 + i01*args.ne00; - const int64_t i3 = n / (ne2*ne1*ne0); - const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); - const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; - const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK5_1; + const int64_t i3 = n / (args.ne2*args.ne1*args.ne0); + const int64_t i2 = (n - i3*args.ne2*args.ne1*args.ne0) / (args.ne1*args.ne0); + const int64_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0) / args.ne0; + const int64_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0)/QK5_1; - device block_q5_1 * dst_data = (device block_q5_1 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + device block_q5_1 * dst_data = (device block_q5_1 *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0); - for (int64_t i00 = tpitg.x*QK5_1; i00 < ne00; i00 += ntg.x*QK5_1) { - device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); + for (int64_t i00 = tpitg.x*QK5_1; i00 < args.ne00; i00 += ntg.x*QK5_1) { + device const float * src = (device float *)(src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + i00*args.nb00); float max = src[0]; float min = src[0]; @@ -3340,47 +4272,28 @@ static inline int best_index_int8(int n, constant float * val, float x) { return x - val[mu-1] < val[mu] - x ? mu-1 : mu; } -constexpr constant static float kvalues_iq4nl_f[16] = { - -127.f, -104.f, -83.f, -65.f, -49.f, -35.f, -22.f, -10.f, 1.f, 13.f, 25.f, 38.f, 53.f, 69.f, 89.f, 113.f -}; - kernel void kernel_cpy_f32_iq4_nl( - device const float * src0, - device void * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig[2]; - const int64_t i02 = tgpig[1]; - const int64_t i01 = tgpig[0]; + constant ggml_metal_kargs_cpy & args, + device const char * src0, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { + const int i03 = tgpig[2]; + const int i02 = tgpig[1]; + const int i01 = tgpig[0]; - const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; + const int64_t n = i03*args.ne02*args.ne01*args.ne00 + i02*args.ne01*args.ne00 + i01*args.ne00; - const int64_t i3 = n / (ne2*ne1*ne0); - const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); - const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; - const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0)/QK4_NL; + const int64_t i3 = n / (args.ne2*args.ne1*args.ne0); + const int64_t i2 = (n - i3*args.ne2*args.ne1*args.ne0) / (args.ne1*args.ne0); + const int64_t i1 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0) / args.ne0; + const int64_t i0 = (n - i3*args.ne2*args.ne1*args.ne0 - i2*args.ne1*args.ne0 - i1*args.ne0)/QK4_NL; - device block_iq4_nl * dst_data = (device block_iq4_nl *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + device block_iq4_nl * dst_data = (device block_iq4_nl *) (dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0); - for (int64_t i00 = tpitg.x*QK4_NL; i00 < ne00; i00 += ntg.x*QK4_NL) { - device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); + for (int64_t i00 = tpitg.x*QK4_NL; i00 < args.ne00; i00 += ntg.x*QK4_NL) { + device const float * src = (device float *)(src0 + i03*args.nb03 + i02*args.nb02 + i01*args.nb01 + i00*args.nb00); float amax = 0.0f; // absolute max float max = 0.0f; @@ -3416,104 +4329,66 @@ kernel void kernel_cpy_f32_iq4_nl( } dst_data[i00/QK4_NL].d = sumq2 > 0 ? sumqx/sumq2 : d; - } } kernel void kernel_concat( + constant ggml_metal_kargs_concat & args, device const char * src0, device const char * src1, device char * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - constant int32_t & dim, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { + uint3 tgpig[[threadgroup_position_in_grid]], + ushort3 tpitg[[thread_position_in_threadgroup]], + ushort3 ntg[[threads_per_threadgroup]]) { - const int64_t i3 = tgpig.z; - const int64_t i2 = tgpig.y; - const int64_t i1 = tgpig.x; + const int i3 = tgpig.z; + const int i2 = tgpig.y; + const int i1 = tgpig.x; - int64_t o[4] = {0, 0, 0, 0}; - o[dim] = dim == 0 ? ne00 : (dim == 1 ? ne01 : (dim == 2 ? ne02 : ne03)); + int o[4] = {0, 0, 0, 0}; + o[args.dim] = args.dim == 0 ? args.ne00 : (args.dim == 1 ? args.ne01 : (args.dim == 2 ? args.ne02 : args.ne03)); device const float * x; - for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { - x = (device const float *)(src0 + (i3 )*nb03 + (i2 )*nb02 + (i1 )*nb01 + (i0 )*nb00); + for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) { + if (i0 < args.ne00 && i1 < args.ne01 && i2 < args.ne02 && i3 < args.ne03) { + x = (device const float *)(src0 + (i3 )*args.nb03 + (i2 )*args.nb02 + (i1 )*args.nb01 + (i0 )*args.nb00); } else { - x = (device const float *)(src1 + (i3 - o[3])*nb13 + (i2 - o[2])*nb12 + (i1 - o[1])*nb11 + (i0 - o[0])*nb10); + x = (device const float *)(src1 + (i3 - o[3])*args.nb13 + (i2 - o[2])*args.nb12 + (i1 - o[1])*args.nb11 + (i0 - o[0])*args.nb10); } - device float * y = (device float *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + device float * y = (device float *)(dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0); *y = *x; } } +template void kernel_mul_mv_q2_K_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { - const int nb = ne00/QK_K; + const int nb = args.ne00/QK_K; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_q2_K * x = (device const block_q2_K *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); + device const block_q2_K * x = (device const block_q2_K *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); float yl[32]; float sumf[N_DST]={0.f}, all_sum; @@ -3562,92 +4437,64 @@ void kernel_mul_mv_q2_K_f32_impl( (acc1[3] + 1.f/256.f * acc2[3]) * (sc[6] & 0xF) * 1.f/64.f) - dmin * (sumy[0] * (sc[0] & 0xF0) + sumy[1] * (sc[2] & 0xF0) + sumy[2] * (sc[4] & 0xF0) + sumy[3] * (sc[6] & 0xF0)); - qs += nb01/2; - sc += nb01; - dh += nb01/2; + qs += args.nb01/2; + sc += args.nb01; + dh += args.nb01/2; } y4 += 4 * QK_K; } + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; + dst_f32[first_row + row] = all_sum; } } } [[host_name("kernel_mul_mv_q2_K_f32")]] kernel void kernel_mul_mv_q2_K_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q2_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); + kernel_mul_mv_q2_K_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } +template void kernel_mul_mv_q3_K_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { - const int nb = ne00/QK_K; + const int nb = args.ne00/QK_K; - const int64_t r0 = tgpig.x; - const int64_t r1 = tgpig.y; - const int64_t im = tgpig.z; + const int r0 = tgpig.x; + const int r1 = tgpig.y; + const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * 2; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_q3_K * x = (device const block_q3_K *) ((device char *) src0 + offset0); - device const float * yy = (device const float *) ((device char *) src1 + offset1); + device const block_q3_K * x = (device const block_q3_K *) (src0 + offset0); + device const float * yy = (device const float *) (src1 + offset1); float yl[32]; @@ -3677,9 +4524,10 @@ void kernel_mul_mv_q3_K_f32_impl( const ushort4 hm = mm[2*ip + il/2]; - const int shift = 2*il; - const float v1 = il == 0 ? 4.f : 64.f; - const float v2 = 4.f * v1; + const short shift = 2*il; + + const float v1 = il == 0 ? 4.f : 64.f; + const float v2 = 4.f * v1; const uint16_t s_shift1 = 4*ip; const uint16_t s_shift2 = s_shift1 + il; @@ -3748,10 +4596,10 @@ void kernel_mul_mv_q3_K_f32_impl( sumf1[row] += d1 * (scales[1] - 32); sumf2[row] += d2 * (scales[3] - 32); - q += nb01/2; - h += nb01/2; - a += nb01/2; - dh += nb01/2; + q += args.nb01/2; + h += args.nb01/2; + a += args.nb01/2; + dh += args.nb01/2; } y1 += 4 * QK_K; @@ -3761,66 +4609,39 @@ void kernel_mul_mv_q3_K_f32_impl( const float sumf = (sumf1[row] + 0.25f * sumf2[row]) / (1 << shift); sumf1[row] = simd_sum(sumf); } + + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + if (tiisg == 0) { for (int row = 0; row < 2; ++row) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = sumf1[row]; + dst_f32[first_row + row] = sumf1[row]; } } } [[host_name("kernel_mul_mv_q3_K_f32")]] kernel void kernel_mul_mv_q3_K_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q3_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); + kernel_mul_mv_q3_K_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } +template void kernel_mul_mv_q4_K_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { const uint16_t kmask1 = 0x3f3f; const uint16_t kmask2 = 0x0f0f; @@ -3831,21 +4652,21 @@ void kernel_mul_mv_q4_K_f32_impl( const int iq = it/4; // 0 or 1 const int ir = it%4; // 0...3 - const int nb = ne00/QK_K; + const int nb = args.ne00/QK_K; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; //const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; const int first_row = r0 * N_DST; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_q4_K * x = (device const block_q4_K *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); + device const block_q4_K * x = (device const block_q4_K *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); float yl[16]; float yh[16]; @@ -3898,92 +4719,64 @@ void kernel_mul_mv_q4_K_f32_impl( (acc2[2] + 1.f/256.f * acc2[3]) * sc8[5] * 1.f/16.f) - dmin * (sumy[0] * sc8[2] + sumy[1] * sc8[3] + sumy[2] * sc8[6] + sumy[3] * sc8[7]); - q1 += nb01/2; - sc += nb01/2; - dh += nb01/2; + q1 += args.nb01/2; + sc += args.nb01/2; + dh += args.nb01/2; } y4 += 4 * QK_K; } + device float * dst_f32 = (device float *) dst + (int64_t)im*args.ne0*args.ne1 + (int64_t)r1*args.ne0; + for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; + dst_f32[first_row + row] = all_sum; } } } [[host_name("kernel_mul_mv_q4_K_f32")]] kernel void kernel_mul_mv_q4_K_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q4_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); + kernel_mul_mv_q4_K_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } +template void kernel_mul_mv_q5_K_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { - const int nb = ne00/QK_K; + const int nb = args.ne00/QK_K; - const int64_t r0 = tgpig.x; - const int64_t r1 = tgpig.y; + const int r0 = tgpig.x; + const int r1 = tgpig.y; const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * 2; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_q5_K * x = (device const block_q5_K *) ((device char *) src0 + offset0); - device const float * yy = (device const float *) ((device char *) src1 + offset1); + device const block_q5_K * x = (device const block_q5_K *) (src0 + offset0); + device const float * yy = (device const float *) (src1 + offset1); float sumf[2]={0.f}; @@ -4057,98 +4850,70 @@ void kernel_mul_mv_q5_K_f32_impl( sc8[5] * (acc1[3]/16.f + 16.f*acc2[3])) - dmin * (sumy[0] * sc8[2] + sumy[1] * sc8[3] + sumy[2] * sc8[6] + sumy[3] * sc8[7]); - q1 += nb01; - qh += nb01; - dh += nb01/2; - a += nb01/2; + q1 += args.nb01; + qh += args.nb01; + dh += args.nb01/2; + a += args.nb01/2; } y1 += 4 * QK_K; } + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + for (int row = 0; row < 2; ++row) { const float tot = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = tot; + dst_f32[first_row + row] = tot; } } } [[host_name("kernel_mul_mv_q5_K_f32")]] kernel void kernel_mul_mv_q5_K_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q5_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); + kernel_mul_mv_q5_K_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } +template void kernel_mul_mv_q6_K_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { const uint8_t kmask1 = 0x03; const uint8_t kmask2 = 0x0C; const uint8_t kmask3 = 0x30; const uint8_t kmask4 = 0xC0; - const int nb = ne00/QK_K; + const int nb = args.ne00/QK_K; - const int64_t r0 = tgpig.x; - const int64_t r1 = tgpig.y; - const int im = tgpig.z; + const int r0 = tgpig.x; + const int r1 = tgpig.y; + const int im = tgpig.z; - const int row = 2 * r0 + sgitg; + const int row = 2*r0 + sgitg; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + const uint64_t offset0 = row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_q6_K * x = (device const block_q6_K *) ((device char *) src0 + offset0); - device const float * yy = (device const float *) ((device char *) src1 + offset1); + device const block_q6_K * x = (device const block_q6_K *) (src0 + offset0); + device const float * yy = (device const float *) (src1 + offset1); float sumf = 0; @@ -4165,7 +4930,6 @@ void kernel_mul_mv_q6_K_f32_impl( const int q_offset_h = 32*ip + l0; for (int i = ix; i < nb; i += 2) { - device const uint8_t * q1 = x[i].ql + q_offset_l; device const uint8_t * q2 = q1 + 32; device const uint8_t * qh = x[i].qh + q_offset_h; @@ -4187,98 +4951,70 @@ void kernel_mul_mv_q6_K_f32_impl( } + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + const float tot = simd_sum(sumf); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + row] = tot; + dst_f32[row] = tot; } } [[host_name("kernel_mul_mv_q6_K_f32")]] kernel void kernel_mul_mv_q6_K_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_q6_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); + kernel_mul_mv_q6_K_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } // ======================= "True" 2-bit +template void kernel_mul_mv_iq2_xxs_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { - const int nb = ne00/QK_K; + const int nb = args.ne00/QK_K; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_iq2_xxs * x = (device const block_iq2_xxs *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); + device const block_iq2_xxs * x = (device const block_iq2_xxs *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); float yl[32]; float sumf[N_DST]={0.f}, all_sum; const int nb32 = nb * (QK_K / 32); - threadgroup uint64_t * values = (threadgroup uint64_t *)shared_values; - threadgroup uint8_t * shared_signs = (threadgroup uint8_t *)(values + 256); + threadgroup uint64_t * svalues = (threadgroup uint64_t *)(shmem); + threadgroup uint8_t * ssigns = (threadgroup uint8_t *)(svalues + 256); { int nval = 4; int pos = (32*sgitg + tiisg)*nval; - for (int i = 0; i < nval; ++i) values[pos + i] = iq2xxs_grid[pos + i]; + for (int i = 0; i < nval; ++i) svalues[pos + i] = iq2xxs_grid[pos + i]; nval = 2; pos = (32*sgitg + tiisg)*nval; - for (int i = 0; i < nval; ++i) shared_signs[pos+i] = ksigns_iq2xs[pos+i]; + for (int i = 0; i < nval; ++i) ssigns[pos+i] = ksigns_iq2xs[pos+i]; threadgroup_barrier(mem_flags::mem_threadgroup); } @@ -4308,114 +5044,85 @@ void kernel_mul_mv_iq2_xxs_f32_impl( float sum = 0; for (int l = 0; l < 4; ++l) { - const threadgroup uint8_t * grid = (const threadgroup uint8_t *)(values + aux8[l]); - const uint8_t signs = shared_signs[(aux32 >> 7*l) & 127]; + const threadgroup uint8_t * grid = (const threadgroup uint8_t *)(svalues + aux8[l]); + const uint8_t signs = ssigns[(aux32 >> 7*l) & 127]; for (int j = 0; j < 8; ++j) { sum += yl[8*l + j] * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); } } sumf[row] += d * sum; - dh += nb01/2; - q2 += nb01/2; + dh += args.nb01/2; + q2 += args.nb01/2; } y4 += 32 * 32; } + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.25f; + dst_f32[first_row + row] = all_sum * 0.25f; } } } [[host_name("kernel_mul_mv_iq2_xxs_f32")]] kernel void kernel_mul_mv_iq2_xxs_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - - kernel_mul_mv_iq2_xxs_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { + kernel_mul_mv_iq2_xxs_f32_impl(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg); } +template void kernel_mul_mv_iq2_xs_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { - const int nb = ne00/QK_K; + const int nb = args.ne00/QK_K; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_iq2_xs * x = (device const block_iq2_xs *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); + device const block_iq2_xs * x = (device const block_iq2_xs *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); float yl[32]; float sumf[N_DST]={0.f}, all_sum; const int nb32 = nb * (QK_K / 32); - threadgroup uint64_t * values = (threadgroup uint64_t *)shared_values; - threadgroup uint8_t * shared_signs = (threadgroup uint8_t *)(values + 512); + threadgroup uint64_t * svalues = (threadgroup uint64_t *)(shmem); + threadgroup uint8_t * ssigns = (threadgroup uint8_t *)(svalues + 512); { int nval = 8; int pos = (32*sgitg + tiisg)*nval; - for (int i = 0; i < nval; ++i) values[pos + i] = iq2xs_grid[pos + i]; + for (int i = 0; i < nval; ++i) svalues[pos + i] = iq2xs_grid[pos + i]; nval = 2; pos = (32*sgitg + tiisg)*nval; - for (int i = 0; i < nval; ++i) shared_signs[pos+i] = ksigns_iq2xs[pos+i]; + for (int i = 0; i < nval; ++i) ssigns[pos+i] = ksigns_iq2xs[pos+i]; threadgroup_barrier(mem_flags::mem_threadgroup); } @@ -4447,122 +5154,94 @@ void kernel_mul_mv_iq2_xs_f32_impl( float sum1 = 0, sum2 = 0; for (int l = 0; l < 2; ++l) { - const threadgroup uint8_t * grid = (const threadgroup uint8_t *)(values + (q2[l] & 511)); - const uint8_t signs = shared_signs[(q2[l] >> 9)]; + const threadgroup uint8_t * grid = (const threadgroup uint8_t *)(svalues + (q2[l] & 511)); + const uint8_t signs = ssigns[(q2[l] >> 9)]; for (int j = 0; j < 8; ++j) { sum1 += yl[8*l + j] * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); } } for (int l = 2; l < 4; ++l) { - const threadgroup uint8_t * grid = (const threadgroup uint8_t *)(values + (q2[l] & 511)); - const uint8_t signs = shared_signs[(q2[l] >> 9)]; + const threadgroup uint8_t * grid = (const threadgroup uint8_t *)(svalues + (q2[l] & 511)); + const uint8_t signs = ssigns[(q2[l] >> 9)]; for (int j = 0; j < 8; ++j) { sum2 += yl[8*l + j] * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); } } sumf[row] += d1 * sum1 + d2 * sum2; - dh += nb01/2; - q2 += nb01/2; - sc += nb01; + dh += args.nb01/2; + q2 += args.nb01/2; + sc += args.nb01; } y4 += 32 * 32; } + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.25f; + dst_f32[first_row + row] = all_sum * 0.25f; } } } [[host_name("kernel_mul_mv_iq2_xs_f32")]] kernel void kernel_mul_mv_iq2_xs_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq2_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); + kernel_mul_mv_iq2_xs_f32_impl(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg); } +template void kernel_mul_mv_iq3_xxs_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { - const int nb = ne00/QK_K; + const int nb = args.ne00/QK_K; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_iq3_xxs * x = (device const block_iq3_xxs *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); + device const block_iq3_xxs * x = (device const block_iq3_xxs *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); float yl[32]; float sumf[N_DST]={0.f}, all_sum; const int nb32 = nb * (QK_K / 32); - threadgroup uint32_t * values = (threadgroup uint32_t *)shared_values; - threadgroup uint8_t * shared_signs = (threadgroup uint8_t *)(values + 256); + threadgroup uint32_t * svalues = (threadgroup uint32_t *)(shmem); + threadgroup uint8_t * ssigns = (threadgroup uint8_t *)(svalues + 256); { int nval = 4; int pos = (32*sgitg + tiisg)*nval; - for (int i = 0; i < nval; ++i) values[pos + i] = iq3xxs_grid[pos + i]; + for (int i = 0; i < nval; ++i) svalues[pos + i] = iq3xxs_grid[pos + i]; nval = 2; pos = (32*sgitg + tiisg)*nval; - for (int i = 0; i < nval; ++i) shared_signs[pos+i] = ksigns_iq2xs[pos+i]; + for (int i = 0; i < nval; ++i) ssigns[pos+i] = ksigns_iq2xs[pos+i]; threadgroup_barrier(mem_flags::mem_threadgroup); } @@ -4571,7 +5250,6 @@ void kernel_mul_mv_iq3_xxs_f32_impl( device const float * y4 = y + 32 * ix; for (int ib32 = ix; ib32 < nb32; ib32 += 32) { - for (int i = 0; i < 32; ++i) { yl[i] = y4[i]; } @@ -4585,16 +5263,15 @@ void kernel_mul_mv_iq3_xxs_f32_impl( device const half * dh = &xr->d; for (int row = 0; row < N_DST; row++) { - const float db = dh[0]; const uint32_t aux32 = gas[0] | (gas[1] << 16); const float d = db * (0.5f + (aux32 >> 28)); float2 sum = {0}; for (int l = 0; l < 4; ++l) { - const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(values + q3[2*l+0]); - const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(values + q3[2*l+1]); - const uint8_t signs = shared_signs[(aux32 >> 7*l) & 127]; + const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(svalues + q3[2*l+0]); + const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(svalues + q3[2*l+1]); + const uint8_t signs = ssigns[(aux32 >> 7*l) & 127]; for (int j = 0; j < 4; ++j) { sum[0] += yl[8*l + j + 0] * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); sum[1] += yl[8*l + j + 4] * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f); @@ -4602,103 +5279,75 @@ void kernel_mul_mv_iq3_xxs_f32_impl( } sumf[row] += d * (sum[0] + sum[1]); - dh += nb01/2; - q3 += nb01; - gas += nb01/2; + dh += args.nb01/2; + q3 += args.nb01; + gas += args.nb01/2; } y4 += 32 * 32; } + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.5f; + dst_f32[first_row + row] = all_sum * 0.5f; } } } [[host_name("kernel_mul_mv_iq3_xxs_f32")]] kernel void kernel_mul_mv_iq3_xxs_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq3_xxs_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); + kernel_mul_mv_iq3_xxs_f32_impl(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg); } +template void kernel_mul_mv_iq3_s_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { - const int nb = ne00/QK_K; + const int nb = args.ne00/QK_K; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_iq3_s * x = (device const block_iq3_s *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); + device const block_iq3_s * x = (device const block_iq3_s *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); float yl[32]; float sumf[N_DST]={0.f}, all_sum; const int nb32 = nb * (QK_K / 32); - threadgroup uint32_t * values = (threadgroup uint32_t *)shared_values; + threadgroup uint32_t * svalues = (threadgroup uint32_t *) shmem; { int nval = 8; int pos = (32*sgitg + tiisg)*nval; - for (int i = 0; i < nval; ++i) values[pos + i] = iq3s_grid[pos + i]; + for (int i = 0; i < nval; ++i) svalues[pos + i] = iq3s_grid[pos + i]; threadgroup_barrier(mem_flags::mem_threadgroup); } @@ -4729,8 +5378,8 @@ void kernel_mul_mv_iq3_s_f32_impl( float2 sum = {0}; for (int l = 0; l < 4; ++l) { - const threadgroup uint32_t * table1 = qh[0] & kmask_iq2xs[2*l+0] ? values + 256 : values; - const threadgroup uint32_t * table2 = qh[0] & kmask_iq2xs[2*l+1] ? values + 256 : values; + const threadgroup uint32_t * table1 = qh[0] & kmask_iq2xs[2*l+0] ? svalues + 256 : svalues; + const threadgroup uint32_t * table2 = qh[0] & kmask_iq2xs[2*l+1] ? svalues + 256 : svalues; const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(table1 + qs[2*l+0]); const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(table2 + qs[2*l+1]); for (int j = 0; j < 4; ++j) { @@ -4740,105 +5389,77 @@ void kernel_mul_mv_iq3_s_f32_impl( } sumf[row] += d * (sum[0] + sum[1]); - dh += nb01/2; - qs += nb01; - qh += nb01; - sc += nb01; - signs += nb01; + dh += args.nb01/2; + qs += args.nb01; + qh += args.nb01; + sc += args.nb01; + signs += args.nb01; } y4 += 32 * 32; } + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; + dst_f32[first_row + row] = all_sum; } } } [[host_name("kernel_mul_mv_iq3_s_f32")]] kernel void kernel_mul_mv_iq3_s_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq3_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); + kernel_mul_mv_iq3_s_f32_impl(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg); } +template void kernel_mul_mv_iq2_s_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { - const int nb = ne00/QK_K; + const int nb = args.ne00/QK_K; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_iq2_s * x = (device const block_iq2_s *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); + device const block_iq2_s * x = (device const block_iq2_s *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); float yl[32]; float sumf[N_DST]={0.f}, all_sum; const int nb32 = nb * (QK_K / 32); - //threadgroup uint64_t * values = (threadgroup uint64_t *)shared_values; + //threadgroup uint64_t * svalues = (threadgroup uint64_t *) shmem; //{ // int nval = 32; // int pos = (32*sgitg + tiisg)*nval; - // for (int i = 0; i < nval; ++i) values[pos + i] = iq2s_grid[pos + i]; + // for (int i = 0; i < nval; ++i) svalues[pos + i] = iq2s_grid[pos + i]; // threadgroup_barrier(mem_flags::mem_threadgroup); //} @@ -4870,8 +5491,8 @@ void kernel_mul_mv_iq2_s_f32_impl( float2 sum = {0}; for (int l = 0; l < 2; ++l) { - //const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(values + (qs[l+0] | ((qh[0] << (8-2*l)) & 0x300))); - //const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(values + (qs[l+2] | ((qh[0] << (4-2*l)) & 0x300))); + //const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(svalues + (qs[l+0] | ((qh[0] << (8-2*l)) & 0x300))); + //const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(svalues + (qs[l+2] | ((qh[0] << (4-2*l)) & 0x300))); constant uint8_t * grid1 = (constant uint8_t *)(iq2s_grid + (qs[l+0] | ((qh[0] << (8-2*l)) & 0x300))); constant uint8_t * grid2 = (constant uint8_t *)(iq2s_grid + (qs[l+2] | ((qh[0] << (4-2*l)) & 0x300))); for (int j = 0; j < 8; ++j) { @@ -4881,94 +5502,66 @@ void kernel_mul_mv_iq2_s_f32_impl( } sumf[row] += d1 * sum[0] + d2 * sum[1]; - dh += nb01/2; - qs += nb01; - qh += nb01; - sc += nb01; - signs += nb01; + dh += args.nb01/2; + qs += args.nb01; + qh += args.nb01; + sc += args.nb01; + signs += args.nb01; } y4 += 32 * 32; } + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.25f; + dst_f32[first_row + row] = all_sum * 0.25f; } } } [[host_name("kernel_mul_mv_iq2_s_f32")]] kernel void kernel_mul_mv_iq2_s_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq2_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); + kernel_mul_mv_iq2_s_f32_impl(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg); } +template void kernel_mul_mv_iq1_s_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_value, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { - const int nb = ne00/QK_K; + const int nb = args.ne00/QK_K; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_iq1_s * x = (device const block_iq1_s *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); + device const block_iq1_s * x = (device const block_iq1_s *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); float yl[32]; float sumf[N_DST]={0.f}, all_sum; @@ -5011,61 +5604,50 @@ void kernel_mul_mv_iq1_s_f32_impl( } sumf[row] += (float)dh[0] * (sum + sumy * (qh[0] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA)) * (2*((qh[0] >> 12) & 7) + 1); - dh += nb01/2; - qs += nb01; - qh += nb01/2; + dh += args.nb01/2; + qs += args.nb01; + qh += args.nb01/2; } y4 += 32 * 32; } + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; + dst_f32[first_row + row] = all_sum; } } } +template void kernel_mul_mv_iq1_m_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_value, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { - const int nb = ne00/QK_K; + const int nb = args.ne00/QK_K; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_iq1_m * x = (device const block_iq1_m *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); + device const block_iq1_m * x = (device const block_iq1_m *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); float yl[32]; float sumf[N_DST]={0.f}, all_sum; @@ -5117,66 +5699,55 @@ void kernel_mul_mv_iq1_m_f32_impl( sumf[row] += (float)scale.f16 * ((sum[0] + delta1) * (2*((sc[ib/2] >> (6*(ib%2)+0)) & 7) + 1) + (sum[1] + delta2) * (2*((sc[ib/2] >> (6*(ib%2)+3)) & 7) + 1)); - sc += nb01/2; - qs += nb01; - qh += nb01; + sc += args.nb01/2; + qs += args.nb01; + qh += args.nb01; } y4 += 32 * 32; } + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; + dst_f32[first_row + row] = all_sum; } } } +template void kernel_mul_mv_iq4_nl_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values_i8, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { - threadgroup float * shared_values = (threadgroup float *)shared_values_i8; - const int nb = ne00/QK4_NL; + threadgroup float * shmem_f32 = (threadgroup float *) shmem; + const int nb = args.ne00/QK4_NL; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; const int first_row = (r0 * 2 + sgitg) * 2; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_iq4_nl * x = (device const block_iq4_nl *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); + device const block_iq4_nl * x = (device const block_iq4_nl *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); const int ix = tiisg/2; // 0...15 const int it = tiisg%2; // 0 or 1 - shared_values[tiisg] = kvalues_iq4nl_f[tiisg%16]; + shmem_f32[tiisg] = kvalues_iq4nl_f[tiisg%16]; threadgroup_barrier(mem_flags::mem_threadgroup); float4 yl[4]; @@ -5194,7 +5765,7 @@ void kernel_mul_mv_iq4_nl_f32_impl( device const float4 * y4 = (device const float4 *)yb; yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5]; - for (int row = 0; row < 2 && first_row + row < ne01; ++row) { + for (int row = 0; row < 2 && first_row + row < args.ne01; ++row) { device const block_iq4_nl & xb = x[row*nb + ib]; device const uint16_t * q4 = (device const uint16_t *)(xb.qs + 8*it); @@ -5204,16 +5775,16 @@ void kernel_mul_mv_iq4_nl_f32_impl( aux32[0] = q4[0] | (q4[1] << 16); aux32[1] = (aux32[0] >> 4) & 0x0f0f0f0f; aux32[0] &= 0x0f0f0f0f; - qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]}; - qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]}; + qf1 = {shmem_f32[q8[0]], shmem_f32[q8[1]], shmem_f32[q8[2]], shmem_f32[q8[3]]}; + qf2 = {shmem_f32[q8[4]], shmem_f32[q8[5]], shmem_f32[q8[6]], shmem_f32[q8[7]]}; acc1 += yl[0] * qf1; acc2 += yl[1] * qf2; aux32[0] = q4[2] | (q4[3] << 16); aux32[1] = (aux32[0] >> 4) & 0x0f0f0f0f; aux32[0] &= 0x0f0f0f0f; - qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]}; - qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]}; + qf1 = {shmem_f32[q8[0]], shmem_f32[q8[1]], shmem_f32[q8[2]], shmem_f32[q8[3]]}; + qf2 = {shmem_f32[q8[4]], shmem_f32[q8[5]], shmem_f32[q8[6]], shmem_f32[q8[7]]}; acc1 += yl[2] * qf1; acc2 += yl[3] * qf2; @@ -5226,60 +5797,49 @@ void kernel_mul_mv_iq4_nl_f32_impl( yb += 16 * QK4_NL; } - for (int row = 0; row < 2 && first_row + row < ne01; ++row) { + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + + for (int row = 0; row < 2 && first_row + row < args.ne01; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; + dst_f32[first_row + row] = all_sum; } } } +template void kernel_mul_mv_iq4_xs_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values_i8, - uint3 tgpig, - uint tiisg, - uint sgitg) { + args_t args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg) { - threadgroup float * shared_values = (threadgroup float *)shared_values_i8; - const int nb = ne00/QK_K; + threadgroup float * shmem_f32 = (threadgroup float *) shmem; + const int nb = args.ne00/QK_K; const int r0 = tgpig.x; const int r1 = tgpig.y; const int im = tgpig.z; const int first_row = (r0 * 2 + sgitg) * 2; - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const uint i12 = im%args.ne12; + const uint i13 = im/args.ne12; - const uint offset0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03; - const uint offset1 = r1*nb11 + (i12 )*nb12 + (i13 )*nb13; + const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const uint64_t offset1 = r1*args.nb11 + (i12 )*args.nb12 + (i13 )*args.nb13; - device const block_iq4_xs * x = (device const block_iq4_xs *) ((device char *) src0 + offset0); - device const float * y = (device const float *) ((device char *) src1 + offset1); + device const block_iq4_xs * x = (device const block_iq4_xs *) (src0 + offset0); + device const float * y = (device const float *) (src1 + offset1); const int ix = tiisg/16; // 0 or 1 const int it = tiisg%16; // 0...15 const int ib = it/2; const int il = it%2; - shared_values[tiisg] = kvalues_iq4nl_f[tiisg%16]; + shmem_f32[tiisg] = kvalues_iq4nl_f[tiisg%16]; threadgroup_barrier(mem_flags::mem_threadgroup); float4 yl[4]; @@ -5293,28 +5853,26 @@ void kernel_mul_mv_iq4_xs_f32_impl( float4 qf1, qf2; for (int ibl = ix; ibl < nb; ibl += 2) { - device const float4 * y4 = (device const float4 *)yb; yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5]; for (int row = 0; row < 2; ++row) { - device const block_iq4_xs & xb = x[row*nb + ibl]; device const uint32_t * q4 = (device const uint32_t *)(xb.qs + 16*ib + 8*il); float4 acc1 = {0.f}, acc2 = {0.f}; - aux32[0] = q4[0] & 0x0f0f0f0f; + aux32[0] = (q4[0] ) & 0x0f0f0f0f; aux32[1] = (q4[0] >> 4) & 0x0f0f0f0f; - qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]}; - qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]}; + qf1 = {shmem_f32[q8[0]], shmem_f32[q8[1]], shmem_f32[q8[2]], shmem_f32[q8[3]]}; + qf2 = {shmem_f32[q8[4]], shmem_f32[q8[5]], shmem_f32[q8[6]], shmem_f32[q8[7]]}; acc1 += yl[0] * qf1; acc2 += yl[1] * qf2; - aux32[0] = q4[1] & 0x0f0f0f0f; + aux32[0] = (q4[1] ) & 0x0f0f0f0f; aux32[1] = (q4[1] >> 4) & 0x0f0f0f0f; - qf1 = {shared_values[q8[0]], shared_values[q8[1]], shared_values[q8[2]], shared_values[q8[3]]}; - qf2 = {shared_values[q8[4]], shared_values[q8[5]], shared_values[q8[6]], shared_values[q8[7]]}; + qf1 = {shmem_f32[q8[0]], shmem_f32[q8[1]], shmem_f32[q8[2]], shmem_f32[q8[3]]}; + qf2 = {shmem_f32[q8[4]], shmem_f32[q8[5]], shmem_f32[q8[6]], shmem_f32[q8[7]]}; acc1 += yl[2] * qf1; acc2 += yl[3] * qf2; @@ -5328,568 +5886,68 @@ void kernel_mul_mv_iq4_xs_f32_impl( yb += 2 * QK_K; } + device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0; + for (int row = 0; row < 2; ++row) { all_sum = simd_sum(sumf[row]); if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; + dst_f32[first_row + row] = all_sum; } } } [[host_name("kernel_mul_mv_iq1_s_f32")]] kernel void kernel_mul_mv_iq1_s_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq1_s_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); + kernel_mul_mv_iq1_s_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } [[host_name("kernel_mul_mv_iq1_m_f32")]] kernel void kernel_mul_mv_iq1_m_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq1_m_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); + kernel_mul_mv_iq1_m_f32_impl(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg); } [[host_name("kernel_mul_mv_iq4_nl_f32")]] kernel void kernel_mul_mv_iq4_nl_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq4_nl_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); + kernel_mul_mv_iq4_nl_f32_impl(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg); } [[host_name("kernel_mul_mv_iq4_xs_f32")]] kernel void kernel_mul_mv_iq4_xs_f32( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mv & args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - kernel_mul_mv_iq4_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb01, nb02, nb03, ne10, ne12, nb11, nb12, nb13, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); -} - -//============================= templates and their specializations ============================= - -// NOTE: this is not dequantizing - we are simply fitting the template -template -void dequantize_f32(device const float4x4 * src, short il, thread type4x4 & reg) { - float4x4 temp = *(((device float4x4 *)src)); - for (int i = 0; i < 16; i++){ - reg[i/4][i%4] = temp[i/4][i%4]; - } -} - -template -void dequantize_f16(device const half4x4 * src, short il, thread type4x4 & reg) { - half4x4 temp = *(((device half4x4 *)src)); - for (int i = 0; i < 16; i++){ - reg[i/4][i%4] = temp[i/4][i%4]; - } -} - -template -void dequantize_q4_0(device const block_q4_0 *xb, short il, thread type4x4 & reg) { - device const uint16_t * qs = ((device const uint16_t *)xb + 1); - const float d1 = il ? (xb->d / 16.h) : xb->d; - const float d2 = d1 / 256.f; - const float md = -8.h * xb->d; - const ushort mask0 = il ? 0x00F0 : 0x000F; - const ushort mask1 = mask0 << 8; - - for (int i=0;i<8;i++) { - reg[i/2][2*(i%2)+0] = d1 * (qs[i] & mask0) + md; - reg[i/2][2*(i%2)+1] = d2 * (qs[i] & mask1) + md; - } -} - -template -void dequantize_q4_1(device const block_q4_1 *xb, short il, thread type4x4 & reg) { - device const uint16_t * qs = ((device const uint16_t *)xb + 2); - const float d1 = il ? (xb->d / 16.h) : xb->d; - const float d2 = d1 / 256.f; - const float m = xb->m; - const ushort mask0 = il ? 0x00F0 : 0x000F; - const ushort mask1 = mask0 << 8; - - for (int i=0;i<8;i++) { - reg[i/2][2*(i%2)+0] = ((qs[i] & mask0) * d1) + m; - reg[i/2][2*(i%2)+1] = ((qs[i] & mask1) * d2) + m; - } -} - -template -void dequantize_q5_0(device const block_q5_0 *xb, short il, thread type4x4 & reg) { - device const uint16_t * qs = ((device const uint16_t *)xb + 3); - const float d = xb->d; - const float md = -16.h * xb->d; - const ushort mask = il ? 0x00F0 : 0x000F; - - const uint32_t qh = *((device const uint32_t *)xb->qh); - - const int x_mv = il ? 4 : 0; - - const int gh_mv = il ? 12 : 0; - const int gh_bk = il ? 0 : 4; - - for (int i = 0; i < 8; i++) { - // extract the 5-th bits for x0 and x1 - const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10; - const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10; - - // combine the 4-bits from qs with the 5th bit - const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0); - const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1); - - reg[i/2][2*(i%2)+0] = d * x0 + md; - reg[i/2][2*(i%2)+1] = d * x1 + md; - } -} - -template -void dequantize_q5_1(device const block_q5_1 *xb, short il, thread type4x4 & reg) { - device const uint16_t * qs = ((device const uint16_t *)xb + 4); - const float d = xb->d; - const float m = xb->m; - const ushort mask = il ? 0x00F0 : 0x000F; - - const uint32_t qh = *((device const uint32_t *)xb->qh); - - const int x_mv = il ? 4 : 0; - - const int gh_mv = il ? 12 : 0; - const int gh_bk = il ? 0 : 4; - - for (int i = 0; i < 8; i++) { - // extract the 5-th bits for x0 and x1 - const uint8_t xh_0 = ((qh >> (gh_mv + 2*i )) << gh_bk) & 0x10; - const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10; - - // combine the 4-bits from qs with the 5th bit - const int32_t x0 = ((((qs[i] ) & mask) >> x_mv) | xh_0); - const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1); - - reg[i/2][2*(i%2)+0] = d * x0 + m; - reg[i/2][2*(i%2)+1] = d * x1 + m; - } -} - -template -void dequantize_q8_0(device const block_q8_0 *xb, short il, thread type4x4 & reg) { - device const int8_t * qs = ((device const int8_t *)xb->qs); - const half d = xb->d; - - for (int i = 0; i < 16; i++) { - reg[i/4][i%4] = (qs[i + 16*il] * d); - } -} - -template -void dequantize_q2_K(device const block_q2_K *xb, short il, thread type4x4 & reg) { - const float d = xb->d; - const float min = xb->dmin; - device const uint8_t * q = (device const uint8_t *)xb->qs; - float dl, ml; - uint8_t sc = xb->scales[il]; - - q = q + 32*(il/8) + 16*(il&1); - il = (il/2)%4; - - half coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h); - uchar mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3); - dl = d * (sc & 0xF) * coef, ml = min * (sc >> 4); - for (int i = 0; i < 16; ++i) { - reg[i/4][i%4] = dl * (q[i] & mask) - ml; - } -} - -template -void dequantize_q3_K(device const block_q3_K *xb, short il, thread type4x4 & reg) { - const half d_all = xb->d; - device const uint8_t * q = (device const uint8_t *)xb->qs; - device const uint8_t * h = (device const uint8_t *)xb->hmask; - device const int8_t * scales = (device const int8_t *)xb->scales; - - q = q + 32 * (il/8) + 16 * (il&1); - h = h + 16 * (il&1); - uint8_t m = 1 << (il/2); - uint16_t kmask1 = (il/4)>1 ? ((il/4)>2 ? 192 : 48) : \ - ((il/4)>0 ? 12 : 3); - uint16_t kmask2 = il/8 ? 0xF0 : 0x0F; - uint16_t scale_2 = scales[il%8], scale_1 = scales[8 + il%4]; - int16_t dl_int = (il/4)&1 ? (scale_2&kmask2) | ((scale_1&kmask1) << 2) - : (scale_2&kmask2) | ((scale_1&kmask1) << 4); - float dl = il<8 ? d_all * (dl_int - 32.f) : d_all * (dl_int / 16.f - 32.f); - const float ml = 4.f * dl; - - il = (il/2) & 3; - const half coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h); - const uint8_t mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3); - dl *= coef; - - for (int i = 0; i < 16; ++i) { - reg[i/4][i%4] = dl * (q[i] & mask) - (h[i] & m ? 0 : ml); - } -} - -static inline uchar2 get_scale_min_k4_just2(int j, int k, device const uchar * q) { - return j < 4 ? uchar2{uchar(q[j+0+k] & 63), uchar(q[j+4+k] & 63)} - : uchar2{uchar((q[j+4+k] & 0xF) | ((q[j-4+k] & 0xc0) >> 2)), uchar((q[j+4+k] >> 4) | ((q[j-0+k] & 0xc0) >> 2))}; -} - -template -void dequantize_q4_K(device const block_q4_K *xb, short il, thread type4x4 & reg) { - device const uchar * q = xb->qs; - - short is = (il/4) * 2; - q = q + (il/4) * 32 + 16 * (il&1); - il = il & 3; - const uchar2 sc = get_scale_min_k4_just2(is, il/2, xb->scales); - const float d = il < 2 ? xb->d : xb->d / 16.h; - const float min = xb->dmin; - const float dl = d * sc[0]; - const float ml = min * sc[1]; - - const ushort mask = il<2 ? 0x0F : 0xF0; - for (int i = 0; i < 16; ++i) { - reg[i/4][i%4] = dl * (q[i] & mask) - ml; - } -} - -template -void dequantize_q5_K(device const block_q5_K *xb, short il, thread type4x4 & reg) { - device const uint8_t * q = xb->qs; - device const uint8_t * qh = xb->qh; - - short is = (il/4) * 2; - q = q + 32 * (il/4) + 16 * (il&1); - qh = qh + 16 * (il&1); - uint8_t ul = 1 << (il/2); - il = il & 3; - const uchar2 sc = get_scale_min_k4_just2(is, il/2, xb->scales); - const float d = il < 2 ? xb->d : xb->d / 16.f; - const float min = xb->dmin; - const float dl = d * sc[0]; - const float ml = min * sc[1]; - - const ushort mask = il<2 ? 0x0F : 0xF0; - const float qh_val = il<2 ? 16.f : 256.f; - for (int i = 0; i < 16; ++i) { - reg[i/4][i%4] = dl * ((q[i] & mask) + (qh[i] & ul ? qh_val : 0)) - ml; - } -} - -template -void dequantize_q6_K(device const block_q6_K *xb, short il, thread type4x4 & reg) { - const half d_all = xb->d; - device const uint8_t * ql = (device const uint8_t *)xb->ql; - device const uint8_t * qh = (device const uint8_t *)xb->qh; - device const int8_t * scales = (device const int8_t *)xb->scales; - - ql = ql + 64*(il/8) + 32*((il/2)&1) + 16*(il&1); - qh = qh + 32*(il/8) + 16*(il&1); - float sc = scales[(il%2) + 2 * ((il/2))]; - il = (il/2) & 3; - - const uint16_t kmask1 = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3); - const uint16_t kmask2 = il>1 ? 0xF0 : 0x0F; - const float coef = il>1 ? 1.f/16.f : 1.f; - const float ml = d_all * sc * 32.f; - const float dl = d_all * sc * coef; - for (int i = 0; i < 16; ++i) { - const half q = il&1 ? ((ql[i] & kmask2) | ((qh[i] & kmask1) << 2)) - : ((ql[i] & kmask2) | ((qh[i] & kmask1) << 4)); - reg[i/4][i%4] = dl * q - ml; - } -} - -template -void dequantize_iq2_xxs(device const block_iq2_xxs * xb, short il, thread type4x4 & reg) { - // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 - const float d = xb->d; - const int ib32 = il/2; - il = il%2; - // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 - // each block of 32 needs 2 uint32_t's for the quants & scale, so 4 uint16_t's. - device const uint16_t * q2 = xb->qs + 4*ib32; - const uint32_t aux32_g = q2[0] | (q2[1] << 16); - const uint32_t aux32_s = q2[2] | (q2[3] << 16); - thread const uint8_t * aux8 = (thread const uint8_t *)&aux32_g; - const float dl = d * (0.5f + (aux32_s >> 28)) * 0.25f; - constant uint8_t * grid = (constant uint8_t *)(iq2xxs_grid + aux8[2*il+0]); - uint8_t signs = ksigns_iq2xs[(aux32_s >> 14*il) & 127]; - for (int i = 0; i < 8; ++i) { - reg[i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); - } - grid = (constant uint8_t *)(iq2xxs_grid + aux8[2*il+1]); - signs = ksigns_iq2xs[(aux32_s >> (14*il+7)) & 127]; - for (int i = 0; i < 8; ++i) { - reg[2+i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); - } -} - -template -void dequantize_iq2_xs(device const block_iq2_xs * xb, short il, thread type4x4 & reg) { - // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 - const float d = xb->d; - const int ib32 = il/2; - il = il%2; - // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 - device const uint16_t * q2 = xb->qs + 4*ib32; - const float dl = d * (0.5f + ((xb->scales[ib32] >> 4*il) & 0xf)) * 0.25f; - constant uint8_t * grid = (constant uint8_t *)(iq2xs_grid + (q2[2*il+0] & 511)); - uint8_t signs = ksigns_iq2xs[q2[2*il+0] >> 9]; - for (int i = 0; i < 8; ++i) { - reg[i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); - } - grid = (constant uint8_t *)(iq2xs_grid + (q2[2*il+1] & 511)); - signs = ksigns_iq2xs[q2[2*il+1] >> 9]; - for (int i = 0; i < 8; ++i) { - reg[2+i/4][i%4] = dl * grid[i] * (signs & kmask_iq2xs[i] ? -1.f : 1.f); - } -} - -template -void dequantize_iq3_xxs(device const block_iq3_xxs * xb, short il, thread type4x4 & reg) { - // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 - const float d = xb->d; - const int ib32 = il/2; - il = il%2; - // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 - device const uint8_t * q3 = xb->qs + 8*ib32; - device const uint16_t * gas = (device const uint16_t *)(xb->qs + QK_K/4) + 2*ib32; - const uint32_t aux32 = gas[0] | (gas[1] << 16); - const float dl = d * (0.5f + (aux32 >> 28)) * 0.5f; - constant uint8_t * grid1 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+0]); - constant uint8_t * grid2 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+1]); - uint8_t signs = ksigns_iq2xs[(aux32 >> 14*il) & 127]; - for (int i = 0; i < 4; ++i) { - reg[0][i] = dl * grid1[i] * (signs & kmask_iq2xs[i+0] ? -1.f : 1.f); - reg[1][i] = dl * grid2[i] * (signs & kmask_iq2xs[i+4] ? -1.f : 1.f); - } - grid1 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+2]); - grid2 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+3]); - signs = ksigns_iq2xs[(aux32 >> (14*il+7)) & 127]; - for (int i = 0; i < 4; ++i) { - reg[2][i] = dl * grid1[i] * (signs & kmask_iq2xs[i+0] ? -1.f : 1.f); - reg[3][i] = dl * grid2[i] * (signs & kmask_iq2xs[i+4] ? -1.f : 1.f); - } -} - -template -void dequantize_iq3_s(device const block_iq3_s * xb, short il, thread type4x4 & reg) { - // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 - const float d = xb->d; - const int ib32 = il/2; - il = il%2; - // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 - device const uint8_t * qs = xb->qs + 8*ib32; - device const uint8_t * signs = xb->signs + 4*ib32 + 2*il; - const uint8_t qh = xb->qh[ib32] >> 4*il; - const float dl = d * (1 + 2*((xb->scales[ib32/2] >> 4*(ib32%2)) & 0xf)); - constant uint8_t * grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+0] | ((qh << 8) & 256))); - constant uint8_t * grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+1] | ((qh << 7) & 256))); - for (int i = 0; i < 4; ++i) { - reg[0][i] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i+0]); - reg[1][i] = dl * grid2[i] * select(1, -1, signs[0] & kmask_iq2xs[i+4]); - } - grid1 = (constant uint8_t *)(iq3s_grid + (qs[4*il+2] | ((qh << 6) & 256))); - grid2 = (constant uint8_t *)(iq3s_grid + (qs[4*il+3] | ((qh << 5) & 256))); - for (int i = 0; i < 4; ++i) { - reg[2][i] = dl * grid1[i] * select(1, -1, signs[1] & kmask_iq2xs[i+0]); - reg[3][i] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i+4]); - } -} - -template -void dequantize_iq2_s(device const block_iq2_s * xb, short il, thread type4x4 & reg) { - // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 - const float d = xb->d; - const int ib32 = il/2; - il = il%2; - // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 - device const uint8_t * qs = xb->qs + 4*ib32 + 2*il; - device const uint8_t * signs = qs + QK_K/8; - const uint8_t qh = xb->qh[ib32] >> 4*il; - const float dl = d * (0.5f + ((xb->scales[ib32] >> 4*il) & 0xf)) * 0.25f; - constant uint8_t * grid1 = (constant uint8_t *)(iq2s_grid + (qs[0] | ((qh << 8) & 0x300))); - constant uint8_t * grid2 = (constant uint8_t *)(iq2s_grid + (qs[1] | ((qh << 6) & 0x300))); - for (int i = 0; i < 8; ++i) { - reg[i/4+0][i%4] = dl * grid1[i] * select(1, -1, signs[0] & kmask_iq2xs[i]); - reg[i/4+2][i%4] = dl * grid2[i] * select(1, -1, signs[1] & kmask_iq2xs[i]); - } -} - -template -void dequantize_iq1_s(device const block_iq1_s * xb, short il, thread type4x4 & reg) { - // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 - const int ib32 = il/2; - il = il%2; - const float d = xb->d; - device const uint8_t * qs = xb->qs + 4*ib32 + 2*il; - device const uint16_t * qh = xb->qh; - const float dl = d * (2*((qh[ib32] >> 12) & 7) + 1); - const float ml = dl * (qh[ib32] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA); - const uint16_t h = qh[ib32] >> 6*il; - constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((h << 8) & 0x700))); - constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((h << 5) & 0x700))); - for (int i = 0; i < 4; ++i) { - reg[0][i] = dl * (grid1[i] & 0xf) + ml; - reg[1][i] = dl * (grid1[i] >> 4) + ml; - reg[2][i] = dl * (grid2[i] & 0xf) + ml; - reg[3][i] = dl * (grid2[i] >> 4) + ml; - } -} - -template -void dequantize_iq1_m(device const block_iq1_m * xb, short il, thread type4x4 & reg) { - // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 - const int ib32 = il/2; - il = il%2; - device const uint16_t * sc = (device const uint16_t *)xb->scales; - - iq1m_scale_t scale; - scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); - const float d = scale.f16; - - device const uint8_t * qs = xb->qs + 4*ib32 + 2*il; - device const uint8_t * qh = xb->qh + 2*ib32 + il; - - const float dl = d * (2*((sc[ib32/2] >> (6*(ib32%2)+3*il)) & 7) + 1); - const float ml1 = dl * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA); - const float ml2 = dl * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA); - constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700))); - constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 4) & 0x700))); - for (int i = 0; i < 4; ++i) { - reg[0][i] = dl * (grid1[i] & 0xf) + ml1; - reg[1][i] = dl * (grid1[i] >> 4) + ml1; - reg[2][i] = dl * (grid2[i] & 0xf) + ml2; - reg[3][i] = dl * (grid2[i] >> 4) + ml2; - } -} - -template -void dequantize_iq4_nl(device const block_iq4_nl * xb, short il, thread type4x4 & reg) { - device const uint16_t * q4 = (device const uint16_t *)xb->qs; - const float d = xb->d; - uint32_t aux32; - thread const uint8_t * q8 = (thread const uint8_t *)&aux32; - for (int i = 0; i < 4; ++i) { - aux32 = ((q4[2*i] | (q4[2*i+1] << 16)) >> 4*il) & 0x0f0f0f0f; - reg[i][0] = d * kvalues_iq4nl_f[q8[0]]; - reg[i][1] = d * kvalues_iq4nl_f[q8[1]]; - reg[i][2] = d * kvalues_iq4nl_f[q8[2]]; - reg[i][3] = d * kvalues_iq4nl_f[q8[3]]; - } -} - -template -void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4 & reg) { - // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 - const int ib32 = il/2; - il = il%2; - // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 - device const uint32_t * q4 = (device const uint32_t *)xb->qs + 4*ib32; - const int ls = ((xb->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((xb->scales_h >> 2*ib32) & 3) << 4); - const float d = (float)xb->d * (ls - 32); - uint32_t aux32; - thread const uint8_t * q8 = (thread const uint8_t *)&aux32; - for (int i = 0; i < 4; ++i) { - aux32 = (q4[i] >> 4*il) & 0x0f0f0f0f; - reg[i][0] = d * kvalues_iq4nl_f[q8[0]]; - reg[i][1] = d * kvalues_iq4nl_f[q8[1]]; - reg[i][2] = d * kvalues_iq4nl_f[q8[2]]; - reg[i][3] = d * kvalues_iq4nl_f[q8[3]]; - } + kernel_mul_mv_iq4_xs_f32_impl(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg); } template @@ -5993,134 +6051,141 @@ kernel void kernel_get_rows_i32( // each block_q contains 16*nl weights template -kernel void kernel_mul_mm(device const uchar * src0, - device const uchar * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne02, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant uint64_t & nb13, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup uchar * shared_memory [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { +kernel void kernel_mul_mm( + constant ggml_metal_kargs_mul_mm & args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiitg[[thread_index_in_threadgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - threadgroup T * sa = (threadgroup T *)(shared_memory); - threadgroup float * sb = (threadgroup float *)(shared_memory + 4096); + threadgroup T * sa = (threadgroup T *)(shmem); + threadgroup float * sb = (threadgroup float *)(shmem + 4096); - const uint r0 = tgpig.y; - const uint r1 = tgpig.x; - const uint im = tgpig.z; + const int r0 = tgpig.y; + const int r1 = tgpig.x; + const int im = tgpig.z; // if this block is of 64x32 shape or smaller - short n_rows = (ne0 - r0 * BLOCK_SIZE_M < BLOCK_SIZE_M) ? (ne0 - r0 * BLOCK_SIZE_M) : BLOCK_SIZE_M; - short n_cols = (ne1 - r1 * BLOCK_SIZE_N < BLOCK_SIZE_N) ? (ne1 - r1 * BLOCK_SIZE_N) : BLOCK_SIZE_N; + const short n_rows = (args.ne0 - r0*BLOCK_SIZE_M < BLOCK_SIZE_M) ? (args.ne0 - r0*BLOCK_SIZE_M) : BLOCK_SIZE_M; + const short n_cols = (args.ne1 - r1*BLOCK_SIZE_N < BLOCK_SIZE_N) ? (args.ne1 - r1*BLOCK_SIZE_N) : BLOCK_SIZE_N; // a thread shouldn't load data outside of the matrix - short thread_row = ((short)tiitg/THREAD_PER_ROW) < n_rows ? ((short)tiitg/THREAD_PER_ROW) : n_rows - 1; - short thread_col = ((short)tiitg/THREAD_PER_COL) < n_cols ? ((short)tiitg/THREAD_PER_COL) : n_cols - 1; + const short thread_row = ((short)tiitg/THREAD_PER_ROW) < n_rows ? ((short)tiitg/THREAD_PER_ROW) : n_rows - 1; + const short thread_col = ((short)tiitg/THREAD_PER_COL) < n_cols ? ((short)tiitg/THREAD_PER_COL) : n_cols - 1; simdgroup_T8x8 ma[4]; simdgroup_float8x8 mb[2]; - simdgroup_float8x8 c_res[8]; - for (int i = 0; i < 8; i++){ - c_res[i] = make_filled_simdgroup_matrix(0.f); + simdgroup_float8x8 mc[8]; + + for (short i = 0; i < 8; i++){ + mc[i] = make_filled_simdgroup_matrix(0.f); } short il = (tiitg % THREAD_PER_ROW); - const uint i12 = im%ne12; - const uint i13 = im/ne12; + const int i12 = im%args.ne12; + const int i13 = im/args.ne12; - uint offset0 = (i12/r2)*nb02 + (i13/r3)*nb03; - ushort offset1 = il/nl; + const uint64_t offset0 = (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03; + const short offset1 = il/nl; + + device const block_q * x = (device const block_q *)(src0 + + args.nb01*(r0*BLOCK_SIZE_M + thread_row) + offset0) + offset1; - device const block_q * x = (device const block_q *)(src0 + (r0 * BLOCK_SIZE_M + thread_row) * nb01 + offset0) + offset1; device const float * y = (device const float *)(src1 - + nb13 * i13 - + nb12 * i12 - + nb11 * (r1 * BLOCK_SIZE_N + thread_col) - + nb10 * (BLOCK_SIZE_K / THREAD_PER_COL * (tiitg % THREAD_PER_COL))); + + args.nb13*i13 + + args.nb12*i12 + + args.nb11*(r1*BLOCK_SIZE_N + thread_col) + + args.nb10*(BLOCK_SIZE_K / THREAD_PER_COL * (tiitg % THREAD_PER_COL))); - for (int loop_k = 0; loop_k < ne00; loop_k += BLOCK_SIZE_K) { + for (int loop_k = 0; loop_k < args.ne00; loop_k += BLOCK_SIZE_K) { // load data and store to threadgroup memory T4x4 temp_a; dequantize_func(x, il, temp_a); + threadgroup_barrier(mem_flags::mem_threadgroup); #pragma unroll(16) - for (int i = 0; i < 16; i++) { - *(sa + SG_MAT_SIZE * ((tiitg / THREAD_PER_ROW / 8) \ - + (tiitg % THREAD_PER_ROW) * 16 + (i / 8) * 8) \ - + (tiitg / THREAD_PER_ROW) % 8 + (i & 7) * 8) = temp_a[i/4][i%4]; + for (short i = 0; i < 16; i++) { + *(sa + SG_MAT_SIZE * ((tiitg/THREAD_PER_ROW/8) \ + + (tiitg%THREAD_PER_ROW)*16 + (i/8)*8) \ + + (tiitg/THREAD_PER_ROW)%8 + (i&7)*8) = temp_a[i/4][i%4]; } - *(threadgroup float2x4 *)(sb + (tiitg % THREAD_PER_COL) * 8 * 32 + 8 * (tiitg / THREAD_PER_COL)) = *((device float2x4 *)y); + *(threadgroup float2x4 *)(sb + 32*8*(tiitg%THREAD_PER_COL) + 8*(tiitg/THREAD_PER_COL)) = *((device float2x4 *) y); il = (il + 2 < nl) ? il + 2 : il % 2; - x = (il < 2) ? x + (2+nl-1)/nl : x; + x = (il < 2) ? x + (2 + nl - 1)/nl : x; y += BLOCK_SIZE_K; threadgroup_barrier(mem_flags::mem_threadgroup); // load matrices from threadgroup memory and conduct outer products - threadgroup T * lsma = (sa + THREAD_MAT_M * SG_MAT_SIZE * (sgitg % 2)); - threadgroup float * lsmb = (sb + THREAD_MAT_N * SG_MAT_SIZE * (sgitg / 2)); + threadgroup const T * lsma = (sa + THREAD_MAT_M*SG_MAT_SIZE*(sgitg%2)); + threadgroup const float * lsmb = (sb + THREAD_MAT_N*SG_MAT_SIZE*(sgitg/2)); #pragma unroll(4) - for (int ik = 0; ik < BLOCK_SIZE_K / 8; ik++) { + for (short ik = 0; ik < BLOCK_SIZE_K/8; ik++) { #pragma unroll(4) - for (int i = 0; i < 4; i++) { - simdgroup_load(ma[i],lsma + SG_MAT_SIZE * i); - } - simdgroup_barrier(mem_flags::mem_none); - #pragma unroll(2) - for (int i = 0; i < 2; i++) { - simdgroup_load(mb[i],lsmb + SG_MAT_SIZE * i); + for (short i = 0; i < 4; i++) { + simdgroup_load(ma[i], lsma + SG_MAT_SIZE * i); } - lsma += BLOCK_SIZE_M / SG_MAT_ROW * SG_MAT_SIZE; - lsmb += BLOCK_SIZE_N / SG_MAT_ROW * SG_MAT_SIZE; + simdgroup_barrier(mem_flags::mem_none); + + #pragma unroll(2) + for (short i = 0; i < 2; i++) { + simdgroup_load(mb[i], lsmb + SG_MAT_SIZE * i); + } #pragma unroll(8) - for (int i = 0; i < 8; i++){ - simdgroup_multiply_accumulate(c_res[i], mb[i/4], ma[i%4], c_res[i]); + for (short i = 0; i < 8; i++){ + simdgroup_multiply_accumulate(mc[i], mb[i/4], ma[i%4], mc[i]); } + + lsma += (BLOCK_SIZE_M/SG_MAT_ROW)*SG_MAT_SIZE; + lsmb += (BLOCK_SIZE_N/SG_MAT_ROW)*SG_MAT_SIZE; } } - if ((r0 + 1) * BLOCK_SIZE_M <= ne0 && (r1 + 1) * BLOCK_SIZE_N <= ne1) { - device float * C = dst + (BLOCK_SIZE_M * r0 + 32 * (sgitg & 1)) \ - + (BLOCK_SIZE_N * r1 + 16 * (sgitg >> 1)) * ne0 + im*ne1*ne0; - for (int i = 0; i < 8; i++) { - simdgroup_store(c_res[i], C + 8 * (i%4) + 8 * ne0 * (i/4), ne0); + if ((r0 + 1) * BLOCK_SIZE_M <= args.ne0 && (r1 + 1) * BLOCK_SIZE_N <= args.ne1) { + device float * C = (device float *) dst + + (BLOCK_SIZE_M * r0 + 32*(sgitg & 1)) + \ + (BLOCK_SIZE_N * r1 + 16*(sgitg >> 1)) * args.ne0 + im*args.ne1*args.ne0; + + for (short i = 0; i < 8; i++) { + simdgroup_store(mc[i], C + 8 * (i%4) + 8 * args.ne0 * (i/4), args.ne0); } } else { // block is smaller than 64x32, we should avoid writing data outside of the matrix threadgroup_barrier(mem_flags::mem_threadgroup); - threadgroup float * temp_str = ((threadgroup float *)shared_memory) \ - + 32 * (sgitg&1) + (16 * (sgitg>>1)) * BLOCK_SIZE_M; - for (int i = 0; i < 8; i++) { - simdgroup_store(c_res[i], temp_str + 8 * (i%4) + 8 * BLOCK_SIZE_M * (i/4), BLOCK_SIZE_M); + threadgroup float * temp_str = ((threadgroup float *) shmem) \ + + 32*(sgitg&1) + (16*(sgitg >> 1))*BLOCK_SIZE_M; + for (short i = 0; i < 8; i++) { + simdgroup_store(mc[i], temp_str + 8*(i%4) + 8*BLOCK_SIZE_M*(i/4), BLOCK_SIZE_M); } threadgroup_barrier(mem_flags::mem_threadgroup); - device float * C = dst + (BLOCK_SIZE_M * r0) + (BLOCK_SIZE_N * r1) * ne0 + im*ne1*ne0; if (sgitg == 0) { - for (int i = 0; i < n_rows; i++) { - for (int j = tiitg; j < n_cols; j += BLOCK_SIZE_N) { - *(C + i + j * ne0) = *(temp_str + i + j * BLOCK_SIZE_M); + for (int j = tiitg; j < n_cols; j += BLOCK_SIZE_N) { + device float * D = (device float *) dst + (r0*BLOCK_SIZE_M) + (r1*BLOCK_SIZE_N + j)*args.ne0 + im*args.ne1*args.ne0; + device float4 * D4 = (device float4 *) D; + + threadgroup float * C = temp_str + (j*BLOCK_SIZE_M); + threadgroup float4 * C4 = (threadgroup float4 *) C; + + int i = 0; + for (; i < n_rows/4; i++) { + *(D4 + i) = *(C4 + i); + } + + i *= 4; + for (; i < n_rows; i++) { + *(D + i) = *(C + i); } } } @@ -6128,36 +6193,37 @@ kernel void kernel_mul_mm(device const uchar * src0, } // same as kernel_mul_mm_impl, but src1 and dst are accessed via indices stored in rowids +// TODO: this kernel needs to be reimplemented from scratch for better performance template void kernel_mul_mm_id_impl( - device const uchar * src0, - device const uchar * src1, + int32_t ne00, + int32_t ne02, + uint64_t nb01, + uint64_t nb02, + int32_t ne11, + int32_t ne12, + uint64_t nb10, + uint64_t nb11, + uint64_t nb12, + int32_t ne0, + int32_t ne1, + int64_t ne0ne1, + device const char * src0, + device const char * src1, threadgroup ushort2 * rowids, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne02, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne11, - constant int64_t & ne12, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - int64_t ne1, - int64_t ne0ne1, - threadgroup uchar * shared_memory, - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + device char * dst, + threadgroup char * shmem, + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiitg[[thread_index_in_threadgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { - threadgroup half * sa = (threadgroup half *)(shared_memory); - threadgroup float * sb = (threadgroup float *)(shared_memory + 4096); + threadgroup half * sa = (threadgroup half *)(shmem); + threadgroup float * sb = (threadgroup float *)(shmem + 4096); - const uint r0 = tgpig.y; - const uint r1 = tgpig.x; + const int r0 = tgpig.y; + const int r1 = tgpig.x; - if (r1 * BLOCK_SIZE_N >= ne1) return; + if (r1*BLOCK_SIZE_N >= ne1) return; // if this block is of 64x32 shape or smaller short n_rows = (ne0 - r0 * BLOCK_SIZE_M < BLOCK_SIZE_M) ? (ne0 - r0 * BLOCK_SIZE_M) : BLOCK_SIZE_M; @@ -6169,9 +6235,9 @@ void kernel_mul_mm_id_impl( simdgroup_half8x8 ma[4]; simdgroup_float8x8 mb[2]; - simdgroup_float8x8 c_res[8]; + simdgroup_float8x8 mc[8]; for (int i = 0; i < 8; i++){ - c_res[i] = make_filled_simdgroup_matrix(0.f); + mc[i] = make_filled_simdgroup_matrix(0.f); } short il = (tiitg % THREAD_PER_ROW); @@ -6209,11 +6275,14 @@ void kernel_mul_mm_id_impl( threadgroup half * lsma = (sa + THREAD_MAT_M * SG_MAT_SIZE * (sgitg % 2)); threadgroup float * lsmb = (sb + THREAD_MAT_N * SG_MAT_SIZE * (sgitg / 2)); + #pragma unroll(BLOCK_SIZE_K/8) for (int ik = 0; ik < BLOCK_SIZE_K / 8; ik++) { + #pragma unroll(4) for (int i = 0; i < 4; i++) { simdgroup_load(ma[i], lsma + SG_MAT_SIZE * i); } simdgroup_barrier(mem_flags::mem_none); + #pragma unroll(2) for (int i = 0; i < 2; i++) { simdgroup_load(mb[i], lsmb + SG_MAT_SIZE * i); } @@ -6221,29 +6290,42 @@ void kernel_mul_mm_id_impl( lsma += BLOCK_SIZE_M / SG_MAT_ROW * SG_MAT_SIZE; lsmb += BLOCK_SIZE_N / SG_MAT_ROW * SG_MAT_SIZE; + #pragma unroll(8) for (int i = 0; i < 8; i++){ - simdgroup_multiply_accumulate(c_res[i], mb[i/4], ma[i%4], c_res[i]); + simdgroup_multiply_accumulate(mc[i], mb[i/4], ma[i%4], mc[i]); } } } { threadgroup_barrier(mem_flags::mem_threadgroup); - threadgroup float * temp_str = ((threadgroup float *)shared_memory) \ + threadgroup float * temp_str = ((threadgroup float *) shmem) \ + 32 * (sgitg&1) + (16 * (sgitg>>1)) * BLOCK_SIZE_M; for (int i = 0; i < 8; i++) { - simdgroup_store(c_res[i], temp_str + 8 * (i%4) + 8 * BLOCK_SIZE_M * (i/4), BLOCK_SIZE_M); + simdgroup_store(mc[i], temp_str + 8 * (i%4) + 8 * BLOCK_SIZE_M * (i/4), BLOCK_SIZE_M); } threadgroup_barrier(mem_flags::mem_threadgroup); - device float * C = dst + (BLOCK_SIZE_M * r0); if (sgitg == 0) { for (int j = tiitg; j < n_cols; j += BLOCK_SIZE_N) { threadgroup const auto & jid = rowids[r1 * BLOCK_SIZE_N + j]; - int joff = jid[0] * ne0 + jid[1] * ne0ne1; - for (int i = 0; i < n_rows; i++) { - *(C + i + joff) = *(temp_str + i + j * BLOCK_SIZE_M); + int64_t joff = jid[0]*ne0 + jid[1]*ne0ne1; + + device float * D = (device float *) dst + (r0*BLOCK_SIZE_M) + joff; + device float4 * D4 = (device float4 *) D; + + threadgroup float * C = temp_str + (j*BLOCK_SIZE_M); + threadgroup float4 * C4 = (threadgroup float4 *) C; + + int i = 0; + for (; i < n_rows/4; i++) { + *(D4 + i) = *(C4 + i); + } + + i *= 4; + for (; i < n_rows; i++) { + *(D + i) = *(C + i); } } } @@ -6252,48 +6334,34 @@ void kernel_mul_mm_id_impl( template kernel void kernel_mul_mm_id( - device const uchar * src0s, - device const uchar * src1, - device float * dst, - device const uchar * ids, - constant int64_t & nei0, - constant int64_t & nei1, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne02, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - threadgroup uchar * shared_memory [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { + constant ggml_metal_kargs_mul_mm_id & args, + device const char * src0s, + device const char * src1, + device char * dst, + device const char * ids, + threadgroup char * shmem [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiitg[[thread_index_in_threadgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { const int32_t i02 = tgpig.z; + tgpig.z = 0; - device const uchar * src0 = src0s + i02*nb02; + device const char * src0 = src0s + i02*args.nb02; // row indices - threadgroup ushort2 * rowids = (threadgroup ushort2 *)(shared_memory + 8192); + threadgroup ushort2 * rowids = (threadgroup ushort2 *)(shmem + 8192); // TODO: parallelize this loop - int64_t _ne1 = 0; - for (ushort ii1 = 0; ii1 < nei1; ii1++) { - for (ushort ii0 = 0; ii0 < nei0; ii0++) { - int32_t id = ((device int32_t *) (ids + ii1*nbi1))[ii0]; + int32_t _ne1 = 0; + for (ushort ii1 = 0; ii1 < args.nei1; ii1++) { + for (ushort ii0 = 0; ii0 < args.nei0; ii0++) { + int32_t id = ((device int32_t *) (ids + ii1*args.nbi1))[ii0]; if (id == i02) { - //if (tiitg == 0) { + if (tiitg == 0) { rowids[_ne1] = ushort2(ii0, ii1); - //} + } _ne1++; } } @@ -6302,23 +6370,23 @@ kernel void kernel_mul_mm_id( threadgroup_barrier(mem_flags::mem_threadgroup); kernel_mul_mm_id_impl( + args.ne00, + args.ne02, + args.nb01, + args.nb02, + args.ne11, + args.ne12, + args.nb10, + args.nb11, + args.nb12, + args.ne0, + _ne1, + (int64_t)args.ne0*args.ne1, src0, src1, rowids, dst, - ne00, - ne02, - nb01, - nb02, - ne11, - ne12, - nb10, - nb11, - nb12, - ne0, - _ne1, - ne0*ne1, - shared_memory, + shmem, tgpig, tiitg, sgitg); @@ -6334,6 +6402,9 @@ typedef decltype(kernel_get_rows_f) get_rows_f_t; template [[host_name("kernel_get_rows_f32")]] kernel get_rows_f_t kernel_get_rows_f; template [[host_name("kernel_get_rows_f16")]] kernel get_rows_f_t kernel_get_rows_f; +#if defined(GGML_METAL_USE_BF16) +template [[host_name("kernel_get_rows_bf16")]] kernel get_rows_f_t kernel_get_rows_f; +#endif typedef decltype(kernel_get_rows_q) get_rows_q_t; @@ -6365,6 +6436,9 @@ typedef decltype(kernel_mul_mm; template [[host_name("kernel_mul_mm_f16_f32")]] kernel mat_mm_t kernel_mul_mm; +#if defined(GGML_METAL_USE_BF16) +template [[host_name("kernel_mul_mm_bf16_f32")]] kernel mat_mm_t kernel_mul_mm; +#endif template [[host_name("kernel_mul_mm_q4_0_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_q4_1_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_q5_0_f32")]] kernel mat_mm_t kernel_mul_mm; @@ -6393,6 +6467,9 @@ typedef decltype(kernel_mul_mm_id) mat_mm_id_t; template [[host_name("kernel_mul_mm_id_f32_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_f16_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +#if defined(GGML_METAL_USE_BF16) +template [[host_name("kernel_mul_mm_id_bf16_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +#endif template [[host_name("kernel_mul_mm_id_q4_0_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_q4_1_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_q5_0_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; @@ -6418,194 +6495,110 @@ template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]] kernel mat_mm_id_t kernel // typedef void (kernel_mul_mv_impl_t)( - device const char * src0, - device const char * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb00, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne11, - int64_t ne12, - uint64_t nb10, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - uint3 tgpig, - uint tiisg); + ggml_metal_kargs_mul_mv args, + device const char * src0, + device const char * src1, + device char * dst, + uint3 tgpig, + ushort tiisg); typedef void (kernel_mul_mv2_impl_t)( - device const void * src0, - device const float * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne12, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiisg, - uint sgitg); + ggml_metal_kargs_mul_mv args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiisg, + ushort sgitg); template void mmv_fn( - device const char * src0, - device const char * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb00, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne11, - int64_t ne12, - int64_t ne13, - uint64_t nb10, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint64_t nb1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiitg, - uint tiisg, - uint sgitg) { - impl_fn(src0,src1,dst,ne00,ne01,ne02,nb00,nb01,nb02,nb03,ne10,ne11,ne12,nb10,nb11,nb12,nb13,ne0,ne1,r2,r3,tgpig,tiisg); + ggml_metal_kargs_mul_mv args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiitg, + ushort tiisg, + ushort sgitg) { + impl_fn(args, src0, src1, dst, tgpig, tiisg); } template void mmv_fn( - device const char * src0, - device const char * src1, - device float * dst, - int64_t ne00, - int64_t ne01, - int64_t ne02, - uint64_t nb00, - uint64_t nb01, - uint64_t nb02, - uint64_t nb03, - int64_t ne10, - int64_t ne11, - int64_t ne12, - int64_t ne13, - uint64_t nb10, - uint64_t nb11, - uint64_t nb12, - uint64_t nb13, - int64_t ne0, - int64_t ne1, - uint64_t nb1, - uint r2, - uint r3, - threadgroup int8_t * shared_values, - uint3 tgpig, - uint tiitg, - uint tiisg, - uint sgitg) { - impl_fn(src0,(const device float *)src1,dst,ne00,ne01,ne02,nb01,nb02,nb03,ne10,ne12,nb11,nb12,nb13,ne0,ne1,r2,r3,shared_values,tgpig,tiisg,sgitg); + ggml_metal_kargs_mul_mv args, + device const char * src0, + device const char * src1, + device char * dst, + threadgroup char * shmem, + uint3 tgpig, + ushort tiitg, + ushort tiisg, + ushort sgitg) { + impl_fn(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg); } -typedef decltype(mmv_fn>) mul_mv_impl_fn_t; +typedef decltype(mmv_fn>) mul_mv_impl_fn_t; template kernel void kernel_mul_mv_id( - device const char * src0s, - device const char * src1, - device float * dst, - device const char * ids, - constant int64_t & nei0, - constant int64_t & nei1, - constant uint64_t & nbi1, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant int64_t & ne10, - constant int64_t & ne11, - constant int64_t & ne12, - constant int64_t & ne13, - constant uint64_t & nb10, - constant uint64_t & nb11, - constant uint64_t & nb12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint64_t & nb1, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiitg[[thread_index_in_threadgroup]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - const int iid1 = tgpig.z/nei0; - const int idx = tgpig.z%nei0; + constant ggml_metal_kargs_mul_mv_id & args, + device const char * src0s, + device const char * src1, + device char * dst, + device const char * ids, + threadgroup char * shmem [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + ushort tiitg[[thread_index_in_threadgroup]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { + const int iid1 = tgpig.z/args.nei0; + const int idx = tgpig.z%args.nei0; tgpig.z = 0; - const int32_t i02 = ((device const int32_t *) (ids + iid1*nbi1))[idx]; + const int32_t i02 = ((device const int32_t *) (ids + iid1*args.nbi1))[idx]; - const int64_t i11 = idx % ne11; + const int64_t i11 = idx % args.ne11; const int64_t i12 = iid1; const int64_t i1 = idx; const int64_t i2 = i12; - device const char * src0_cur = src0s + i02*nb02; - device const char * src1_cur = src1 + i11*nb11 + i12*nb12; - device float * dst_cur = dst + i1*ne0 + i2*ne1*ne0; + device const char * src0_cur = src0s + i02*args.nb02; + device const char * src1_cur = src1 + i11*args.nb11 + i12*args.nb12; + + device char * dst_cur = dst + (i1*args.ne0 + i2*args.ne1*args.ne0)*sizeof(float); + + ggml_metal_kargs_mul_mv args0 = { + /*.ne00 =*/ args.ne00, + /*.ne01 =*/ args.ne01, + /*.ne02 =*/ 1, // args.ne02, + /*.nb00 =*/ args.nb00, + /*.nb01 =*/ args.nb01, + /*.nb02 =*/ args.nb02, + /*.nb03 =*/ args.nb02, // args.ne02 == 1 + /*.ne10 =*/ args.ne10, + /*.ne11 =*/ 1, // args.ne11, + /*.ne12 =*/ 1, // args.ne12, + /*.nb10 =*/ args.nb10, + /*.nb11 =*/ args.nb11, + /*.nb12 =*/ args.nb12, + /*.nb13 =*/ args.nb12, // ne12 == 1 + /*.ne0 =*/ args.ne0, + /*.ne1 =*/ 1, // args.ne1, + /*.r2 =*/ 1, + /*.r3 =*/ 1, + }; impl_fn( + args0, /* src0 */ src0_cur, /* src1 */ src1_cur, /* dst */ dst_cur, - /* ne00 */ ne00, - /* ne01 */ ne01, - /* ne02 */ 1, // ne02, - /* nb00 */ nb00, - /* nb01 */ nb01, - /* nb02 */ nb02, - /* nb03 */ nb02, // ne02 == 1 - /* ne10 */ ne10, - /* ne11 */ 1, // ne11, - /* ne12 */ 1, // ne12, - /* ne13 */ 1, // ne13, - /* nb10 */ nb10, - /* nb11 */ nb11, - /* nb12 */ nb12, - /* ne13 */ nb12, // ne12 == 1 - /* ne0 */ ne0, - /* ne1 */ 1, // ne1, - /* nb1 */ nb1, - /* r2 */ 1, - /* r3 */ 1, - shared_values, + shmem, tgpig, tiitg, tiisg, @@ -6616,6 +6609,9 @@ typedef decltype(kernel_mul_mv_id>>; template [[host_name("kernel_mul_mv_id_f16_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>>; +#if defined(GGML_METAL_USE_BF16) +template [[host_name("kernel_mul_mv_id_bf16_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>>; +#endif template [[host_name("kernel_mul_mv_id_q8_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; template [[host_name("kernel_mul_mv_id_q4_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>>; template [[host_name("kernel_mul_mv_id_q4_1_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>>; diff --git a/ggml/src/ggml-musa/CMakeLists.txt b/ggml/src/ggml-musa/CMakeLists.txt new file mode 100644 index 000000000..415b2b2e0 --- /dev/null +++ b/ggml/src/ggml-musa/CMakeLists.txt @@ -0,0 +1,107 @@ +if (NOT EXISTS $ENV{MUSA_PATH}) + if (NOT EXISTS /opt/musa) + set(MUSA_PATH /usr/local/musa) + else() + set(MUSA_PATH /opt/musa) + endif() +else() + set(MUSA_PATH $ENV{MUSA_PATH}) +endif() + +set(CMAKE_C_COMPILER "${MUSA_PATH}/bin/clang") +set(CMAKE_C_EXTENSIONS OFF) +set(CMAKE_CXX_COMPILER "${MUSA_PATH}/bin/clang++") +set(CMAKE_CXX_EXTENSIONS OFF) + +list(APPEND CMAKE_MODULE_PATH "${MUSA_PATH}/cmake") + +find_package(MUSAToolkit) + +if (MUSAToolkit_FOUND) + message(STATUS "MUSA Toolkit found") + + if (NOT DEFINED MUSA_ARCHITECTURES) + set(MUSA_ARCHITECTURES "21;22") + endif() + message(STATUS "Using MUSA architectures: ${MUSA_ARCHITECTURES}") + + file(GLOB GGML_HEADERS_MUSA "../ggml-cuda/*.cuh") + list(APPEND GGML_HEADERS_MUSA "../../include/ggml-cuda.h") + + file(GLOB GGML_SOURCES_MUSA "../ggml-cuda/*.cu") + file(GLOB SRCS "../ggml-cuda/template-instances/fattn-wmma*.cu") + list(APPEND GGML_SOURCES_MUSA ${SRCS}) + file(GLOB SRCS "../ggml-cuda/template-instances/mmq*.cu") + list(APPEND GGML_SOURCES_MUSA ${SRCS}) + + if (GGML_CUDA_FA_ALL_QUANTS) + file(GLOB SRCS "../ggml-cuda/template-instances/fattn-vec*.cu") + list(APPEND GGML_SOURCES_MUSA ${SRCS}) + add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS) + else() + file(GLOB SRCS "../ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu") + list(APPEND GGML_SOURCES_MUSA ${SRCS}) + file(GLOB SRCS "../ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu") + list(APPEND GGML_SOURCES_MUSA ${SRCS}) + file(GLOB SRCS "../ggml-cuda/template-instances/fattn-vec*f16-f16.cu") + list(APPEND GGML_SOURCES_MUSA ${SRCS}) + endif() + + set_source_files_properties(${GGML_SOURCES_MUSA} PROPERTIES LANGUAGE CXX) + foreach(SOURCE ${GGML_SOURCES_MUSA}) + set(COMPILE_FLAGS "-x musa -mtgpu") + foreach(ARCH ${MUSA_ARCHITECTURES}) + set(COMPILE_FLAGS "${COMPILE_FLAGS} --cuda-gpu-arch=mp_${ARCH}") + endforeach() + set_property(SOURCE ${SOURCE} PROPERTY COMPILE_FLAGS ${COMPILE_FLAGS}) + endforeach() + + ggml_add_backend_library(ggml-musa + ${GGML_HEADERS_MUSA} + ${GGML_SOURCES_MUSA} + ) + + # TODO: do not use CUDA definitions for MUSA + target_compile_definitions(ggml PUBLIC GGML_USE_CUDA) + + add_compile_definitions(GGML_USE_MUSA) + add_compile_definitions(GGML_CUDA_PEER_MAX_BATCH_SIZE=${GGML_CUDA_PEER_MAX_BATCH_SIZE}) + + if (GGML_CUDA_GRAPHS) + add_compile_definitions(GGML_CUDA_USE_GRAPHS) + endif() + + if (GGML_CUDA_FORCE_MMQ) + add_compile_definitions(GGML_CUDA_FORCE_MMQ) + endif() + + if (GGML_CUDA_FORCE_CUBLAS) + add_compile_definitions(GGML_CUDA_FORCE_CUBLAS) + endif() + + if (GGML_CUDA_NO_VMM) + add_compile_definitions(GGML_CUDA_NO_VMM) + endif() + + if (GGML_CUDA_F16 OR GGML_CUDA_DMMV_F16) + add_compile_definitions(GGML_CUDA_F16) + endif() + + if (GGML_CUDA_NO_PEER_COPY) + add_compile_definitions(GGML_CUDA_NO_PEER_COPY) + endif() + + if (GGML_STATIC) + target_link_libraries(ggml-musa PRIVATE MUSA::musart_static MUSA::mublas_static) + else() + target_link_libraries(ggml-musa PRIVATE MUSA::musart MUSA::mublas) + endif() + + if (GGML_CUDA_NO_VMM) + # No VMM requested, no need to link directly with the musa driver lib (libmusa.so) + else() + target_link_libraries(ggml-musa PRIVATE MUSA::musa_driver) + endif() +else() + message(FATAL_ERROR "MUSA Toolkit not found") +endif() diff --git a/ggml/src/ggml-opt.cpp b/ggml/src/ggml-opt.cpp new file mode 100644 index 000000000..7c3e24103 --- /dev/null +++ b/ggml/src/ggml-opt.cpp @@ -0,0 +1,854 @@ +#include "ggml-opt.h" + +#include "ggml.h" +#include "ggml-alloc.h" +#include "ggml-backend.h" +#include "ggml-impl.h" + +#include +#include +#include +#include +#include +#include +#include + +struct ggml_opt_dataset { + struct ggml_context * ctx = nullptr; + ggml_backend_buffer_t buf = nullptr; + struct ggml_tensor * data = nullptr; + struct ggml_tensor * labels = nullptr; + + int64_t ndata = -1; + int64_t ndata_shard = -1; + size_t nbs_data = -1; + size_t nbs_labels = -1; + + std::vector permutation; +}; + +struct ggml_opt_context { + ggml_backend_sched_t backend_sched = nullptr; + ggml_cgraph * allocated_graph = nullptr; + ggml_cgraph * allocated_graph_copy = nullptr; + struct ggml_context * ctx_static = nullptr; + struct ggml_context * ctx_static_cpu = nullptr; + struct ggml_context * ctx_compute = nullptr; + struct ggml_context * ctx_copy = nullptr; + ggml_backend_buffer_t buf_static = nullptr; + ggml_backend_buffer_t buf_static_cpu = nullptr; + std::mt19937 rng; + + struct ggml_tensor * inputs = nullptr; + struct ggml_tensor * outputs = nullptr; + struct ggml_tensor * labels = nullptr; + + struct ggml_tensor * loss = nullptr; + struct ggml_tensor * pred = nullptr; + struct ggml_tensor * ncorrect = nullptr; + + struct ggml_cgraph * gf = nullptr; + struct ggml_cgraph * gb_grad = nullptr; + struct ggml_cgraph * gb_opt = nullptr; + + int64_t iter = 1; + int32_t opt_period = 1; + int32_t opt_i = 0; + bool loss_per_datapoint = false; + + ggml_opt_get_optimizer_params get_opt_pars = nullptr; + void * get_opt_pars_ud = nullptr; + struct ggml_tensor * adamw_params = nullptr; +}; + +struct ggml_opt_result { + int64_t ndata = 0; + std::vector loss; + std::vector pred; + int64_t ncorrect = 0; + + int64_t opt_period = -1; + bool loss_per_datapoint = false; +}; + +// ====== Dataset ====== + +ggml_opt_dataset_t ggml_opt_dataset_init(int64_t ne_datapoint, int64_t ne_label, int64_t ndata, int64_t ndata_shard) { + GGML_ASSERT(ne_datapoint > 0); + GGML_ASSERT(ne_label >= 0); + GGML_ASSERT(ndata > 0); + GGML_ASSERT(ndata_shard > 0); + + ggml_opt_dataset_t result = new ggml_opt_dataset; + result->ndata = ndata; + result->ndata_shard = ndata_shard; + + { + struct ggml_init_params params = { + /*.mem_size =*/ 2*ggml_tensor_overhead(), + /*.mem_buffer =*/ nullptr, + /*.no_alloc =*/ true, + }; + result->ctx = ggml_init(params); + } + + result->data = ggml_new_tensor_2d(result->ctx, GGML_TYPE_F32, ne_datapoint, ndata); + result->nbs_data = ggml_nbytes(result->data) * ndata_shard/ndata; + + if (ne_label > 0) { + result->labels = ggml_new_tensor_2d(result->ctx, GGML_TYPE_F32, ne_label, ndata); + result->nbs_labels = ggml_nbytes(result->labels) * ndata_shard/ndata; + } else { + result->labels = nullptr; + result->nbs_labels = 0; + } + + result->buf = ggml_backend_alloc_ctx_tensors_from_buft(result->ctx, ggml_backend_cpu_buffer_type()); + + const int64_t nshards = ndata/ndata_shard; + result->permutation.resize(nshards); + for (int64_t i = 0; i < nshards; ++i) { + result->permutation[i] = i; + } + return result; +} + +void ggml_opt_dataset_free(ggml_opt_dataset_t dataset) { + ggml_backend_buffer_free(dataset->buf); + ggml_free(dataset->ctx); + delete dataset; +} + +struct ggml_tensor * ggml_opt_dataset_data(ggml_opt_dataset_t dataset) { + return dataset->data; +} + +struct ggml_tensor * ggml_opt_dataset_labels(ggml_opt_dataset_t dataset) { + return dataset->labels; +} + +void ggml_opt_dataset_shuffle(ggml_opt_context_t opt_ctx, ggml_opt_dataset_t dataset, int64_t idata) { + GGML_ASSERT(idata <= dataset->ndata); + + if (idata < 0) { + std::shuffle(dataset->permutation.begin(), dataset->permutation.end(), opt_ctx->rng); + return; + } + + GGML_ASSERT(idata % dataset->ndata_shard == 0); + const int64_t ishard_max = idata / dataset->ndata_shard; + std::shuffle(dataset->permutation.begin(), dataset->permutation.begin() + ishard_max, opt_ctx->rng); +} + +void ggml_opt_dataset_get_batch(ggml_opt_dataset_t dataset, struct ggml_tensor * data_batch, struct ggml_tensor * labels_batch, int64_t ibatch) { + GGML_ASSERT( data_batch && ggml_is_contiguous(data_batch)); + GGML_ASSERT(!labels_batch || ggml_is_contiguous(labels_batch)); + GGML_ASSERT((labels_batch == nullptr) == (dataset->labels == nullptr)); + + const size_t nb_data_batch = ggml_nbytes(data_batch); + GGML_ASSERT(nb_data_batch % dataset->nbs_data == 0); + const int64_t shards_per_batch = nb_data_batch / dataset->nbs_data; + + if (labels_batch) { + const size_t nb_labels_batch = ggml_nbytes(labels_batch); + GGML_ASSERT(nb_labels_batch == shards_per_batch*dataset->nbs_labels); + } + + GGML_ASSERT((ibatch + 1)*shards_per_batch <= int64_t(dataset->permutation.size())); + + for (int64_t ishard_batch = 0; ishard_batch < shards_per_batch; ++ishard_batch) { + const int64_t ishard = dataset->permutation[ibatch*shards_per_batch + ishard_batch]; + + const char * ptr_data = (const char *) dataset->data->data + ishard*dataset->nbs_data; + ggml_backend_tensor_set(data_batch, ptr_data, ishard_batch*dataset->nbs_data, dataset->nbs_data); + + if (!labels_batch) { + continue; + } + + const char * ptr_labels = (const char *) dataset->labels->data + ishard*dataset->nbs_labels; + ggml_backend_tensor_set(labels_batch, ptr_labels, ishard_batch*dataset->nbs_labels, dataset->nbs_labels); + } +} + +// ====== Model / Context ====== + +struct ggml_opt_optimizer_params ggml_opt_get_default_optimizer_params(void * userdata) { + GGML_UNUSED(userdata); + + ggml_opt_optimizer_params result; + + result.adamw.alpha = 0.001f; + result.adamw.beta1 = 0.9f; + result.adamw.beta2 = 0.999f; + result.adamw.eps = 1e-8f; + result.adamw.wd = 0.0f; + + return result; +} + +struct ggml_opt_params ggml_opt_default_params( + ggml_backend_sched_t backend_sched, + struct ggml_context * ctx_compute, + struct ggml_tensor * inputs, + struct ggml_tensor * outputs, + enum ggml_opt_loss_type loss_type) { + return { + /*backend_sched =*/ backend_sched, + /*ctx_compute =*/ ctx_compute, + /*inputs =*/ inputs, + /*logits =*/ outputs, + /*loss_type =*/ loss_type, + /*build_type =*/ GGML_OPT_BUILD_TYPE_OPT, + /*opt_period =*/ 1, + /*get_opt_pars =*/ ggml_opt_get_default_optimizer_params, + /*get_opt_pars_ud =*/ nullptr, + }; +} + +static ggml_tensor * map_tensor(std::map & tensor_map, ggml_context * ctx, ggml_tensor * tensor) { + if (!tensor) { + return nullptr; + } + + if (tensor_map.find(tensor) != tensor_map.end()) { + return tensor_map[tensor]; + } + + ggml_tensor * new_tensor = ggml_dup_tensor(ctx, tensor); + tensor_map[tensor] = new_tensor; + + new_tensor->op = tensor->op; + for (int i = 0; i < GGML_MAX_DIMS; i++) { + new_tensor->nb[i] = tensor->nb[i]; + } + new_tensor->flags = tensor->flags; + memcpy(new_tensor->op_params, tensor->op_params, sizeof(tensor->op_params)); + strcpy(new_tensor->name, tensor->name); + new_tensor->data = tensor->data; + new_tensor->buffer = tensor->buffer; + new_tensor->extra = tensor->extra; + new_tensor->view_offs = tensor->view_offs; + new_tensor->view_src = map_tensor(tensor_map, ctx, tensor->view_src); + for (int i = 0; i < GGML_MAX_SRC; i++) { + new_tensor->src[i] = map_tensor(tensor_map, ctx, tensor->src[i]); + } + + return new_tensor; +} + +static ggml_cgraph * dup_graph(ggml_context * ctx, ggml_cgraph * src) { + std::map tensor_map; + + ggml_cgraph * dst = ggml_new_graph_custom(ctx, src->size, /*grads =*/ true); + + for (int i = 0; i < src->n_leafs; i++) { + ggml_build_forward_expand(dst, map_tensor(tensor_map, ctx, src->leafs[i])); + } + GGML_ASSERT(dst->n_leafs == src->n_leafs); + for (int i = 0; i < src->n_nodes; i++) { + ggml_build_forward_expand(dst, map_tensor(tensor_map, ctx, src->nodes[i])); + } + GGML_ASSERT(dst->n_nodes == src->n_nodes); + for (int i = 0; i < src->n_nodes; ++i) { + const size_t igrad_src = ggml_hash_find(&src->visited_hash_set, src->nodes[i]); + const size_t igrad_dst = ggml_hash_find(&dst->visited_hash_set, dst->nodes[i]); + + GGML_ASSERT(igrad_src != GGML_HASHSET_FULL); + GGML_ASSERT(ggml_bitset_get(src->visited_hash_set.used, igrad_src)); + GGML_ASSERT(igrad_dst != GGML_HASHSET_FULL); + GGML_ASSERT(ggml_bitset_get(dst->visited_hash_set.used, igrad_dst)); + + dst->grads[igrad_dst] = src->grads[igrad_src]; + dst->grad_accs[igrad_dst] = src->grad_accs[igrad_src]; + } + + return dst; +} + +static void ggml_opt_alloc_graph(ggml_opt_context_t opt_ctx, ggml_cgraph * graph) { + GGML_ASSERT(graph); + if (opt_ctx->allocated_graph == graph) { + return; + } + + ggml_backend_sched_reset(opt_ctx->backend_sched); // clear allocation of previous graph + + { + ggml_init_params params = { + /*.mem_size =*/ ggml_tensor_overhead() * GGML_DEFAULT_GRAPH_SIZE, + /*.mem_buffer =*/ nullptr, + /*.no_alloc =*/ true, + }; + ggml_free(opt_ctx->ctx_copy); + opt_ctx->ctx_copy = ggml_init(params); + } + + opt_ctx->allocated_graph_copy = dup_graph(opt_ctx->ctx_copy, graph); + + ggml_backend_sched_alloc_graph(opt_ctx->backend_sched, opt_ctx->allocated_graph_copy); + opt_ctx->allocated_graph = graph; +} + +ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) { + ggml_opt_context_t result = new struct ggml_opt_context; + result->backend_sched = params.backend_sched; + result->ctx_compute = params.ctx_compute; + result->inputs = params.inputs; + result->outputs = params.outputs; + result->opt_period = params.opt_period; + result->get_opt_pars = params.get_opt_pars; + result->get_opt_pars_ud = params.get_opt_pars_ud; + + GGML_ASSERT(result->inputs->data && "the inputs must be allocated statically"); + GGML_ASSERT(result->opt_period >= 1); + + const bool accumulate = params.build_type == GGML_OPT_BUILD_TYPE_GRAD || + (params.build_type == GGML_OPT_BUILD_TYPE_OPT && result->opt_period > 1); + + ggml_set_input(result->inputs); + ggml_set_output(result->outputs); + + result->gf = ggml_new_graph_custom(result->ctx_compute, GGML_DEFAULT_GRAPH_SIZE, /*grads =*/ true); // Forward pass. + ggml_build_forward_expand(result->gf, result->outputs); + + int n_param = 0; + for (int i = 0; i < result->gf->n_nodes; ++i) { + if (result->gf->nodes[i]->flags & GGML_TENSOR_FLAG_PARAM) { + n_param++; + } + } + + { + // The static context is used for: + // - gradients (1 tensor per param if using gradient accumulation) + // - optimizer momenta (2 tensors per param) + // - labels + // - loss + its gradient (up to 5 tensors) + // - pred + // - ncorrect (2 tensors). + const size_t tensors_per_param = (accumulate ? 1 : 0) + (params.build_type == GGML_OPT_BUILD_TYPE_OPT ? 2 : 0); + const size_t size_meta = (tensors_per_param*n_param + 9) * ggml_tensor_overhead(); + struct ggml_init_params params = { + /*.mem_size =*/ size_meta, + /*.mem_buffer =*/ nullptr, + /*.no_alloc =*/ true, + }; + result->ctx_static = ggml_init(params); + } + { + // The static cpu context is used for: + // - optimizer parameters (1 for the entire context) + const size_t size_meta = 1 * ggml_tensor_overhead(); + struct ggml_init_params params = { + /*.mem_size =*/ size_meta, + /*.mem_buffer =*/ nullptr, + /*.no_alloc =*/ true, + }; + result->ctx_static_cpu = ggml_init(params); + } + + + switch (params.loss_type) { + case GGML_OPT_LOSS_TYPE_MEAN: { + result->loss = ggml_sum(result->ctx_static, result->outputs); + ggml_set_name(result->loss, "loss_sum"); + const float scale = 1.0f / (result->opt_period * ggml_nelements(result->outputs)); + result->loss = ggml_scale(result->ctx_static, result->loss, scale); + ggml_set_name(result->loss, "loss_mean"); + result->loss_per_datapoint = true; + break; + } + case GGML_OPT_LOSS_TYPE_SUM: { + result->loss = ggml_sum(result->ctx_static, result->outputs); + ggml_set_name(result->loss, "loss_sum"); + result->loss_per_datapoint = false; + break; + } + case GGML_OPT_LOSS_TYPE_CROSS_ENTROPY: { + result->labels = ggml_dup_tensor(result->ctx_static, result->outputs); + ggml_set_input(result->labels); + ggml_set_name(result->labels, "labels"); + result->loss = ggml_cross_entropy_loss(result->ctx_static, result->outputs, result->labels); + ggml_set_name(result->loss, "loss_cross_entropy"); + if (result->opt_period > 1) { + result->loss = ggml_scale(result->ctx_static, result->loss, 1.0f / result->opt_period); + ggml_set_name(result->loss, "loss_cross_entropy_scaled"); + } + result->loss_per_datapoint = true; + break; + } + case GGML_OPT_LOSS_TYPE_MEAN_SQUARED_ERROR: { + result->labels = ggml_dup_tensor(result->ctx_static, result->outputs); + ggml_set_input(result->labels); + ggml_set_name(result->labels, "labels"); + result->loss = ggml_sub(result->ctx_static, result->outputs, result->labels); + ggml_set_name(result->loss, "loss_error"); + result->loss = ggml_sqr(result->ctx_static, result->loss); + ggml_set_name(result->loss, "loss_squared_error"); + result->loss = ggml_sum(result->ctx_static, result->loss); + ggml_set_name(result->loss, "loss_sum_squared_error"); + const float scale = 1.0f / (result->opt_period * ggml_nelements(result->outputs)); + result->loss = ggml_scale(result->ctx_static, result->loss, scale); + ggml_set_name(result->loss, "loss_mean_squared_error"); + result->loss_per_datapoint = true; + break; + } + } + ggml_set_output(result->loss); + ggml_set_loss(result->loss); + ggml_build_forward_expand(result->gf, result->loss); + + result->pred = ggml_argmax(result->ctx_static, result->outputs); + ggml_set_name(result->pred, "pred"); + ggml_set_output(result->pred); + ggml_build_forward_expand(result->gf, result->pred); + + if (result->labels) { + result->ncorrect = ggml_count_equal(result->ctx_static, result->pred, ggml_argmax(result->ctx_static, result->labels)); + ggml_set_name(result->ncorrect, "ncorrect"); + ggml_set_output(result->ncorrect); + ggml_build_forward_expand(result->gf, result->ncorrect); + } else { + result->ncorrect = nullptr; + } + + if (params.build_type == GGML_OPT_BUILD_TYPE_FORWARD) { + result->buf_static = ggml_backend_alloc_ctx_tensors(result->ctx_static, ggml_backend_sched_get_backend(result->backend_sched, 0)); + return result; + } + + // gb_grad == graph backward gradients, forward pass, then backward pass to calculate gradients. + result->gb_grad = ggml_graph_dup(result->ctx_compute, result->gf); + ggml_build_backward_expand(result->ctx_static, result->ctx_compute, result->gb_grad, accumulate); + + if (params.build_type == GGML_OPT_BUILD_TYPE_GRAD) { + result->buf_static = ggml_backend_alloc_ctx_tensors(result->ctx_static, ggml_backend_sched_get_backend(result->backend_sched, 0)); + ggml_graph_reset(result->gb_grad); + return result; + } + + GGML_ASSERT(params.build_type == GGML_OPT_BUILD_TYPE_OPT); + + // gb_opt == graph backward optimize, forward pass, then backward pass to calculate gradients, then optimizer step. + result->gb_opt = ggml_graph_dup(result->ctx_compute, result->gb_grad); + + result->adamw_params = ggml_new_tensor_1d(result->ctx_static_cpu, GGML_TYPE_F32, 7); + ggml_set_input(result->adamw_params); + ggml_set_name(result->adamw_params, "adamw_params"); + + for (int i = result->gf->n_nodes-1; i >= 0; --i) { + struct ggml_tensor * node = result->gb_opt->nodes[i]; + struct ggml_tensor * grad = ggml_graph_get_grad(result->gb_opt, node); + + if (node->flags & GGML_TENSOR_FLAG_PARAM) { + struct ggml_tensor * m = ggml_dup_tensor(result->ctx_static, node); + struct ggml_tensor * v = ggml_dup_tensor(result->ctx_static, node); + struct ggml_tensor * opt_step = ggml_opt_step_adamw(result->ctx_compute, node, grad, m, v, result->adamw_params); + ggml_build_forward_expand(result->gb_opt, opt_step); + } + } + + result->buf_static = ggml_backend_alloc_ctx_tensors( + result->ctx_static, ggml_backend_sched_get_backend(result->backend_sched, 0)); + + result->buf_static_cpu = ggml_backend_alloc_ctx_tensors_from_buft(result->ctx_static_cpu, ggml_backend_cpu_buffer_type()); + + ggml_graph_reset(result->gb_opt); + + return result; +} + +void ggml_opt_free(ggml_opt_context_t opt_ctx) { + if (opt_ctx == nullptr) { + return; + } + ggml_backend_buffer_free(opt_ctx->buf_static); + ggml_backend_buffer_free(opt_ctx->buf_static_cpu); + ggml_free(opt_ctx->ctx_static); + ggml_free(opt_ctx->ctx_static_cpu); + delete opt_ctx; +} + +void ggml_opt_reset(ggml_opt_context_t opt_ctx, bool optimizer) { + if (optimizer) { + ggml_graph_reset(opt_ctx->gb_opt); + opt_ctx->iter = 1; + } else { + ggml_graph_reset(opt_ctx->gb_grad); + } +} + +struct ggml_tensor * ggml_opt_inputs(ggml_opt_context_t opt_ctx) { + return opt_ctx->inputs; +} + +struct ggml_tensor * ggml_opt_outputs(ggml_opt_context_t opt_ctx) { + return opt_ctx->outputs; +} + +struct ggml_tensor * ggml_opt_labels(ggml_opt_context_t opt_ctx) { + return opt_ctx->labels; +} + +struct ggml_tensor * ggml_opt_loss(ggml_opt_context_t opt_ctx) { + return opt_ctx->loss; +} + +struct ggml_tensor * ggml_opt_pred(ggml_opt_context_t opt_ctx) { + return opt_ctx->pred; +} + +struct ggml_tensor * ggml_opt_ncorrect(ggml_opt_context_t opt_ctx) { + return opt_ctx->ncorrect; +} + +struct ggml_tensor * ggml_opt_grad_acc(ggml_opt_context_t opt_ctx, struct ggml_tensor * node) { + return ggml_graph_get_grad_acc(opt_ctx->gb_opt, node); +} + +// ====== Optimization Result ====== + +ggml_opt_result_t ggml_opt_result_init() { + return new ggml_opt_result; +} + +void ggml_opt_result_free(ggml_opt_result_t result) { + delete result; +} + +void ggml_opt_result_reset(ggml_opt_result_t result) { + result->ndata = 0; + result->loss.clear(); + result->pred.clear(); + result->ncorrect = 0; +} + +void ggml_opt_result_ndata(ggml_opt_result_t result, int64_t * ndata) { + *ndata = result->ndata; +} + +void ggml_opt_result_loss(ggml_opt_result_t result, double * loss, double * unc) { + const int64_t nbatches = result->loss.size(); // Number of physical batches. + + if (nbatches == 0) { + *loss = 0.0; + *unc = NAN; + return; + } + + double sum = 0.0; + double sum_squared = 0.0; + + for (const float & loss : result->loss) { + // If the loss is per datapoint it was scaled by 1.0f/opt_period for each physical batch. + const float loss_scaled = result->loss_per_datapoint ? loss*result->opt_period : loss; + sum += loss_scaled; + sum_squared += loss_scaled*loss_scaled; + } + + const double mean = sum/nbatches; + *loss = result->loss_per_datapoint ? mean : sum; + + if (!unc) { + return; + } + + if (nbatches < 2) { + *unc = NAN; + return; + } + + const double var_sum = sum_squared/nbatches - mean*mean; // variance without Bessel's correction, i.e. nbatches/(nbatches-1) + *unc = result->loss_per_datapoint ? sqrt(var_sum / (nbatches - 1)) : sqrt(var_sum * nbatches/(nbatches - 1)); +} + +void ggml_opt_result_pred(ggml_opt_result_t result, int32_t * pred) { + for (size_t i = 0; i < result->pred.size(); ++i) { + pred[i] = result->pred[i]; + } +} + +void ggml_opt_result_accuracy(ggml_opt_result_t result, double * accuracy, double * unc) { + *accuracy = result->ncorrect >= 0 ? double(result->ncorrect) / double(result->ndata) : NAN; + + if (!unc) { + return; + } + + *unc = result->ncorrect >= 0 && result->ndata >= 2 ? + sqrt((*accuracy) * (1.0 - (*accuracy)) / double(result->ndata - 1)) : NAN; +} + +// ====== Computation ====== + +static void ggml_opt_eval_graph(ggml_opt_context_t opt_ctx, ggml_cgraph * graph, ggml_opt_result * result) { + if (graph != opt_ctx->gf) { + struct ggml_opt_optimizer_params opt_pars = opt_ctx->get_opt_pars(opt_ctx->get_opt_pars_ud); + + GGML_ASSERT(opt_pars.adamw.alpha > 0.0f); + GGML_ASSERT(opt_pars.adamw.beta1 >= 0.0f); + GGML_ASSERT(opt_pars.adamw.beta1 <= 1.0f); + GGML_ASSERT(opt_pars.adamw.beta2 >= 0.0f); + GGML_ASSERT(opt_pars.adamw.beta2 <= 1.0f); + GGML_ASSERT(opt_pars.adamw.eps >= 0.0f); + GGML_ASSERT(opt_pars.adamw.wd >= 0.0f); + GGML_ASSERT(opt_pars.adamw.wd <= 1.0f); + + // beta1, beta2 after applying warmup + const float beta1h = 1.0f/(1.0f - powf(opt_pars.adamw.beta1, opt_ctx->iter)); + const float beta2h = 1.0f/(1.0f - powf(opt_pars.adamw.beta2, opt_ctx->iter)); + + float * adamw_par_data = ggml_get_data_f32(opt_ctx->adamw_params); + adamw_par_data[0] = opt_pars.adamw.alpha; + adamw_par_data[1] = opt_pars.adamw.beta1; + adamw_par_data[2] = opt_pars.adamw.beta2; + adamw_par_data[3] = opt_pars.adamw.eps; + adamw_par_data[4] = opt_pars.adamw.wd; + adamw_par_data[5] = beta1h; + adamw_par_data[6] = beta2h; + } + + ggml_opt_alloc_graph(opt_ctx, graph); + ggml_backend_sched_graph_compute(opt_ctx->backend_sched, opt_ctx->allocated_graph_copy); + opt_ctx->iter += opt_ctx->allocated_graph == opt_ctx->gb_opt; + + if (!result) { + return; + } + + if (result->ndata == 0) { + result->loss_per_datapoint = opt_ctx->loss_per_datapoint; + result->opt_period = opt_ctx->opt_period; + } else { + GGML_ASSERT(result->loss_per_datapoint == opt_ctx->loss_per_datapoint); + GGML_ASSERT(result->opt_period == opt_ctx->opt_period); + } + + const int64_t ndata = opt_ctx->outputs->ne[1]; + GGML_ASSERT(result->ndata == ndata*int64_t(result->loss.size()) && "varying batch size not supported"); + result->ndata += ndata; + + GGML_ASSERT(ggml_is_scalar(opt_ctx->loss)); + GGML_ASSERT(opt_ctx->loss->type == GGML_TYPE_F32); + float loss; + ggml_backend_tensor_get(opt_ctx->loss, &loss, 0, ggml_nbytes(opt_ctx->loss)); + result->loss.push_back(loss); + + GGML_ASSERT(opt_ctx->pred->type == GGML_TYPE_I32); + std::vector pred(ndata); + ggml_backend_tensor_get(opt_ctx->pred, pred.data(), 0, ggml_nbytes(opt_ctx->pred)); + result->pred.insert(result->pred.end(), pred.begin(), pred.end()); + + if (!opt_ctx->labels || result->ncorrect < 0) { + result->ncorrect = -1; + return; + } + + GGML_ASSERT(ggml_is_scalar(opt_ctx->ncorrect)); + GGML_ASSERT(opt_ctx->ncorrect->type == GGML_TYPE_I64); + int64_t ncorrect; + ggml_backend_tensor_get(opt_ctx->ncorrect, &ncorrect, 0, ggml_nbytes(opt_ctx->ncorrect)); + result->ncorrect += ncorrect; +} + +void ggml_opt_forward(ggml_opt_context_t opt_ctx, ggml_opt_result * result) { + ggml_opt_eval_graph(opt_ctx, opt_ctx->gf, result); +} + +void ggml_opt_forward_backward(ggml_opt_context_t opt_ctx, ggml_opt_result * result) { + if (opt_ctx->opt_period == 1) { + ggml_opt_eval_graph(opt_ctx, opt_ctx->gb_opt, result); + return; + } + + const int32_t opt_i_next = (opt_ctx->opt_i + 1) % opt_ctx->opt_period; + if (opt_i_next == 0) { + ggml_opt_eval_graph(opt_ctx, opt_ctx->gb_opt, result); + ggml_opt_reset(opt_ctx, /*optimizer =*/ false); + } else { + ggml_opt_eval_graph(opt_ctx, opt_ctx->gb_grad, result); + } + opt_ctx->opt_i = opt_i_next; +} + +// ====== High-Level Functions ====== + +void ggml_opt_epoch( + ggml_opt_context_t opt_ctx, + ggml_opt_dataset_t dataset, + ggml_opt_result_t result_train, + ggml_opt_result_t result_eval, + int64_t idata_split, + ggml_opt_epoch_callback callback_train, + ggml_opt_epoch_callback callback_eval) { + struct ggml_tensor * inputs = ggml_opt_inputs(opt_ctx); + struct ggml_tensor * labels = ggml_opt_labels(opt_ctx); + struct ggml_tensor * data = ggml_opt_dataset_data(dataset); + GGML_ASSERT(data->ne[0] == inputs->ne[0]); + + const int64_t ndata = data->ne[1]; + const int64_t ndata_batch = inputs->ne[1]; + + GGML_ASSERT(data->ne[1] % inputs->ne[1] == 0); + const int64_t nbatches = ndata/ndata_batch; + + idata_split = idata_split < 0 ? ndata : idata_split; + GGML_ASSERT(idata_split % ndata_batch == 0); + const int64_t ibatch_split = idata_split / ndata_batch; + + int64_t ibatch = 0; + int64_t t_loop_start = ggml_time_us(); + for (; ibatch < ibatch_split; ++ibatch) { + ggml_opt_dataset_get_batch(dataset, inputs, labels, ibatch); + ggml_opt_forward_backward(opt_ctx, result_train); + if (callback_train) { + callback_train(true, opt_ctx, dataset, result_train, ibatch+1, ibatch_split, t_loop_start); + } + } + t_loop_start = ggml_time_us(); + for (; ibatch < nbatches; ++ibatch) { + ggml_opt_dataset_get_batch(dataset, inputs, labels, ibatch); + ggml_opt_forward(opt_ctx, result_eval); + if (callback_eval) { + callback_eval(false, opt_ctx, dataset, result_eval, ibatch+1-ibatch_split, nbatches-ibatch_split, t_loop_start); + } + } +} + +void ggml_opt_epoch_callback_progress_bar( + bool train, + ggml_opt_context_t opt_ctx, + ggml_opt_dataset_t dataset, + ggml_opt_result_t result, + int64_t ibatch, + int64_t ibatch_max, + int64_t t_start_us) { + fprintf(stderr, "%s[", train ? "train: " : "val: "); + + constexpr int64_t bar_length = 25; + for (int64_t j = 0; j < bar_length; ++j) { + const int64_t ibatch_j = ibatch_max * j/bar_length; + if (ibatch_j < ibatch) { + fprintf(stderr, "="); + } else if (ibatch_max * (j - 1)/bar_length < ibatch) { + fprintf(stderr, ">"); + } else { + fprintf(stderr, " "); + } + } + + const int64_t batch_size = ggml_opt_inputs(opt_ctx)->ne[1]; + const int64_t idata = ibatch*batch_size; + const int64_t idata_max = ibatch_max*batch_size; + + double loss; + double loss_unc; + ggml_opt_result_loss(result, &loss, &loss_unc); + + double accuracy; + double accuracy_unc; + ggml_opt_result_accuracy(result, &accuracy, &accuracy_unc); + + const int64_t t_ibatch_us = ggml_time_us() - t_start_us; + int64_t t_ibatch_s = t_ibatch_us / 1000000; + const int64_t t_ibatch_h = t_ibatch_s / 3600; + t_ibatch_s -= t_ibatch_h * 3600; + const int64_t t_ibatch_m = t_ibatch_s / 60; + t_ibatch_s -= t_ibatch_m * 60; + + const int64_t t_eta_us = t_ibatch_us * (ibatch_max - ibatch)/ibatch; + int64_t t_eta_s = t_eta_us / 1000000; + const int64_t t_eta_h = t_eta_s / 3600; + t_eta_s -= t_eta_h * 3600; + const int64_t t_eta_m = t_eta_s / 60; + t_eta_s -= t_eta_m * 60; + + fprintf(stderr, "| data=%06" PRId64 "/%06" PRId64 ", loss=%.6lf+-%.6lf, accuracy=%.2lf+-%.2lf%%, " + "t=%02" PRId64 ":%02" PRId64 ":%02" PRId64 ", ETA=%02" PRId64 ":%02" PRId64 ":%02" PRId64 "]\r", + idata, idata_max, loss, loss_unc, 100.0*accuracy, 100.0*accuracy_unc, + t_ibatch_h, t_ibatch_m, t_ibatch_s, t_eta_h, t_eta_m, t_eta_s); + if (ibatch == ibatch_max) { + fprintf(stderr, "\n"); + } + fflush(stderr); + + GGML_UNUSED(dataset); +} + +void ggml_opt_fit( + ggml_backend_sched_t backend_sched, + ggml_context * ctx_compute, + ggml_tensor * inputs, + ggml_tensor * outputs, + ggml_opt_dataset_t dataset, + enum ggml_opt_loss_type loss_type, + ggml_opt_get_optimizer_params get_opt_pars, + int64_t nepoch, + int64_t nbatch_logical, + float val_split, + bool silent) { + ggml_time_init(); + const int64_t t_start_us = ggml_time_us(); + + const int64_t ndata = ggml_opt_dataset_data(dataset)->ne[1]; + const int64_t nbatch_physical = inputs->ne[1]; + GGML_ASSERT(ndata % nbatch_logical == 0); + GGML_ASSERT(nbatch_logical % nbatch_physical == 0); + + const int64_t opt_period = nbatch_logical / nbatch_physical; + const int64_t nbatches_logical = ndata / nbatch_logical; + + GGML_ASSERT(val_split >= 0.0f); + GGML_ASSERT(val_split < 1.0f); + const int64_t ibatch_split = int64_t(((1.0f - val_split) * nbatches_logical)) * opt_period; // train <-> val split index (physical) + const int64_t idata_split = ibatch_split * nbatch_physical; + + int64_t epoch = 1; + + ggml_opt_params params = ggml_opt_default_params(backend_sched, ctx_compute, inputs, outputs, loss_type); + params.opt_period = opt_period; + params.get_opt_pars = get_opt_pars; + params.get_opt_pars_ud = &epoch; + ggml_opt_context_t opt_ctx = ggml_opt_init(params); + + // Shuffling the data is generally useful but there is only a point if not all data is used in a single batch. + if (nbatch_logical < ndata) { + ggml_opt_dataset_shuffle(opt_ctx, dataset, -1); // Shuffle all data (train + validation). + } + + ggml_opt_result_t result_train = ggml_opt_result_init(); + ggml_opt_result_t result_val = ggml_opt_result_init(); + + ggml_opt_epoch_callback epoch_callback = silent ? nullptr : ggml_opt_epoch_callback_progress_bar; + + for (; epoch <= nepoch; ++epoch) { + if (nbatch_logical < idata_split) { + ggml_opt_dataset_shuffle(opt_ctx, dataset, idata_split); + } + + ggml_opt_result_reset(result_train); + ggml_opt_result_reset(result_val); + + if (!silent) { + fprintf(stderr, "%s: epoch %04" PRId64 "/%04" PRId64 ":\n", __func__, epoch, nepoch); + } + ggml_opt_epoch(opt_ctx, dataset, result_train, result_val, idata_split, epoch_callback, epoch_callback); + if (!silent) { + fprintf(stderr, "\n"); + } + } + + if (!silent) { + int64_t t_total_s = (ggml_time_us() - t_start_us) / 1000000; + const int64_t t_total_h = t_total_s / 3600; + t_total_s -= t_total_h * 3600; + const int64_t t_total_m = t_total_s / 60; + t_total_s -= t_total_m * 60; + fprintf(stderr, "%s: training took %02" PRId64 ":%02" PRId64 ":%02" PRId64 "\n", __func__, t_total_h, t_total_m, t_total_s); + } + + ggml_opt_free(opt_ctx); + ggml_opt_result_free(result_train); + ggml_opt_result_free(result_val); +} diff --git a/ggml/src/ggml-quants.c b/ggml/src/ggml-quants.c index 7aa6dce89..7301a9c6c 100644 --- a/ggml/src/ggml-quants.c +++ b/ggml/src/ggml-quants.c @@ -3,8 +3,8 @@ #include "ggml-quants.h" #include "ggml-impl.h" -#include "ggml-cpu-impl.h" - +#include "ggml-cpu/ggml-cpu-impl.h" +#include "ggml-cpu.h" #include #include @@ -27,643 +27,6 @@ #define UNUSED GGML_UNUSED -// some compilers don't provide _mm256_set_m128i, e.g. gcc 7 -#define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1) - -#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) -// multiply int8_t, add results pairwise twice -static inline __m128i mul_sum_i8_pairs(const __m128i x, const __m128i y) { - // Get absolute values of x vectors - const __m128i ax = _mm_sign_epi8(x, x); - // Sign the values of the y vectors - const __m128i sy = _mm_sign_epi8(y, x); - // Perform multiplication and create 16-bit values - const __m128i dot = _mm_maddubs_epi16(ax, sy); - const __m128i ones = _mm_set1_epi16(1); - return _mm_madd_epi16(ones, dot); -} - -#if __AVX__ || __AVX2__ || __AVX512F__ -// horizontally add 8 floats -static inline float hsum_float_8(const __m256 x) { - __m128 res = _mm256_extractf128_ps(x, 1); - res = _mm_add_ps(res, _mm256_castps256_ps128(x)); - res = _mm_add_ps(res, _mm_movehl_ps(res, res)); - res = _mm_add_ss(res, _mm_movehdup_ps(res)); - return _mm_cvtss_f32(res); -} - -// horizontally add 8 int32_t -static inline int hsum_i32_8(const __m256i a) { - const __m128i sum128 = _mm_add_epi32(_mm256_castsi256_si128(a), _mm256_extractf128_si256(a, 1)); - const __m128i hi64 = _mm_unpackhi_epi64(sum128, sum128); - const __m128i sum64 = _mm_add_epi32(hi64, sum128); - const __m128i hi32 = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1)); - return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32)); -} - -// horizontally add 4 int32_t -static inline int hsum_i32_4(const __m128i a) { - const __m128i hi64 = _mm_unpackhi_epi64(a, a); - const __m128i sum64 = _mm_add_epi32(hi64, a); - const __m128i hi32 = _mm_shuffle_epi32(sum64, _MM_SHUFFLE(2, 3, 0, 1)); - return _mm_cvtsi128_si32(_mm_add_epi32(sum64, hi32)); -} - -#if defined(__AVX2__) || defined(__AVX512F__) -// spread 32 bits to 32 bytes { 0x00, 0xFF } -static inline __m256i bytes_from_bits_32(const uint8_t * x) { - uint32_t x32; - memcpy(&x32, x, sizeof(uint32_t)); - const __m256i shuf_mask = _mm256_set_epi64x( - 0x0303030303030303, 0x0202020202020202, - 0x0101010101010101, 0x0000000000000000); - __m256i bytes = _mm256_shuffle_epi8(_mm256_set1_epi32(x32), shuf_mask); - const __m256i bit_mask = _mm256_set1_epi64x(0x7fbfdfeff7fbfdfe); - bytes = _mm256_or_si256(bytes, bit_mask); - return _mm256_cmpeq_epi8(bytes, _mm256_set1_epi64x(-1)); -} - -// Unpack 32 4-bit fields into 32 bytes -// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval -static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) -{ - const __m128i tmp = _mm_loadu_si128((const __m128i *)rsi); - const __m256i bytes = MM256_SET_M128I(_mm_srli_epi16(tmp, 4), tmp); - const __m256i lowMask = _mm256_set1_epi8( 0xF ); - return _mm256_and_si256(lowMask, bytes); -} - -// add int16_t pairwise and return as float vector -static inline __m256 sum_i16_pairs_float(const __m256i x) { - const __m256i ones = _mm256_set1_epi16(1); - const __m256i summed_pairs = _mm256_madd_epi16(ones, x); - return _mm256_cvtepi32_ps(summed_pairs); -} - -static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) { -#if defined(__AVXVNNI__) || (defined(__AVX512VNNI__) && defined(__AVX512VL__)) - const __m256i zero = _mm256_setzero_si256(); - const __m256i summed_pairs = _mm256_dpbusd_epi32(zero, ax, sy); - return _mm256_cvtepi32_ps(summed_pairs); -#else - // Perform multiplication and create 16-bit values - const __m256i dot = _mm256_maddubs_epi16(ax, sy); - return sum_i16_pairs_float(dot); -#endif -} - -// multiply int8_t, add results pairwise twice and return as float vector -static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) { -#if __AVXVNNIINT8__ - const __m256i zero = _mm256_setzero_si256(); - const __m256i summed_pairs = _mm256_dpbssd_epi32(zero, x, y); - return _mm256_cvtepi32_ps(summed_pairs); -#else - // Get absolute values of x vectors - const __m256i ax = _mm256_sign_epi8(x, x); - // Sign the values of the y vectors - const __m256i sy = _mm256_sign_epi8(y, x); - return mul_sum_us8_pairs_float(ax, sy); -#endif -} - -static inline __m128i packNibbles( __m256i bytes ) -{ - // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh -#if __AVX512F__ - const __m256i bytes_srli_4 = _mm256_srli_epi16(bytes, 4); // 0000_0000_abcd_0000 - bytes = _mm256_or_si256(bytes, bytes_srli_4); // 0000_abcd_abcd_efgh - return _mm256_cvtepi16_epi8(bytes); // abcd_efgh -#else - const __m256i lowByte = _mm256_set1_epi16( 0xFF ); - __m256i high = _mm256_andnot_si256( lowByte, bytes ); - __m256i low = _mm256_and_si256( lowByte, bytes ); - high = _mm256_srli_epi16( high, 4 ); - bytes = _mm256_or_si256( low, high ); - - // Compress uint16_t lanes into bytes - __m128i r0 = _mm256_castsi256_si128( bytes ); - __m128i r1 = _mm256_extracti128_si256( bytes, 1 ); - return _mm_packus_epi16( r0, r1 ); -#endif -} -#elif defined(__AVX__) -// spread 32 bits to 32 bytes { 0x00, 0xFF } -static inline __m256i bytes_from_bits_32(const uint8_t * x) { - uint32_t x32; - memcpy(&x32, x, sizeof(uint32_t)); - const __m128i shuf_maskl = _mm_set_epi64x(0x0101010101010101, 0x0000000000000000); - const __m128i shuf_maskh = _mm_set_epi64x(0x0303030303030303, 0x0202020202020202); - __m128i bytesl = _mm_shuffle_epi8(_mm_set1_epi32(x32), shuf_maskl); - __m128i bytesh = _mm_shuffle_epi8(_mm_set1_epi32(x32), shuf_maskh); - const __m128i bit_mask = _mm_set1_epi64x(0x7fbfdfeff7fbfdfe); - bytesl = _mm_or_si128(bytesl, bit_mask); - bytesh = _mm_or_si128(bytesh, bit_mask); - bytesl = _mm_cmpeq_epi8(bytesl, _mm_set1_epi64x(-1)); - bytesh = _mm_cmpeq_epi8(bytesh, _mm_set1_epi64x(-1)); - return MM256_SET_M128I(bytesh, bytesl); -} - -// Unpack 32 4-bit fields into 32 bytes -// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval -static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) -{ - // Load 16 bytes from memory - __m128i tmpl = _mm_loadu_si128((const __m128i *)rsi); - __m128i tmph = _mm_srli_epi16(tmpl, 4); - const __m128i lowMask = _mm_set1_epi8(0xF); - tmpl = _mm_and_si128(lowMask, tmpl); - tmph = _mm_and_si128(lowMask, tmph); - return MM256_SET_M128I(tmph, tmpl); -} - -// add int16_t pairwise and return as float vector -static inline __m256 sum_i16_pairs_float(const __m128i xh, const __m128i xl) { - const __m128i ones = _mm_set1_epi16(1); - const __m128i summed_pairsl = _mm_madd_epi16(ones, xl); - const __m128i summed_pairsh = _mm_madd_epi16(ones, xh); - const __m256i summed_pairs = MM256_SET_M128I(summed_pairsh, summed_pairsl); - return _mm256_cvtepi32_ps(summed_pairs); -} - -static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) { - const __m128i axl = _mm256_castsi256_si128(ax); - const __m128i axh = _mm256_extractf128_si256(ax, 1); - const __m128i syl = _mm256_castsi256_si128(sy); - const __m128i syh = _mm256_extractf128_si256(sy, 1); - // Perform multiplication and create 16-bit values - const __m128i dotl = _mm_maddubs_epi16(axl, syl); - const __m128i doth = _mm_maddubs_epi16(axh, syh); - return sum_i16_pairs_float(doth, dotl); -} - -// multiply int8_t, add results pairwise twice and return as float vector -static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) { - const __m128i xl = _mm256_castsi256_si128(x); - const __m128i xh = _mm256_extractf128_si256(x, 1); - const __m128i yl = _mm256_castsi256_si128(y); - const __m128i yh = _mm256_extractf128_si256(y, 1); - // Get absolute values of x vectors - const __m128i axl = _mm_sign_epi8(xl, xl); - const __m128i axh = _mm_sign_epi8(xh, xh); - // Sign the values of the y vectors - const __m128i syl = _mm_sign_epi8(yl, xl); - const __m128i syh = _mm_sign_epi8(yh, xh); - // Perform multiplication and create 16-bit values - const __m128i dotl = _mm_maddubs_epi16(axl, syl); - const __m128i doth = _mm_maddubs_epi16(axh, syh); - return sum_i16_pairs_float(doth, dotl); -} - -static inline __m128i packNibbles( __m128i bytes1, __m128i bytes2 ) -{ - // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh - const __m128i lowByte = _mm_set1_epi16( 0xFF ); - __m128i high = _mm_andnot_si128( lowByte, bytes1 ); - __m128i low = _mm_and_si128( lowByte, bytes1 ); - high = _mm_srli_epi16( high, 4 ); - bytes1 = _mm_or_si128( low, high ); - high = _mm_andnot_si128( lowByte, bytes2 ); - low = _mm_and_si128( lowByte, bytes2 ); - high = _mm_srli_epi16( high, 4 ); - bytes2 = _mm_or_si128( low, high ); - - return _mm_packus_epi16( bytes1, bytes2); -} - -static inline __m128i mul_add_epi8_sse(const __m128i x, const __m128i y) { - const __m128i ax = _mm_sign_epi8(x, x); - const __m128i sy = _mm_sign_epi8(y, x); - return _mm_maddubs_epi16(ax, sy); -} -#endif -#elif defined(__SSSE3__) -// horizontally add 4x4 floats -static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128 c, const __m128 d) { - __m128 res_0 =_mm_hadd_ps(a, b); - __m128 res_1 =_mm_hadd_ps(c, d); - __m128 res =_mm_hadd_ps(res_0, res_1); - res =_mm_hadd_ps(res, res); - res =_mm_hadd_ps(res, res); - - return _mm_cvtss_f32(res); -} -#endif // __AVX__ || __AVX2__ || __AVX512F__ -#endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) - -#if defined(__ARM_NEON) || defined(__wasm_simd128__) || defined(__POWER9_VECTOR__) -#define B1(c,s,n) 0x ## n ## c , 0x ## n ## s -#define B2(c,s,n) B1(c,s,n ## c), B1(c,s,n ## s) -#define B3(c,s,n) B2(c,s,n ## c), B2(c,s,n ## s) -#define B4(c,s,n) B3(c,s,n ## c), B3(c,s,n ## s) -#define B5(c,s,n) B4(c,s,n ## c), B4(c,s,n ## s) -#define B6(c,s,n) B5(c,s,n ## c), B5(c,s,n ## s) -#define B7(c,s,n) B6(c,s,n ## c), B6(c,s,n ## s) -#define B8(c,s ) B7(c,s, c), B7(c,s, s) - -// precomputed tables for expanding 8bits to 8 bytes: -static const uint64_t table_b2b_0[1 << 8] = { B8(00, 10) }; // ( b) << 4 -static const uint64_t table_b2b_1[1 << 8] = { B8(10, 00) }; // (!b) << 4 -#endif - -#if defined(__loongarch_asx) - -#ifdef __clang__ -#define VREGS_PREFIX "$vr" -#define XREGS_PREFIX "$xr" -#else // GCC -#define VREGS_PREFIX "$f" -#define XREGS_PREFIX "$f" -#endif -#define __ALL_REGS "0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31" -// Convert __m128i to __m256i -static inline __m256i ____m256i(__m128i in) { - __m256i out = __lasx_xvldi(0); - __asm__ volatile ( - ".irp i," __ALL_REGS "\n\t" - " .ifc %[out], " XREGS_PREFIX"\\i \n\t" - " .irp j," __ALL_REGS "\n\t" - " .ifc %[in], " VREGS_PREFIX "\\j \n\t" - " xvpermi.q $xr\\i, $xr\\j, 0x20 \n\t" - " .endif \n\t" - " .endr \n\t" - " .endif \n\t" - ".endr \n\t" - : [out] "+f" (out) : [in] "f" (in) - ); - return out; -} -// Convert two __m128i to __m256i -static inline __m256i lasx_set_q(__m128i inhi, __m128i inlo) { - __m256i out; - __asm__ volatile ( - ".irp i," __ALL_REGS "\n\t" - " .ifc %[hi], " VREGS_PREFIX "\\i \n\t" - " .irp j," __ALL_REGS "\n\t" - " .ifc %[lo], " VREGS_PREFIX "\\j \n\t" - " xvpermi.q $xr\\i, $xr\\j, 0x20 \n\t" - " .endif \n\t" - " .endr \n\t" - " .endif \n\t" - ".endr \n\t" - ".ifnc %[out], %[hi] \n\t" - ".irp i," __ALL_REGS "\n\t" - " .ifc %[out], " XREGS_PREFIX "\\i \n\t" - " .irp j," __ALL_REGS "\n\t" - " .ifc %[hi], " VREGS_PREFIX "\\j \n\t" - " xvori.b $xr\\i, $xr\\j, 0 \n\t" - " .endif \n\t" - " .endr \n\t" - " .endif \n\t" - ".endr \n\t" - ".endif \n\t" - : [out] "=f" (out), [hi] "+f" (inhi) - : [lo] "f" (inlo) - ); - return out; -} -// Convert __m256i low part to __m128i -static inline __m128i lasx_extracti128_lo(__m256i in) { - __m128i out; - __asm__ volatile ( - ".ifnc %[out], %[in] \n\t" - ".irp i," __ALL_REGS "\n\t" - " .ifc %[out], " VREGS_PREFIX "\\i \n\t" - " .irp j," __ALL_REGS "\n\t" - " .ifc %[in], " XREGS_PREFIX "\\j \n\t" - " vori.b $vr\\i, $vr\\j, 0 \n\t" - " .endif \n\t" - " .endr \n\t" - " .endif \n\t" - ".endr \n\t" - ".endif \n\t" - : [out] "=f" (out) : [in] "f" (in) - ); - return out; -} -// Convert __m256i high part to __m128i -static inline __m128i lasx_extracti128_hi(__m256i in) { - __m128i out; - __asm__ volatile ( - ".irp i," __ALL_REGS "\n\t" - " .ifc %[out], " VREGS_PREFIX "\\i \n\t" - " .irp j," __ALL_REGS "\n\t" - " .ifc %[in], " XREGS_PREFIX "\\j \n\t" - " xvpermi.q $xr\\i, $xr\\j, 0x11 \n\t" - " .endif \n\t" - " .endr \n\t" - " .endif \n\t" - ".endr \n\t" - : [out] "=f" (out) : [in] "f" (in) - ); - return out; -} - -static __m256i lasx_set_w(int e7, int e6, int e5, int e4, int e3, int e2, int e1, int e0) { - v8i32 __ret = {e0, e1, e2, e3, e4, e5, e6, e7}; - return (__m256i)__ret; -} - -static __m128i lsx_set_w(int32_t a, int32_t b, int32_t c, int32_t d) { - v4i32 __ret = {d, c, b, a}; - return (__m128i)__ret; -} - -static __m256i lasx_set_d(int64_t a, int64_t b, int64_t c, int64_t d) { - v4i64 __ret = {d, c, b, a}; - return (__m256i)__ret; -} - -static __m256i lasx_insertf128( __m128i x, __m128i y) { - return lasx_set_q(x, y); -} - -static __m128i lsx_shuffle_b(__m128i a, __m128i b) { - __m128i mask_f, zero, tmp0, tmp2, mask; - int f = 0x8f; - mask_f = __lsx_vreplgr2vr_b(f); - zero = __lsx_vldi(0); - tmp0 = __lsx_vand_v(b, mask_f); // get mask with low 4 bit and sign bits - tmp0 = __lsx_vori_b(tmp0, 0x10); // make each mask or with 0x10 prepare for positive - mask = __lsx_vsle_b(zero, tmp0); // if mask >= 0, set mask - tmp2 = __lsx_vand_v(tmp0, mask); // maskout the in2 < ones - return __lsx_vshuf_b(a, zero, tmp2); -} - -static __m256i lasx_shuffle_b(__m256i a, __m256i b) { - __m256i mask_f, zero, tmp0, tmp2, mask; - int f = 0x8f; - mask_f = __lasx_xvreplgr2vr_b(f); - zero = __lasx_xvldi(0); - tmp0 = __lasx_xvand_v(b, mask_f); // get mask with low 4 bit and sign bits - tmp0 = __lasx_xvori_b(tmp0, 0x10); // make each mask or with 0x10 prepare for positive - mask = __lasx_xvsle_b(zero, tmp0); // if mask >= 0, set mask - tmp2 = __lasx_xvand_v(tmp0, mask); // maskout the in2 < ones - return __lasx_xvshuf_b(a, zero, tmp2); -} - -static __m256i lasx_extu8_16(__m128i a) { - __m128i zero = __lsx_vldi(0); - __m128i vlo = __lsx_vilvl_b(zero, a); - __m128i vhi = __lsx_vilvh_b(zero, a); - return lasx_set_q(vhi, vlo); -} - -static __m256i lasx_ext8_16(__m128i a) { - __m128i sign = __lsx_vslti_b(a, 0); - __m128i vlo = __lsx_vilvl_b(sign, a); - __m128i vhi = __lsx_vilvh_b(sign, a); - return lasx_set_q(vhi, vlo); -} - -static __m256i lasx_ext16_32(__m128i a) { - __m256i tmp1; - tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 0), 0); - tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 1), 1); - tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 2), 2); - tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 3), 3); - tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 4), 4); - tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 5), 5); - tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 6), 6); - tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 7), 7); - return tmp1; -} - -static __m128i lasx_extracti128( __m256i a, int pos) { - __m128i ret; - if( pos == 0) - { - ret = lasx_extracti128_lo(a); - } else { - ret = lasx_extracti128_hi(a); - } - return ret; -} - -static __m128 lasx_extractf128( __m256 a, int pos) { - __m128 ret; - if( pos == 0) - { - ret = (__m128)lasx_extracti128_lo((__m256i)a); - } else { - ret = (__m128)lasx_extracti128_hi((__m256i)a); - } - return ret; -} - -static __m128i lsx_hadd_h(__m128i a, __m128i b) { - __m128i tmp1 = __lsx_vpickev_h(b, a); - __m128i tmp2 = __lsx_vpickod_h(b, a); - return __lsx_vadd_h(tmp1, tmp2); -} - -static __m128i lsx_hadd_w(__m128i a, __m128i b) { - __m128i tmp1 = __lsx_vpickev_w(b, a); - __m128i tmp2 = __lsx_vpickod_w(b, a); - return __lsx_vadd_w(tmp1, tmp2); -} - -static __m128 lsx_hadd_s(__m128 a, __m128 b) { - __m128 tmp1 = (__m128)__lsx_vpickev_w((__m128i)b, (__m128i)a); - __m128 tmp2 = (__m128)__lsx_vpickod_w((__m128i)b, (__m128i)a); - - return __lsx_vfadd_s(tmp1, tmp2); -} - -static __m256i lasx_maddubs_h(__m256i a, __m256i b) { - __m256i tmp1, tmp2; - tmp1 = __lasx_xvmulwev_h_b(a, b); - tmp2 = __lasx_xvmulwod_h_b(a, b); - return __lasx_xvsadd_h(tmp1, tmp2); -} - -static __m256i lasx_madd_h(__m256i a, __m256i b) { - __m256i tmp1, tmp2; - tmp1 = __lasx_xvmulwev_w_h(a, b); - tmp2 = __lasx_xvmulwod_w_h(a, b); - return __lasx_xvadd_w(tmp1, tmp2); -} - -static __m256i lasx_packs_w(__m256i a, __m256i b) { - __m256i tmp, tmp1; - tmp = __lasx_xvsat_w(a, 15); - tmp1 = __lasx_xvsat_w(b, 15); - return __lasx_xvpickev_h(tmp1, tmp); -} - -static __m256i lasx_packs_h(__m256i a, __m256i b) { - __m256i tmp, tmp1; - tmp = __lasx_xvsat_h(a, 7); - tmp1 = __lasx_xvsat_h(b, 7); - return __lasx_xvpickev_b(tmp1, tmp); -} - -static __m128i lsx_packs_w(__m128i a, __m128i b) { - __m128i tmp, tmp1; - tmp = __lsx_vsat_w(a, 15); - tmp1 = __lsx_vsat_w(b, 15); - return __lsx_vpickev_h(tmp1, tmp); -} - -static __m128i lsx_packs_h(__m128i a, __m128i b) { - __m128i tmp, tmp1; - tmp = __lsx_vsat_h(a, 7); - tmp1 = __lsx_vsat_h(b, 7); - return __lsx_vpickev_b(tmp1, tmp); -} - -static __m128i lsx_packus_h(__m128i a, __m128i b) { - __m128i tmp, tmp1; - tmp = __lsx_vsat_hu(a, 7); - tmp1 = __lsx_vsat_hu(b, 7); - return __lsx_vpickev_b(tmp1, tmp); -} - - -static __m128i lsx_maddubs_h(__m128i a, __m128i b) { - __m128i tmp1, tmp2; - tmp1 = __lsx_vmulwev_h_b(a, b); - tmp2 = __lsx_vmulwod_h_b(a, b); - return __lsx_vsadd_h(tmp1, tmp2); -} - -static __m128i lsx_madd_h(__m128i a, __m128i b) { - __m128i tmp1, tmp2; - tmp1 = __lsx_vmulwev_w_h(a, b); - tmp2 = __lsx_vmulwod_w_h(a, b); - return __lsx_vadd_w(tmp1, tmp2); -} - -// multiply int8_t, add results pairwise twice -static inline __m128i mul_sum_i8_pairs(const __m128i x, const __m128i y) { - // Get absolute values of x vectors - const __m128i ax = __lsx_vsigncov_b(x, x); - // Sign the values of the y vectors - const __m128i sy = __lsx_vsigncov_b(x, y); - // Perform multiplication and create 16-bit values - const __m128i dot = lsx_maddubs_h(ax, sy); - const __m128i ones = __lsx_vreplgr2vr_h(1); - return lsx_madd_h(ones, dot); -} - -// horizontally add 8 floats -static inline float hsum_float_8(const __m256 x) { - __m128 res = lasx_extractf128(x, 1); - ft_union tmp; - res = __lsx_vfadd_s(res, lasx_extractf128(x, 0)); - res = __lsx_vfadd_s(res, (__m128)__lsx_vpickod_d((__m128i)res, (__m128i)res)); - res = __lsx_vfadd_s(res, (__m128)__lsx_vinsgr2vr_w(__lsx_vldi(0), __lsx_vpickve2gr_w(res, 1), 0)); - tmp.i = __lsx_vpickve2gr_w(res, 0); - return tmp.f; -} - -// horizontally add 8 int32_t -static inline int hsum_i32_8(const __m256i a) { - - __m256i tmp1 = __lasx_xvpermi_q(a, a, 0x11); - __m256i tmp2 = __lasx_xvpermi_q(a, a, 0x00); - - __m128i tmp1_128 = lasx_extracti128_lo(tmp1); - __m128i tmp2_128 = lasx_extracti128_lo(tmp2); - - __m128i sum128 = __lsx_vadd_w(tmp1_128, tmp2_128); - - __m128i ev = __lsx_vpickev_w(sum128, sum128); - __m128i od = __lsx_vpickod_w(sum128, sum128); - __m128i sum64 = __lsx_vadd_w(ev, od); - - int sum64_1, sum64_2; - sum64_1 = __lsx_vpickve2gr_w(sum64, 0); - sum64_2 = __lsx_vpickve2gr_w(sum64, 1); - - return sum64_1 + sum64_2; -} - -// horizontally add 4 int32_t -static inline int hsum_i32_4(const __m128i a) { - __m128i ev = __lsx_vpickev_w(a, a); - __m128i od = __lsx_vpickod_w(a, a); - __m128i sum64 = __lsx_vadd_w(ev, od); - - int sum64_1, sum64_2; - sum64_1 = __lsx_vpickve2gr_w(sum64, 0); - sum64_2 = __lsx_vpickve2gr_w(sum64, 1); - - return sum64_1 + sum64_2; -} - -// spread 32 bits to 32 bytes { 0x00, 0xFF } -static inline __m256i bytes_from_bits_32(const uint8_t * x) { - - uint32_t x32; - memcpy(&x32, x, sizeof(uint32_t)); - const __m256i shuf_mask = lasx_set_d( - 0x0303030303030303, 0x0202020202020202, - 0x0101010101010101, 0x0000000000000000); - - __m256i bytes = lasx_shuffle_b(__lasx_xvreplgr2vr_w(x32), shuf_mask); - const __m256i bit_mask = __lasx_xvreplgr2vr_d(0x7fbfdfeff7fbfdfe); - bytes = __lasx_xvor_v(bytes, bit_mask); - return __lasx_xvseq_b(bytes, __lasx_xvreplgr2vr_d(-1)); -} - -// Unpack 32 4-bit fields into 32 bytes -// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval -static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) { - const __m128i lo = __lsx_vld((const __m128i *)rsi, 0); - __m128i hi = __lsx_vsrli_h(lo, 4); - return __lasx_xvandi_b(lasx_insertf128(hi, lo), 0xf); -} - -// add int16_t pairwise and return as float vector -static inline __m256 sum_i16_pairs_float(const __m256i x) { - __m256i v = __lasx_xvpackod_h(x, x); - __m256i summed_pairs = __lasx_xvaddwev_w_h(x, v); - return __lasx_xvffint_s_w(summed_pairs); -} - -static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) { - // Perform multiplication and create 16-bit values - const __m256i dot = lasx_maddubs_h(ax, sy); - return sum_i16_pairs_float(dot); -} - -// multiply int8_t, add results pairwise twice and return as float vector -static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) { - - // Get absolute values of x vectors - const __m256i ax = __lasx_xvsigncov_b(x, x); - // Sign the values of the y vectors - const __m256i sy = __lasx_xvsigncov_b(x, y); - - return mul_sum_us8_pairs_float(ax, sy); -} - -static inline __m128i packNibbles( __m256i bytes ) { - // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh - const __m256i lowByte = __lasx_xvreplgr2vr_h(0xFF); - __m256i high = __lasx_xvandn_v(lowByte, bytes); - __m256i low = __lasx_xvand_v(lowByte, bytes); - high = __lasx_xvsrli_h(high, 4); - bytes = __lasx_xvor_v(low, high); - // Compress uint16_t lanes into bytes - __m128i *r0 = (__m128i *)&bytes; - __m256i tmp_h128 = __lasx_xvpermi_q(bytes, bytes, 0x11); - __m128i *r1 = (__m128i *)&tmp_h128; - - __m128i zero = __lsx_vldi(0); - __m128i tmp, tmp2, tmp3; - - tmp = __lsx_vmax_h(zero, *r0); - tmp2 = __lsx_vsat_hu(tmp, 7); - - tmp = __lsx_vmax_h(zero, *r1); - tmp3 = __lsx_vsat_hu(tmp, 7); - return __lsx_vpickev_b(tmp3, tmp2); -} -#endif //__loongarch_asx - // reference implementation for deterministic creation of model files void quantize_row_q4_0_ref(const float * restrict x, block_q4_0 * restrict y, int64_t k) { static const int qk = QK4_0; @@ -702,11 +65,6 @@ void quantize_row_q4_0_ref(const float * restrict x, block_q4_0 * restrict y, in } } -void quantize_row_q4_0(const float * restrict x, void * restrict y, int64_t k) { - quantize_row_q4_0_ref(x, y, k); -} - - void quantize_row_q4_1_ref(const float * restrict x, block_q4_1 * restrict y, int64_t k) { const int qk = QK4_1; @@ -744,10 +102,6 @@ void quantize_row_q4_1_ref(const float * restrict x, block_q4_1 * restrict y, in } } -void quantize_row_q4_1(const float * restrict x, void * restrict y, int64_t k) { - quantize_row_q4_1_ref(x, y, k); -} - void quantize_row_q5_0_ref(const float * restrict x, block_q5_0 * restrict y, int64_t k) { static const int qk = QK5_0; @@ -792,10 +146,6 @@ void quantize_row_q5_0_ref(const float * restrict x, block_q5_0 * restrict y, in } } -void quantize_row_q5_0(const float * restrict x, void * restrict y, int64_t k) { - quantize_row_q5_0_ref(x, y, k); -} - void quantize_row_q5_1_ref(const float * restrict x, block_q5_1 * restrict y, int64_t k) { const int qk = QK5_1; @@ -840,10 +190,6 @@ void quantize_row_q5_1_ref(const float * restrict x, block_q5_1 * restrict y, in } } -void quantize_row_q5_1(const float * restrict x, void * restrict y, int64_t k) { - quantize_row_q5_1_ref(x, y, k); -} - // reference implementation for deterministic creation of model files void quantize_row_q8_0_ref(const float * restrict x, block_q8_0 * restrict y, int64_t k) { assert(k % QK8_0 == 0); @@ -870,291 +216,6 @@ void quantize_row_q8_0_ref(const float * restrict x, block_q8_0 * restrict y, in } } -void quantize_row_q8_0(const float * restrict x, void * restrict vy, int64_t k) { - assert(QK8_0 == 32); - assert(k % QK8_0 == 0); - const int nb = k / QK8_0; - - block_q8_0 * restrict y = vy; - -#if defined(__ARM_NEON) - for (int i = 0; i < nb; i++) { - float32x4_t srcv [8]; - float32x4_t asrcv[8]; - float32x4_t amaxv[8]; - - for (int j = 0; j < 8; j++) srcv[j] = vld1q_f32(x + i*32 + 4*j); - for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[j]); - - for (int j = 0; j < 4; j++) amaxv[2*j] = vmaxq_f32(asrcv[2*j], asrcv[2*j+1]); - for (int j = 0; j < 2; j++) amaxv[4*j] = vmaxq_f32(amaxv[4*j], amaxv[4*j+2]); - for (int j = 0; j < 1; j++) amaxv[8*j] = vmaxq_f32(amaxv[8*j], amaxv[8*j+4]); - - const float amax = vmaxvq_f32(amaxv[0]); - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - - y[i].d = GGML_FP32_TO_FP16(d); - - for (int j = 0; j < 8; j++) { - const float32x4_t v = vmulq_n_f32(srcv[j], id); - const int32x4_t vi = vcvtnq_s32_f32(v); - - y[i].qs[4*j + 0] = vgetq_lane_s32(vi, 0); - y[i].qs[4*j + 1] = vgetq_lane_s32(vi, 1); - y[i].qs[4*j + 2] = vgetq_lane_s32(vi, 2); - y[i].qs[4*j + 3] = vgetq_lane_s32(vi, 3); - } - } -#elif defined(__wasm_simd128__) - for (int i = 0; i < nb; i++) { - v128_t srcv [8]; - v128_t asrcv[8]; - v128_t amaxv[8]; - - for (int j = 0; j < 8; j++) srcv[j] = wasm_v128_load(x + i*32 + 4*j); - for (int j = 0; j < 8; j++) asrcv[j] = wasm_f32x4_abs(srcv[j]); - - for (int j = 0; j < 4; j++) amaxv[2*j] = wasm_f32x4_max(asrcv[2*j], asrcv[2*j+1]); - for (int j = 0; j < 2; j++) amaxv[4*j] = wasm_f32x4_max(amaxv[4*j], amaxv[4*j+2]); - for (int j = 0; j < 1; j++) amaxv[8*j] = wasm_f32x4_max(amaxv[8*j], amaxv[8*j+4]); - - const float amax = MAX(MAX(wasm_f32x4_extract_lane(amaxv[0], 0), - wasm_f32x4_extract_lane(amaxv[0], 1)), - MAX(wasm_f32x4_extract_lane(amaxv[0], 2), - wasm_f32x4_extract_lane(amaxv[0], 3))); - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - - y[i].d = GGML_FP32_TO_FP16(d); - - for (int j = 0; j < 8; j++) { - const v128_t v = wasm_f32x4_mul(srcv[j], wasm_f32x4_splat(id)); - const v128_t vi = wasm_i32x4_trunc_sat_f32x4(v); - - y[i].qs[4*j + 0] = wasm_i32x4_extract_lane(vi, 0); - y[i].qs[4*j + 1] = wasm_i32x4_extract_lane(vi, 1); - y[i].qs[4*j + 2] = wasm_i32x4_extract_lane(vi, 2); - y[i].qs[4*j + 3] = wasm_i32x4_extract_lane(vi, 3); - } - } -#elif defined(__AVX2__) || defined(__AVX__) - for (int i = 0; i < nb; i++) { - // Load elements into 4 AVX vectors - __m256 v0 = _mm256_loadu_ps( x ); - __m256 v1 = _mm256_loadu_ps( x + 8 ); - __m256 v2 = _mm256_loadu_ps( x + 16 ); - __m256 v3 = _mm256_loadu_ps( x + 24 ); - x += 32; - - // Compute max(abs(e)) for the block - const __m256 signBit = _mm256_set1_ps( -0.0f ); - __m256 maxAbs = _mm256_andnot_ps( signBit, v0 ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) ); - - __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) ); - max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) ); - max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) ); - const float maxScalar = _mm_cvtss_f32( max4 ); - - // Quantize these floats - const float d = maxScalar / 127.f; - y[i].d = GGML_FP32_TO_FP16(d); - const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f; - const __m256 mul = _mm256_set1_ps( id ); - - // Apply the multiplier - v0 = _mm256_mul_ps( v0, mul ); - v1 = _mm256_mul_ps( v1, mul ); - v2 = _mm256_mul_ps( v2, mul ); - v3 = _mm256_mul_ps( v3, mul ); - - // Round to nearest integer - v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST ); - v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST ); - v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST ); - v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST ); - - // Convert floats to integers - __m256i i0 = _mm256_cvtps_epi32( v0 ); - __m256i i1 = _mm256_cvtps_epi32( v1 ); - __m256i i2 = _mm256_cvtps_epi32( v2 ); - __m256i i3 = _mm256_cvtps_epi32( v3 ); - -#if defined(__AVX2__) - // Convert int32 to int16 - i0 = _mm256_packs_epi32( i0, i1 ); // 0, 1, 2, 3, 8, 9, 10, 11, 4, 5, 6, 7, 12, 13, 14, 15 - i2 = _mm256_packs_epi32( i2, i3 ); // 16, 17, 18, 19, 24, 25, 26, 27, 20, 21, 22, 23, 28, 29, 30, 31 - // Convert int16 to int8 - i0 = _mm256_packs_epi16( i0, i2 ); // 0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27, 4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31 - - // We got our precious signed bytes, but the order is now wrong - // These AVX2 pack instructions process 16-byte pieces independently - // The following instruction is fixing the order - const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 ); - i0 = _mm256_permutevar8x32_epi32( i0, perm ); - - _mm256_storeu_si256((__m256i *)y[i].qs, i0); -#else - // Since we don't have in AVX some necessary functions, - // we split the registers in half and call AVX2 analogs from SSE - __m128i ni0 = _mm256_castsi256_si128( i0 ); - __m128i ni1 = _mm256_extractf128_si256( i0, 1); - __m128i ni2 = _mm256_castsi256_si128( i1 ); - __m128i ni3 = _mm256_extractf128_si256( i1, 1); - __m128i ni4 = _mm256_castsi256_si128( i2 ); - __m128i ni5 = _mm256_extractf128_si256( i2, 1); - __m128i ni6 = _mm256_castsi256_si128( i3 ); - __m128i ni7 = _mm256_extractf128_si256( i3, 1); - - // Convert int32 to int16 - ni0 = _mm_packs_epi32( ni0, ni1 ); - ni2 = _mm_packs_epi32( ni2, ni3 ); - ni4 = _mm_packs_epi32( ni4, ni5 ); - ni6 = _mm_packs_epi32( ni6, ni7 ); - // Convert int16 to int8 - ni0 = _mm_packs_epi16( ni0, ni2 ); - ni4 = _mm_packs_epi16( ni4, ni6 ); - - _mm_storeu_si128((__m128i *)(y[i].qs + 0), ni0); - _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4); -#endif - } -#elif defined(__riscv_v_intrinsic) - - size_t vl = __riscv_vsetvl_e32m4(QK8_0); - - for (int i = 0; i < nb; i++) { - // load elements - vfloat32m4_t v_x = __riscv_vle32_v_f32m4(x+i*QK8_0, vl); - - vfloat32m4_t vfabs = __riscv_vfabs_v_f32m4(v_x, vl); - vfloat32m1_t tmp = __riscv_vfmv_v_f_f32m1(0.0f, vl); - vfloat32m1_t vmax = __riscv_vfredmax_vs_f32m4_f32m1(vfabs, tmp, vl); - float amax = __riscv_vfmv_f_s_f32m1_f32(vmax); - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - - y[i].d = GGML_FP32_TO_FP16(d); - - vfloat32m4_t x0 = __riscv_vfmul_vf_f32m4(v_x, id, vl); - - // convert to integer - vint16m2_t vi = __riscv_vfncvt_x_f_w_i16m2(x0, vl); - vint8m1_t vs = __riscv_vncvt_x_x_w_i8m1(vi, vl); - - // store result - __riscv_vse8_v_i8m1(y[i].qs , vs, vl); - } - -#elif defined(__POWER9_VECTOR__) - for (int i = 0; i < nb; i++) { - vector float srcv [8]; - vector float asrcv[8]; - vector float amaxv[8]; - vector signed int vi[8]; - - for (int j = 0; j < 8; j++) srcv[j] = vec_xl(0, x + i*32 + 4*j); - for (int j = 0; j < 8; j++) asrcv[j] = vec_abs(srcv[j]); - - for (int j = 0; j < 4; j++) amaxv[2*j] = vec_max(asrcv[2*j], asrcv[2*j+1]); - for (int j = 0; j < 2; j++) amaxv[4*j] = vec_max(amaxv[4*j], amaxv[4*j+2]); - for (int j = 0; j < 1; j++) amaxv[8*j] = vec_max(amaxv[8*j], amaxv[8*j+4]); - - const float amax = MAX(MAX(vec_extract(amaxv[0], 0), - vec_extract(amaxv[0], 1)), - MAX(vec_extract(amaxv[0], 2), - vec_extract(amaxv[0], 3))); - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - const vector float vid = vec_splats(id); - - y[i].d = GGML_FP32_TO_FP16(d); - - for (int j = 0; j < 8; j++) { - const vector float v = vec_round(vec_mul(srcv[j], vid)); - vi[j] = vec_cts(v, 0); - } - vec_xst(vec_pack(vec_pack(vi[0], vi[1]), vec_pack(vi[2], vi[3])), 0, &y[i].qs[0]); - vec_xst(vec_pack(vec_pack(vi[4], vi[5]), vec_pack(vi[6], vi[7])), 16, &y[i].qs[0]); - } - -#elif defined(__loongarch_asx) - for (int i = 0; i < nb; i++) { - ft_union fi; - __m256 v0 = (__m256)__lasx_xvld( x , 0); - __m256 v1 = (__m256)__lasx_xvld( x , 32); - __m256 v2 = (__m256)__lasx_xvld( x , 64); - __m256 v3 = (__m256)__lasx_xvld( x , 96); - x += 32; - - // Compute max(abs(e)) for the block - const __m256 sign_bit = __lasx_xvreplfr2vr_s( -0.0f ); - __m256 max_abs = (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v0 ); - max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v1 ) ); - max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v2 ) ); - max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v3 ) ); - - __m128 max4 = __lsx_vfmax_s( lasx_extractf128( max_abs, 1 ), lasx_extractf128( max_abs , 0) ); - max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vpickod_d((__m128i) max4, (__m128i)max4 ) ); - __m128 tmp = max4; - max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vinsgr2vr_w(tmp, __lsx_vpickve2gr_w( max4, 1 ), 0 )); - fi.i = __lsx_vpickve2gr_w( (__m128i)max4, 0 ); - const float max_scalar = fi.f; - - // Quantize these floats - const float d = max_scalar / 127.f; - y[i].d = GGML_FP32_TO_FP16(d); - const float id = ( max_scalar != 0.0f ) ? 127.f / max_scalar : 0.0f; - const __m256 mul = (__m256)__lasx_xvreplfr2vr_s( id ); - - // Apply the multiplier - v0 = __lasx_xvfmul_s( v0, mul ); - v1 = __lasx_xvfmul_s( v1, mul ); - v2 = __lasx_xvfmul_s( v2, mul ); - v3 = __lasx_xvfmul_s( v3, mul ); - - // Round to nearest integer - __m256i i0 = __lasx_xvftintrne_w_s( v0 ); - __m256i i1 = __lasx_xvftintrne_w_s( v1 ); - __m256i i2 = __lasx_xvftintrne_w_s( v2 ); - __m256i i3 = __lasx_xvftintrne_w_s( v3 ); - - __m128i ni0 = lasx_extracti128( i0, 0 ); - __m128i ni1 = lasx_extracti128( i0, 1); - __m128i ni2 = lasx_extracti128( i1, 0); - __m128i ni3 = lasx_extracti128( i1, 1); - __m128i ni4 = lasx_extracti128( i2, 0); - __m128i ni5 = lasx_extracti128( i2, 1); - __m128i ni6 = lasx_extracti128( i3, 0); - __m128i ni7 = lasx_extracti128( i3, 1); - - // Convert int32 to int16 - ni0 = lsx_packs_w( ni0, ni1 ); - ni2 = lsx_packs_w( ni2, ni3 ); - ni4 = lsx_packs_w( ni4, ni5 ); - ni6 = lsx_packs_w( ni6, ni7 ); - // Convert int16 to int8 - ni0 = lsx_packs_h( ni0, ni2 ); - ni4 = lsx_packs_h( ni4, ni6 ); - - __lsx_vst(ni0, (__m128i *)(y[i].qs + 0), 0); - __lsx_vst(ni4, (__m128i *)(y[i].qs + 16), 0); - - } -#else - GGML_UNUSED(nb); - // scalar - quantize_row_q8_0_ref(x, y, k); -#endif -} - // reference implementation for deterministic creation of model files void quantize_row_q8_1_ref(const float * restrict x, block_q8_1 * restrict y, int64_t k) { assert(QK8_1 == 32); @@ -1191,334 +252,6 @@ void quantize_row_q8_1_ref(const float * restrict x, block_q8_1 * restrict y, in } } -void quantize_row_q8_1(const float * restrict x, void * restrict vy, int64_t k) { - assert(k % QK8_1 == 0); - const int nb = k / QK8_1; - - block_q8_1 * restrict y = vy; - -#if defined(__ARM_NEON) - for (int i = 0; i < nb; i++) { - float32x4_t srcv [8]; - float32x4_t asrcv[8]; - float32x4_t amaxv[8]; - - for (int j = 0; j < 8; j++) srcv[j] = vld1q_f32(x + i*32 + 4*j); - for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[j]); - - for (int j = 0; j < 4; j++) amaxv[2*j] = vmaxq_f32(asrcv[2*j], asrcv[2*j+1]); - for (int j = 0; j < 2; j++) amaxv[4*j] = vmaxq_f32(amaxv[4*j], amaxv[4*j+2]); - for (int j = 0; j < 1; j++) amaxv[8*j] = vmaxq_f32(amaxv[8*j], amaxv[8*j+4]); - - const float amax = vmaxvq_f32(amaxv[0]); - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - - y[i].d = GGML_FP32_TO_FP16(d); - - int32x4_t accv = vdupq_n_s32(0); - - for (int j = 0; j < 8; j++) { - const float32x4_t v = vmulq_n_f32(srcv[j], id); - const int32x4_t vi = vcvtnq_s32_f32(v); - - y[i].qs[4*j + 0] = vgetq_lane_s32(vi, 0); - y[i].qs[4*j + 1] = vgetq_lane_s32(vi, 1); - y[i].qs[4*j + 2] = vgetq_lane_s32(vi, 2); - y[i].qs[4*j + 3] = vgetq_lane_s32(vi, 3); - - accv = vaddq_s32(accv, vi); - } - - y[i].s = GGML_FP32_TO_FP16(d * vaddvq_s32(accv)); - } -#elif defined(__wasm_simd128__) - for (int i = 0; i < nb; i++) { - v128_t srcv [8]; - v128_t asrcv[8]; - v128_t amaxv[8]; - - for (int j = 0; j < 8; j++) srcv[j] = wasm_v128_load(x + i*32 + 4*j); - for (int j = 0; j < 8; j++) asrcv[j] = wasm_f32x4_abs(srcv[j]); - - for (int j = 0; j < 4; j++) amaxv[2*j] = wasm_f32x4_max(asrcv[2*j], asrcv[2*j+1]); - for (int j = 0; j < 2; j++) amaxv[4*j] = wasm_f32x4_max(amaxv[4*j], amaxv[4*j+2]); - for (int j = 0; j < 1; j++) amaxv[8*j] = wasm_f32x4_max(amaxv[8*j], amaxv[8*j+4]); - - const float amax = MAX(MAX(wasm_f32x4_extract_lane(amaxv[0], 0), - wasm_f32x4_extract_lane(amaxv[0], 1)), - MAX(wasm_f32x4_extract_lane(amaxv[0], 2), - wasm_f32x4_extract_lane(amaxv[0], 3))); - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - - y[i].d = GGML_FP32_TO_FP16(d); - - v128_t accv = wasm_i32x4_splat(0); - - for (int j = 0; j < 8; j++) { - const v128_t v = wasm_f32x4_mul(srcv[j], wasm_f32x4_splat(id)); - const v128_t vi = wasm_i32x4_trunc_sat_f32x4(v); - - y[i].qs[4*j + 0] = wasm_i32x4_extract_lane(vi, 0); - y[i].qs[4*j + 1] = wasm_i32x4_extract_lane(vi, 1); - y[i].qs[4*j + 2] = wasm_i32x4_extract_lane(vi, 2); - y[i].qs[4*j + 3] = wasm_i32x4_extract_lane(vi, 3); - - accv = wasm_i32x4_add(accv, vi); - } - - y[i].s = GGML_FP32_TO_FP16( - d * (wasm_i32x4_extract_lane(accv, 0) + - wasm_i32x4_extract_lane(accv, 1) + - wasm_i32x4_extract_lane(accv, 2) + - wasm_i32x4_extract_lane(accv, 3))); - } -#elif defined(__AVX2__) || defined(__AVX__) - for (int i = 0; i < nb; i++) { - // Load elements into 4 AVX vectors - __m256 v0 = _mm256_loadu_ps( x ); - __m256 v1 = _mm256_loadu_ps( x + 8 ); - __m256 v2 = _mm256_loadu_ps( x + 16 ); - __m256 v3 = _mm256_loadu_ps( x + 24 ); - x += 32; - - // Compute max(abs(e)) for the block - const __m256 signBit = _mm256_set1_ps( -0.0f ); - __m256 maxAbs = _mm256_andnot_ps( signBit, v0 ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v1 ) ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v2 ) ); - maxAbs = _mm256_max_ps( maxAbs, _mm256_andnot_ps( signBit, v3 ) ); - - __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) ); - max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) ); - max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) ); - const float max_scalar = _mm_cvtss_f32( max4 ); - - // Quantize these floats - const float d = max_scalar / 127.f; - y[i].d = GGML_FP32_TO_FP16(d); - const float id = ( max_scalar != 0.0f ) ? 127.f / max_scalar : 0.0f; - const __m256 mul = _mm256_set1_ps( id ); - - // Apply the multiplier - v0 = _mm256_mul_ps( v0, mul ); - v1 = _mm256_mul_ps( v1, mul ); - v2 = _mm256_mul_ps( v2, mul ); - v3 = _mm256_mul_ps( v3, mul ); - - // Round to nearest integer - v0 = _mm256_round_ps( v0, _MM_ROUND_NEAREST ); - v1 = _mm256_round_ps( v1, _MM_ROUND_NEAREST ); - v2 = _mm256_round_ps( v2, _MM_ROUND_NEAREST ); - v3 = _mm256_round_ps( v3, _MM_ROUND_NEAREST ); - - // Convert floats to integers - __m256i i0 = _mm256_cvtps_epi32( v0 ); - __m256i i1 = _mm256_cvtps_epi32( v1 ); - __m256i i2 = _mm256_cvtps_epi32( v2 ); - __m256i i3 = _mm256_cvtps_epi32( v3 ); - -#if defined(__AVX2__) - // Compute the sum of the quants and set y[i].s - y[i].s = GGML_FP32_TO_FP16(d * hsum_i32_8(_mm256_add_epi32(_mm256_add_epi32(i0, i1), _mm256_add_epi32(i2, i3)))); - - // Convert int32 to int16 - i0 = _mm256_packs_epi32( i0, i1 ); // 0, 1, 2, 3, 8, 9, 10, 11, 4, 5, 6, 7, 12, 13, 14, 15 - i2 = _mm256_packs_epi32( i2, i3 ); // 16, 17, 18, 19, 24, 25, 26, 27, 20, 21, 22, 23, 28, 29, 30, 31 - // Convert int16 to int8 - i0 = _mm256_packs_epi16( i0, i2 ); // 0, 1, 2, 3, 8, 9, 10, 11, 16, 17, 18, 19, 24, 25, 26, 27, 4, 5, 6, 7, 12, 13, 14, 15, 20, 21, 22, 23, 28, 29, 30, 31 - - // We got our precious signed bytes, but the order is now wrong - // These AVX2 pack instructions process 16-byte pieces independently - // The following instruction is fixing the order - const __m256i perm = _mm256_setr_epi32( 0, 4, 1, 5, 2, 6, 3, 7 ); - i0 = _mm256_permutevar8x32_epi32( i0, perm ); - - _mm256_storeu_si256((__m256i *)y[i].qs, i0); -#else - // Since we don't have in AVX some necessary functions, - // we split the registers in half and call AVX2 analogs from SSE - __m128i ni0 = _mm256_castsi256_si128( i0 ); - __m128i ni1 = _mm256_extractf128_si256( i0, 1); - __m128i ni2 = _mm256_castsi256_si128( i1 ); - __m128i ni3 = _mm256_extractf128_si256( i1, 1); - __m128i ni4 = _mm256_castsi256_si128( i2 ); - __m128i ni5 = _mm256_extractf128_si256( i2, 1); - __m128i ni6 = _mm256_castsi256_si128( i3 ); - __m128i ni7 = _mm256_extractf128_si256( i3, 1); - - // Compute the sum of the quants and set y[i].s - const __m128i s0 = _mm_add_epi32(_mm_add_epi32(ni0, ni1), _mm_add_epi32(ni2, ni3)); - const __m128i s1 = _mm_add_epi32(_mm_add_epi32(ni4, ni5), _mm_add_epi32(ni6, ni7)); - y[i].s = GGML_FP32_TO_FP16(d * hsum_i32_4(_mm_add_epi32(s0, s1))); - - // Convert int32 to int16 - ni0 = _mm_packs_epi32( ni0, ni1 ); - ni2 = _mm_packs_epi32( ni2, ni3 ); - ni4 = _mm_packs_epi32( ni4, ni5 ); - ni6 = _mm_packs_epi32( ni6, ni7 ); - // Convert int16 to int8 - ni0 = _mm_packs_epi16( ni0, ni2 ); - ni4 = _mm_packs_epi16( ni4, ni6 ); - - _mm_storeu_si128((__m128i *)(y[i].qs + 0), ni0); - _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4); -#endif - } -#elif defined(__riscv_v_intrinsic) - - size_t vl = __riscv_vsetvl_e32m4(QK8_1); - - for (int i = 0; i < nb; i++) { - // load elements - vfloat32m4_t v_x = __riscv_vle32_v_f32m4(x+i*QK8_1, vl); - - vfloat32m4_t vfabs = __riscv_vfabs_v_f32m4(v_x, vl); - vfloat32m1_t tmp = __riscv_vfmv_v_f_f32m1(0.0, vl); - vfloat32m1_t vmax = __riscv_vfredmax_vs_f32m4_f32m1(vfabs, tmp, vl); - float amax = __riscv_vfmv_f_s_f32m1_f32(vmax); - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - - y[i].d = GGML_FP32_TO_FP16(d); - - vfloat32m4_t x0 = __riscv_vfmul_vf_f32m4(v_x, id, vl); - - // convert to integer - vint16m2_t vi = __riscv_vfncvt_x_f_w_i16m2(x0, vl); - vint8m1_t vs = __riscv_vncvt_x_x_w_i8m1(vi, vl); - - // store result - __riscv_vse8_v_i8m1(y[i].qs , vs, vl); - - // compute sum for y[i].s - vint16m1_t tmp2 = __riscv_vmv_v_x_i16m1(0, vl); - vint16m1_t vwrs = __riscv_vwredsum_vs_i8m1_i16m1(vs, tmp2, vl); - - // set y[i].s - int sum = __riscv_vmv_x_s_i16m1_i16(vwrs); - y[i].s = GGML_FP32_TO_FP16(sum*d); - } - -#elif defined(__POWER9_VECTOR__) - for (int i = 0; i < nb; i++) { - vector float srcv [8]; - vector float asrcv[8]; - vector float amaxv[8]; - vector signed int vi[8]; - - for (int j = 0; j < 8; j++) srcv[j] = vec_xl(0, x + i*32 + 4*j); - for (int j = 0; j < 8; j++) asrcv[j] = vec_abs(srcv[j]); - - for (int j = 0; j < 4; j++) amaxv[2*j] = vec_max(asrcv[2*j], asrcv[2*j+1]); - for (int j = 0; j < 2; j++) amaxv[4*j] = vec_max(amaxv[4*j], amaxv[4*j+2]); - for (int j = 0; j < 1; j++) amaxv[8*j] = vec_max(amaxv[8*j], amaxv[8*j+4]); - - const float amax = MAX(MAX(vec_extract(amaxv[0], 0), - vec_extract(amaxv[0], 1)), - MAX(vec_extract(amaxv[0], 2), - vec_extract(amaxv[0], 3))); - - const float d = amax / ((1 << 7) - 1); - const float id = d ? 1.0f/d : 0.0f; - const vector float vid = vec_splats(id); - - y[i].d = GGML_FP32_TO_FP16(d); - - vector int accv = vec_splats(0); - - for (int j = 0; j < 8; j++) { - const vector float v = vec_round(vec_mul(srcv[j], vid)); - vi[j] = vec_cts(v, 0); - - accv = vec_add(accv, vi[j]); - } - vec_xst(vec_pack(vec_pack(vi[0], vi[1]), vec_pack(vi[2], vi[3])), 0, &y[i].qs[0]); - vec_xst(vec_pack(vec_pack(vi[4], vi[5]), vec_pack(vi[6], vi[7])), 16, &y[i].qs[0]); - - accv = vec_add(accv, vec_sld(accv, accv, 4)); - accv = vec_add(accv, vec_sld(accv, accv, 8)); - y[i].s = GGML_FP32_TO_FP16(d * vec_extract(accv, 0)); - } - -#elif defined(__loongarch_asx) - for (int i = 0; i < nb; i++) { - ft_union ft; - __m256 v0 = (__m256)__lasx_xvld( x , 0 ); - __m256 v1 = (__m256)__lasx_xvld( x , 32 ); - __m256 v2 = (__m256)__lasx_xvld( x , 64 ); - __m256 v3 = (__m256)__lasx_xvld( x , 96 ); - x += 32; - - // Compute max(abs(e)) for the block - const __m256 sign_bit = __lasx_xvreplfr2vr_s( -0.0f ); - __m256 max_abs = (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v0 ); - max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v1 ) ); - max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v2 ) ); - max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v3 ) ); - - __m128 max4 = __lsx_vfmax_s( lasx_extractf128( max_abs, 1 ), lasx_extractf128( max_abs, 0) ); - max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vpickod_d((__m128i) max4, (__m128i)max4 ) ); - __m128 tmp = max4; - max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vextrins_w((__m128i)tmp, (__m128i)max4, 0x10 )); - ft.i = __lsx_vpickve2gr_w( (__m128i)max4, 0 ); - const float max_scalar = ft.f; - - // Quantize these floats - const float d = max_scalar / 127.f; - y[i].d = GGML_FP32_TO_FP16(d); - const float id = ( max_scalar != 0.0f ) ? 127.f / max_scalar : 0.0f; - const __m256 mul = __lasx_xvreplfr2vr_s( id ); - - // Apply the multiplier - v0 = __lasx_xvfmul_s( v0, mul ); - v1 = __lasx_xvfmul_s( v1, mul ); - v2 = __lasx_xvfmul_s( v2, mul ); - v3 = __lasx_xvfmul_s( v3, mul ); - - // Round to nearest integer - __m256i i0 = __lasx_xvftintrne_w_s( v0 ); - __m256i i1 = __lasx_xvftintrne_w_s( v1 ); - __m256i i2 = __lasx_xvftintrne_w_s( v2 ); - __m256i i3 = __lasx_xvftintrne_w_s( v3 ); - - __m128i ni0 = lasx_extracti128(i0, 0); - __m128i ni1 = lasx_extracti128( i0, 1); - __m128i ni2 = lasx_extracti128( i1, 0); - __m128i ni3 = lasx_extracti128( i1, 1); - __m128i ni4 = lasx_extracti128( i2, 0 ); - __m128i ni5 = lasx_extracti128( i2, 1); - __m128i ni6 = lasx_extracti128( i3, 0); - __m128i ni7 = lasx_extracti128( i3, 1); - - // Compute the sum of the quants and set y[i].s - const __m128i s0 = __lsx_vadd_w(__lsx_vadd_w(ni0, ni1), __lsx_vadd_w(ni2, ni3)); - const __m128i s1 = __lsx_vadd_w(__lsx_vadd_w(ni4, ni5), __lsx_vadd_w(ni6, ni7)); - y[i].s = GGML_FP32_TO_FP16(d * hsum_i32_4(__lsx_vadd_w(s0, s1))); - - // Convert int32 to int16 - ni0 = lsx_packs_w( ni0, ni1 ); - ni2 = lsx_packs_w( ni2, ni3 ); - ni4 = lsx_packs_w( ni4, ni5 ); - ni6 = lsx_packs_w( ni6, ni7 ); - // Convert int16 to int8 - ni0 = lsx_packs_h( ni0, ni2 ); - ni4 = lsx_packs_h( ni4, ni6 ); - - __lsx_vst(ni0, (__m128i *)(y[i].qs + 0), 0); - __lsx_vst(ni4, (__m128i *)(y[i].qs + 16), 0); - } -#else - GGML_UNUSED(nb); - // scalar - quantize_row_q8_1_ref(x, y, k); -#endif -} - void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int64_t k) { static const int qk = QK4_0; @@ -2008,10 +741,6 @@ void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int6 } } -void quantize_row_q2_K(const float * restrict x, void * restrict vy, int64_t k) { - quantize_row_q2_K_ref(x, vy, k); -} - static float make_qkx3_quants(int n, int nmax, const float * restrict x, const float * restrict weights, uint8_t * restrict L, float * restrict the_min, uint8_t * restrict Laux, float rmin, float rdelta, int nstep, bool use_mad) { @@ -2374,10 +1103,6 @@ void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int6 } } -void quantize_row_q3_K(const float * restrict x, void * restrict vy, int64_t k) { - quantize_row_q3_K_ref(x, vy, k); -} - static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restrict y, int64_t n_per_row, const float * restrict quant_weights) { assert(n_per_row % QK_K == 0); const int nb = n_per_row / QK_K; @@ -2576,12 +1301,6 @@ void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int6 } } -void quantize_row_q4_K(const float * restrict x, void * restrict vy, int64_t k) { - assert(k % QK_K == 0); - block_q4_K * restrict y = vy; - quantize_row_q4_K_ref(x, y, k); -} - static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restrict y, int64_t n_per_row, const float * quant_weights) { assert(n_per_row % QK_K == 0); const int64_t nb = n_per_row / QK_K; @@ -2787,12 +1506,6 @@ void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int6 } } -void quantize_row_q5_K(const float * restrict x, void * restrict vy, int64_t k) { - assert(k % QK_K == 0); - block_q5_K * restrict y = vy; - quantize_row_q5_K_ref(x, y, k); -} - static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restrict y, int64_t n_per_row, const float * quant_weights) { assert(n_per_row % QK_K == 0); const int64_t nb = n_per_row / QK_K; @@ -3005,12 +1718,6 @@ void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int6 } } -void quantize_row_q6_K(const float * restrict x, void * restrict vy, int64_t k) { - assert(k % QK_K == 0); - block_q6_K * restrict y = vy; - quantize_row_q6_K_ref(x, y, k); -} - static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restrict y, int64_t n_per_row, const float * quant_weights) { assert(n_per_row % QK_K == 0); const int64_t nb = n_per_row / QK_K; @@ -3413,33 +2120,20 @@ void quantize_row_tq2_0_ref(const float * restrict x, block_tq2_0 * restrict y, } } -void quantize_row_tq1_0(const float * restrict x, void * restrict vy, int64_t k) { - assert(k % QK_K == 0); - block_tq1_0 * restrict y = vy; - quantize_row_tq1_0_ref(x, y, k); -} - -void quantize_row_tq2_0(const float * restrict x, void * restrict vy, int64_t k) { - assert(k % QK_K == 0); - block_tq2_0 * restrict y = vy; - quantize_row_tq2_0_ref(x, y, k); -} - size_t quantize_tq1_0(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { (void)quant_weights; // not used const size_t row_size = ggml_row_size(GGML_TYPE_TQ1_0, n_per_row); - quantize_row_tq1_0(src, dst, (int64_t)nrow*n_per_row); + quantize_row_tq1_0_ref(src, dst, (int64_t)nrow*n_per_row); return nrow * row_size; } size_t quantize_tq2_0(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { (void)quant_weights; // not used const size_t row_size = ggml_row_size(GGML_TYPE_TQ2_0, n_per_row); - quantize_row_tq2_0(src, dst, (int64_t)nrow*n_per_row); + quantize_row_tq2_0_ref(src, dst, (int64_t)nrow*n_per_row); return nrow * row_size; } - void dequantize_row_tq1_0(const block_tq1_0 * restrict x, float * restrict y, int64_t k) { assert(k % QK_K == 0); const int64_t nb = k / QK_K; @@ -3832,9179 +2526,6 @@ void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int6 } } -void quantize_row_q8_K(const float * restrict x, void * restrict y, int64_t k) { - quantize_row_q8_K_ref(x, y, k); -} - -//===================================== Dot products ================================= - -// -// Helper functions -// -#if __AVX__ || __AVX2__ || __AVX512F__ - -// shuffles to pick the required scales in dot products -static inline __m256i get_scale_shuffle_q3k(int i) { - static const uint8_t k_shuffle[128] = { - 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, - 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, - 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11, - 12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13, 14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15, - }; - return _mm256_loadu_si256((const __m256i*)k_shuffle + i); -} -static inline __m256i get_scale_shuffle_k4(int i) { - static const uint8_t k_shuffle[256] = { - 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, - 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, - 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, - 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, - 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, - 10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11, - 12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13, - 14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15 - }; - return _mm256_loadu_si256((const __m256i*)k_shuffle + i); -} -static inline __m128i get_scale_shuffle(int i) { - static const uint8_t k_shuffle[128] = { - 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, - 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, - 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, - 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, - 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, - 10,10,10,10,10,10,10,10, 11,11,11,11,11,11,11,11, - 12,12,12,12,12,12,12,12, 13,13,13,13,13,13,13,13, - 14,14,14,14,14,14,14,14, 15,15,15,15,15,15,15,15 - }; - return _mm_loadu_si128((const __m128i*)k_shuffle + i); -} -#elif defined(__loongarch_asx) -// shuffles to pick the required scales in dot products -static inline __m256i get_scale_shuffle_q3k(int i) { - static const uint8_t k_shuffle[128] = { - 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, - 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, - 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11, - 12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13, 14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15, - }; - return __lasx_xvld((const __m256i*)k_shuffle + i, 0); -} -static inline __m256i get_scale_shuffle_k4(int i) { - static const uint8_t k_shuffle[256] = { - 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, - 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, - 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, - 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, - 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, - 10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11, - 12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13, - 14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15 - }; - return __lasx_xvld((const __m256i*)k_shuffle + i, 0); -} -static inline __m128i get_scale_shuffle(int i) { - static const uint8_t k_shuffle[128] = { - 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, - 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, - 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, - 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, - 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, - 10,10,10,10,10,10,10,10, 11,11,11,11,11,11,11,11, - 12,12,12,12,12,12,12,12, 13,13,13,13,13,13,13,13, - 14,14,14,14,14,14,14,14, 15,15,15,15,15,15,15,15 - }; - return __lsx_vld((const __m128i*)k_shuffle + i, 0); -} -#endif - -void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - const int qk = QK8_0; - const int nb = n / qk; - - assert(n % qk == 0); -#if defined(__ARM_FEATURE_MATMUL_INT8) - assert((nrc == 2) || (nrc == 1)); -#else - assert(nrc == 1); -#endif - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q4_0 * restrict x = vx; - const block_q8_0 * restrict y = vy; - -#if defined(__ARM_FEATURE_MATMUL_INT8) - if (nrc == 2) { - const block_q4_0 * restrict vx0 = vx; - const block_q4_0 * restrict vx1 = (const block_q4_0 *) ((const uint8_t*)vx + bx); - const block_q8_0 * restrict vy0 = vy; - const block_q8_0 * restrict vy1 = (const block_q8_0 *) ((const uint8_t*)vy + by); - - float32x4_t sumv0 = vdupq_n_f32(0.0f); - - for (int i = 0; i < nb; i++) { - const block_q4_0 * restrict b_x0 = &vx0[i]; - const block_q4_0 * restrict b_x1 = &vx1[i]; - const block_q8_0 * restrict b_y0 = &vy0[i]; - const block_q8_0 * restrict b_y1 = &vy1[i]; - - const uint8x16_t m4b = vdupq_n_u8(0x0F); - const int8x16_t s8b = vdupq_n_s8(0x8); - - const uint8x16_t v0_0 = vld1q_u8(b_x0->qs); - const uint8x16_t v0_1 = vld1q_u8(b_x1->qs); - - // 4-bit -> 8-bit - const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); - const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); - const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); - const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); - - // sub 8 - const int8x16_t x0_l = vsubq_s8(v0_0l, s8b); - const int8x16_t x0_h = vsubq_s8(v0_0h, s8b); - const int8x16_t x1_l = vsubq_s8(v0_1l, s8b); - const int8x16_t x1_h = vsubq_s8(v0_1h, s8b); - - // load y - const int8x16_t y0_l = vld1q_s8(b_y0->qs); - const int8x16_t y0_h = vld1q_s8(b_y0->qs + 16); - const int8x16_t y1_l = vld1q_s8(b_y1->qs); - const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); - - float32_t _scale[4] = { GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)}; - - float32x4_t scale = vld1q_f32(_scale); - - int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); - int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); - - int8x16_t l2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); - int8x16_t l3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); - - int8x16_t r0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); - int8x16_t r1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); - - int8x16_t r2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); - int8x16_t r3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); - - sumv0 = vmlaq_f32(sumv0,(vcvtq_f32_s32(vmmlaq_s32((vmmlaq_s32((vmmlaq_s32((vmmlaq_s32(vdupq_n_s32(0), l0, r0)), - l1, r1)), l2, r2)), l3, r3))), scale); - } - float32x4_t sumv1 = vextq_f32(sumv0, sumv0, 2); - float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1); - - vst1_f32(s, vget_low_f32(sumv2)); - vst1_f32(s + bs, vget_high_f32(sumv2)); - return; - } -#endif - - int ib = 0; - float sumf = 0; - -#if defined(__ARM_FEATURE_SVE) - svfloat32_t sumv0 = svdup_n_f32(0.0f); - svfloat32_t sumv1 = svdup_n_f32(0.0f); - - const int vector_length = ggml_cpu_get_sve_cnt()*8; - - // VLA Implementation using switch case - switch (vector_length) { - case 128: - { - // predicate for activating higher lanes for 4 float32 elements - const svbool_t ph4 = svptrue_pat_b32(SV_VL4); - - for (; ib + 1 < nb; ib += 2) { - const block_q4_0 * restrict x0 = &x[ib + 0]; - const block_q4_0 * restrict x1 = &x[ib + 1]; - const block_q8_0 * restrict y0 = &y[ib + 0]; - const block_q8_0 * restrict y1 = &y[ib + 1]; - - // load x - const svuint8_t qx0r = svld1rq_u8(svptrue_b8(), x0->qs); - const svuint8_t qx1r = svld1rq_u8(svptrue_b8(), x1->qs); - - // 4-bit -> 8-bit - const svint8_t qx0l = svreinterpret_s8_u8(svand_n_u8_m(svptrue_b8(), qx0r, 0x0F)); - const svint8_t qx0h = svreinterpret_s8_u8(svlsr_n_u8_m(svptrue_b8(), qx0r, 0x04)); - const svint8_t qx1l = svreinterpret_s8_u8(svand_n_u8_m(svptrue_b8(), qx1r, 0x0F)); - const svint8_t qx1h = svreinterpret_s8_u8(svlsr_n_u8_m(svptrue_b8(), qx1r, 0x04)); - - // sub 8 - const svint8_t qx0ls = svsub_n_s8_x(svptrue_b8(), qx0h, 8); - const svint8_t qx0hs = svsub_n_s8_x(svptrue_b8(), qx0l, 8); - const svint8_t qx1ls = svsub_n_s8_x(svptrue_b8(), qx1h, 8); - const svint8_t qx1hs = svsub_n_s8_x(svptrue_b8(), qx1l, 8); - - // load y - const svint8_t qy0h = svld1_s8(svptrue_b8(), y0->qs); - const svint8_t qy0l = svld1_s8(svptrue_b8(), y0->qs + 16); - const svint8_t qy1h = svld1_s8(svptrue_b8(), y1->qs); - const svint8_t qy1l = svld1_s8(svptrue_b8(), y1->qs + 16); - - // dot product - sumv0 = svmla_n_f32_x(ph4, sumv0, svcvt_f32_s32_x(ph4, svadd_x(ph4, - svdot_s32(svdup_n_s32(0), qx0ls, qy0l), - svdot_s32(svdup_n_s32(0), qx0hs, qy0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); - sumv1 = svmla_n_f32_x(ph4, sumv1, svcvt_f32_s32_x(ph4, svadd_x(ph4, - svdot_s32(svdup_n_s32(0), qx1ls, qy1l), - svdot_s32(svdup_n_s32(0), qx1hs, qy1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); - } - - sumf = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1)); - } break; - case 256: - { - // predicate for activating higher lanes for 16 int8 elements - const svbool_t ph16 = svptrue_pat_b8(SV_VL16); - // predicate for activating lower lanes for 16 int8 elements - const svbool_t pl16 = svnot_b_z(svptrue_b8(), ph16); - - for (; ib + 1 < nb; ib += 2) { - const block_q4_0 * restrict x0 = &x[ib + 0]; - const block_q4_0 * restrict x1 = &x[ib + 1]; - const block_q8_0 * restrict y0 = &y[ib + 0]; - const block_q8_0 * restrict y1 = &y[ib + 1]; - - // load x - const svuint8_t qx0r = svld1rq_u8(svptrue_b8(), x0->qs); - const svuint8_t qx1r = svld1rq_u8(svptrue_b8(), x1->qs); - - // 4-bit -> 8-bit - const svint8_t qx0 = svreinterpret_s8_u8(svlsr_n_u8_m(pl16, svand_n_u8_m(ph16, qx0r, 0x0F), 0x04)); - const svint8_t qx1 = svreinterpret_s8_u8(svlsr_n_u8_m(pl16, svand_n_u8_m(ph16, qx1r, 0x0F), 0x04)); - - // sub 8 - const svint8_t qx0s = svsub_n_s8_x(svptrue_b8(), qx0, 8); - const svint8_t qx1s = svsub_n_s8_x(svptrue_b8(), qx1, 8); - - // load y - const svint8_t qy0 = svld1_s8(svptrue_b8(), y0->qs); - const svint8_t qy1 = svld1_s8(svptrue_b8(), y1->qs); - - // dot product - sumv0 = svmla_n_f32_x(svptrue_b32(), sumv0, svcvt_f32_s32_x(svptrue_b32(), - svdot_s32(svdup_n_s32(0), qx0s, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); - sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), - svdot_s32(svdup_n_s32(0), qx1s, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); - } - - sumf = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1)); - } break; - case 512: - { - // predicate for activating higher lanes for 32 int8 elements - const svbool_t ph32 = svptrue_pat_b8(SV_VL32); - - // predicate for activating higher lanes for 16 int8 elements - const svbool_t ph16 = svptrue_pat_b8(SV_VL16); - // predicate for activating lower lanes for 16 int8 elements from first 32 int8 activated lanes - const svbool_t pl16 = svnot_b_z(ph32, ph16); - - for (; ib + 1 < nb; ib += 2) { - const block_q4_0 * restrict x0 = &x[ib + 0]; - const block_q4_0 * restrict x1 = &x[ib + 1]; - const block_q8_0 * restrict y0 = &y[ib + 0]; - const block_q8_0 * restrict y1 = &y[ib + 1]; - - // load x - const svuint8_t qx0r = svld1rq_u8(ph32, x0->qs); - const svuint8_t qx1r = svld1rq_u8(ph32, x1->qs); - - // 4-bit -> 8-bit - const svint8_t qx0 = svreinterpret_s8_u8(svlsr_n_u8_m(pl16, svand_n_u8_m(ph16, qx0r, 0x0F), 0x04)); - const svint8_t qx1 = svreinterpret_s8_u8(svlsr_n_u8_m(pl16, svand_n_u8_m(ph16, qx1r, 0x0F), 0x04)); - - // sub 8 - const svint8_t qx0s = svsub_n_s8_x(ph32, qx0, 8); - const svint8_t qx1s = svsub_n_s8_x(ph32, qx1, 8); - - // load y - const svint8_t qy0 = svld1_s8(ph32, y0->qs); - const svint8_t qy1 = svld1_s8(ph32, y1->qs); - - // dot product - sumv0 = svmla_n_f32_x(ph32, sumv0, svcvt_f32_s32_x(ph32, - svdot_s32(svdup_n_s32(0), qx0s, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); - sumv1 = svmla_n_f32_x(ph32, sumv1, svcvt_f32_s32_x(ph32, - svdot_s32(svdup_n_s32(0), qx1s, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); - } - - sumf = svaddv_f32(ph32, svadd_f32_x(ph32, sumv0, sumv1)); - } break; - default: - assert(false && "Unsupported vector length"); - break; - } - -#elif defined(__ARM_NEON) - float32x4_t sumv0 = vdupq_n_f32(0.0f); - float32x4_t sumv1 = vdupq_n_f32(0.0f); - - for (; ib + 1 < nb; ib += 2) { - const block_q4_0 * restrict x0 = &x[ib + 0]; - const block_q4_0 * restrict x1 = &x[ib + 1]; - const block_q8_0 * restrict y0 = &y[ib + 0]; - const block_q8_0 * restrict y1 = &y[ib + 1]; - - const uint8x16_t m4b = vdupq_n_u8(0x0F); - const int8x16_t s8b = vdupq_n_s8(0x8); - - const uint8x16_t v0_0 = vld1q_u8(x0->qs); - const uint8x16_t v0_1 = vld1q_u8(x1->qs); - - // 4-bit -> 8-bit - const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); - const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); - const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); - const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); - - // sub 8 - const int8x16_t v0_0ls = vsubq_s8(v0_0l, s8b); - const int8x16_t v0_0hs = vsubq_s8(v0_0h, s8b); - const int8x16_t v0_1ls = vsubq_s8(v0_1l, s8b); - const int8x16_t v0_1hs = vsubq_s8(v0_1h, s8b); - - // load y - const int8x16_t v1_0l = vld1q_s8(y0->qs); - const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); - const int8x16_t v1_1l = vld1q_s8(y1->qs); - const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); - - // dot product into int32x4_t - const int32x4_t p_0 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_0ls, v1_0l), v0_0hs, v1_0h); - const int32x4_t p_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_1ls, v1_1l), v0_1hs, v1_1h); - - sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); - sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); - } - - sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); -#elif defined(__AVX2__) - // Initialize accumulator with zeros - __m256 acc = _mm256_setzero_ps(); - - // Main loop - for (; ib < nb; ++ib) { - /* Compute combined scale for the block */ - const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) ); - - __m256i qx = bytes_from_nibbles_32(x[ib].qs); - - // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval. - const __m256i off = _mm256_set1_epi8( 8 ); - qx = _mm256_sub_epi8( qx, off ); - - __m256i qy = _mm256_loadu_si256((const __m256i *)y[ib].qs); - - const __m256 q = mul_sum_i8_pairs_float(qx, qy); - - /* Multiply q with scale and accumulate */ - acc = _mm256_fmadd_ps( d, q, acc ); - } - - sumf = hsum_float_8(acc); -#elif defined(__AVX__) - const __m128i mone = _mm_set1_epi16(1); - - __m256 accum1 = _mm256_setzero_ps(); - __m256 accum2 = _mm256_setzero_ps(); - for (; ib + 1 < nb; ib += 2) { - const __m128i q4bits_1 = _mm_loadu_si128((const __m128i *)x[ib + 0].qs); - const __m128i q4bits_2 = _mm_loadu_si128((const __m128i *)x[ib + 1].qs); - const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)y[ib + 0].qs); - const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)y[ib + 0].qs + 1); - const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs); - const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs + 1); - - const __m128i q4b_1_0 = _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), q4bits_1), _mm_set1_epi8(8)); - const __m128i q4b_1_1 = _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), _mm_srli_epi16(q4bits_1, 4)), _mm_set1_epi8(8)); - const __m128i q4b_2_0 = _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), q4bits_2), _mm_set1_epi8(8)); - const __m128i q4b_2_1 = _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), _mm_srli_epi16(q4bits_2, 4)), _mm_set1_epi8(8)); - const __m128i p16_1_0 = mul_add_epi8_sse(q4b_1_0, q8b_1_0); - const __m128i p16_1_1 = mul_add_epi8_sse(q4b_1_1, q8b_1_1); - const __m128i p16_2_0 = mul_add_epi8_sse(q4b_2_0, q8b_2_0); - const __m128i p16_2_1 = mul_add_epi8_sse(q4b_2_1, q8b_2_1); - const __m128i p_1_0 = _mm_madd_epi16(p16_1_0, mone); - const __m128i p_1_1 = _mm_madd_epi16(p16_1_1, mone); - const __m128i p_2_0 = _mm_madd_epi16(p16_2_0, mone); - const __m128i p_2_1 = _mm_madd_epi16(p16_2_1, mone); - accum1 = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[ib + 0].d)*GGML_FP16_TO_FP32(x[ib + 0].d)), - _mm256_cvtepi32_ps(MM256_SET_M128I(p_1_1, p_1_0))), accum1); - accum2 = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[ib + 1].d)*GGML_FP16_TO_FP32(x[ib + 1].d)), - _mm256_cvtepi32_ps(MM256_SET_M128I(p_2_1, p_2_0))), accum2); - } - - sumf = hsum_float_8(_mm256_add_ps(accum1, accum2)); -#elif defined(__SSSE3__) - // set constants - const __m128i lowMask = _mm_set1_epi8(0xF); - const __m128i off = _mm_set1_epi8(8); - - // Initialize accumulator with zeros - __m128 acc_0 = _mm_setzero_ps(); - __m128 acc_1 = _mm_setzero_ps(); - __m128 acc_2 = _mm_setzero_ps(); - __m128 acc_3 = _mm_setzero_ps(); - - for (; ib + 1 < nb; ib += 2) { - _mm_prefetch(&x[ib] + sizeof(block_q4_0), _MM_HINT_T0); - _mm_prefetch(&y[ib] + sizeof(block_q8_0), _MM_HINT_T0); - - // Compute combined scale for the block 0 and 1 - const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) ); - - const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[ib].qs); - - __m128i bx_0 = _mm_and_si128(lowMask, tmp_0_1); - __m128i by_0 = _mm_loadu_si128((const __m128i *)y[ib].qs); - bx_0 = _mm_sub_epi8(bx_0, off); - const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0); - - __m128i bx_1 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_0_1, 4)); - __m128i by_1 = _mm_loadu_si128((const __m128i *)(y[ib].qs + 16)); - bx_1 = _mm_sub_epi8(bx_1, off); - const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1); - - _mm_prefetch(&x[ib] + 2 * sizeof(block_q4_0), _MM_HINT_T0); - _mm_prefetch(&y[ib] + 2 * sizeof(block_q8_0), _MM_HINT_T0); - - // Compute combined scale for the block 2 and 3 - const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[ib + 1].d) * GGML_FP16_TO_FP32(y[ib + 1].d) ); - - const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[ib + 1].qs); - - __m128i bx_2 = _mm_and_si128(lowMask, tmp_2_3); - __m128i by_2 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs); - bx_2 = _mm_sub_epi8(bx_2, off); - const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2); - - __m128i bx_3 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_2_3, 4)); - __m128i by_3 = _mm_loadu_si128((const __m128i *)(y[ib + 1].qs + 16)); - bx_3 = _mm_sub_epi8(bx_3, off); - const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3); - - // Convert int32_t to float - __m128 p0 = _mm_cvtepi32_ps(i32_0); - __m128 p1 = _mm_cvtepi32_ps(i32_1); - __m128 p2 = _mm_cvtepi32_ps(i32_2); - __m128 p3 = _mm_cvtepi32_ps(i32_3); - - // Apply the scale - __m128 p0_d = _mm_mul_ps( d_0_1, p0 ); - __m128 p1_d = _mm_mul_ps( d_0_1, p1 ); - __m128 p2_d = _mm_mul_ps( d_2_3, p2 ); - __m128 p3_d = _mm_mul_ps( d_2_3, p3 ); - - // Acummulate - acc_0 = _mm_add_ps(p0_d, acc_0); - acc_1 = _mm_add_ps(p1_d, acc_1); - acc_2 = _mm_add_ps(p2_d, acc_2); - acc_3 = _mm_add_ps(p3_d, acc_3); - } - - sumf = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3); -#elif defined(__riscv_v_intrinsic) - size_t vl = __riscv_vsetvl_e8m1(qk/2); - - for (; ib < nb; ++ib) { - // load elements - vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[ib].qs, vl); - - vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[ib].qs, vl); - vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[ib].qs+16, vl); - - // mask and store lower part of x, and then upper part - vuint8mf2_t x_a = __riscv_vand_vx_u8mf2(tx, 0x0F, vl); - vuint8mf2_t x_l = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl); - - vint8mf2_t x_ai = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a); - vint8mf2_t x_li = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l); - - // subtract offset - vint8mf2_t v0 = __riscv_vsub_vx_i8mf2(x_ai, 8, vl); - vint8mf2_t v1 = __riscv_vsub_vx_i8mf2(x_li, 8, vl); - - vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl); - vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl); - - vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); - - vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl); - vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl); - - int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); - - sumf += sumi*GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d); - } - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0xF); - const vector signed int v0 = vec_splats((int32_t)0); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - const vector signed char v8 = vec_splats((signed char)0x8); - - vector float vsumf0 = vec_splats(0.0f); - -#pragma GCC unroll 8 - for (; ib < nb; ++ib) { - __builtin_prefetch(x[ib].qs, 0, 1); - __builtin_prefetch(y[ib].qs, 0, 1); - - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); - vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); - vector float vd = vec_mul(vxd, vyd); - - vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs); - vector signed char q8y0 = vec_xl( 0, y[ib].qs); - vector signed char q8y1 = vec_xl(16, y[ib].qs); - - vector signed char q4x0 = vec_and(qxs, lowMask); - vector signed char q4x1 = vec_sr(qxs, v4); - - q4x0 = vec_sub(q4x0, v8); - q4x1 = vec_sub(q4x1, v8); - - vector signed short qv0 = vec_add(vec_mule(q4x0, q8y0), vec_mulo(q4x0, q8y0)); - vector signed short qv1 = vec_add(vec_mule(q4x1, q8y1), vec_mulo(q4x1, q8y1)); - - vector signed int vsumi0 = v0; - - vsumi0 = vec_sum4s(qv0, vsumi0); - vsumi0 = vec_sum4s(qv1, vsumi0); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - } - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - sumf = vec_extract(vsumf0, 0); - -#elif defined(__loongarch_asx) - // Initialize accumulator with zeros - __m256 acc = (__m256)__lasx_xvldi(0); - - // Main loop - for (; ib < nb; ++ib) { - /* Compute combined scale for the block */ - const __m256 d = __lasx_xvreplfr2vr_s( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) ); - - __m256i qx = bytes_from_nibbles_32(x[ib].qs); - - // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval. - const __m256i off = __lasx_xvreplgr2vr_b( 8 ); - qx = __lasx_xvsub_b( qx, off ); - - __m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0); - - const __m256 q = mul_sum_i8_pairs_float(qx, qy); - - /* Multiply q with scale and accumulate */ - acc = __lasx_xvfmadd_s( d, q, acc ); - } - - sumf = hsum_float_8(acc); -#elif defined(__loongarch_sx) - // set constants - const __m128i low_mask = __lsx_vreplgr2vr_b(0xF); - const __m128i off = __lsx_vreplgr2vr_b(8); - - // Initialize accumulator with zeros - __m128 acc_0 = __lsx_vldi(0); - __m128 acc_1 = __lsx_vldi(0); - __m128 acc_2 = __lsx_vldi(0); - __m128 acc_3 = __lsx_vldi(0); - - for (; ib + 1 < nb; ib += 2) { - - // Compute combined scale for the block 0 and 1 - const __m128 d_0_1 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) ); - - const __m128i tmp_0_1 = __lsx_vld((const __m128i *)x[ib].qs, 0); - - __m128i bx_0 = __lsx_vand_v(low_mask, tmp_0_1); - __m128i by_0 = __lsx_vld((const __m128i *)y[ib].qs, 0); - bx_0 = __lsx_vsub_b(bx_0, off); - const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0); - - __m128i bx_1 = __lsx_vand_v(low_mask, __lsx_vsrli_d(tmp_0_1, 4)); - __m128i by_1 = __lsx_vld((const __m128i *)(y[ib].qs + 16), 0); - bx_1 = __lsx_vsub_b(bx_1, off); - const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1); - - //_mm_prefetch(&x[ib] + 2 * sizeof(block_q4_0), _MM_HINT_T0); - //_mm_prefetch(&y[ib] + 2 * sizeof(block_q8_0), _MM_HINT_T0); - - // Compute combined scale for the block 2 and 3 - const __m128 d_2_3 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[ib + 1].d) * GGML_FP16_TO_FP32(y[ib + 1].d) ); - - const __m128i tmp_2_3 = __lsx_vld((const __m128i *)x[ib + 1].qs, 0); - - __m128i bx_2 = __lsx_vand_v(low_mask, tmp_2_3); - __m128i by_2 = __lsx_vld((const __m128i *)y[ib + 1].qs, 0); - bx_2 = __lsx_vsub_b(bx_2, off); - const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2); - - __m128i bx_3 = __lsx_vand_v(low_mask, __lsx_vsrli_d(tmp_2_3, 4)); - __m128i by_3 = __lsx_vld((const __m128i *)(y[ib + 1].qs + 16), 0); - bx_3 = __lsx_vsub_b(bx_3, off); - const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3); - - // Convert int32_t to float - __m128 p0 = __lsx_vffint_s_w(i32_0); - __m128 p1 = __lsx_vffint_s_w(i32_1); - __m128 p2 = __lsx_vffint_s_w(i32_2); - __m128 p3 = __lsx_vffint_s_w(i32_3); - - // Apply the scale - __m128 p0_d = __lsx_vfmul_s( d_0_1, p0 ); - __m128 p1_d = __lsx_vfmul_s( d_0_1, p1 ); - __m128 p2_d = __lsx_vfmul_s( d_2_3, p2 ); - __m128 p3_d = __lsx_vfmul_s( d_2_3, p3 ); - - // Acummulate - acc_0 = __lsx_vfadd_s(p0_d, acc_0); - acc_1 = __lsx_vfadd_s(p1_d, acc_1); - acc_2 = __lsx_vfadd_s(p2_d, acc_2); - acc_3 = __lsx_vfadd_s(p3_d, acc_3); - } - - sumf = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3); -#endif - for (; ib < nb; ++ib) { - int sumi0 = 0; - int sumi1 = 0; - - for (int j = 0; j < qk/2; ++j) { - const int v0 = (x[ib].qs[j] & 0x0F) - 8; - const int v1 = (x[ib].qs[j] >> 4) - 8; - - sumi0 += (v0 * y[ib].qs[j]); - sumi1 += (v1 * y[ib].qs[j + qk/2]); - } - - int sumi = sumi0 + sumi1; - sumf += sumi*GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d); - } - - *s = sumf; -} - -void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - const int qk = QK8_1; - const int nb = n / qk; - - assert(n % qk == 0); -#if defined(__ARM_FEATURE_MATMUL_INT8) - assert((nrc == 2) || (nrc == 1)); -#else - assert(nrc == 1); -#endif - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q4_1 * restrict x = vx; - const block_q8_1 * restrict y = vy; - -#if defined(__ARM_FEATURE_MATMUL_INT8) - if (nrc == 2) { - const block_q4_1 * restrict vx0 = vx; - const block_q4_1 * restrict vx1 = (const block_q4_1 *) ((const uint8_t*)vx + bx); - const block_q8_1 * restrict vy0 = vy; - const block_q8_1 * restrict vy1 = (const block_q8_1 *) ((const uint8_t*)vy + by); - - float32x4_t sumv0 = vdupq_n_f32(0.0f); - float32x4_t summs0 = vdupq_n_f32(0.0f); - - for (int i = 0; i < nb; i++) { - const block_q4_1 * restrict b_x0 = &vx0[i]; - const block_q4_1 * restrict b_x1 = &vx1[i]; - const block_q8_1 * restrict b_y0 = &vy0[i]; - const block_q8_1 * restrict b_y1 = &vy1[i]; - - float32_t summs_t[4] = {GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y0->s), - GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y0->s), - GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y1->s), - GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y1->s)}; - summs0 = vaddq_f32(summs0, vld1q_f32(summs_t)); - - const uint8x16_t m4b = vdupq_n_u8(0x0F); - - const uint8x16_t v0_0 = vld1q_u8(b_x0->qs); - const uint8x16_t v0_1 = vld1q_u8(b_x1->qs); - - // 4-bit -> 8-bit - const int8x16_t x0_l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); - const int8x16_t x0_h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); - const int8x16_t x1_l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); - const int8x16_t x1_h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); - - // load y - const int8x16_t y0_l = vld1q_s8(b_y0->qs); - const int8x16_t y0_h = vld1q_s8(b_y0->qs + 16); - const int8x16_t y1_l = vld1q_s8(b_y1->qs); - const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); - - // mmla into int32x4_t - float32_t _scale[4] = {GGML_FP16_TO_FP32(b_x0->d)*b_y0->d, - GGML_FP16_TO_FP32(b_x0->d)*b_y1->d, - GGML_FP16_TO_FP32(b_x1->d)*b_y0->d, - GGML_FP16_TO_FP32(b_x1->d)*b_y1->d}; - float32x4_t scale = vld1q_f32(_scale); - - int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); - int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); - - int8x16_t l2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); - int8x16_t l3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); - - int8x16_t r0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); - int8x16_t r1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); - - int8x16_t r2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); - int8x16_t r3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); - sumv0 = vmlaq_f32(sumv0,(vcvtq_f32_s32(vmmlaq_s32((vmmlaq_s32((vmmlaq_s32((vmmlaq_s32(vdupq_n_s32(0), l0, r0)), - l1, r1)), l2, r2)), l3, r3))), scale); - } - - float32x4_t sumv1 = vextq_f32(sumv0, sumv0, 2); - float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1); - sumv2 = vaddq_f32(sumv2, summs0); - - vst1_f32(s, vget_low_f32 (sumv2)); - vst1_f32(s + bs, vget_high_f32(sumv2)); - return; - } -#endif - - int ib = 0; - float sumf = 0; - - // TODO: add WASM SIMD -#if defined(__ARM_NEON) - float32x4_t sumv0 = vdupq_n_f32(0.0f); - float32x4_t sumv1 = vdupq_n_f32(0.0f); - - float summs = 0; - - for (; ib + 1 < nb; ib += 2) { - const block_q4_1 * restrict x0 = &x[ib + 0]; - const block_q4_1 * restrict x1 = &x[ib + 1]; - const block_q8_1 * restrict y0 = &y[ib + 0]; - const block_q8_1 * restrict y1 = &y[ib + 1]; - - summs += GGML_FP16_TO_FP32(x0->m) * GGML_FP16_TO_FP32(y0->s) + GGML_FP16_TO_FP32(x1->m) * GGML_FP16_TO_FP32(y1->s); - - const uint8x16_t m4b = vdupq_n_u8(0x0F); - - const uint8x16_t v0_0 = vld1q_u8(x0->qs); - const uint8x16_t v0_1 = vld1q_u8(x1->qs); - - // 4-bit -> 8-bit - const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); - const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); - const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); - const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); - - // load y - const int8x16_t v1_0l = vld1q_s8(y0->qs); - const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); - const int8x16_t v1_1l = vld1q_s8(y1->qs); - const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); - - // dot product into int32x4_t - const int32x4_t p_0 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0l), v0_0h, v1_0h); - const int32x4_t p_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1l), v0_1h, v1_1h); - - sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(p_0), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); - sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); - } - - sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs; -#elif defined(__AVX2__) || defined(__AVX__) - // Initialize accumulator with zeros - __m256 acc = _mm256_setzero_ps(); - - float summs = 0; - - // Main loop - for (; ib < nb; ++ib) { - const float d0 = GGML_FP16_TO_FP32(x[ib].d); - const float d1 = GGML_FP16_TO_FP32(y[ib].d); - - summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s); - - const __m256 d0v = _mm256_set1_ps( d0 ); - const __m256 d1v = _mm256_set1_ps( d1 ); - - // Compute combined scales - const __m256 d0d1 = _mm256_mul_ps( d0v, d1v ); - - // Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes - const __m256i qx = bytes_from_nibbles_32(x[ib].qs); - const __m256i qy = _mm256_loadu_si256( (const __m256i *)y[ib].qs ); - - const __m256 xy = mul_sum_us8_pairs_float(qx, qy); - - // Accumulate d0*d1*x*y -#if defined(__AVX2__) - acc = _mm256_fmadd_ps( d0d1, xy, acc ); -#else - acc = _mm256_add_ps( _mm256_mul_ps( d0d1, xy ), acc ); -#endif - } - - sumf = hsum_float_8(acc) + summs; -#elif defined(__riscv_v_intrinsic) - size_t vl = __riscv_vsetvl_e8m1(qk/2); - - for (; ib < nb; ++ib) { - // load elements - vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[ib].qs, vl); - - vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[ib].qs, vl); - vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[ib].qs+16, vl); - - // mask and store lower part of x, and then upper part - vuint8mf2_t x_a = __riscv_vand_vx_u8mf2(tx, 0x0F, vl); - vuint8mf2_t x_l = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl); - - vint8mf2_t v0 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a); - vint8mf2_t v1 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l); - - vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl); - vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl); - - vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); - - vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl); - vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl); - - int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); - - sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s); - } - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0xF); - const vector signed int v0 = vec_splats((int32_t)0); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - - vector float vsumf0 = vec_splats(0.0f); - -#pragma GCC unroll 4 - for (; ib < nb; ++ib) { - __builtin_prefetch(x[ib].qs, 0, 1); - __builtin_prefetch(y[ib].qs, 0, 1); - - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); - vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); - vector float vd = vec_mul(vxd, vyd); - - vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[ib].m)); - vector float vys = {GGML_FP16_TO_FP32(y[ib].s), 0.0f, 0.0f, 0.0f}; - vsumf0 = vec_madd(vxmin, vys, vsumf0); - - vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs); - vector signed char q8y0 = vec_xl( 0, y[ib].qs); - vector signed char q8y1 = vec_xl(16, y[ib].qs); - - vector unsigned char q4x0 = (vector unsigned char)vec_and(qxs, lowMask); - vector unsigned char q4x1 = (vector unsigned char)vec_sr(qxs, v4); - - vector signed int vsumi0 = v0; - - vsumi0 = vec_msum(q8y0, q4x0, vsumi0); - vsumi0 = vec_msum(q8y1, q4x1, vsumi0); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - } - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - sumf = vec_extract(vsumf0, 0); - -#elif defined(__loongarch_asx) - // Initialize accumulator with zeros - __m256 acc = (__m256)__lasx_xvldi(0); - - float summs = 0; - - // Main loop - for (; ib < nb; ++ib) { - const float d0 = GGML_FP16_TO_FP32(x[ib].d); - const float d1 = GGML_FP16_TO_FP32(y[ib].d); - - summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s); - - const __m256 d0v = __lasx_xvreplfr2vr_s( d0 ); - const __m256 d1v = __lasx_xvreplfr2vr_s( d1 ); - - // Compute combined scales - const __m256 d0d1 = __lasx_xvfmul_s( d0v, d1v ); - - // Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes - const __m256i qx = bytes_from_nibbles_32(x[ib].qs); - const __m256i qy = __lasx_xvld( (const __m256i *)y[ib].qs, 0); - - const __m256 xy = mul_sum_us8_pairs_float(qx, qy); - - // Accumulate d0*d1*x*y - acc = __lasx_xvfmadd_s( d0d1, xy, acc ); - } - - sumf = hsum_float_8(acc) + summs; -#endif - for (; ib < nb; ++ib) { - int sumi0 = 0; - int sumi1 = 0; - - for (int j = 0; j < qk/2; ++j) { - const int v0 = (x[ib].qs[j] & 0x0F); - const int v1 = (x[ib].qs[j] >> 4); - - sumi0 += (v0 * y[ib].qs[j]); - sumi1 += (v1 * y[ib].qs[j + qk/2]); - } - - int sumi = sumi0 + sumi1; - sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s); - } - - *s = sumf; -} - -void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - const int qk = QK8_0; - const int nb = n / qk; - - int ib = 0; - float sumf = 0; - - assert(n % qk == 0); - assert(qk == QK5_0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q5_0 * restrict x = vx; - const block_q8_0 * restrict y = vy; - -#if defined(__ARM_NEON) - float32x4_t sumv0 = vdupq_n_f32(0.0f); - float32x4_t sumv1 = vdupq_n_f32(0.0f); - - uint32_t qh0; - uint32_t qh1; - - uint64_t tmp0[4]; - uint64_t tmp1[4]; - - for (; ib + 1 < nb; ib += 2) { - const block_q5_0 * restrict x0 = &x[ib]; - const block_q5_0 * restrict x1 = &x[ib + 1]; - const block_q8_0 * restrict y0 = &y[ib]; - const block_q8_0 * restrict y1 = &y[ib + 1]; - - const uint8x16_t m4b = vdupq_n_u8(0x0F); - - // extract the 5th bit via lookup table ((!b) << 4) - memcpy(&qh0, x0->qh, sizeof(qh0)); - memcpy(&qh1, x1->qh, sizeof(qh1)); - - tmp0[0] = table_b2b_1[(qh0 >> 0) & 0xFF]; - tmp0[1] = table_b2b_1[(qh0 >> 8) & 0xFF]; - tmp0[2] = table_b2b_1[(qh0 >> 16) & 0xFF]; - tmp0[3] = table_b2b_1[(qh0 >> 24) ]; - - tmp1[0] = table_b2b_1[(qh1 >> 0) & 0xFF]; - tmp1[1] = table_b2b_1[(qh1 >> 8) & 0xFF]; - tmp1[2] = table_b2b_1[(qh1 >> 16) & 0xFF]; - tmp1[3] = table_b2b_1[(qh1 >> 24) ]; - - const int8x16_t qhl0 = vld1q_s8((const int8_t *)(tmp0 + 0)); - const int8x16_t qhh0 = vld1q_s8((const int8_t *)(tmp0 + 2)); - const int8x16_t qhl1 = vld1q_s8((const int8_t *)(tmp1 + 0)); - const int8x16_t qhh1 = vld1q_s8((const int8_t *)(tmp1 + 2)); - - const uint8x16_t v0_0 = vld1q_u8(x0->qs); - const uint8x16_t v0_1 = vld1q_u8(x1->qs); - - // 4-bit -> 8-bit - int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); - int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); - int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); - int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); - - // add high bit and sub 16 (equivalent to sub 0x10 when bit is zero) - const int8x16_t v0_0lf = vsubq_s8(v0_0l, qhl0); - const int8x16_t v0_0hf = vsubq_s8(v0_0h, qhh0); - const int8x16_t v0_1lf = vsubq_s8(v0_1l, qhl1); - const int8x16_t v0_1hf = vsubq_s8(v0_1h, qhh1); - - // load y - const int8x16_t v1_0l = vld1q_s8(y0->qs); - const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); - const int8x16_t v1_1l = vld1q_s8(y1->qs); - const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); - - sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( - ggml_vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l), - ggml_vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); - sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( - ggml_vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l), - ggml_vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); - } - - sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); -#elif defined(__wasm_simd128__) - v128_t sumv = wasm_f32x4_splat(0.0f); - - uint32_t qh; - uint64_t tmp[4]; - - // TODO: check if unrolling this is better - for (; ib < nb; ++ib) { - const block_q5_0 * restrict x0 = &x[ib]; - const block_q8_0 * restrict y0 = &y[ib]; - - const v128_t m4b = wasm_i8x16_splat(0x0F); - - // extract the 5th bit - memcpy(&qh, x0->qh, sizeof(qh)); - - tmp[0] = table_b2b_1[(qh >> 0) & 0xFF]; - tmp[1] = table_b2b_1[(qh >> 8) & 0xFF]; - tmp[2] = table_b2b_1[(qh >> 16) & 0xFF]; - tmp[3] = table_b2b_1[(qh >> 24) ]; - - const v128_t qhl = wasm_v128_load(tmp + 0); - const v128_t qhh = wasm_v128_load(tmp + 2); - - const v128_t v0 = wasm_v128_load(x0->qs); - - // 4-bit -> 8-bit - const v128_t v0l = wasm_v128_and (v0, m4b); - const v128_t v0h = wasm_u8x16_shr(v0, 4); - - // add high bit and sub 16 (equivalent to sub 0x10 when bit is zero) - const v128_t v0lf = wasm_i8x16_sub(v0l, qhl); - const v128_t v0hf = wasm_i8x16_sub(v0h, qhh); - - // load y - const v128_t v1l = wasm_v128_load(y0->qs); - const v128_t v1h = wasm_v128_load(y0->qs + 16); - - // int8x16 -> int16x8 - const v128_t v0lfl = wasm_i16x8_extend_low_i8x16 (v0lf); - const v128_t v0lfh = wasm_i16x8_extend_high_i8x16(v0lf); - const v128_t v0hfl = wasm_i16x8_extend_low_i8x16 (v0hf); - const v128_t v0hfh = wasm_i16x8_extend_high_i8x16(v0hf); - - const v128_t v1ll = wasm_i16x8_extend_low_i8x16 (v1l); - const v128_t v1lh = wasm_i16x8_extend_high_i8x16(v1l); - const v128_t v1hl = wasm_i16x8_extend_low_i8x16 (v1h); - const v128_t v1hh = wasm_i16x8_extend_high_i8x16(v1h); - - // dot product - sumv = wasm_f32x4_add(sumv, wasm_f32x4_mul(wasm_f32x4_convert_i32x4( - wasm_i32x4_add( - wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll), - wasm_i32x4_dot_i16x8(v0lfh, v1lh)), - wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl), - wasm_i32x4_dot_i16x8(v0hfh, v1hh)))), - wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d)))); - } - - sumf = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) + - wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3); -#elif defined(__AVX2__) - // Initialize accumulator with zeros - __m256 acc = _mm256_setzero_ps(); - - // Main loop - for (; ib < nb; ++ib) { - /* Compute combined scale for the block */ - const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d)); - - __m256i qx = bytes_from_nibbles_32(x[ib].qs); - __m256i bxhi = bytes_from_bits_32(x[ib].qh); - bxhi = _mm256_andnot_si256(bxhi, _mm256_set1_epi8((char)0xF0)); - qx = _mm256_or_si256(qx, bxhi); - - __m256i qy = _mm256_loadu_si256((const __m256i *)y[ib].qs); - - const __m256 q = mul_sum_i8_pairs_float(qx, qy); - - /* Multiply q with scale and accumulate */ - acc = _mm256_fmadd_ps(d, q, acc); - } - - sumf = hsum_float_8(acc); -#elif defined(__AVX__) - // Initialize accumulator with zeros - __m256 acc = _mm256_setzero_ps(); - __m128i mask = _mm_set1_epi8((char)0xF0); - - // Main loop - for (; ib < nb; ++ib) { - /* Compute combined scale for the block */ - const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d)); - - __m256i bx_0 = bytes_from_nibbles_32(x[ib].qs); - const __m256i bxhi = bytes_from_bits_32(x[ib].qh); - __m128i bxhil = _mm256_castsi256_si128(bxhi); - __m128i bxhih = _mm256_extractf128_si256(bxhi, 1); - bxhil = _mm_andnot_si128(bxhil, mask); - bxhih = _mm_andnot_si128(bxhih, mask); - __m128i bxl = _mm256_castsi256_si128(bx_0); - __m128i bxh = _mm256_extractf128_si256(bx_0, 1); - bxl = _mm_or_si128(bxl, bxhil); - bxh = _mm_or_si128(bxh, bxhih); - bx_0 = MM256_SET_M128I(bxh, bxl); - - const __m256i by_0 = _mm256_loadu_si256((const __m256i *)y[ib].qs); - - const __m256 q = mul_sum_i8_pairs_float(bx_0, by_0); - - /* Multiply q with scale and accumulate */ - acc = _mm256_add_ps(_mm256_mul_ps(d, q), acc); - } - - sumf = hsum_float_8(acc); -#elif defined(__riscv_v_intrinsic) - uint32_t qh; - - size_t vl = __riscv_vsetvl_e8m1(qk/2); - - // These temporary registers are for masking and shift operations - vuint32m2_t vt_1 = __riscv_vid_v_u32m2(vl); - vuint32m2_t vt_2 = __riscv_vsll_vv_u32m2(__riscv_vmv_v_x_u32m2(1, vl), vt_1, vl); - - vuint32m2_t vt_3 = __riscv_vsll_vx_u32m2(vt_2, 16, vl); - vuint32m2_t vt_4 = __riscv_vadd_vx_u32m2(vt_1, 12, vl); - - for (; ib < nb; ++ib) { - memcpy(&qh, x[ib].qh, sizeof(uint32_t)); - - // ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4; - vuint32m2_t xha_0 = __riscv_vand_vx_u32m2(vt_2, qh, vl); - vuint32m2_t xhr_0 = __riscv_vsrl_vv_u32m2(xha_0, vt_1, vl); - vuint32m2_t xhl_0 = __riscv_vsll_vx_u32m2(xhr_0, 4, vl); - - // ((qh & (1u << (j + 16))) >> (j + 12)); - vuint32m2_t xha_1 = __riscv_vand_vx_u32m2(vt_3, qh, vl); - vuint32m2_t xhl_1 = __riscv_vsrl_vv_u32m2(xha_1, vt_4, vl); - - // narrowing - vuint16m1_t xhc_0 = __riscv_vncvt_x_x_w_u16m1(xhl_0, vl); - vuint8mf2_t xh_0 = __riscv_vncvt_x_x_w_u8mf2(xhc_0, vl); - - vuint16m1_t xhc_1 = __riscv_vncvt_x_x_w_u16m1(xhl_1, vl); - vuint8mf2_t xh_1 = __riscv_vncvt_x_x_w_u8mf2(xhc_1, vl); - - // load - vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[ib].qs, vl); - - vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[ib].qs, vl); - vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[ib].qs+16, vl); - - vuint8mf2_t x_at = __riscv_vand_vx_u8mf2(tx, 0x0F, vl); - vuint8mf2_t x_lt = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl); - - vuint8mf2_t x_a = __riscv_vor_vv_u8mf2(x_at, xh_0, vl); - vuint8mf2_t x_l = __riscv_vor_vv_u8mf2(x_lt, xh_1, vl); - - vint8mf2_t x_ai = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a); - vint8mf2_t x_li = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l); - - vint8mf2_t v0 = __riscv_vsub_vx_i8mf2(x_ai, 16, vl); - vint8mf2_t v1 = __riscv_vsub_vx_i8mf2(x_li, 16, vl); - - vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl); - vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl); - - vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); - - vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl); - vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl); - - int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); - - sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d)) * sumi; - } - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0xF); - const vector unsigned char v4 = vec_splats((unsigned char)4); - - vector float vsumf0 = vec_splats(0.0f); - -#pragma GCC unroll 4 - for (; ib < nb; ++ib) { - __builtin_prefetch(x[ib].qs, 0, 1); - __builtin_prefetch(y[ib].qs, 0, 1); - - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); - vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); - vector float vd = vec_mul(vxd, vyd); - - vector signed long long aux64x2_0 = {(uint64_t)(table_b2b_1[x[ib].qh[0]]), (uint64_t)(table_b2b_1[x[ib].qh[1]])}; - vector signed long long aux64x2_1 = {(uint64_t)(table_b2b_1[x[ib].qh[2]]), (uint64_t)(table_b2b_1[x[ib].qh[3]])}; - - vector signed char qh0 = (vector signed char)aux64x2_0; - vector signed char qh1 = (vector signed char)aux64x2_1; - - vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs); - - vector signed char q5x0 = vec_sub(vec_and (qxs, lowMask), qh0); - vector signed char q5x1 = vec_sub(vec_sr(qxs, v4), qh1); - - vector signed char q8y0 = vec_xl( 0, y[ib].qs); - vector signed char q8y1 = vec_xl( 16, y[ib].qs); - - vector signed short qv0 = vec_add(vec_mule(q5x0, q8y0), vec_mulo(q5x0, q8y0)); - vector signed short qv1 = vec_add(vec_mule(q5x1, q8y1), vec_mulo(q5x1, q8y1)); - - qv0 = vec_add(qv0, qv1); - - vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackl(qv0)); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - } - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - sumf = vec_extract(vsumf0, 0); - -#elif defined(__loongarch_asx) - // Initialize accumulator with zeros - __m256 acc = (__m256)__lasx_xvldi(0); - - // Main loop - for (; ib < nb; ++ib) { - /* Compute combined scale for the block */ - const __m256 d = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d)); //FIXME - - __m256i qx = bytes_from_nibbles_32(x[ib].qs); - __m256i bxhi = bytes_from_bits_32(x[ib].qh); - bxhi = __lasx_xvandn_v(bxhi, __lasx_xvreplgr2vr_b((char)0xF0)); - qx = __lasx_xvor_v(qx, bxhi); - - __m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0); - - const __m256 q = mul_sum_i8_pairs_float(qx, qy); - - /* Multiply q with scale and accumulate */ - acc = __lasx_xvfmadd_s(d, q, acc); - } - - sumf = hsum_float_8(acc); -#endif - for (; ib < nb; ++ib) { - uint32_t qh; - memcpy(&qh, x[ib].qh, sizeof(qh)); - - int sumi0 = 0; - int sumi1 = 0; - - for (int j = 0; j < qk/2; ++j) { - const uint8_t xh_0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4; - const uint8_t xh_1 = ((qh & (1u << (j + 16))) >> (j + 12)); - - const int32_t x0 = (int8_t)(((x[ib].qs[j] & 0x0F) | xh_0) - 16); - const int32_t x1 = (int8_t)(((x[ib].qs[j] >> 4) | xh_1) - 16); - - sumi0 += (x0 * y[ib].qs[j]); - sumi1 += (x1 * y[ib].qs[j + qk/2]); - } - - int sumi = sumi0 + sumi1; - sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d)) * sumi; - } - - *s = sumf; -} - -void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - const int qk = QK8_1; - const int nb = n / qk; - - int ib = 0; - float sumf = 0; - - assert(n % qk == 0); - assert(qk == QK5_1); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q5_1 * restrict x = vx; - const block_q8_1 * restrict y = vy; - -#if defined(__ARM_NEON) - float32x4_t sumv0 = vdupq_n_f32(0.0f); - float32x4_t sumv1 = vdupq_n_f32(0.0f); - - float summs0 = 0.0f; - float summs1 = 0.0f; - - uint32_t qh0; - uint32_t qh1; - - uint64_t tmp0[4]; - uint64_t tmp1[4]; - - for (; ib + 1 < nb; ib += 2) { - const block_q5_1 * restrict x0 = &x[ib]; - const block_q5_1 * restrict x1 = &x[ib + 1]; - const block_q8_1 * restrict y0 = &y[ib]; - const block_q8_1 * restrict y1 = &y[ib + 1]; - - const uint8x16_t m4b = vdupq_n_u8(0x0F); - - summs0 += GGML_FP16_TO_FP32(x0->m) * GGML_FP16_TO_FP32(y0->s); - summs1 += GGML_FP16_TO_FP32(x1->m) * GGML_FP16_TO_FP32(y1->s); - - // extract the 5th bit via lookup table ((b) << 4) - memcpy(&qh0, x0->qh, sizeof(qh0)); - memcpy(&qh1, x1->qh, sizeof(qh1)); - - tmp0[0] = table_b2b_0[(qh0 >> 0) & 0xFF]; - tmp0[1] = table_b2b_0[(qh0 >> 8) & 0xFF]; - tmp0[2] = table_b2b_0[(qh0 >> 16) & 0xFF]; - tmp0[3] = table_b2b_0[(qh0 >> 24) ]; - - tmp1[0] = table_b2b_0[(qh1 >> 0) & 0xFF]; - tmp1[1] = table_b2b_0[(qh1 >> 8) & 0xFF]; - tmp1[2] = table_b2b_0[(qh1 >> 16) & 0xFF]; - tmp1[3] = table_b2b_0[(qh1 >> 24) ]; - - const int8x16_t qhl0 = vld1q_s8((const int8_t *)(tmp0 + 0)); - const int8x16_t qhh0 = vld1q_s8((const int8_t *)(tmp0 + 2)); - const int8x16_t qhl1 = vld1q_s8((const int8_t *)(tmp1 + 0)); - const int8x16_t qhh1 = vld1q_s8((const int8_t *)(tmp1 + 2)); - - const uint8x16_t v0_0 = vld1q_u8(x0->qs); - const uint8x16_t v0_1 = vld1q_u8(x1->qs); - - // 4-bit -> 8-bit - const int8x16_t v0_0l = vreinterpretq_s8_u8(vandq_u8 (v0_0, m4b)); - const int8x16_t v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4)); - const int8x16_t v0_1l = vreinterpretq_s8_u8(vandq_u8 (v0_1, m4b)); - const int8x16_t v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4)); - - // add high bit - const int8x16_t v0_0lf = vorrq_s8(v0_0l, qhl0); - const int8x16_t v0_0hf = vorrq_s8(v0_0h, qhh0); - const int8x16_t v0_1lf = vorrq_s8(v0_1l, qhl1); - const int8x16_t v0_1hf = vorrq_s8(v0_1h, qhh1); - - // load y - const int8x16_t v1_0l = vld1q_s8(y0->qs); - const int8x16_t v1_0h = vld1q_s8(y0->qs + 16); - const int8x16_t v1_1l = vld1q_s8(y1->qs); - const int8x16_t v1_1h = vld1q_s8(y1->qs + 16); - - sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( - ggml_vdotq_s32(vdupq_n_s32(0), v0_0lf, v1_0l), - ggml_vdotq_s32(vdupq_n_s32(0), v0_0hf, v1_0h))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); - sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( - ggml_vdotq_s32(vdupq_n_s32(0), v0_1lf, v1_1l), - ggml_vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); - } - - sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs0 + summs1; -#elif defined(__wasm_simd128__) - v128_t sumv = wasm_f32x4_splat(0.0f); - - float summs = 0.0f; - - uint32_t qh; - uint64_t tmp[4]; - - // TODO: check if unrolling this is better - for (; ib < nb; ++ib) { - const block_q5_1 * restrict x0 = &x[ib]; - const block_q8_1 * restrict y0 = &y[ib]; - - summs += GGML_FP16_TO_FP32(x0->m) * GGML_FP16_TO_FP32(y0->s); - - const v128_t m4b = wasm_i8x16_splat(0x0F); - - // extract the 5th bit - memcpy(&qh, x0->qh, sizeof(qh)); - - tmp[0] = table_b2b_0[(qh >> 0) & 0xFF]; - tmp[1] = table_b2b_0[(qh >> 8) & 0xFF]; - tmp[2] = table_b2b_0[(qh >> 16) & 0xFF]; - tmp[3] = table_b2b_0[(qh >> 24) ]; - - const v128_t qhl = wasm_v128_load(tmp + 0); - const v128_t qhh = wasm_v128_load(tmp + 2); - - const v128_t v0 = wasm_v128_load(x0->qs); - - // 4-bit -> 8-bit - const v128_t v0l = wasm_v128_and (v0, m4b); - const v128_t v0h = wasm_u8x16_shr(v0, 4); - - // add high bit - const v128_t v0lf = wasm_v128_or(v0l, qhl); - const v128_t v0hf = wasm_v128_or(v0h, qhh); - - // load y - const v128_t v1l = wasm_v128_load(y0->qs); - const v128_t v1h = wasm_v128_load(y0->qs + 16); - - // int8x16 -> int16x8 - const v128_t v0lfl = wasm_i16x8_extend_low_i8x16 (v0lf); - const v128_t v0lfh = wasm_i16x8_extend_high_i8x16(v0lf); - const v128_t v0hfl = wasm_i16x8_extend_low_i8x16 (v0hf); - const v128_t v0hfh = wasm_i16x8_extend_high_i8x16(v0hf); - - const v128_t v1ll = wasm_i16x8_extend_low_i8x16 (v1l); - const v128_t v1lh = wasm_i16x8_extend_high_i8x16(v1l); - const v128_t v1hl = wasm_i16x8_extend_low_i8x16 (v1h); - const v128_t v1hh = wasm_i16x8_extend_high_i8x16(v1h); - - // dot product - sumv = wasm_f32x4_add(sumv, - wasm_f32x4_mul(wasm_f32x4_convert_i32x4(wasm_i32x4_add( - wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0lfl, v1ll), - wasm_i32x4_dot_i16x8(v0lfh, v1lh)), - wasm_i32x4_add(wasm_i32x4_dot_i16x8(v0hfl, v1hl), - wasm_i32x4_dot_i16x8(v0hfh, v1hh)))), - wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d)))); - } - - sumf = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) + - wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3) + summs; -#elif defined(__AVX2__) - // Initialize accumulator with zeros - __m256 acc = _mm256_setzero_ps(); - - float summs = 0.0f; - - // Main loop - for (; ib < nb; ++ib) { - const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d)); - - summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s); - - __m256i qx = bytes_from_nibbles_32(x[ib].qs); - __m256i bxhi = bytes_from_bits_32(x[ib].qh); - bxhi = _mm256_and_si256(bxhi, _mm256_set1_epi8(0x10)); - qx = _mm256_or_si256(qx, bxhi); - - const __m256 dy = _mm256_set1_ps(GGML_FP16_TO_FP32(y[ib].d)); - const __m256i qy = _mm256_loadu_si256((const __m256i *)y[ib].qs); - - const __m256 q = mul_sum_us8_pairs_float(qx, qy); - - acc = _mm256_fmadd_ps(q, _mm256_mul_ps(dx, dy), acc); - } - - sumf = hsum_float_8(acc) + summs; -#elif defined(__AVX__) - // Initialize accumulator with zeros - __m256 acc = _mm256_setzero_ps(); - __m128i mask = _mm_set1_epi8(0x10); - - float summs = 0.0f; - - // Main loop - for (; ib < nb; ++ib) { - const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d)); - - summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s); - - __m256i bx_0 = bytes_from_nibbles_32(x[ib].qs); - const __m256i bxhi = bytes_from_bits_32(x[ib].qh); - __m128i bxhil = _mm256_castsi256_si128(bxhi); - __m128i bxhih = _mm256_extractf128_si256(bxhi, 1); - bxhil = _mm_and_si128(bxhil, mask); - bxhih = _mm_and_si128(bxhih, mask); - __m128i bxl = _mm256_castsi256_si128(bx_0); - __m128i bxh = _mm256_extractf128_si256(bx_0, 1); - bxl = _mm_or_si128(bxl, bxhil); - bxh = _mm_or_si128(bxh, bxhih); - bx_0 = MM256_SET_M128I(bxh, bxl); - - const __m256 dy = _mm256_set1_ps(GGML_FP16_TO_FP32(y[ib].d)); - const __m256i by_0 = _mm256_loadu_si256((const __m256i *)y[ib].qs); - - const __m256 q = mul_sum_us8_pairs_float(bx_0, by_0); - - acc = _mm256_add_ps(_mm256_mul_ps(q, _mm256_mul_ps(dx, dy)), acc); - } - - sumf = hsum_float_8(acc) + summs; -#elif defined(__riscv_v_intrinsic) - uint32_t qh; - - size_t vl = __riscv_vsetvl_e8m1(qk/2); - - // temporary registers for shift operations - vuint32m2_t vt_1 = __riscv_vid_v_u32m2(vl); - vuint32m2_t vt_2 = __riscv_vadd_vx_u32m2(vt_1, 12, vl); - - for (; ib < nb; ++ib) { - memcpy(&qh, x[ib].qh, sizeof(uint32_t)); - - // load qh - vuint32m2_t vqh = __riscv_vmv_v_x_u32m2(qh, vl); - - // ((qh >> (j + 0)) << 4) & 0x10; - vuint32m2_t xhr_0 = __riscv_vsrl_vv_u32m2(vqh, vt_1, vl); - vuint32m2_t xhl_0 = __riscv_vsll_vx_u32m2(xhr_0, 4, vl); - vuint32m2_t xha_0 = __riscv_vand_vx_u32m2(xhl_0, 0x10, vl); - - // ((qh >> (j + 12)) ) & 0x10; - vuint32m2_t xhr_1 = __riscv_vsrl_vv_u32m2(vqh, vt_2, vl); - vuint32m2_t xha_1 = __riscv_vand_vx_u32m2(xhr_1, 0x10, vl); - - // narrowing - vuint16m1_t xhc_0 = __riscv_vncvt_x_x_w_u16m1(xha_0, vl); - vuint8mf2_t xh_0 = __riscv_vncvt_x_x_w_u8mf2(xhc_0, vl); - - vuint16m1_t xhc_1 = __riscv_vncvt_x_x_w_u16m1(xha_1, vl); - vuint8mf2_t xh_1 = __riscv_vncvt_x_x_w_u8mf2(xhc_1, vl); - - // load - vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[ib].qs, vl); - - vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[ib].qs, vl); - vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[ib].qs+16, vl); - - vuint8mf2_t x_at = __riscv_vand_vx_u8mf2(tx, 0x0F, vl); - vuint8mf2_t x_lt = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl); - - vuint8mf2_t x_a = __riscv_vor_vv_u8mf2(x_at, xh_0, vl); - vuint8mf2_t x_l = __riscv_vor_vv_u8mf2(x_lt, xh_1, vl); - - vint8mf2_t v0 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_a); - vint8mf2_t v1 = __riscv_vreinterpret_v_u8mf2_i8mf2(x_l); - - vint16m1_t vec_mul1 = __riscv_vwmul_vv_i16m1(v0, y0, vl); - vint16m1_t vec_mul2 = __riscv_vwmul_vv_i16m1(v1, y1, vl); - - vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); - - vint32m1_t vs1 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul1, vec_zero, vl); - vint32m1_t vs2 = __riscv_vwredsum_vs_i16m1_i32m1(vec_mul2, vs1, vl); - - int sumi = __riscv_vmv_x_s_i32m1_i32(vs2); - - sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s); - } - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0xF); - const vector signed int v0 = vec_splats((int32_t)0); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - - vector float vsumf0 = vec_splats(0.0f); - -#pragma GCC unroll 4 - for (; ib < nb; ++ib) { - __builtin_prefetch(x[ib].qs, 0, 1); - __builtin_prefetch(y[ib].qs, 0, 1); - - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); - vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); - vector float vd = vec_mul(vxd, vyd); - - vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[ib].m)); - vector float vys = {GGML_FP16_TO_FP32(y[ib].s), 0.f, 0.f, 0.f}; - vsumf0 = vec_madd(vxmin, vys, vsumf0); - - vector unsigned long long aux64x2_0 = {(uint64_t)(table_b2b_0[x[ib].qh[0]]), (uint64_t)(table_b2b_0[x[ib].qh[1]])}; - vector unsigned long long aux64x2_1 = {(uint64_t)(table_b2b_0[x[ib].qh[2]]), (uint64_t)(table_b2b_0[x[ib].qh[3]])}; - - vector signed char qh0 = (vector signed char)aux64x2_0; - vector signed char qh1 = (vector signed char)aux64x2_1; - - vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs); - - vector unsigned char q5x0 = (vector unsigned char)vec_or(vec_and(qxs, lowMask), qh0); - vector unsigned char q5x1 = (vector unsigned char)vec_or(vec_sr(qxs, v4), qh1); - - vector signed char q8y0 = vec_xl( 0, y[ib].qs); - vector signed char q8y1 = vec_xl( 16, y[ib].qs); - - vector signed int vsumi0 = v0; - - vsumi0 = vec_msum(q8y0, q5x0, vsumi0); - vsumi0 = vec_msum(q8y1, q5x1, vsumi0); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - } - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - sumf = vec_extract(vsumf0, 0); - -#elif defined(__loongarch_asx) - // Initialize accumulator with zeros - __m256 acc = (__m256)__lasx_xvldi(0); - - float summs = 0.0f; - - // Main loop - for (; ib < nb; ++ib) { - const __m256 dx = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ib].d)); - - summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s); - - __m256i qx = bytes_from_nibbles_32(x[ib].qs); - __m256i bxhi = bytes_from_bits_32(x[ib].qh); - bxhi = __lasx_xvand_v(bxhi, __lasx_xvreplgr2vr_b(0x10)); - qx = __lasx_xvor_v(qx, bxhi); - - const __m256 dy = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[ib].d)); - const __m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0); - - const __m256 q = mul_sum_us8_pairs_float(qx, qy); - - acc = __lasx_xvfmadd_s(q, __lasx_xvfmul_s(dx, dy), acc); - } - - sumf = hsum_float_8(acc) + summs; -#endif - for (; ib < nb; ++ib) { - uint32_t qh; - memcpy(&qh, x[ib].qh, sizeof(qh)); - - int sumi0 = 0; - int sumi1 = 0; - - for (int j = 0; j < qk/2; ++j) { - const uint8_t xh_0 = ((qh >> (j + 0)) << 4) & 0x10; - const uint8_t xh_1 = ((qh >> (j + 12)) ) & 0x10; - - const int32_t x0 = (x[ib].qs[j] & 0xF) | xh_0; - const int32_t x1 = (x[ib].qs[j] >> 4) | xh_1; - - sumi0 += (x0 * y[ib].qs[j]); - sumi1 += (x1 * y[ib].qs[j + qk/2]); - } - - int sumi = sumi0 + sumi1; - sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s); - } - - *s = sumf; -} - -void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - const int qk = QK8_0; - const int nb = n / qk; - - assert(n % qk == 0); -#if defined(__ARM_FEATURE_MATMUL_INT8) - assert((nrc == 2) || (nrc == 1)); -#else - assert(nrc == 1); -#endif - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q8_0 * restrict x = vx; - const block_q8_0 * restrict y = vy; - -#if defined(__ARM_FEATURE_MATMUL_INT8) - if (nrc == 2) { - const block_q8_0 * restrict vx0 = vx; - const block_q8_0 * restrict vx1 = (const block_q8_0 *) ((const uint8_t*)vx + bx); - const block_q8_0 * restrict vy0 = vy; - const block_q8_0 * restrict vy1 = (const block_q8_0 *) ((const uint8_t*)vy + by); - - float32x4_t sumv0 = vdupq_n_f32(0.0f); - - for (int i = 0; i < nb; i++) { - const block_q8_0 * restrict b_x0 = &vx0[i]; - const block_q8_0 * restrict b_y0 = &vy0[i]; - - const block_q8_0 * restrict b_x1 = &vx1[i]; - const block_q8_0 * restrict b_y1 = &vy1[i]; - - const int8x16_t x0_l = vld1q_s8(b_x0->qs); - const int8x16_t x0_h = vld1q_s8(b_x0->qs + 16); - const int8x16_t x1_l = vld1q_s8(b_x1->qs); - const int8x16_t x1_h = vld1q_s8(b_x1->qs + 16); - - // load y - const int8x16_t y0_l = vld1q_s8(b_y0->qs); - const int8x16_t y0_h = vld1q_s8(b_y0->qs + 16); - const int8x16_t y1_l = vld1q_s8(b_y1->qs); - const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); - - float32_t _scale[4] = {GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)}; - float32x4_t scale = vld1q_f32(_scale); - - int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); - int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); - - int8x16_t l2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); - int8x16_t l3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_h), vreinterpretq_s64_s8(x1_h))); - - int8x16_t r0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); - int8x16_t r1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_l), vreinterpretq_s64_s8(y1_l))); - - int8x16_t r2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); - int8x16_t r3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); - - sumv0 = vmlaq_f32(sumv0,(vcvtq_f32_s32(vmmlaq_s32((vmmlaq_s32((vmmlaq_s32((vmmlaq_s32(vdupq_n_s32(0), l0, r0)), - l1, r1)), l2, r2)), l3, r3))), scale); - } - float32x4_t sumv1 = vextq_f32(sumv0, sumv0, 2); - float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1); - - vst1_f32(s, vget_low_f32(sumv2)); - vst1_f32(s + bs, vget_high_f32(sumv2)); - return; - } -#endif - - int ib = 0; - float sumf = 0; - -#if defined(__ARM_FEATURE_SVE) - svfloat32_t sumv0 = svdup_n_f32(0.0f); - svfloat32_t sumv1 = svdup_n_f32(0.0f); - - const int vector_length = ggml_cpu_get_sve_cnt()*8; - - //VLA Implemenation for SVE - switch (vector_length) { - case 128: - { - // predicate for activating lanes for 16 Int8 elements - const svbool_t ph16 = svptrue_pat_b8 (SV_VL16); - const svbool_t pl16 = svptrue_pat_b32(SV_VL4); - - for (; ib + 1 < nb; ib += 2) { - const block_q8_0 * restrict x0 = &x[ib + 0]; - const block_q8_0 * restrict x1 = &x[ib + 1]; - const block_q8_0 * restrict y0 = &y[ib + 0]; - const block_q8_0 * restrict y1 = &y[ib + 1]; - - // load x - const svint8_t qx0_0 = svld1_s8(ph16, x0->qs); - const svint8_t qx0_1 = svld1_s8(ph16, x0->qs+16); - const svint8_t qx1_0 = svld1_s8(ph16, x1->qs); - const svint8_t qx1_1 = svld1_s8(ph16, x1->qs+16); - - // load y - const svint8_t qy0_0 = svld1_s8(ph16, y0->qs); - const svint8_t qy0_1 = svld1_s8(ph16, y0->qs+16); - const svint8_t qy1_0 = svld1_s8(ph16, y1->qs); - const svint8_t qy1_1 = svld1_s8(ph16, y1->qs+16); - - sumv0 = svmla_n_f32_x(pl16, sumv0, svcvt_f32_s32_x(pl16, svadd_x(pl16, - svdot_s32(svdup_n_s32(0), qx0_0, qy0_0), - svdot_s32(svdup_n_s32(0), qx0_1, qy0_1))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); - sumv1 = svmla_n_f32_x(pl16, sumv1, svcvt_f32_s32_x(pl16, svadd_x(pl16, - svdot_s32(svdup_n_s32(0), qx1_0, qy1_0), - svdot_s32(svdup_n_s32(0), qx1_1, qy1_1))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); - } - - sumf = svaddv_f32(pl16, svadd_f32_x(pl16, sumv0, sumv1)); - } break; - case 256: - { - //printf("sve256"); - for (; ib + 1 < nb; ib += 2) { - const block_q8_0 * restrict x0 = &x[ib + 0]; - const block_q8_0 * restrict x1 = &x[ib + 1]; - const block_q8_0 * restrict y0 = &y[ib + 0]; - const block_q8_0 * restrict y1 = &y[ib + 1]; - - // load x - const svint8_t qx0 = svld1_s8(svptrue_b8(), x0->qs); - const svint8_t qx1 = svld1_s8(svptrue_b8(), x1->qs); - - // load y - const svint8_t qy0 = svld1_s8(svptrue_b8(), y0->qs); - const svint8_t qy1 = svld1_s8(svptrue_b8(), y1->qs); - - sumv0 = svmla_n_f32_x(svptrue_b32(), sumv0, svcvt_f32_s32_x(svptrue_b32(), - svdot_s32(svdup_n_s32(0), qx0, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); - sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), - svdot_s32(svdup_n_s32(0), qx1, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); - } - - sumf = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1)); - } break; - case 512: - { - // predicate for activating high 256 bit - const svbool_t ph32 = svptrue_pat_b8(SV_VL32); - // predicate for activating low 256 bit - const svbool_t pl32 = svnot_b_z(svptrue_b8(), ph32); - - // predicate for activating high lanes for 8 float32 elements - const svbool_t ph8 = svptrue_pat_b32(SV_VL8); - // predicate for activating low lanes for 8 float32 elements - const svbool_t pl8 = svnot_b_z(svptrue_b32(), ph8); - - svfloat32_t sumv00 = svdup_n_f32(0.0f); - - for (; ib + 1 < nb; ib += 2) { - const block_q8_0 * restrict x0 = &x[ib + 0]; - const block_q8_0 * restrict x1 = &x[ib + 1]; - const block_q8_0 * restrict y0 = &y[ib + 0]; - const block_q8_0 * restrict y1 = &y[ib + 1]; - - //load 32 int8_t in first half of vector and put another 32 int8_t in second vector lower bits - // and add them to make one 64 element vector - // load x - const svint8_t qx_32 = svld1_s8(ph32, x0->qs); - svint8_t qx_64 = svld1_s8(pl32, x0->qs + 2); - - qx_64 = svadd_s8_x(svptrue_b8(), qx_32, qx_64); - - // load y - const svint8_t qy_32 = svld1_s8(ph32, y0->qs); - svint8_t qy_64 = svld1_s8(pl32, y0->qs + 2); - - qy_64 = svadd_s8_x(svptrue_b8(), qy_32, qy_64); - - // scale creation - const float32_t deq1 = GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d); - const float32_t deq2 = GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d); - - // duplicate deq1 in first half of vector and deq2 in second half of vector - const svfloat32_t temp = svdup_f32_m(svdup_f32_z(ph8, deq1), pl8, deq2); - - const svfloat32_t sumvt = svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx_64, qy_64)); - - sumv00 = svmla_f32_m(svptrue_b32(), sumv00, sumvt, temp); - } - - sumf = svaddv_f32(svptrue_b32(), sumv00); - break; - } - default: - assert(false && "Unsupported vector length"); - break; - } -#elif defined(__ARM_NEON) - float32x4_t sumv0 = vdupq_n_f32(0.0f); - float32x4_t sumv1 = vdupq_n_f32(0.0f); - - for (; ib + 1 < nb; ib += 2) { - const block_q8_0 * restrict x0 = &x[ib + 0]; - const block_q8_0 * restrict x1 = &x[ib + 1]; - const block_q8_0 * restrict y0 = &y[ib + 0]; - const block_q8_0 * restrict y1 = &y[ib + 1]; - - const int8x16_t x0_0 = vld1q_s8(x0->qs); - const int8x16_t x0_1 = vld1q_s8(x0->qs + 16); - const int8x16_t x1_0 = vld1q_s8(x1->qs); - const int8x16_t x1_1 = vld1q_s8(x1->qs + 16); - - // load y - const int8x16_t y0_0 = vld1q_s8(y0->qs); - const int8x16_t y0_1 = vld1q_s8(y0->qs + 16); - const int8x16_t y1_0 = vld1q_s8(y1->qs); - const int8x16_t y1_1 = vld1q_s8(y1->qs + 16); - - sumv0 = vmlaq_n_f32(sumv0, vcvtq_f32_s32(vaddq_s32( - ggml_vdotq_s32(vdupq_n_s32(0), x0_0, y0_0), - ggml_vdotq_s32(vdupq_n_s32(0), x0_1, y0_1))), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); - - sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(vaddq_s32( - ggml_vdotq_s32(vdupq_n_s32(0), x1_0, y1_0), - ggml_vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); - } - - sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1); -#elif defined(__AVX2__) || defined(__AVX__) - // Initialize accumulator with zeros - __m256 acc = _mm256_setzero_ps(); - - // Main loop - for (; ib < nb; ++ib) { - // Compute combined scale for the block - const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d)); - __m256i qx = _mm256_loadu_si256((const __m256i *)x[ib].qs); - __m256i qy = _mm256_loadu_si256((const __m256i *)y[ib].qs); - - const __m256 q = mul_sum_i8_pairs_float(qx, qy); - - // Multiply q with scale and accumulate -#if defined(__AVX2__) - acc = _mm256_fmadd_ps( d, q, acc ); -#else - acc = _mm256_add_ps( _mm256_mul_ps( d, q ), acc ); -#endif - } - - sumf = hsum_float_8(acc); -#elif defined(__riscv_v_intrinsic) - size_t vl = __riscv_vsetvl_e8m1(qk); - - for (; ib < nb; ++ib) { - // load elements - vint8m1_t bx_0 = __riscv_vle8_v_i8m1(x[ib].qs, vl); - vint8m1_t by_0 = __riscv_vle8_v_i8m1(y[ib].qs, vl); - - vint16m2_t vw_mul = __riscv_vwmul_vv_i16m2(bx_0, by_0, vl); - - vint32m1_t v_zero = __riscv_vmv_v_x_i32m1(0, vl); - vint32m1_t v_sum = __riscv_vwredsum_vs_i16m2_i32m1(vw_mul, v_zero, vl); - - int sumi = __riscv_vmv_x_s_i32m1_i32(v_sum); - - sumf += sumi*(GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d)); - } -#elif defined(__POWER9_VECTOR__) - const vector signed int v0 = vec_splats((int32_t)0); - vector float vsumf0 = vec_splats(0.0f); - -#pragma GCC unroll 8 - for (; ib < nb; ++ib) { - __builtin_prefetch(x[ib].qs, 0, 1); - __builtin_prefetch(y[ib].qs, 0, 1); - - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); - vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); - vector float vd = vec_mul(vxd, vyd); - - vector signed char q8x0 = vec_xl( 0, x[ib].qs); - vector signed char q8x1 = vec_xl(16, x[ib].qs); - vector signed char q8y0 = vec_xl( 0, y[ib].qs); - vector signed char q8y1 = vec_xl(16, y[ib].qs); - - vector signed short qv0 = vec_mule(q8x0, q8y0); - vector signed short qv1 = vec_mulo(q8x0, q8y0); - vector signed short qv2 = vec_mule(q8x1, q8y1); - vector signed short qv3 = vec_mulo(q8x1, q8y1); - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - - vsumi0 = vec_sum4s(qv0, vsumi0); - vsumi1 = vec_sum4s(qv1, vsumi1); - vsumi0 = vec_sum4s(qv2, vsumi0); - vsumi1 = vec_sum4s(qv3, vsumi1); - - vsumi0 = vec_add(vsumi0, vsumi1); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - } - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - sumf = vec_extract(vsumf0, 0); - -#elif defined(__loongarch_asx) - // Initialize accumulator with zeros - __m256 acc = (__m256)__lasx_xvldi(0); - - // Main loop - for (; ib < nb; ++ib) { - // Compute combined scale for the block - const __m256 d = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d)); - __m256i qx = __lasx_xvld((const __m256i *)x[ib].qs, 0); - __m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0); - - const __m256 q = mul_sum_i8_pairs_float(qx, qy); - - // Multiply q with scale and accumulate - acc = __lasx_xvfmadd_s( d, q, acc ); - } - - sumf = hsum_float_8(acc); -#endif - for (; ib < nb; ++ib) { - int sumi = 0; - - for (int j = 0; j < qk; j++) { - sumi += x[ib].qs[j]*y[ib].qs[j]; - } - - sumf += sumi*(GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d)); - } - - *s = sumf; -} - -void ggml_vec_dot_tq1_0_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_tq1_0 * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#if defined(__ARM_NEON) - float sumf = 0.0f; - - uint8_t k_shift[16] = {1, 1, 1, 1, 3, 3, 3, 3, 9, 9, 9, 9, 27, 27, 27, 27}; - - const uint8x16_t shift = vld1q_u8(k_shift); - - for (int i = 0; i < nb; ++i) { -#if defined(__ARM_FEATURE_DOTPROD) - int32x4_t sumi0 = vdupq_n_s32(0); - int32x4_t sumi1 = vdupq_n_s32(0); -#else - int16x8_t sumi0 = vdupq_n_s16(0); - int16x8_t sumi1 = vdupq_n_s16(0); -#endif - - // first 32 bytes of 5 elements - { - uint8x16_t qx0 = vld1q_u8(x[i].qs + 0); - uint8x16_t qx1 = vld1q_u8(x[i].qs + 16); - uint8x16_t qx2 = vmulq_u8(qx0, vdupq_n_u8(3)); - uint8x16_t qx3 = vmulq_u8(qx1, vdupq_n_u8(3)); - uint8x16_t qx4 = vmulq_u8(qx0, vdupq_n_u8(9)); - uint8x16_t qx5 = vmulq_u8(qx1, vdupq_n_u8(9)); - uint8x16_t qx6 = vmulq_u8(qx0, vdupq_n_u8(27)); - uint8x16_t qx7 = vmulq_u8(qx1, vdupq_n_u8(27)); - uint8x16_t qx8 = vmulq_u8(qx0, vdupq_n_u8(81)); - uint8x16_t qx9 = vmulq_u8(qx1, vdupq_n_u8(81)); - - // multiply by 3 and keep the 2 bits above 8 bits - int8x16_t sqx0 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx0, vshrq_n_u8(qx0, 1)), 6)); - int8x16_t sqx1 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx1, vshrq_n_u8(qx1, 1)), 6)); - int8x16_t sqx2 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx2, vshrq_n_u8(qx2, 1)), 6)); - int8x16_t sqx3 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx3, vshrq_n_u8(qx3, 1)), 6)); - int8x16_t sqx4 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx4, vshrq_n_u8(qx4, 1)), 6)); - int8x16_t sqx5 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx5, vshrq_n_u8(qx5, 1)), 6)); - int8x16_t sqx6 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx6, vshrq_n_u8(qx6, 1)), 6)); - int8x16_t sqx7 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx7, vshrq_n_u8(qx7, 1)), 6)); - int8x16_t sqx8 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx8, vshrq_n_u8(qx8, 1)), 6)); - int8x16_t sqx9 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx9, vshrq_n_u8(qx9, 1)), 6)); - - const int8x16_t qy0 = vld1q_s8(y[i].qs + 0); - const int8x16_t qy1 = vld1q_s8(y[i].qs + 16); - const int8x16_t qy2 = vld1q_s8(y[i].qs + 32); - const int8x16_t qy3 = vld1q_s8(y[i].qs + 48); - const int8x16_t qy4 = vld1q_s8(y[i].qs + 64); - const int8x16_t qy5 = vld1q_s8(y[i].qs + 80); - const int8x16_t qy6 = vld1q_s8(y[i].qs + 96); - const int8x16_t qy7 = vld1q_s8(y[i].qs + 112); - const int8x16_t qy8 = vld1q_s8(y[i].qs + 128); - const int8x16_t qy9 = vld1q_s8(y[i].qs + 144); - -#if defined(__ARM_FEATURE_DOTPROD) - sumi0 = vdotq_s32(sumi0, sqx0, qy0); - sumi1 = vdotq_s32(sumi1, sqx1, qy1); - sumi0 = vdotq_s32(sumi0, sqx2, qy2); - sumi1 = vdotq_s32(sumi1, sqx3, qy3); - sumi0 = vdotq_s32(sumi0, sqx4, qy4); - sumi1 = vdotq_s32(sumi1, sqx5, qy5); - sumi0 = vdotq_s32(sumi0, sqx6, qy6); - sumi1 = vdotq_s32(sumi1, sqx7, qy7); - sumi0 = vdotq_s32(sumi0, sqx8, qy8); - sumi1 = vdotq_s32(sumi1, sqx9, qy9); -#else - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx0), vget_low_s8(qy0)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx0), vget_high_s8(qy0)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx1), vget_low_s8(qy1)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx1), vget_high_s8(qy1)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx2), vget_low_s8(qy2)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx2), vget_high_s8(qy2)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx3), vget_low_s8(qy3)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx3), vget_high_s8(qy3)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx4), vget_low_s8(qy4)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx4), vget_high_s8(qy4)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx5), vget_low_s8(qy5)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx5), vget_high_s8(qy5)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx6), vget_low_s8(qy6)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx6), vget_high_s8(qy6)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx7), vget_low_s8(qy7)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx7), vget_high_s8(qy7)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx8), vget_low_s8(qy8)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx8), vget_high_s8(qy8)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx9), vget_low_s8(qy9)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx9), vget_high_s8(qy9)); -#endif - } - - // last 16 bytes of 5-element, along with the 4 bytes of 4 elements - { - uint8x16_t qx0 = vld1q_u8(x[i].qs + 32); - uint8x16_t qx1 = vmulq_u8(qx0, vdupq_n_u8(3)); - uint8x16_t qx2 = vmulq_u8(qx0, vdupq_n_u8(9)); - uint8x16_t qx3 = vmulq_u8(qx0, vdupq_n_u8(27)); - uint8x16_t qx4 = vmulq_u8(qx0, vdupq_n_u8(81)); - uint32_t qh; - memcpy(&qh, x[i].qh, sizeof(qh)); // potentially unaligned - uint8x16_t qx5 = vreinterpretq_u8_u32(vdupq_n_u32(qh)); - qx5 = vmulq_u8(qx5, shift); - - // multiply by 3 and keep the 2 bits above 8 bits - int8x16_t sqx0 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx0, vshrq_n_u8(qx0, 1)), 6)); - int8x16_t sqx1 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx1, vshrq_n_u8(qx1, 1)), 6)); - int8x16_t sqx2 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx2, vshrq_n_u8(qx2, 1)), 6)); - int8x16_t sqx3 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx3, vshrq_n_u8(qx3, 1)), 6)); - int8x16_t sqx4 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx4, vshrq_n_u8(qx4, 1)), 6)); - int8x16_t sqx5 = vreinterpretq_s8_u8(vshrq_n_u8(vhaddq_u8(qx5, vshrq_n_u8(qx5, 1)), 6)); - - const int8x16_t qy0 = vld1q_s8(y[i].qs + 160); - const int8x16_t qy1 = vld1q_s8(y[i].qs + 176); - const int8x16_t qy2 = vld1q_s8(y[i].qs + 192); - const int8x16_t qy3 = vld1q_s8(y[i].qs + 208); - const int8x16_t qy4 = vld1q_s8(y[i].qs + 224); - const int8x16_t qy5 = vld1q_s8(y[i].qs + 240); - -#if defined(__ARM_FEATURE_DOTPROD) - sumi0 = vdotq_s32(sumi0, sqx0, qy0); - sumi1 = vdotq_s32(sumi1, sqx1, qy1); - sumi0 = vdotq_s32(sumi0, sqx2, qy2); - sumi1 = vdotq_s32(sumi1, sqx3, qy3); - sumi0 = vdotq_s32(sumi0, sqx4, qy4); - sumi1 = vdotq_s32(sumi1, sqx5, qy5); -#else - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx0), vget_low_s8(qy0)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx0), vget_high_s8(qy0)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx1), vget_low_s8(qy1)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx1), vget_high_s8(qy1)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx2), vget_low_s8(qy2)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx2), vget_high_s8(qy2)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx3), vget_low_s8(qy3)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx3), vget_high_s8(qy3)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx4), vget_low_s8(qy4)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx4), vget_high_s8(qy4)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx5), vget_low_s8(qy5)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx5), vget_high_s8(qy5)); -#endif - } - - const int16x8_t ysum0 = vld1q_s16(y[i].bsums); - const int16x8_t ysum1 = vld1q_s16(y[i].bsums + 8); - - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - -#if defined(__ARM_FEATURE_DOTPROD) - sumi0 = vaddq_s32(sumi0, sumi1); - sumi0 = vsubq_s32(sumi0, vpaddlq_s16(vaddq_s16(ysum0, ysum1))); - - sumf += d * (float) vaddvq_s32(sumi0); -#else - sumi0 = vaddq_s16(sumi0, sumi1); - sumi0 = vsubq_s16(sumi0, vaddq_s16(ysum0, ysum1)); - - sumf += d * (float) vaddlvq_s16(sumi0); -#endif - } - - *s = sumf; - -#elif defined(__AVX2__) - __m256 sumf = _mm256_setzero_ps(); - - for (int i = 0; i < nb; ++i) { - // 16-bit sums - __m256i sumi0 = _mm256_setzero_si256(); - __m256i sumi1 = _mm256_setzero_si256(); - __m256i sumi2 = _mm256_setzero_si256(); - - // first 32 bytes of 5 elements - { - __m256i qx0 = _mm256_loadu_si256((const __m256i *) (x[i].qs)); - // 8-bit multiplies with shifts, masks and adds - __m256i qx1 = _mm256_add_epi8(qx0, _mm256_add_epi8(qx0, qx0)); // 1 * 3 - __m256i qx2 = _mm256_add_epi8(_mm256_and_si256(_mm256_slli_epi16(qx0, 3), _mm256_set1_epi8(-8)), qx0); // 1 * 9 - __m256i qx3 = _mm256_add_epi8(_mm256_and_si256(_mm256_slli_epi16(qx1, 3), _mm256_set1_epi8(-8)), qx1); // 3 * 9 - __m256i qx4 = _mm256_add_epi8(_mm256_and_si256(_mm256_slli_epi16(qx2, 3), _mm256_set1_epi8(-8)), qx2); // 9 * 9 - - // TODO: can _mm256_mulhi_epu16 be faster even if 16-bits? - - // Cancel the +1 from avg so that it behaves like a halving add - qx0 = _mm256_subs_epu8(qx0, _mm256_set1_epi8(1)); - qx1 = _mm256_subs_epu8(qx1, _mm256_set1_epi8(1)); - qx2 = _mm256_subs_epu8(qx2, _mm256_set1_epi8(1)); - qx3 = _mm256_subs_epu8(qx3, _mm256_set1_epi8(1)); - qx4 = _mm256_subs_epu8(qx4, _mm256_set1_epi8(1)); - // Multiply by 3 and get the top 2 bits - qx0 = _mm256_avg_epu8(qx0, _mm256_avg_epu8(qx0, _mm256_setzero_si256())); - qx1 = _mm256_avg_epu8(qx1, _mm256_avg_epu8(qx1, _mm256_setzero_si256())); - qx2 = _mm256_avg_epu8(qx2, _mm256_avg_epu8(qx2, _mm256_setzero_si256())); - qx3 = _mm256_avg_epu8(qx3, _mm256_avg_epu8(qx3, _mm256_setzero_si256())); - qx4 = _mm256_avg_epu8(qx4, _mm256_avg_epu8(qx4, _mm256_setzero_si256())); - qx0 = _mm256_and_si256(_mm256_srli_epi16(qx0, 6), _mm256_set1_epi8(3)); - qx1 = _mm256_and_si256(_mm256_srli_epi16(qx1, 6), _mm256_set1_epi8(3)); - qx2 = _mm256_and_si256(_mm256_srli_epi16(qx2, 6), _mm256_set1_epi8(3)); - qx3 = _mm256_and_si256(_mm256_srli_epi16(qx3, 6), _mm256_set1_epi8(3)); - qx4 = _mm256_and_si256(_mm256_srli_epi16(qx4, 6), _mm256_set1_epi8(3)); - - const __m256i qy0 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 0)); - const __m256i qy1 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 32)); - const __m256i qy2 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 64)); - const __m256i qy3 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 96)); - const __m256i qy4 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 128)); - - qx0 = _mm256_maddubs_epi16(qx0, qy0); - qx1 = _mm256_maddubs_epi16(qx1, qy1); - qx2 = _mm256_maddubs_epi16(qx2, qy2); - qx3 = _mm256_maddubs_epi16(qx3, qy3); - qx4 = _mm256_maddubs_epi16(qx4, qy4); - - sumi0 = _mm256_add_epi16(sumi0, _mm256_add_epi16(qx0, qx1)); - sumi1 = _mm256_add_epi16(sumi1, _mm256_add_epi16(qx2, qx3)); - sumi2 = _mm256_add_epi16(sumi2, qx4); - } - - // last 16 bytes of 5-element, along with the 4 bytes of 4 elements - { - __m128i qx0 = _mm_loadu_si128((const __m128i *) (x[i].qs + 32)); - uint32_t qh; - memcpy(&qh, x[i].qh, sizeof(qh)); // potentially unaligned - __m256i qx5_l = _mm256_cvtepu8_epi16(_mm_set1_epi32(qh)); - __m128i qx1 = _mm_add_epi8(qx0, _mm_add_epi8(qx0, qx0)); // 1 * 3 - __m128i qx2 = _mm_add_epi8(_mm_and_si128(_mm_slli_epi16(qx0, 3), _mm_set1_epi8(-8)), qx0); // 1 * 9 - __m128i qx3 = _mm_add_epi8(_mm_and_si128(_mm_slli_epi16(qx1, 3), _mm_set1_epi8(-8)), qx1); // 3 * 9 - __m128i qx4 = _mm_add_epi8(_mm_and_si128(_mm_slli_epi16(qx2, 3), _mm_set1_epi8(-8)), qx2); // 9 * 9 - __m256i qx01 = MM256_SET_M128I(qx1, qx0); - __m256i qx23 = MM256_SET_M128I(qx3, qx2); - - // avx2 does not have 8-bit multiplies, so 16-bit it is. - qx5_l = _mm256_mullo_epi16(qx5_l, _mm256_set_epi16(27, 27, 27, 27, 9, 9, 9, 9, 3, 3, 3, 3, 1, 1, 1, 1)); - qx5_l = _mm256_and_si256(qx5_l, _mm256_set1_epi16(0xFF)); - __m128i qx5 = _mm_packus_epi16(_mm256_castsi256_si128(qx5_l), _mm256_extracti128_si256(qx5_l, 1)); - - __m256i qx45 = MM256_SET_M128I(qx5, qx4); - - // Cancel the +1 from avg so that it behaves like a halving add - qx01 = _mm256_subs_epu8(qx01, _mm256_set1_epi8(1)); - qx23 = _mm256_subs_epu8(qx23, _mm256_set1_epi8(1)); - qx45 = _mm256_subs_epu8(qx45, _mm256_set1_epi8(1)); - // Multiply by 3 and get the top 2 bits - qx01 = _mm256_avg_epu8(qx01, _mm256_avg_epu8(qx01, _mm256_setzero_si256())); - qx23 = _mm256_avg_epu8(qx23, _mm256_avg_epu8(qx23, _mm256_setzero_si256())); - qx45 = _mm256_avg_epu8(qx45, _mm256_avg_epu8(qx45, _mm256_setzero_si256())); - qx01 = _mm256_and_si256(_mm256_srli_epi16(qx01, 6), _mm256_set1_epi8(3)); - qx23 = _mm256_and_si256(_mm256_srli_epi16(qx23, 6), _mm256_set1_epi8(3)); - qx45 = _mm256_and_si256(_mm256_srli_epi16(qx45, 6), _mm256_set1_epi8(3)); - - const __m256i qy01 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 160)); - const __m256i qy23 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 192)); - const __m256i qy45 = _mm256_loadu_si256((const __m256i *) (y[i].qs + 224)); - - qx01 = _mm256_maddubs_epi16(qx01, qy01); - qx23 = _mm256_maddubs_epi16(qx23, qy23); - qx45 = _mm256_maddubs_epi16(qx45, qy45); - - sumi0 = _mm256_add_epi16(sumi0, qx01); - sumi1 = _mm256_add_epi16(sumi1, qx23); - sumi2 = _mm256_add_epi16(sumi2, qx45); - } - - const __m256i ysum = _mm256_loadu_si256((const __m256i *) y[i].bsums); - const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(x[i].d)); - - sumi0 = _mm256_sub_epi16(sumi0, ysum); - sumi0 = _mm256_add_epi16(sumi0, _mm256_add_epi16(sumi1, sumi2)); - sumi0 = _mm256_madd_epi16(sumi0, _mm256_set1_epi16(1)); - - sumf = _mm256_add_ps(_mm256_mul_ps(_mm256_cvtepi32_ps(sumi0), d), sumf); - } - - *s = hsum_float_8(sumf); - -#else - const uint8_t pow3[6] = {1, 3, 9, 27, 81, 243}; - - float sumf = 0.0f; - - for (int i = 0; i < nb; ++i) { - int sum = 0; - - for (size_t j = 0; j < sizeof(x->qs) - sizeof(x->qs) % 32; j += 32) { - for (size_t l = 0; l < 5; ++l) { - for (size_t m = 0; m < 32; ++m) { - uint8_t q = x[i].qs[j + m] * pow3[l]; - uint16_t xi = ((uint16_t) q * 3) >> 8; - sum += (xi - 1) * y[i].qs[j*5 + l*32 + m]; - } - } - } - for (size_t j = sizeof(x->qs) - sizeof(x->qs) % 32; j < sizeof(x->qs); j += 16) { - for (size_t l = 0; l < 5; ++l) { - for (size_t m = 0; m < 16; ++m) { - uint8_t q = x[i].qs[j + m] * pow3[l]; - uint16_t xi = ((uint16_t) q * 3) >> 8; - sum += (xi - 1) * y[i].qs[j*5 + l*16 + m]; - } - } - } - - for (size_t l = 0; l < 4; ++l) { - for (size_t j = 0; j < sizeof(x->qh); ++j) { - uint8_t q = x[i].qh[j] * pow3[l]; - uint16_t xi = ((uint16_t) q * 3) >> 8; - sum += (xi - 1) * y[i].qs[sizeof(x->qs)*5 + l*sizeof(x->qh) + j]; - } - } - - sumf += (float) sum * (GGML_FP16_TO_FP32(x[i].d) * y[i].d); - } - - *s = sumf; -#endif -} - -void ggml_vec_dot_tq2_0_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_tq2_0 * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#if defined(__ARM_NEON) - float sumf = 0.0f; - - const uint8x16_t m3 = vdupq_n_u8(3); - - for (int i = 0; i < nb; ++i) { -#if defined(__ARM_FEATURE_DOTPROD) - int32x4_t sumi0 = vdupq_n_s32(0); - int32x4_t sumi1 = vdupq_n_s32(0); -#else - int16x8_t sumi0 = vdupq_n_s16(0); - int16x8_t sumi1 = vdupq_n_s16(0); -#endif - - for (size_t j = 0; j < sizeof(x->qs); j += 32) { - uint8x16_t qx0 = vld1q_u8(x[i].qs + j); - uint8x16_t qx1 = vld1q_u8(x[i].qs + j + 16); - uint8x16_t qx2 = vshrq_n_u8(qx0, 2); - uint8x16_t qx3 = vshrq_n_u8(qx1, 2); - uint8x16_t qx4 = vshrq_n_u8(qx0, 4); - uint8x16_t qx5 = vshrq_n_u8(qx1, 4); - uint8x16_t qx6 = vshrq_n_u8(qx0, 6); - uint8x16_t qx7 = vshrq_n_u8(qx1, 6); - - int8x16_t sqx0 = vreinterpretq_s8_u8(vandq_u8(qx0, m3)); - int8x16_t sqx1 = vreinterpretq_s8_u8(vandq_u8(qx1, m3)); - int8x16_t sqx2 = vreinterpretq_s8_u8(vandq_u8(qx2, m3)); - int8x16_t sqx3 = vreinterpretq_s8_u8(vandq_u8(qx3, m3)); - int8x16_t sqx4 = vreinterpretq_s8_u8(vandq_u8(qx4, m3)); - int8x16_t sqx5 = vreinterpretq_s8_u8(vandq_u8(qx5, m3)); - int8x16_t sqx6 = vreinterpretq_s8_u8(vandq_u8(qx6, m3)); - int8x16_t sqx7 = vreinterpretq_s8_u8(vandq_u8(qx7, m3)); - - const int8x16_t qy0 = vld1q_s8(y[i].qs + j*4 + 0); - const int8x16_t qy1 = vld1q_s8(y[i].qs + j*4 + 16); - const int8x16_t qy2 = vld1q_s8(y[i].qs + j*4 + 32); - const int8x16_t qy3 = vld1q_s8(y[i].qs + j*4 + 48); - const int8x16_t qy4 = vld1q_s8(y[i].qs + j*4 + 64); - const int8x16_t qy5 = vld1q_s8(y[i].qs + j*4 + 80); - const int8x16_t qy6 = vld1q_s8(y[i].qs + j*4 + 96); - const int8x16_t qy7 = vld1q_s8(y[i].qs + j*4 + 112); - -#if defined(__ARM_FEATURE_DOTPROD) - sumi0 = vdotq_s32(sumi0, sqx0, qy0); - sumi1 = vdotq_s32(sumi1, sqx1, qy1); - sumi0 = vdotq_s32(sumi0, sqx2, qy2); - sumi1 = vdotq_s32(sumi1, sqx3, qy3); - sumi0 = vdotq_s32(sumi0, sqx4, qy4); - sumi1 = vdotq_s32(sumi1, sqx5, qy5); - sumi0 = vdotq_s32(sumi0, sqx6, qy6); - sumi1 = vdotq_s32(sumi1, sqx7, qy7); -#else - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx0), vget_low_s8(qy0)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx0), vget_high_s8(qy0)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx1), vget_low_s8(qy1)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx1), vget_high_s8(qy1)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx2), vget_low_s8(qy2)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx2), vget_high_s8(qy2)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx3), vget_low_s8(qy3)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx3), vget_high_s8(qy3)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx4), vget_low_s8(qy4)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx4), vget_high_s8(qy4)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx5), vget_low_s8(qy5)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx5), vget_high_s8(qy5)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx6), vget_low_s8(qy6)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx6), vget_high_s8(qy6)); - sumi0 = vmlal_s8(sumi0, vget_low_s8(sqx7), vget_low_s8(qy7)); - sumi1 = vmlal_s8(sumi1, vget_high_s8(sqx7), vget_high_s8(qy7)); -#endif - } - - const int16x8_t ysum0 = vld1q_s16(y[i].bsums); - const int16x8_t ysum1 = vld1q_s16(y[i].bsums + 8); - - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - -#if defined(__ARM_FEATURE_DOTPROD) - sumi0 = vaddq_s32(sumi0, sumi1); - sumi0 = vsubq_s32(sumi0, vpaddlq_s16(vaddq_s16(ysum0, ysum1))); - - sumf += d * (float) vaddvq_s32(sumi0); -#else - sumi0 = vaddq_s16(sumi0, sumi1); - sumi0 = vsubq_s16(sumi0, vaddq_s16(ysum0, ysum1)); - - sumf += d * (float) vaddlvq_s16(sumi0); -#endif - } - - *s = sumf; - -#elif defined(__AVX2__) - __m256 sumf = _mm256_setzero_ps(); - - for (int i = 0; i < nb; ++i) { - // 16-bit sums, because 256*127 still fits - __m256i sumi0 = _mm256_setzero_si256(); - __m256i sumi1 = _mm256_setzero_si256(); - - for (size_t j = 0; j < sizeof(x->qs); j += 32) { - __m256i qx0 = _mm256_loadu_si256((const __m256i *) (x[i].qs + j)); - __m256i qx1 = _mm256_srli_epi16(qx0, 2); - __m256i qx2 = _mm256_srli_epi16(qx0, 4); - __m256i qx3 = _mm256_srli_epi16(qx0, 6); - - // 0, 1, 2 (should not be 3) - qx0 = _mm256_and_si256(qx0, _mm256_set1_epi8(3)); - qx1 = _mm256_and_si256(qx1, _mm256_set1_epi8(3)); - qx2 = _mm256_and_si256(qx2, _mm256_set1_epi8(3)); - qx3 = _mm256_and_si256(qx3, _mm256_set1_epi8(3)); - - const __m256i qy0 = _mm256_loadu_si256((const __m256i *) (y[i].qs + j*4 + 0)); - const __m256i qy1 = _mm256_loadu_si256((const __m256i *) (y[i].qs + j*4 + 32)); - const __m256i qy2 = _mm256_loadu_si256((const __m256i *) (y[i].qs + j*4 + 64)); - const __m256i qy3 = _mm256_loadu_si256((const __m256i *) (y[i].qs + j*4 + 96)); - - qx0 = _mm256_maddubs_epi16(qx0, qy0); - qx1 = _mm256_maddubs_epi16(qx1, qy1); - qx2 = _mm256_maddubs_epi16(qx2, qy2); - qx3 = _mm256_maddubs_epi16(qx3, qy3); - - sumi0 = _mm256_add_epi16(sumi0, _mm256_add_epi16(qx0, qx1)); - sumi1 = _mm256_add_epi16(sumi1, _mm256_add_epi16(qx2, qx3)); - } - - const __m256i ysum = _mm256_loadu_si256((const __m256i *) y[i].bsums); - const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(x[i].d)); - - sumi0 = _mm256_add_epi16(sumi0, sumi1); - sumi0 = _mm256_sub_epi16(sumi0, ysum); - sumi0 = _mm256_madd_epi16(sumi0, _mm256_set1_epi16(1)); - - sumf = _mm256_add_ps(_mm256_mul_ps(_mm256_cvtepi32_ps(sumi0), d), sumf); - } - - *s = hsum_float_8(sumf); - -#else - float sumf = 0.0f; - - for (int i = 0; i < nb; ++i) { - int32_t sumi = 0; - - for (size_t j = 0; j < sizeof(x->qs); j += 32) { - for (size_t l = 0; l < 4; ++l) { - for (size_t k = 0; k < 32; ++k) { - sumi += y[i].qs[j*4 + l*32 + k] * (((x[i].qs[j + k] >> (l*2)) & 3) - 1); - } - } - } - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - sumf += (float) sumi * d; - } - - *s = sumf; -#endif -} - -void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q2_K * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#ifdef __ARM_NEON - const uint8x16_t m3 = vdupq_n_u8(0x3); - const uint8x16_t m4 = vdupq_n_u8(0xF); - - const int32x4_t vzero = vdupq_n_s32(0); - - ggml_int8x16x2_t q2bytes; - uint8_t aux[16]; - - float sum = 0; - - for (int i = 0; i < nb; ++i) { - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const uint8_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - const uint8_t * restrict sc = x[i].scales; - - const uint8x16_t mins_and_scales = vld1q_u8(sc); - const uint8x16_t scales = vandq_u8(mins_and_scales, m4); - vst1q_u8(aux, scales); - - const uint8x16_t mins = vshrq_n_u8(mins_and_scales, 4); - const ggml_int16x8x2_t q8sums = ggml_vld1q_s16_x2(y[i].bsums); - const ggml_int16x8x2_t mins16 = {{vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(mins))), vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(mins)))}}; - const int32x4_t s0 = vaddq_s32(vmull_s16(vget_low_s16 (mins16.val[0]), vget_low_s16 (q8sums.val[0])), - vmull_s16(vget_high_s16(mins16.val[0]), vget_high_s16(q8sums.val[0]))); - const int32x4_t s1 = vaddq_s32(vmull_s16(vget_low_s16 (mins16.val[1]), vget_low_s16 (q8sums.val[1])), - vmull_s16(vget_high_s16(mins16.val[1]), vget_high_s16(q8sums.val[1]))); - sum += dmin * vaddvq_s32(vaddq_s32(s0, s1)); - - int isum = 0; - int is = 0; - -// We use this macro instead of a function call because for some reason -// the code runs 2-3% slower, even if the function is declared inline -#define MULTIPLY_ACCUM_WITH_SCALE(index)\ - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * aux[is+(index)];\ - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * aux[is+1+(index)]; - -#define SHIFT_MULTIPLY_ACCUM_WITH_SCALE(shift, index)\ - q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32;\ - q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits.val[0], (shift)), m3));\ - q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits.val[1], (shift)), m3));\ - MULTIPLY_ACCUM_WITH_SCALE((index)); - - for (int j = 0; j < QK_K/128; ++j) { - const ggml_uint8x16x2_t q2bits = ggml_vld1q_u8_x2(q2); q2 += 32; - - ggml_int8x16x2_t q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; - q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(q2bits.val[0], m3)); - q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(q2bits.val[1], m3)); - - MULTIPLY_ACCUM_WITH_SCALE(0); - - SHIFT_MULTIPLY_ACCUM_WITH_SCALE(2, 2); - SHIFT_MULTIPLY_ACCUM_WITH_SCALE(4, 4); - SHIFT_MULTIPLY_ACCUM_WITH_SCALE(6, 6); - - is += 8; - } - - sum += d * isum; - } - - *s = sum; - -#elif defined __AVX2__ - - const __m256i m3 = _mm256_set1_epi8(3); - const __m128i m4 = _mm_set1_epi8(0xF); - - __m256 acc = _mm256_setzero_ps(); - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const uint8_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const __m128i mins_and_scales = _mm_loadu_si128((const __m128i*)x[i].scales); - const __m128i scales8 = _mm_and_si128(mins_and_scales, m4); - const __m128i mins8 = _mm_and_si128(_mm_srli_epi16(mins_and_scales, 4), m4); - const __m256i mins = _mm256_cvtepi8_epi16(mins8); - const __m256i prod = _mm256_madd_epi16(mins, _mm256_loadu_si256((const __m256i*)y[i].bsums)); - - acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&dmin), _mm256_cvtepi32_ps(prod), acc); - - const __m256i all_scales = _mm256_cvtepi8_epi16(scales8); - const __m128i l_scales = _mm256_extracti128_si256(all_scales, 0); - const __m128i h_scales = _mm256_extracti128_si256(all_scales, 1); - const __m256i scales[2] = {MM256_SET_M128I(l_scales, l_scales), MM256_SET_M128I(h_scales, h_scales)}; - - __m256i sumi = _mm256_setzero_si256(); - - for (int j = 0; j < QK_K/128; ++j) { - - const __m256i q2bits = _mm256_loadu_si256((const __m256i*)q2); q2 += 32; - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q8_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q8_3 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - - const __m256i q2_0 = _mm256_and_si256(q2bits, m3); - const __m256i q2_1 = _mm256_and_si256(_mm256_srli_epi16(q2bits, 2), m3); - const __m256i q2_2 = _mm256_and_si256(_mm256_srli_epi16(q2bits, 4), m3); - const __m256i q2_3 = _mm256_and_si256(_mm256_srli_epi16(q2bits, 6), m3); - - __m256i p0 = _mm256_maddubs_epi16(q2_0, q8_0); - __m256i p1 = _mm256_maddubs_epi16(q2_1, q8_1); - __m256i p2 = _mm256_maddubs_epi16(q2_2, q8_2); - __m256i p3 = _mm256_maddubs_epi16(q2_3, q8_3); - - p0 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(0)), p0); - p1 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(1)), p1); - p2 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(2)), p2); - p3 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(3)), p3); - - p0 = _mm256_add_epi32(p0, p1); - p2 = _mm256_add_epi32(p2, p3); - - sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p0, p2)); - } - - acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi), acc); - - } - - *s = hsum_float_8(acc); - -#elif defined __AVX__ - - const __m128i m3 = _mm_set1_epi8(0x3); - const __m128i m4 = _mm_set1_epi8(0xF); - const __m128i m2 = _mm_set1_epi8(0x2); - - __m256 acc = _mm256_setzero_ps(); - - for (int i = 0; i < nb; ++i) { - - const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const uint8_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - // load mins and scales from block_q2_K.scales[QK_K/16] - const __m128i mins_and_scales = _mm_loadu_si128((const __m128i*)x[i].scales); - const __m128i scales16 = _mm_and_si128(mins_and_scales, m4); - const __m128i mins16 = _mm_and_si128(_mm_srli_epi16(mins_and_scales, 4), m4); - const __m128i mins_0 = _mm_cvtepi8_epi16(mins16); - const __m128i mins_1 = _mm_cvtepi8_epi16(_mm_unpackhi_epi64(mins16, mins16)); - - // summs = y[i].bsums * (x[i].scales >> 4) in 16bits*8*2 to 32bits*4*2 - const __m128i summs_0 = _mm_madd_epi16(mins_0, _mm_loadu_si128((const __m128i*)&y[i].bsums[0])); - const __m128i summs_1 = _mm_madd_epi16(mins_1, _mm_loadu_si128((const __m128i*)&y[i].bsums[8])); - - // sumf += -dmin * summs in 32bits*8 - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&dmin), _mm256_cvtepi32_ps(MM256_SET_M128I(summs_1, summs_0))), acc); - - const __m128i scales_0 = _mm_cvtepi8_epi16(scales16); - const __m128i scales_1 = _mm_cvtepi8_epi16(_mm_unpackhi_epi64(scales16, scales16)); - const __m128i scales[2] = { scales_0, scales_1 }; - - __m128i sumi_0 = _mm_setzero_si128(); - __m128i sumi_1 = _mm_setzero_si128(); - - for (int j = 0; j < QK_K/128; ++j) { - - // load Q8 quants int8*16*8 from block_q8_K.qs[QK_K] - const __m128i q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_2 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_3 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_4 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_5 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_6 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_7 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - - // load 2bits*16*8 from block_q2_K.qs[QK_K/4] - __m128i q2bits = _mm_loadu_si128((const __m128i*)q2); q2 += 16; - const __m128i q2_0 = _mm_and_si128(q2bits, m3); - const __m128i q2_2 = _mm_and_si128(_mm_srli_epi16(q2bits, 2), m3); - const __m128i q2_4 = _mm_and_si128(_mm_srli_epi16(q2bits, 4), m3); - const __m128i q2_6 = _mm_and_si128(_mm_srli_epi16(q2bits, 6), m3); - q2bits = _mm_loadu_si128((const __m128i*)q2); q2 += 16; - const __m128i q2_1 = _mm_and_si128(q2bits, m3); - const __m128i q2_3 = _mm_and_si128(_mm_srli_epi16(q2bits, 2), m3); - const __m128i q2_5 = _mm_and_si128(_mm_srli_epi16(q2bits, 4), m3); - const __m128i q2_7 = _mm_and_si128(_mm_srli_epi16(q2bits, 6), m3); - - // isuml = q8[l] * ((q2[l] >> shift) & 3) in 8bits*16*8 to 16bits*8*8 - __m128i p0 = _mm_maddubs_epi16(q2_0, q8_0); - __m128i p1 = _mm_maddubs_epi16(q2_1, q8_1); - __m128i p2 = _mm_maddubs_epi16(q2_2, q8_2); - __m128i p3 = _mm_maddubs_epi16(q2_3, q8_3); - __m128i p4 = _mm_maddubs_epi16(q2_4, q8_4); - __m128i p5 = _mm_maddubs_epi16(q2_5, q8_5); - __m128i p6 = _mm_maddubs_epi16(q2_6, q8_6); - __m128i p7 = _mm_maddubs_epi16(q2_7, q8_7); - - // isum += (x[i].scales[is++] & 0xF) * isuml in 16bits*8*8 to 32bits*4*8 - __m128i shuffle = _mm_set1_epi16(0x0100); - p0 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p0); - shuffle = _mm_add_epi16(shuffle, m2); - p1 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p1); - shuffle = _mm_add_epi16(shuffle, m2); - p2 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p2); - shuffle = _mm_add_epi16(shuffle, m2); - p3 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p3); - shuffle = _mm_add_epi16(shuffle, m2); - p4 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p4); - shuffle = _mm_add_epi16(shuffle, m2); - p5 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p5); - shuffle = _mm_add_epi16(shuffle, m2); - p6 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p6); - shuffle = _mm_add_epi16(shuffle, m2); - p7 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p7); - - p0 = _mm_add_epi32(p0, p1); - p2 = _mm_add_epi32(p2, p3); - p4 = _mm_add_epi32(p4, p5); - p6 = _mm_add_epi32(p6, p7); - - // isum in 32bits*4*2 - sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p0, p2)); - sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p4, p6)); - } - - // sumf += dall * isum - dmin * summs in 32bits - __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0); - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&dall), _mm256_cvtepi32_ps(sumi)), acc); - } - - *s = hsum_float_8(acc); - -#elif defined __riscv_v_intrinsic - - float sumf = 0; - uint8_t temp_01[32] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}; - - for (int i = 0; i < nb; ++i) { - - const uint8_t * q2 = x[i].qs; - const int8_t * q8 = y[i].qs; - const uint8_t * sc = x[i].scales; - - const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - size_t vl = 16; - - vuint8m1_t scales = __riscv_vle8_v_u8m1(sc, vl); - vuint8m1_t aux = __riscv_vand_vx_u8m1(scales, 0x0F, vl); - - vint16m1_t q8sums = __riscv_vle16_v_i16m1(y[i].bsums, vl); - - vuint8mf2_t scales_2 = __riscv_vle8_v_u8mf2(sc, vl); - vuint8mf2_t mins8 = __riscv_vsrl_vx_u8mf2(scales_2, 0x4, vl); - vint16m1_t mins = __riscv_vreinterpret_v_u16m1_i16m1(__riscv_vzext_vf2_u16m1(mins8, vl)); - vint32m2_t prod = __riscv_vwmul_vv_i32m2(q8sums, mins, vl); - vint32m1_t vsums = __riscv_vredsum_vs_i32m2_i32m1(prod, __riscv_vmv_v_x_i32m1(0, 1), vl); - - sumf += dmin * __riscv_vmv_x_s_i32m1_i32(vsums); - - vl = 32; - - vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); - vuint8m1_t v_b = __riscv_vle8_v_u8m1(temp_01, vl); - - uint8_t is=0; - int isum=0; - - for (int j = 0; j < QK_K/128; ++j) { - // load Q2 - vuint8m1_t q2_x = __riscv_vle8_v_u8m1(q2, vl); - - vuint8m1_t q2_0 = __riscv_vand_vx_u8m1(q2_x, 0x03, vl); - vuint8m1_t q2_1 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q2_x, 0x2, vl), 0x03 , vl); - vuint8m1_t q2_2 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q2_x, 0x4, vl), 0x03 , vl); - vuint8m1_t q2_3 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q2_x, 0x6, vl), 0x03 , vl); - - // duplicate scale elements for product - vuint8m1_t sc0 = __riscv_vrgather_vv_u8m1(aux, __riscv_vadd_vx_u8m1(v_b, 0+is, vl), vl); - vuint8m1_t sc1 = __riscv_vrgather_vv_u8m1(aux, __riscv_vadd_vx_u8m1(v_b, 2+is, vl), vl); - vuint8m1_t sc2 = __riscv_vrgather_vv_u8m1(aux, __riscv_vadd_vx_u8m1(v_b, 4+is, vl), vl); - vuint8m1_t sc3 = __riscv_vrgather_vv_u8m1(aux, __riscv_vadd_vx_u8m1(v_b, 6+is, vl), vl); - - vint16m2_t p0 = __riscv_vreinterpret_v_u16m2_i16m2(__riscv_vwmulu_vv_u16m2(q2_0, sc0, vl)); - vint16m2_t p1 = __riscv_vreinterpret_v_u16m2_i16m2(__riscv_vwmulu_vv_u16m2(q2_1, sc1, vl)); - vint16m2_t p2 = __riscv_vreinterpret_v_u16m2_i16m2(__riscv_vwmulu_vv_u16m2(q2_2, sc2, vl)); - vint16m2_t p3 = __riscv_vreinterpret_v_u16m2_i16m2(__riscv_vwmulu_vv_u16m2(q2_3, sc3, vl)); - - // load Q8 - vint8m1_t q8_0 = __riscv_vle8_v_i8m1(q8, vl); - vint8m1_t q8_1 = __riscv_vle8_v_i8m1(q8+32, vl); - vint8m1_t q8_2 = __riscv_vle8_v_i8m1(q8+64, vl); - vint8m1_t q8_3 = __riscv_vle8_v_i8m1(q8+96, vl); - - vint32m4_t s0 = __riscv_vwmul_vv_i32m4(p0, __riscv_vwcvt_x_x_v_i16m2(q8_0, vl), vl); - vint32m4_t s1 = __riscv_vwmul_vv_i32m4(p1, __riscv_vwcvt_x_x_v_i16m2(q8_1, vl), vl); - vint32m4_t s2 = __riscv_vwmul_vv_i32m4(p2, __riscv_vwcvt_x_x_v_i16m2(q8_2, vl), vl); - vint32m4_t s3 = __riscv_vwmul_vv_i32m4(p3, __riscv_vwcvt_x_x_v_i16m2(q8_3, vl), vl); - - vint32m1_t isum0 = __riscv_vredsum_vs_i32m4_i32m1(__riscv_vadd_vv_i32m4(s0, s1, vl), vzero, vl); - vint32m1_t isum1 = __riscv_vredsum_vs_i32m4_i32m1(__riscv_vadd_vv_i32m4(s2, s3, vl), isum0, vl); - - isum += __riscv_vmv_x_s_i32m1_i32(isum1); - - q2+=32; q8+=128; is=8; - - } - - sumf += dall * isum; - - } - - *s = sumf; - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0x3); - const vector signed char lowScaleMask = vec_splats((signed char)0xF); - const vector int v0 = vec_splats((int32_t)0); - const vector unsigned char v2 = vec_splats((unsigned char)0x2); - const vector unsigned char v6 = vec_splats((unsigned char)0x6); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - - for (int i = 0; i < nb; ++i) { - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].dmin)); - vector float vdmin = vec_mul(vxmin, vyd); - - vector signed short q8ysums0 = vec_xl( 0, y[i].bsums); - vector signed short q8ysums1 = vec_xl(16, y[i].bsums); - - vector signed char q2xmins = (vector signed char)vec_xl( 0, x[i].scales); - vector signed char vscales = vec_and(q2xmins, lowScaleMask); - - q2xmins = vec_sr(q2xmins, v4); - vector signed short q2xmins0 = vec_unpackh(q2xmins); - vector signed short q2xmins1 = vec_unpackl(q2xmins); - - vector signed int prod0 = vec_mule(q2xmins0, q8ysums0); - vector signed int prod1 = vec_mulo(q2xmins0, q8ysums0); - vector signed int prod2 = vec_mule(q2xmins1, q8ysums1); - vector signed int prod3 = vec_mulo(q2xmins1, q8ysums1); - - vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vdmin, vsumf0); - vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vdmin, vsumf1); - vsumf2 = vec_nmsub(vec_ctf(prod2, 0), vdmin, vsumf2); - vsumf3 = vec_nmsub(vec_ctf(prod3, 0), vdmin, vsumf3); - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - vector signed int vsumi2 = v0; - vector signed int vsumi3 = v0; - vector signed int vsumi4 = v0; - vector signed int vsumi5 = v0; - vector signed int vsumi6 = v0; - vector signed int vsumi7 = v0; - - const uint8_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - for (int j = 0; j < QK_K/128; ++j) { - __builtin_prefetch(q2, 0, 1); - __builtin_prefetch(q8, 0, 1); - - vector signed char qxs0 = (vector signed char)vec_xl( 0, q2); - vector signed char qxs1 = (vector signed char)vec_xl(16, q2); - q2 += 32; - - vector unsigned char q2x00 = (vector unsigned char)vec_and(qxs0, lowMask); - vector unsigned char q2x01 = (vector unsigned char)vec_and(vec_sr(qxs0, v2), lowMask); - vector unsigned char q2x02 = (vector unsigned char)vec_and(vec_sr(qxs0, v4), lowMask); - vector unsigned char q2x03 = (vector unsigned char)vec_and(vec_sr(qxs0, v6), lowMask); - vector unsigned char q2x10 = (vector unsigned char)vec_and(qxs1, lowMask); - vector unsigned char q2x11 = (vector unsigned char)vec_and(vec_sr(qxs1, v2), lowMask); - vector unsigned char q2x12 = (vector unsigned char)vec_and(vec_sr(qxs1, v4), lowMask); - vector unsigned char q2x13 = (vector unsigned char)vec_and(vec_sr(qxs1, v6), lowMask); - - vector signed char q8y00 = vec_xl( 0, q8); - vector signed char q8y10 = vec_xl( 16, q8); - vector signed char q8y01 = vec_xl( 32, q8); - vector signed char q8y11 = vec_xl( 48, q8); - vector signed char q8y02 = vec_xl( 64, q8); - vector signed char q8y12 = vec_xl( 80, q8); - vector signed char q8y03 = vec_xl( 96, q8); - vector signed char q8y13 = vec_xl(112, q8); - q8 += 128; - - vector signed int qv0 = vec_msum(q8y00, q2x00, v0); - vector signed int qv1 = vec_msum(q8y01, q2x01, v0); - vector signed int qv2 = vec_msum(q8y02, q2x02, v0); - vector signed int qv3 = vec_msum(q8y03, q2x03, v0); - vector signed int qv4 = vec_msum(q8y10, q2x10, v0); - vector signed int qv5 = vec_msum(q8y11, q2x11, v0); - vector signed int qv6 = vec_msum(q8y12, q2x12, v0); - vector signed int qv7 = vec_msum(q8y13, q2x13, v0); - - vector signed short vscales_07 = vec_unpackh(vscales); - vector signed int vscales_03 = vec_unpackh(vscales_07); - vector signed int vscales_47 = vec_unpackl(vscales_07); - vector signed int vs0 = vec_splat(vscales_03, 0); - vector signed int vs1 = vec_splat(vscales_03, 1); - vector signed int vs2 = vec_splat(vscales_03, 2); - vector signed int vs3 = vec_splat(vscales_03, 3); - vector signed int vs4 = vec_splat(vscales_47, 0); - vector signed int vs5 = vec_splat(vscales_47, 1); - vector signed int vs6 = vec_splat(vscales_47, 2); - vector signed int vs7 = vec_splat(vscales_47, 3); - vscales = vec_sld(vscales, vscales, 8); - - vsumi0 = vec_add(vec_mul(qv0, vs0), vsumi0); - vsumi1 = vec_add(vec_mul(qv1, vs2), vsumi1); - vsumi2 = vec_add(vec_mul(qv2, vs4), vsumi2); - vsumi3 = vec_add(vec_mul(qv3, vs6), vsumi3); - vsumi4 = vec_add(vec_mul(qv4, vs1), vsumi4); - vsumi5 = vec_add(vec_mul(qv5, vs3), vsumi5); - vsumi6 = vec_add(vec_mul(qv6, vs5), vsumi6); - vsumi7 = vec_add(vec_mul(qv7, vs7), vsumi7); - } - - vsumi0 = vec_add(vsumi0, vsumi4); - vsumi1 = vec_add(vsumi1, vsumi5); - vsumi2 = vec_add(vsumi2, vsumi6); - vsumi3 = vec_add(vsumi3, vsumi7); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined __loongarch_asx - - const __m256i m3 = __lasx_xvreplgr2vr_b(3); - const __m128i m4 = __lsx_vreplgr2vr_b(0xF); - - __m256 acc = (__m256)__lasx_xvldi(0); - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const uint8_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const __m128i mins_and_scales = __lsx_vld((const __m128i*)x[i].scales, 0); - const __m128i scales8 = __lsx_vand_v(mins_and_scales, m4); - const __m128i mins8 = __lsx_vand_v(__lsx_vsrli_h(mins_and_scales, 4), m4); - const __m256i mins = lasx_ext8_16(mins8); - const __m256i prod = lasx_madd_h(mins, __lasx_xvld((const __m256i*)y[i].bsums, 0)); - - acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(dmin), __lasx_xvffint_s_w(prod), acc); - - const __m256i all_scales = lasx_ext8_16(scales8); - const __m128i l_scales = lasx_extracti128(all_scales, 0); - const __m128i h_scales = lasx_extracti128(all_scales, 1); - const __m256i scales[2] = {lasx_insertf128(l_scales, l_scales), lasx_insertf128(h_scales, h_scales)}; - - __m256i sumi = __lasx_xvldi(0); - - for (int j = 0; j < QK_K/128; ++j) { - - const __m256i q2bits = __lasx_xvld((const __m256i*)q2, 0); q2 += 32; - - const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - const __m256i q8_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - const __m256i q8_3 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - - const __m256i q2_0 = __lasx_xvand_v(q2bits, m3); - const __m256i q2_1 = __lasx_xvand_v(__lasx_xvsrli_h(q2bits, 2), m3); - const __m256i q2_2 = __lasx_xvand_v(__lasx_xvsrli_h(q2bits, 4), m3); - const __m256i q2_3 = __lasx_xvand_v(__lasx_xvsrli_h(q2bits, 6), m3); - - __m256i p0 = lasx_maddubs_h(q2_0, q8_0); - __m256i p1 = lasx_maddubs_h(q2_1, q8_1); - __m256i p2 = lasx_maddubs_h(q2_2, q8_2); - __m256i p3 = lasx_maddubs_h(q2_3, q8_3); - - p0 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(0)), p0); - p1 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(1)), p1); - p2 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(2)), p2); - p3 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(3)), p3); - - p0 = __lasx_xvadd_w(p0, p1); - p2 = __lasx_xvadd_w(p2, p3); - - sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p0, p2)); - } - - acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), acc); - - } - - *s = hsum_float_8(acc); - -#else - - float sumf = 0; - - for (int i = 0; i < nb; ++i) { - - const uint8_t * q2 = x[i].qs; - const int8_t * q8 = y[i].qs; - const uint8_t * sc = x[i].scales; - - int summs = 0; - for (int j = 0; j < 16; ++j) { - summs += y[i].bsums[j] * (sc[j] >> 4); - } - - const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - int isum = 0; - int is = 0; - int d; - for (int k = 0; k < QK_K/128; ++k) { - int shift = 0; - for (int j = 0; j < 4; ++j) { - d = sc[is++] & 0xF; - int isuml = 0; - for (int l = 0; l < 16; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3); - isum += d * isuml; - d = sc[is++] & 0xF; - isuml = 0; - for (int l = 16; l < 32; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3); - isum += d * isuml; - shift += 2; - q8 += 32; - } - q2 += 32; - } - sumf += dall * isum - dmin * summs; - } - *s = sumf; -#endif -} - -void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const uint32_t kmask1 = 0x03030303; - const uint32_t kmask2 = 0x0f0f0f0f; - - const block_q3_K * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#ifdef __ARM_NEON - - uint32_t aux[3]; - uint32_t utmp[4]; - - const uint8x16_t m3b = vdupq_n_u8(0x3); - const int32x4_t vzero = vdupq_n_s32(0); - - const uint8x16_t m0 = vdupq_n_u8(1); - const uint8x16_t m1 = vshlq_n_u8(m0, 1); - const uint8x16_t m2 = vshlq_n_u8(m0, 2); - const uint8x16_t m3 = vshlq_n_u8(m0, 3); - const int8_t m32 = 32; - - ggml_int8x16x4_t q3bytes; - - float sum = 0; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q3 = x[i].qs; - const uint8_t * restrict qh = x[i].hmask; - const int8_t * restrict q8 = y[i].qs; - - ggml_uint8x16x2_t qhbits = ggml_vld1q_u8_x2(qh); - - ggml_uint8x16x4_t q3h; - - int32_t isum = 0; - - // Set up scales - memcpy(aux, x[i].scales, 12); - utmp[3] = ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4); - utmp[2] = ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4); - utmp[1] = (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4); - utmp[0] = (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4); - - int8_t * scale = (int8_t *)utmp; - for (int j = 0; j < 16; ++j) scale[j] -= m32; - - for (int j = 0; j < QK_K/128; ++j) { - - const ggml_uint8x16x2_t q3bits = ggml_vld1q_u8_x2(q3); q3 += 32; - const ggml_int8x16x4_t q8bytes_1 = ggml_vld1q_s8_x4(q8); q8 += 64; - const ggml_int8x16x4_t q8bytes_2 = ggml_vld1q_s8_x4(q8); q8 += 64; - - q3h.val[0] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[0]), 2); - q3h.val[1] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[1]), 2); - q3h.val[2] = vshlq_n_u8(vbicq_u8(m1, qhbits.val[0]), 1); - q3h.val[3] = vshlq_n_u8(vbicq_u8(m1, qhbits.val[1]), 1); - - q3bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q3bits.val[0], m3b)), vreinterpretq_s8_u8(q3h.val[0])); - q3bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q3bits.val[1], m3b)), vreinterpretq_s8_u8(q3h.val[1])); - q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 2), m3b)), vreinterpretq_s8_u8(q3h.val[2])); - q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 2), m3b)), vreinterpretq_s8_u8(q3h.val[3])); - - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes_1.val[0])) * scale[0]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes_1.val[1])) * scale[1]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes_1.val[2])) * scale[2]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes_1.val[3])) * scale[3]; - - scale += 4; - - q3h.val[0] = vbicq_u8(m2, qhbits.val[0]); - q3h.val[1] = vbicq_u8(m2, qhbits.val[1]); - q3h.val[2] = vshrq_n_u8(vbicq_u8(m3, qhbits.val[0]), 1); - q3h.val[3] = vshrq_n_u8(vbicq_u8(m3, qhbits.val[1]), 1); - - q3bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 4), m3b)), vreinterpretq_s8_u8(q3h.val[0])); - q3bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 4), m3b)), vreinterpretq_s8_u8(q3h.val[1])); - q3bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[0], 6), m3b)), vreinterpretq_s8_u8(q3h.val[2])); - q3bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q3bits.val[1], 6), m3b)), vreinterpretq_s8_u8(q3h.val[3])); - - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes_2.val[0])) * scale[0]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes_2.val[1])) * scale[1]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes_2.val[2])) * scale[2]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes_2.val[3])) * scale[3]; - - scale += 4; - - if (j == 0) { - qhbits.val[0] = vshrq_n_u8(qhbits.val[0], 4); - qhbits.val[1] = vshrq_n_u8(qhbits.val[1], 4); - } - - } - sum += d * isum; - - } - - *s = sum; - -#elif defined __AVX2__ - - const __m256i m3 = _mm256_set1_epi8(3); - const __m256i mone = _mm256_set1_epi8(1); - const __m128i m32 = _mm_set1_epi8(32); - - __m256 acc = _mm256_setzero_ps(); - - uint32_t aux[3]; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q3 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - // Set up scales - memcpy(aux, x[i].scales, 12); - __m128i scales128 = _mm_set_epi32( - ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4), - ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4), - (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4), - (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4)); - scales128 = _mm_sub_epi8(scales128, m32); - const __m256i all_scales = _mm256_cvtepi8_epi16(scales128); - const __m128i l_scales = _mm256_extracti128_si256(all_scales, 0); - const __m128i h_scales = _mm256_extracti128_si256(all_scales, 1); - const __m256i scales[2] = {MM256_SET_M128I(l_scales, l_scales), MM256_SET_M128I(h_scales, h_scales)}; - - // high bit - const __m256i hbits = _mm256_loadu_si256((const __m256i*)x[i].hmask); - - // integer accumulator - __m256i sumi = _mm256_setzero_si256(); - - int bit = 0; - int is = 0; - - for (int j = 0; j < QK_K/128; ++j) { - // load low 2 bits - const __m256i q3bits = _mm256_loadu_si256((const __m256i*)q3); q3 += 32; - - // prepare low and high bits - const __m256i q3l_0 = _mm256_and_si256(q3bits, m3); - const __m256i q3h_0 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, bit)), bit), 2); - ++bit; - - const __m256i q3l_1 = _mm256_and_si256(_mm256_srli_epi16(q3bits, 2), m3); - const __m256i q3h_1 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, bit)), bit), 2); - ++bit; - - const __m256i q3l_2 = _mm256_and_si256(_mm256_srli_epi16(q3bits, 4), m3); - const __m256i q3h_2 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, bit)), bit), 2); - ++bit; - - const __m256i q3l_3 = _mm256_and_si256(_mm256_srli_epi16(q3bits, 6), m3); - const __m256i q3h_3 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, bit)), bit), 2); - ++bit; - - // load Q8 quants - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q8_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q8_3 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - - // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use _mm256_maddubs_epi16, - // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set, - // and 2 if the high bit was set) - __m256i q8s_0 = _mm256_maddubs_epi16(q3h_0, q8_0); - __m256i q8s_1 = _mm256_maddubs_epi16(q3h_1, q8_1); - __m256i q8s_2 = _mm256_maddubs_epi16(q3h_2, q8_2); - __m256i q8s_3 = _mm256_maddubs_epi16(q3h_3, q8_3); - - __m256i p16_0 = _mm256_maddubs_epi16(q3l_0, q8_0); - __m256i p16_1 = _mm256_maddubs_epi16(q3l_1, q8_1); - __m256i p16_2 = _mm256_maddubs_epi16(q3l_2, q8_2); - __m256i p16_3 = _mm256_maddubs_epi16(q3l_3, q8_3); - - p16_0 = _mm256_sub_epi16(p16_0, q8s_0); - p16_1 = _mm256_sub_epi16(p16_1, q8s_1); - p16_2 = _mm256_sub_epi16(p16_2, q8s_2); - p16_3 = _mm256_sub_epi16(p16_3, q8s_3); - - // multiply with scales - p16_0 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(is + 0)), p16_0); - p16_1 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(is + 1)), p16_1); - p16_2 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(is + 2)), p16_2); - p16_3 = _mm256_madd_epi16(_mm256_shuffle_epi8(scales[j], get_scale_shuffle_q3k(is + 3)), p16_3); - - // accumulate - p16_0 = _mm256_add_epi32(p16_0, p16_1); - p16_2 = _mm256_add_epi32(p16_2, p16_3); - sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_0, p16_2)); - - } - - // multiply with block scale and accumulate - acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi), acc); - - } - - *s = hsum_float_8(acc); - -#elif defined __AVX__ - - const __m128i m3 = _mm_set1_epi8(3); - const __m128i mone = _mm_set1_epi8(1); - const __m128i m32 = _mm_set1_epi8(32); - const __m128i m2 = _mm_set1_epi8(2); - - __m256 acc = _mm256_setzero_ps(); - - const uint32_t *aux; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q3 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - // Set up scales - aux = (const uint32_t *)x[i].scales; - __m128i scales128 = _mm_set_epi32( - ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4), - ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4), - (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4), - (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4)); - scales128 = _mm_sub_epi8(scales128, m32); - const __m128i scales_0 = _mm_cvtepi8_epi16(scales128); - const __m128i scales_1 = _mm_cvtepi8_epi16(_mm_unpackhi_epi64(scales128, scales128)); - const __m128i scales[2] = { scales_0, scales_1 }; - - // high bit *128*2 from block_q3_K.hmask[QK_K/8] - const __m128i hbits_0 = _mm_loadu_si128((const __m128i*)&x[i].hmask[0]); - const __m128i hbits_1 = _mm_loadu_si128((const __m128i*)&x[i].hmask[16]); - - // integer accumulator - __m128i sumi_0 = _mm_setzero_si128(); - __m128i sumi_1 = _mm_setzero_si128(); - - for (int j = 0; j < QK_K/128; ++j) { - // load low 2 bits *64*2 from block_q3_K.qs[QK_K/4] - const __m128i q3bits_0 = _mm_loadu_si128((const __m128i*)q3); q3 += 16; - const __m128i q3bits_1 = _mm_loadu_si128((const __m128i*)q3); q3 += 16; - - // prepare low and high bits - const int bit = j << 2; - - const __m128i q3l_0 = _mm_and_si128(q3bits_0, m3); - const __m128i q3l_1 = _mm_and_si128(q3bits_1, m3); - const __m128i q3h_0 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_0, _mm_slli_epi16(mone, bit)), bit), 2); - const __m128i q3h_1 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_1, _mm_slli_epi16(mone, bit)), bit), 2); - - const __m128i q3l_2 = _mm_and_si128(_mm_srli_epi16(q3bits_0, 2), m3); - const __m128i q3l_3 = _mm_and_si128(_mm_srli_epi16(q3bits_1, 2), m3); - const __m128i q3h_2 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_0, _mm_slli_epi16(mone, bit+1)), bit+1), 2); - const __m128i q3h_3 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_1, _mm_slli_epi16(mone, bit+1)), bit+1), 2); - - const __m128i q3l_4 = _mm_and_si128(_mm_srli_epi16(q3bits_0, 4), m3); - const __m128i q3l_5 = _mm_and_si128(_mm_srli_epi16(q3bits_1, 4), m3); - const __m128i q3h_4 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_0, _mm_slli_epi16(mone, bit+2)), bit+2), 2); - const __m128i q3h_5 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_1, _mm_slli_epi16(mone, bit+2)), bit+2), 2); - - const __m128i q3l_6 = _mm_and_si128(_mm_srli_epi16(q3bits_0, 6), m3); - const __m128i q3l_7 = _mm_and_si128(_mm_srli_epi16(q3bits_1, 6), m3); - const __m128i q3h_6 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_0, _mm_slli_epi16(mone, bit+3)), bit+3), 2); - const __m128i q3h_7 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_1, _mm_slli_epi16(mone, bit+3)), bit+3), 2); - - // load Q8 quants from block_q8_K.qs[QK_K] - const __m128i q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_2 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_3 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_4 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_5 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_6 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_7 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - - // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use _mm256_maddubs_epi16, - // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set, - // and 2 if the high bit was set) - __m128i q8s_0 = _mm_maddubs_epi16(q3h_0, q8_0); - __m128i q8s_1 = _mm_maddubs_epi16(q3h_1, q8_1); - __m128i q8s_2 = _mm_maddubs_epi16(q3h_2, q8_2); - __m128i q8s_3 = _mm_maddubs_epi16(q3h_3, q8_3); - __m128i q8s_4 = _mm_maddubs_epi16(q3h_4, q8_4); - __m128i q8s_5 = _mm_maddubs_epi16(q3h_5, q8_5); - __m128i q8s_6 = _mm_maddubs_epi16(q3h_6, q8_6); - __m128i q8s_7 = _mm_maddubs_epi16(q3h_7, q8_7); - - __m128i p16_0 = _mm_maddubs_epi16(q3l_0, q8_0); - __m128i p16_1 = _mm_maddubs_epi16(q3l_1, q8_1); - __m128i p16_2 = _mm_maddubs_epi16(q3l_2, q8_2); - __m128i p16_3 = _mm_maddubs_epi16(q3l_3, q8_3); - __m128i p16_4 = _mm_maddubs_epi16(q3l_4, q8_4); - __m128i p16_5 = _mm_maddubs_epi16(q3l_5, q8_5); - __m128i p16_6 = _mm_maddubs_epi16(q3l_6, q8_6); - __m128i p16_7 = _mm_maddubs_epi16(q3l_7, q8_7); - - p16_0 = _mm_sub_epi16(p16_0, q8s_0); - p16_1 = _mm_sub_epi16(p16_1, q8s_1); - p16_2 = _mm_sub_epi16(p16_2, q8s_2); - p16_3 = _mm_sub_epi16(p16_3, q8s_3); - p16_4 = _mm_sub_epi16(p16_4, q8s_4); - p16_5 = _mm_sub_epi16(p16_5, q8s_5); - p16_6 = _mm_sub_epi16(p16_6, q8s_6); - p16_7 = _mm_sub_epi16(p16_7, q8s_7); - - // multiply with scales - __m128i shuffle = _mm_set1_epi16(0x0100); - p16_0 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_0); - shuffle = _mm_add_epi16(shuffle, m2); - p16_1 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_1); - shuffle = _mm_add_epi16(shuffle, m2); - p16_2 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_2); - shuffle = _mm_add_epi16(shuffle, m2); - p16_3 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_3); - shuffle = _mm_add_epi16(shuffle, m2); - p16_4 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_4); - shuffle = _mm_add_epi16(shuffle, m2); - p16_5 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_5); - shuffle = _mm_add_epi16(shuffle, m2); - p16_6 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_6); - shuffle = _mm_add_epi16(shuffle, m2); - p16_7 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_7); - - // accumulate - p16_0 = _mm_add_epi32(p16_0, p16_1); - p16_2 = _mm_add_epi32(p16_2, p16_3); - p16_4 = _mm_add_epi32(p16_4, p16_5); - p16_6 = _mm_add_epi32(p16_6, p16_7); - sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2)); - sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_4, p16_6)); - - } - - // multiply with block scale and accumulate - __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0); - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi)), acc); - - } - - *s = hsum_float_8(acc); - -#elif defined __riscv_v_intrinsic - - uint32_t aux[3]; - uint32_t utmp[4]; - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - - const uint8_t * restrict q3 = x[i].qs; - const uint8_t * restrict qh = x[i].hmask; - const int8_t * restrict q8 = y[i].qs; - - memcpy(aux, x[i].scales, 12); - utmp[3] = ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4); - utmp[2] = ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4); - utmp[1] = (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4); - utmp[0] = (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4); - - int8_t * scale = (int8_t *)utmp; - for (int j = 0; j < 16; ++j) scale[j] -= 32; - - - size_t vl = 32; - uint8_t m = 1; - - vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); - vuint8m1_t vqh = __riscv_vle8_v_u8m1(qh, vl); - - int sum_t = 0; - - for (int j = 0; j < QK_K; j += 128) { - - vl = 32; - - // load Q3 - vuint8m1_t q3_x = __riscv_vle8_v_u8m1(q3, vl); - - vint8m1_t q3_0 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(q3_x, 0x03, vl)); - vint8m1_t q3_1 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q3_x, 0x2, vl), 0x03 , vl)); - vint8m1_t q3_2 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q3_x, 0x4, vl), 0x03 , vl)); - vint8m1_t q3_3 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(q3_x, 0x6, vl), 0x03 , vl)); - - // compute mask for subtraction - vuint8m1_t qh_m0 = __riscv_vand_vx_u8m1(vqh, m, vl); - vbool8_t vmask_0 = __riscv_vmseq_vx_u8m1_b8(qh_m0, 0, vl); - vint8m1_t q3_m0 = __riscv_vsub_vx_i8m1_mu(vmask_0, q3_0, q3_0, 0x4, vl); - m <<= 1; - - vuint8m1_t qh_m1 = __riscv_vand_vx_u8m1(vqh, m, vl); - vbool8_t vmask_1 = __riscv_vmseq_vx_u8m1_b8(qh_m1, 0, vl); - vint8m1_t q3_m1 = __riscv_vsub_vx_i8m1_mu(vmask_1, q3_1, q3_1, 0x4, vl); - m <<= 1; - - vuint8m1_t qh_m2 = __riscv_vand_vx_u8m1(vqh, m, vl); - vbool8_t vmask_2 = __riscv_vmseq_vx_u8m1_b8(qh_m2, 0, vl); - vint8m1_t q3_m2 = __riscv_vsub_vx_i8m1_mu(vmask_2, q3_2, q3_2, 0x4, vl); - m <<= 1; - - vuint8m1_t qh_m3 = __riscv_vand_vx_u8m1(vqh, m, vl); - vbool8_t vmask_3 = __riscv_vmseq_vx_u8m1_b8(qh_m3, 0, vl); - vint8m1_t q3_m3 = __riscv_vsub_vx_i8m1_mu(vmask_3, q3_3, q3_3, 0x4, vl); - m <<= 1; - - // load Q8 and take product with Q3 - vint16m2_t a0 = __riscv_vwmul_vv_i16m2(q3_m0, __riscv_vle8_v_i8m1(q8, vl), vl); - vint16m2_t a1 = __riscv_vwmul_vv_i16m2(q3_m1, __riscv_vle8_v_i8m1(q8+32, vl), vl); - vint16m2_t a2 = __riscv_vwmul_vv_i16m2(q3_m2, __riscv_vle8_v_i8m1(q8+64, vl), vl); - vint16m2_t a3 = __riscv_vwmul_vv_i16m2(q3_m3, __riscv_vle8_v_i8m1(q8+96, vl), vl); - - vl = 16; - - // retrieve lane to multiply with scale - vint32m2_t aux0_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a0, 0), (scale[0]), vl); - vint32m2_t aux0_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a0, 1), (scale[1]), vl); - vint32m2_t aux1_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a1, 0), (scale[2]), vl); - vint32m2_t aux1_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a1, 1), (scale[3]), vl); - vint32m2_t aux2_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a2, 0), (scale[4]), vl); - vint32m2_t aux2_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a2, 1), (scale[5]), vl); - vint32m2_t aux3_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a3, 0), (scale[6]), vl); - vint32m2_t aux3_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(a3, 1), (scale[7]), vl); - - vint32m1_t isum0 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux0_0, aux0_1, vl), vzero, vl); - vint32m1_t isum1 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux1_0, aux1_1, vl), isum0, vl); - vint32m1_t isum2 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux2_0, aux2_1, vl), isum1, vl); - vint32m1_t isum3 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(aux3_0, aux3_1, vl), isum2, vl); - - sum_t += __riscv_vmv_x_s_i32m1_i32(isum3); - - q3 += 32; q8 += 128; scale += 8; - - } - - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - - sumf += d*sum_t; - - } - - *s = sumf; - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0x3); - const vector signed char lowMask1 = vec_splats((int8_t)0xf); - const vector signed char lowMask2 = vec_splats((int8_t)0x30); - const vector int v0 = vec_splats((int32_t)0); - const vector signed char v1 = vec_splats((signed char)0x1); - const vector unsigned char v2 = vec_splats((unsigned char)0x2); - const vector unsigned char v3 = vec_splats((unsigned char)0x3); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - const vector unsigned char v6 = vec_splats((unsigned char)0x6); - const vector signed char off = vec_splats((signed char)0x20); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - - for (int i = 0; i < nb; ++i) { - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - UNUSED(kmask1); - UNUSED(kmask2); - - vector signed char u0 = (vector signed char)vec_xl_len(x[i].scales, 8); - vector signed char u1 = vec_and(u0, lowMask1); - vector signed char u2 = (vector signed char)vec_xl_len(x[i].scales + 8, 4); - vector signed char u3 = (vector signed char)vec_mergeh((vector signed int)u2, (vector signed int)vec_sr(u2, v2)); - vector signed char u30 = vec_sl(vec_and(u3, lowMask), v4); - vector signed char u31 = vec_and(u3, lowMask2); - - u1 = vec_or(u1, u30); - u2 = vec_or(vec_sr(u0, v4), u31); - - vector signed char vscales = (vector signed char)vec_mergeh((vector signed long long)u1, (vector signed long long)u2); - vector signed char qxhs0 = (vector signed char)vec_xl( 0, x[i].hmask); - vector signed char qxhs1 = (vector signed char)vec_xl(16, x[i].hmask); - - vscales = vec_sub(vscales, off); - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - vector signed int vsumi2 = v0; - vector signed int vsumi3 = v0; - vector signed int vsumi4 = v0; - vector signed int vsumi5 = v0; - vector signed int vsumi6 = v0; - vector signed int vsumi7 = v0; - - const uint8_t * restrict q3 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - for (int j = 0; j < QK_K/128; ++j) { - __builtin_prefetch(q3, 0, 1); - __builtin_prefetch(q8, 0, 1); - - vector signed char qxs0 = (vector signed char)vec_xl( 0, q3); - vector signed char qxs1 = (vector signed char)vec_xl(16, q3); - q3 += 32; - - //the low 2 bits - vector signed char qxs00 = vec_and(qxs0, lowMask); - vector signed char qxs01 = vec_and(vec_sr(qxs0, v2), lowMask); - vector signed char qxs02 = vec_and(vec_sr(qxs0, v4), lowMask); - vector signed char qxs03 = vec_and(vec_sr(qxs0, v6), lowMask); - vector signed char qxs10 = vec_and(qxs1, lowMask); - vector signed char qxs11 = vec_and(vec_sr(qxs1, v2), lowMask); - vector signed char qxs12 = vec_and(vec_sr(qxs1, v4), lowMask); - vector signed char qxs13 = vec_and(vec_sr(qxs1, v6), lowMask); - - //the 3rd bit - vector signed char qxh00 = vec_sl(vec_andc(v1, qxhs0), v2); - vector signed char qxh01 = vec_sl(vec_andc(v1, vec_sr(qxhs0, (vector unsigned char)v1)), v2); - vector signed char qxh02 = vec_sl(vec_andc(v1, vec_sr(qxhs0, v2)), v2); - vector signed char qxh03 = vec_sl(vec_andc(v1, vec_sr(qxhs0, v3)), v2); - vector signed char qxh10 = vec_sl(vec_andc(v1, qxhs1), v2); - vector signed char qxh11 = vec_sl(vec_andc(v1, vec_sr(qxhs1, (vector unsigned char)v1)), v2); - vector signed char qxh12 = vec_sl(vec_andc(v1, vec_sr(qxhs1, v2)), v2); - vector signed char qxh13 = vec_sl(vec_andc(v1, vec_sr(qxhs1, v3)), v2); - qxhs0 = vec_sr(qxhs0, v4); - qxhs1 = vec_sr(qxhs1, v4); - - vector signed char q3x00 = vec_sub(qxs00, qxh00); - vector signed char q3x01 = vec_sub(qxs01, qxh01); - vector signed char q3x02 = vec_sub(qxs02, qxh02); - vector signed char q3x03 = vec_sub(qxs03, qxh03); - vector signed char q3x10 = vec_sub(qxs10, qxh10); - vector signed char q3x11 = vec_sub(qxs11, qxh11); - vector signed char q3x12 = vec_sub(qxs12, qxh12); - vector signed char q3x13 = vec_sub(qxs13, qxh13); - - vector signed char q8y00 = vec_xl( 0, q8); - vector signed char q8y10 = vec_xl( 16, q8); - vector signed char q8y01 = vec_xl( 32, q8); - vector signed char q8y11 = vec_xl( 48, q8); - vector signed char q8y02 = vec_xl( 64, q8); - vector signed char q8y12 = vec_xl( 80, q8); - vector signed char q8y03 = vec_xl( 96, q8); - vector signed char q8y13 = vec_xl(112, q8); - q8 += 128; - - vector signed short vscales_h = vec_unpackh(vscales); - vector signed short vs0 = vec_splat(vscales_h, 0); - vector signed short vs1 = vec_splat(vscales_h, 1); - vector signed short vs2 = vec_splat(vscales_h, 2); - vector signed short vs3 = vec_splat(vscales_h, 3); - vector signed short vs4 = vec_splat(vscales_h, 4); - vector signed short vs5 = vec_splat(vscales_h, 5); - vector signed short vs6 = vec_splat(vscales_h, 6); - vector signed short vs7 = vec_splat(vscales_h, 7); - vscales = vec_sld(vscales, vscales, 8); - - vector signed short qv00 = vec_add(vec_mule(q3x00, q8y00), vec_mulo(q3x00, q8y00)); - vector signed short qv01 = vec_add(vec_mule(q3x01, q8y01), vec_mulo(q3x01, q8y01)); - vector signed short qv02 = vec_add(vec_mule(q3x02, q8y02), vec_mulo(q3x02, q8y02)); - vector signed short qv03 = vec_add(vec_mule(q3x03, q8y03), vec_mulo(q3x03, q8y03)); - vector signed short qv10 = vec_add(vec_mule(q3x10, q8y10), vec_mulo(q3x10, q8y10)); - vector signed short qv11 = vec_add(vec_mule(q3x11, q8y11), vec_mulo(q3x11, q8y11)); - vector signed short qv12 = vec_add(vec_mule(q3x12, q8y12), vec_mulo(q3x12, q8y12)); - vector signed short qv13 = vec_add(vec_mule(q3x13, q8y13), vec_mulo(q3x13, q8y13)); - - vsumi0 = vec_msum(qv00, vs0, vsumi0); - vsumi1 = vec_msum(qv01, vs2, vsumi1); - vsumi2 = vec_msum(qv02, vs4, vsumi2); - vsumi3 = vec_msum(qv03, vs6, vsumi3); - vsumi4 = vec_msum(qv10, vs1, vsumi4); - vsumi5 = vec_msum(qv11, vs3, vsumi5); - vsumi6 = vec_msum(qv12, vs5, vsumi6); - vsumi7 = vec_msum(qv13, vs7, vsumi7); - } - - vsumi0 = vec_add(vsumi0, vsumi4); - vsumi1 = vec_add(vsumi1, vsumi5); - vsumi2 = vec_add(vsumi2, vsumi6); - vsumi3 = vec_add(vsumi3, vsumi7); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined __loongarch_asx - - const __m256i m3 = __lasx_xvreplgr2vr_b(3); - const __m256i mone = __lasx_xvreplgr2vr_b(1); - const __m128i m32 = __lsx_vreplgr2vr_b(32); - - __m256 acc = (__m256)__lasx_xvldi(0); - - uint32_t aux[3]; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const uint8_t * restrict q3 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - // Set up scales - memcpy(aux, x[i].scales, 12); - __m128i scales128 = lsx_set_w( - ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4), - ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4), - (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4), - (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4)); - scales128 = __lsx_vsub_b(scales128, m32); - const __m256i all_scales = lasx_ext8_16(scales128); - const __m128i l_scales = lasx_extracti128(all_scales, 0); - const __m128i h_scales = lasx_extracti128(all_scales, 1); - const __m256i scales[2] = {lasx_insertf128(l_scales, l_scales), lasx_insertf128(h_scales, h_scales)}; - - // high bit - const __m256i hbits = __lasx_xvld((const __m256i*)x[i].hmask, 0); - - // integer accumulator - __m256i sumi = __lasx_xvldi(0); - - int bit = 0; - int is = 0; - __m256i xvbit; - - - for (int j = 0; j < QK_K/128; ++j) { - // load low 2 bits - const __m256i q3bits = __lasx_xvld((const __m256i*)q3, 0); q3 += 32; - - xvbit = __lasx_xvreplgr2vr_h(bit); - // prepare low and high bits - const __m256i q3l_0 = __lasx_xvand_v(q3bits, m3); - const __m256i q3h_0 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); - ++bit; - - xvbit = __lasx_xvreplgr2vr_h(bit); - const __m256i q3l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 2), m3); - const __m256i q3h_1 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); - ++bit; - - xvbit = __lasx_xvreplgr2vr_h(bit); - const __m256i q3l_2 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 4), m3); - const __m256i q3h_2 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); - ++bit; - - xvbit = __lasx_xvreplgr2vr_h(bit); - const __m256i q3l_3 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 6), m3); - const __m256i q3h_3 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); - ++bit; - - // load Q8 quants - const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - const __m256i q8_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - const __m256i q8_3 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - - // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use lasx_maddubs_h, - // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set, - // and 2 if the high bit was set) - __m256i q8s_0 = lasx_maddubs_h(q3h_0, q8_0); - __m256i q8s_1 = lasx_maddubs_h(q3h_1, q8_1); - __m256i q8s_2 = lasx_maddubs_h(q3h_2, q8_2); - __m256i q8s_3 = lasx_maddubs_h(q3h_3, q8_3); - - __m256i p16_0 = lasx_maddubs_h(q3l_0, q8_0); - __m256i p16_1 = lasx_maddubs_h(q3l_1, q8_1); - __m256i p16_2 = lasx_maddubs_h(q3l_2, q8_2); - __m256i p16_3 = lasx_maddubs_h(q3l_3, q8_3); - - p16_0 = __lasx_xvsub_h(p16_0, q8s_0); - p16_1 = __lasx_xvsub_h(p16_1, q8s_1); - p16_2 = __lasx_xvsub_h(p16_2, q8s_2); - p16_3 = __lasx_xvsub_h(p16_3, q8s_3); - - // multiply with scales - p16_0 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 0)), p16_0); - p16_1 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 1)), p16_1); - p16_2 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 2)), p16_2); - p16_3 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 3)), p16_3); - - // accumulate - p16_0 = __lasx_xvadd_w(p16_0, p16_1); - p16_2 = __lasx_xvadd_w(p16_2, p16_3); - sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_2)); - } - // multiply with block scale and accumulate - acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), acc);//FIXME - } - - *s = hsum_float_8(acc); - -#else - // scalar version - // This function is written like this so the compiler can manage to vectorize most of it - // Using -Ofast, GCC and clang manage to produce code that is within a factor of 2 or so from the - // manually vectorized version above. Every other version I tried would run at least 4 times slower. - // The ideal situation would be if we could just write the code once, and the compiler would - // automatically produce the best possible set of machine instructions, instead of us having to manually - // write vectorized versions for AVX, ARM_NEON, etc. - - int8_t aux8[QK_K]; - int16_t aux16[8]; - float sums [8]; - int32_t aux32[8]; - memset(sums, 0, 8*sizeof(float)); - - uint32_t auxs[4]; - const int8_t * scales = (const int8_t*)auxs; - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const uint8_t * restrict q3 = x[i].qs; - const uint8_t * restrict hm = x[i].hmask; - const int8_t * restrict q8 = y[i].qs; - memset(aux32, 0, 8*sizeof(int32_t)); - int8_t * restrict a = aux8; - uint8_t m = 1; - for (int j = 0; j < QK_K; j += 128) { - for (int l = 0; l < 32; ++l) a[l] = q3[l] & 3; - for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4); - a += 32; m <<= 1; - for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 2) & 3; - for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4); - a += 32; m <<= 1; - for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 4) & 3; - for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4); - a += 32; m <<= 1; - for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 6) & 3; - for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4); - a += 32; m <<= 1; - q3 += 32; - } - a = aux8; - - memcpy(auxs, x[i].scales, 12); - uint32_t tmp = auxs[2]; - auxs[2] = ((auxs[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4); - auxs[3] = ((auxs[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4); - auxs[0] = (auxs[0] & kmask2) | (((tmp >> 0) & kmask1) << 4); - auxs[1] = (auxs[1] & kmask2) | (((tmp >> 2) & kmask1) << 4); - for (int j = 0; j < QK_K/16; ++j) { - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l]; - q8 += 8; a += 8; - } - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; - } - for (int l = 0; l < 8; ++l) sumf += sums[l]; - *s = sumf; - -#endif - -} - -void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q4_K * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - - static const uint32_t kmask1 = 0x3f3f3f3f; - static const uint32_t kmask2 = 0x0f0f0f0f; - static const uint32_t kmask3 = 0x03030303; - - uint32_t utmp[4]; - -#ifdef __ARM_NEON - const uint8x16_t m4b = vdupq_n_u8(0xf); - const int32x4_t mzero = vdupq_n_s32(0); - - ggml_int8x16x2_t q4bytes; - ggml_int8x16x2_t q8bytes; - - float sumf = 0; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const int16x8_t q8sums = vpaddq_s16(vld1q_s16(y[i].bsums), vld1q_s16(y[i].bsums + 8)); - - memcpy(utmp, x[i].scales, 12); - - uint32x2_t mins8 = { 0 }; - mins8 = vset_lane_u32(utmp[1] & kmask1, mins8, 0); - mins8 = vset_lane_u32(((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4), mins8, 1); - - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[0] &= kmask1; - - const int16x8_t mins = vreinterpretq_s16_u16(vmovl_u8(vreinterpret_u8_u32(mins8))); - const int32x4_t prod = vaddq_s32(vmull_s16(vget_low_s16 (q8sums), vget_low_s16 (mins)), - vmull_s16(vget_high_s16(q8sums), vget_high_s16(mins))); - sumf -= dmin * vaddvq_s32(prod); - - const uint8_t * scales = (const uint8_t *)utmp; - - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - int32_t sumi1 = 0; - int32_t sumi2 = 0; - - for (int j = 0; j < QK_K/64; ++j) { - const ggml_uint8x16x2_t q4bits = ggml_vld1q_u8_x2(q4); q4 += 32; - - q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; - q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b)); - q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b)); - - const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); - sumi1 += vaddvq_s32(p1) * scales[2*j+0]; - - q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32; - q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4)); - q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4)); - - const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); - - sumi2 += vaddvq_s32(p2) * scales[2*j+1]; - } - - sumf += d * (sumi1 + sumi2); - - } - - *s = sumf; - -#elif defined __AVX2__ - - const __m256i m4 = _mm256_set1_epi8(0xF); - - __m256 acc = _mm256_setzero_ps(); - __m128 acc_m = _mm_setzero_ps(); - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - memcpy(utmp, x[i].scales, 12); - utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); - const uint32_t uaux = utmp[1] & kmask1; - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[2] = uaux; - utmp[0] &= kmask1; - - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const __m256i mins_and_scales = _mm256_cvtepu8_epi16(_mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0])); - - const __m256i q8sums = _mm256_loadu_si256((const __m256i*)y[i].bsums); - const __m128i q8s = _mm_hadd_epi16(_mm256_extracti128_si256(q8sums, 0), _mm256_extracti128_si256(q8sums, 1)); - const __m128i prod = _mm_madd_epi16(_mm256_extracti128_si256(mins_and_scales, 1), q8s); - acc_m = _mm_fmadd_ps(_mm_set1_ps(dmin), _mm_cvtepi32_ps(prod), acc_m); - - const __m128i sc128 = _mm256_extracti128_si256(mins_and_scales, 0); - const __m256i scales = MM256_SET_M128I(sc128, sc128); - - __m256i sumi = _mm256_setzero_si256(); - - for (int j = 0; j < QK_K/64; ++j) { - - const __m256i scale_l = _mm256_shuffle_epi8(scales, get_scale_shuffle_k4(2*j+0)); - const __m256i scale_h = _mm256_shuffle_epi8(scales, get_scale_shuffle_k4(2*j+1)); - - const __m256i q4bits = _mm256_loadu_si256((const __m256i*)q4); q4 += 32; - const __m256i q4l = _mm256_and_si256(q4bits, m4); - const __m256i q4h = _mm256_and_si256(_mm256_srli_epi16(q4bits, 4), m4); - - const __m256i q8l = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - __m256i p16l = _mm256_maddubs_epi16(q4l, q8l); - p16l = _mm256_madd_epi16(scale_l, p16l); - - const __m256i q8h = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - __m256i p16h = _mm256_maddubs_epi16(q4h, q8h); - p16h = _mm256_madd_epi16(scale_h, p16h); - const __m256i sumj = _mm256_add_epi32(p16l, p16h); - - sumi = _mm256_add_epi32(sumi, sumj); - } - - __m256 vd = _mm256_set1_ps(d); - acc = _mm256_fmadd_ps(vd, _mm256_cvtepi32_ps(sumi), acc); - - } - - acc_m = _mm_add_ps(acc_m, _mm_movehl_ps(acc_m, acc_m)); - acc_m = _mm_add_ss(acc_m, _mm_movehdup_ps(acc_m)); - - *s = hsum_float_8(acc) + _mm_cvtss_f32(acc_m); - -#elif defined __AVX__ - - const __m128i m4 = _mm_set1_epi8(0xF); - const __m128i m2 = _mm_set1_epi8(0x2); - - __m256 acc = _mm256_setzero_ps(); - __m128 acc_m = _mm_setzero_ps(); - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - memcpy(utmp, x[i].scales, 12); - utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); - const uint32_t uaux = utmp[1] & kmask1; - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[2] = uaux; - utmp[0] &= kmask1; - - const __m128i utmps = _mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0]); - const __m128i scales = _mm_cvtepu8_epi16(utmps); - const __m128i mins = _mm_cvtepu8_epi16(_mm_unpackhi_epi64(utmps, utmps)); - - const __m128i q8sums_0 = _mm_loadu_si128((const __m128i*)&y[i].bsums[0]); - const __m128i q8sums_1 = _mm_loadu_si128((const __m128i*)&y[i].bsums[8]); - const __m128i q8s = _mm_hadd_epi16(q8sums_0, q8sums_1); - const __m128i prod = _mm_madd_epi16(mins, q8s); - acc_m = _mm_add_ps(_mm_mul_ps(_mm_set1_ps(dmin), _mm_cvtepi32_ps(prod)), acc_m); - - __m128i sumi_0 = _mm_setzero_si128(); - __m128i sumi_1 = _mm_setzero_si128(); - - __m128i shuffle = _mm_set1_epi16(0x0100); - for (int j = 0; j < QK_K/64; ++j) { - - const __m128i scale_l = _mm_shuffle_epi8(scales, shuffle); - shuffle = _mm_add_epi16(shuffle, m2); - const __m128i scale_h = _mm_shuffle_epi8(scales, shuffle); - shuffle = _mm_add_epi16(shuffle, m2); - - __m128i q4bits = _mm_loadu_si128((const __m128i*)q4); q4 += 16; - const __m128i q4l_0 = _mm_and_si128(q4bits, m4); - const __m128i q4h_0 = _mm_and_si128(_mm_srli_epi16(q4bits, 4), m4); - q4bits = _mm_loadu_si128((const __m128i*)q4); q4 += 16; - const __m128i q4l_1 = _mm_and_si128(q4bits, m4); - const __m128i q4h_1 = _mm_and_si128(_mm_srli_epi16(q4bits, 4), m4); - - const __m128i q8l_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - __m128i p16l = _mm_maddubs_epi16(q4l_0, q8l_0); - p16l = _mm_madd_epi16(scale_l, p16l); - sumi_0 = _mm_add_epi32(sumi_0, p16l); - const __m128i q8l_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - p16l = _mm_maddubs_epi16(q4l_1, q8l_1); - p16l = _mm_madd_epi16(scale_l, p16l); - sumi_1 = _mm_add_epi32(sumi_1, p16l); - - const __m128i q8h_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - __m128i p16h = _mm_maddubs_epi16(q4h_0, q8h_0); - p16h = _mm_madd_epi16(scale_h, p16h); - sumi_0 = _mm_add_epi32(sumi_0, p16h); - const __m128i q8h_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - p16h = _mm_maddubs_epi16(q4h_1, q8h_1); - p16h = _mm_madd_epi16(scale_h, p16h); - sumi_1 = _mm_add_epi32(sumi_1, p16h); - - } - - __m256 vd = _mm256_set1_ps(d); - __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0); - acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(sumi)), acc); - - } - - acc_m = _mm_add_ps(acc_m, _mm_movehl_ps(acc_m, acc_m)); - acc_m = _mm_add_ss(acc_m, _mm_movehdup_ps(acc_m)); - - *s = hsum_float_8(acc) + _mm_cvtss_f32(acc_m); - -#elif defined __riscv_v_intrinsic - - const uint8_t * scales = (const uint8_t*)&utmp[0]; - const uint8_t * mins = (const uint8_t*)&utmp[2]; - - float sumf = 0; - - for (int i = 0; i < nb; ++i) { - - size_t vl = 8; - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - vint16mf2_t q8sums_0 = __riscv_vlse16_v_i16mf2(y[i].bsums, 4, vl); - vint16mf2_t q8sums_1 = __riscv_vlse16_v_i16mf2(y[i].bsums+1, 4, vl); - vint16mf2_t q8sums = __riscv_vadd_vv_i16mf2(q8sums_0, q8sums_1, vl); - - memcpy(utmp, x[i].scales, 12); - utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); - const uint32_t uaux = utmp[1] & kmask1; - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[2] = uaux; - utmp[0] &= kmask1; - - vuint8mf4_t mins8 = __riscv_vle8_v_u8mf4(mins, vl); - vint16mf2_t v_mins = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vzext_vf2_u16mf2(mins8, vl)); - vint32m1_t prod = __riscv_vwmul_vv_i32m1(q8sums, v_mins, vl); - - vint32m1_t sumi = __riscv_vredsum_vs_i32m1_i32m1(prod, __riscv_vmv_v_x_i32m1(0, 1), vl); - sumf -= dmin * __riscv_vmv_x_s_i32m1_i32(sumi); - - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - vl = 32; - - int32_t sum_1 = 0; - int32_t sum_2 = 0; - - vint16m1_t vzero = __riscv_vmv_v_x_i16m1(0, 1); - - for (int j = 0; j < QK_K/64; ++j) { - // load Q4 - vuint8m1_t q4_x = __riscv_vle8_v_u8m1(q4, vl); - - // load Q8 and multiply it with lower Q4 nibble - vint8m1_t q8_0 = __riscv_vle8_v_i8m1(q8, vl); - vint8m1_t q4_0 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(q4_x, 0x0F, vl)); - vint16m2_t qv_0 = __riscv_vwmul_vv_i16m2(q4_0, q8_0, vl); - vint16m1_t vs_0 = __riscv_vredsum_vs_i16m2_i16m1(qv_0, vzero, vl); - - sum_1 += __riscv_vmv_x_s_i16m1_i16(vs_0) * scales[2*j+0]; - - // load Q8 and multiply it with upper Q4 nibble - vint8m1_t q8_1 = __riscv_vle8_v_i8m1(q8+32, vl); - vint8m1_t q4_1 = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vsrl_vx_u8m1(q4_x, 0x04, vl)); - vint16m2_t qv_1 = __riscv_vwmul_vv_i16m2(q4_1, q8_1, vl); - vint16m1_t vs_1 = __riscv_vredsum_vs_i16m2_i16m1(qv_1, vzero, vl); - - sum_2 += __riscv_vmv_x_s_i16m1_i16(vs_1) * scales[2*j+1]; - - q4 += 32; q8 += 64; - - } - - sumf += d*(sum_1 + sum_2); - - } - - *s = sumf; - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0xF); - const vector signed char lowMask1 = vec_splats((int8_t)0x3f); - const vector signed char lowMask2 = vec_splats((int8_t)0x30); - const vector int v0 = vec_splats((int32_t)0); - const vector unsigned char v2 = vec_splats((uint8_t)2); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - - for (int i = 0; i < nb; ++i) { - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].dmin)); - vector float vdmin = vec_mul(vxmin, vyd); - - vector signed short q8ysums0 = vec_xl( 0, y[i].bsums); - vector signed short q8ysums1 = vec_xl(16, y[i].bsums); - - UNUSED(kmask1); - UNUSED(kmask2); - UNUSED(kmask3); - UNUSED(utmp); - - vector signed char u0 = (vector signed char)vec_xl_len(x[i].scales, 8); - vector signed char u1 = vec_and(vec_sr(u0, v2), lowMask2); - vector signed char u2 = (vector signed char)vec_xl_len(x[i].scales + 8, 4); - vector signed char u3 = vec_sr(u2, v4); - - vector signed char u30 = u1; - vector signed char u31 = (vector signed char)vec_mergeh((vector signed int)vec_and(u2, lowMask), (vector signed int)u3); - - u1 = vec_and(u0, lowMask1); - u2 = vec_or(u30, u31); - - vector signed char utmps = (vector signed char)vec_mergeh((vector signed int)u1, (vector signed int)u2); - - vector signed short vscales = vec_unpackh(utmps); - vector signed short q4xmins = vec_unpackl(utmps); - vector signed short q4xmins0 = vec_mergeh(q4xmins, q4xmins); - vector signed short q4xmins1 = vec_mergel(q4xmins, q4xmins); - - vector signed int prod0 = vec_mule(q4xmins0, q8ysums0); - vector signed int prod1 = vec_mule(q4xmins1, q8ysums1); - vector signed int prod2 = vec_mulo(q4xmins0, q8ysums0); - vector signed int prod3 = vec_mulo(q4xmins1, q8ysums1); - - vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vdmin, vsumf0); - vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vdmin, vsumf1); - vsumf2 = vec_nmsub(vec_ctf(prod2, 0), vdmin, vsumf2); - vsumf3 = vec_nmsub(vec_ctf(prod3, 0), vdmin, vsumf3); - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - vector signed int vsumi2 = v0; - vector signed int vsumi3 = v0; - - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - for (int j = 0; j < QK_K/64; j+=2) { - __builtin_prefetch(q4, 0, 1); - __builtin_prefetch(q8, 0, 1); - - vector signed char qxs0 = (vector signed char)vec_xl( 0, q4); - vector signed char qxs1 = (vector signed char)vec_xl(16, q4); - vector signed char qxs2 = (vector signed char)vec_xl(32, q4); - vector signed char qxs3 = (vector signed char)vec_xl(48, q4); - q4 += 64; - - vector unsigned char q4x00 = (vector unsigned char)vec_and(qxs0, lowMask); - vector unsigned char q4x01 = (vector unsigned char)vec_sr(qxs0, v4); - vector unsigned char q4x10 = (vector unsigned char)vec_and(qxs1, lowMask); - vector unsigned char q4x11 = (vector unsigned char)vec_sr(qxs1, v4); - vector unsigned char q4x20 = (vector unsigned char)vec_and(qxs2, lowMask); - vector unsigned char q4x21 = (vector unsigned char)vec_sr(qxs2, v4); - vector unsigned char q4x30 = (vector unsigned char)vec_and(qxs3, lowMask); - vector unsigned char q4x31 = (vector unsigned char)vec_sr(qxs3, v4); - - vector signed char q8y00 = vec_xl( 0, q8); - vector signed char q8y10 = vec_xl( 16, q8); - vector signed char q8y01 = vec_xl( 32, q8); - vector signed char q8y11 = vec_xl( 48, q8); - vector signed char q8y20 = vec_xl( 64, q8); - vector signed char q8y30 = vec_xl( 80, q8); - vector signed char q8y21 = vec_xl( 96, q8); - vector signed char q8y31 = vec_xl(112, q8); - q8 += 128; - - vector signed int qv00 = vec_msum(q8y00, q4x00, v0); - vector signed int qv01 = vec_msum(q8y01, q4x01, v0); - vector signed int qv10 = vec_msum(q8y10, q4x10, v0); - vector signed int qv11 = vec_msum(q8y11, q4x11, v0); - vector signed int qv20 = vec_msum(q8y20, q4x20, v0); - vector signed int qv21 = vec_msum(q8y21, q4x21, v0); - vector signed int qv30 = vec_msum(q8y30, q4x30, v0); - vector signed int qv31 = vec_msum(q8y31, q4x31, v0); - - vector signed int vscales_h = vec_unpackh(vscales); - vector signed int vs0 = vec_splat(vscales_h, 0); - vector signed int vs1 = vec_splat(vscales_h, 1); - vector signed int vs2 = vec_splat(vscales_h, 2); - vector signed int vs3 = vec_splat(vscales_h, 3); - vscales = vec_sld(vscales, vscales, 8); - - vsumi0 = vec_add(vec_mul(qv00, vs0), vsumi0); - vsumi1 = vec_add(vec_mul(qv01, vs1), vsumi1); - vsumi2 = vec_add(vec_mul(qv20, vs2), vsumi2); - vsumi3 = vec_add(vec_mul(qv21, vs3), vsumi3); - - vsumi0 = vec_add(vec_mul(qv10, vs0), vsumi0); - vsumi1 = vec_add(vec_mul(qv11, vs1), vsumi1); - vsumi2 = vec_add(vec_mul(qv30, vs2), vsumi2); - vsumi3 = vec_add(vec_mul(qv31, vs3), vsumi3); - } - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined __loongarch_asx - GGML_UNUSED(kmask1); - GGML_UNUSED(kmask2); - GGML_UNUSED(kmask3); - - const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); - - __m256 acc = (__m256)__lasx_xvldi(0); - __m128 acc_m = (__m128)__lsx_vldi(0); - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - memcpy(utmp, x[i].scales, 12); - utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); - const uint32_t uaux = utmp[1] & kmask1; - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[2] = uaux; - utmp[0] &= kmask1; - - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const __m256i mins_and_scales = lasx_extu8_16(lsx_set_w(utmp[3], utmp[2], utmp[1], utmp[0])); - - const __m256i q8sums = __lasx_xvld((const __m256i*)y[i].bsums, 0); - const __m128i q8s = lsx_hadd_h(lasx_extracti128(q8sums, 0), lasx_extracti128(q8sums, 1)); - const __m128i prod = lsx_madd_h(lasx_extracti128(mins_and_scales, 1), q8s); - acc_m = __lsx_vfmadd_s(__lsx_vreplfr2vr_s(dmin), __lsx_vffint_s_w(prod), acc_m); - - const __m128i sc128 = lasx_extracti128(mins_and_scales, 0); - const __m256i scales = lasx_insertf128(sc128, sc128); - - __m256i sumi = __lasx_xvldi(0); - - for (int j = 0; j < QK_K/64; ++j) { - - const __m256i scale_l = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+0)); - const __m256i scale_h = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+1)); - - const __m256i q4bits = __lasx_xvld((const __m256i*)q4, 0); q4 += 32; - const __m256i q4l = __lasx_xvand_v(q4bits, m4); - const __m256i q4h = __lasx_xvand_v(__lasx_xvsrli_h(q4bits, 4), m4); - - const __m256i q8l = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - __m256i p16l = lasx_maddubs_h(q4l, q8l); - p16l = lasx_madd_h(scale_l, p16l); - - const __m256i q8h = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - __m256i p16h = lasx_maddubs_h(q4h, q8h); - p16h = lasx_madd_h(scale_h, p16h); - const __m256i sumj = __lasx_xvadd_w(p16l, p16h); - - sumi = __lasx_xvadd_w(sumi, sumj); - } - - __m256 vd = __lasx_xvreplfr2vr_s(d); - acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(sumi), acc); - - } - - acc_m = __lsx_vfadd_s(acc_m, (__m128)__lsx_vpermi_w((__m128i)acc_m, (__m128i)acc_m, 0xee)); - __m128i tmp1 = __lsx_vinsgr2vr_w(__lsx_vldi(0), __lsx_vpickve2gr_w((__m128i)acc_m, 1), 0); - acc_m = __lsx_vfadd_s(acc_m, (__m128)tmp1); - - - ft_union fi; - fi.i = __lsx_vpickve2gr_w(acc_m, 0); - *s = hsum_float_8(acc) + fi.f ; -#else - - const uint8_t * scales = (const uint8_t*)&utmp[0]; - const uint8_t * mins = (const uint8_t*)&utmp[2]; - - int8_t aux8[QK_K]; - int16_t aux16[8]; - float sums [8]; - int32_t aux32[8]; - memset(sums, 0, 8*sizeof(float)); - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - memset(aux32, 0, 8*sizeof(int32_t)); - int8_t * restrict a = aux8; - for (int j = 0; j < QK_K/64; ++j) { - for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF); - a += 32; - for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] >> 4); - a += 32; q4 += 32; - } - memcpy(utmp, x[i].scales, 12); - utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); - const uint32_t uaux = utmp[1] & kmask1; - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[2] = uaux; - utmp[0] &= kmask1; - - int sumi = 0; - for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2]; - a = aux8; - int is = 0; - for (int j = 0; j < QK_K/32; ++j) { - int32_t scale = scales[is++]; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - } - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; - const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d; - sumf -= dmin * sumi; - } - for (int l = 0; l < 8; ++l) sumf += sums[l]; - *s = sumf; -#endif -} - -void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q5_K * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - - static const uint32_t kmask1 = 0x3f3f3f3f; - static const uint32_t kmask2 = 0x0f0f0f0f; - static const uint32_t kmask3 = 0x03030303; - - uint32_t utmp[4]; - -#ifdef __ARM_NEON - const uint8x16_t m4b = vdupq_n_u8(0xf); - const uint8x16_t mone = vdupq_n_u8(1); - const uint8x16_t mtwo = vdupq_n_u8(2); - const int32x4_t mzero = vdupq_n_s32(0); - - ggml_int8x16x4_t q5bytes; - - float sumf = 0; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const int16x8_t q8sums = vpaddq_s16(vld1q_s16(y[i].bsums), vld1q_s16(y[i].bsums + 8)); - - memcpy(utmp, x[i].scales, 12); - utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); - const uint32_t uaux = utmp[1] & kmask1; - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[2] = uaux; - utmp[0] &= kmask1; - - const uint8x8_t mins8 = vld1_u8((const uint8_t*)utmp + 8); - const int16x8_t mins = vreinterpretq_s16_u16(vmovl_u8(mins8)); - const int32x4_t prod = vaddq_s32(vmull_s16(vget_low_s16 (q8sums), vget_low_s16 (mins)), - vmull_s16(vget_high_s16(q8sums), vget_high_s16(mins))); - int32_t sumi_mins = vaddvq_s32(prod); - - const uint8_t * scales = (const uint8_t *)utmp; - - const uint8_t * restrict q5 = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - ggml_uint8x16x2_t qhbits = ggml_vld1q_u8_x2(qh); - - ggml_uint8x16x4_t q5h; - - int32_t sumi = 0; - - for (int j = 0; j < QK_K/64; ++j) { - - const ggml_uint8x16x2_t q5bits = ggml_vld1q_u8_x2(q5); q5 += 32; - const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); q8 += 64; - - q5h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits.val[0]), 4); - q5h.val[1] = vshlq_n_u8(vandq_u8(mone, qhbits.val[1]), 4); - q5h.val[2] = vshlq_n_u8(vandq_u8(mtwo, qhbits.val[0]), 3); - q5h.val[3] = vshlq_n_u8(vandq_u8(mtwo, qhbits.val[1]), 3); - qhbits.val[0] = vshrq_n_u8(qhbits.val[0], 2); - qhbits.val[1] = vshrq_n_u8(qhbits.val[1], 2); - - q5bytes.val[0] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q5bits.val[0], m4b), q5h.val[0])); - q5bytes.val[1] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q5bits.val[1], m4b), q5h.val[1])); - q5bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5bits.val[0], 4), q5h.val[2])); - q5bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5bits.val[1], 4), q5h.val[3])); - - sumi += vaddvq_s32(ggml_vdotq_s32(ggml_vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0]), q5bytes.val[1], q8bytes.val[1])) * *scales++; - sumi += vaddvq_s32(ggml_vdotq_s32(ggml_vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2]), q5bytes.val[3], q8bytes.val[3])) * *scales++; - } - - sumf += d * sumi - dmin * sumi_mins; - } - - *s = sumf; - -#elif defined __AVX2__ - - const __m256i m4 = _mm256_set1_epi8(0xF); - const __m128i mzero = _mm_setzero_si128(); - const __m256i mone = _mm256_set1_epi8(1); - - __m256 acc = _mm256_setzero_ps(); - - float summs = 0.f; - - for (int i = 0; i < nb; ++i) { - const uint8_t * restrict q5 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - memcpy(utmp, x[i].scales, 12); - utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); - const uint32_t uaux = utmp[1] & kmask1; - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[2] = uaux; - utmp[0] &= kmask1; - - const __m256i mins_and_scales = _mm256_cvtepu8_epi16(_mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0])); - - const __m256i q8sums = _mm256_loadu_si256((const __m256i*)y[i].bsums); - const __m128i q8s = _mm_hadd_epi16(_mm256_extracti128_si256(q8sums, 0), _mm256_extracti128_si256(q8sums, 1)); - const __m128i prod = _mm_madd_epi16(_mm256_extracti128_si256(mins_and_scales, 1), q8s); - const __m128i hsum = _mm_hadd_epi32(_mm_hadd_epi32(prod, mzero), mzero); - summs += dmin * _mm_extract_epi32(hsum, 0); - - const __m128i sc128 = _mm256_extracti128_si256(mins_and_scales, 0); - const __m256i scales = MM256_SET_M128I(sc128, sc128); - - const __m256i hbits = _mm256_loadu_si256((const __m256i*)x[i].qh); - __m256i hmask = mone; - - __m256i sumi = _mm256_setzero_si256(); - - int bit = 0; - - for (int j = 0; j < QK_K/64; ++j) { - - const __m256i scale_0 = _mm256_shuffle_epi8(scales, get_scale_shuffle_k4(2*j+0)); - const __m256i scale_1 = _mm256_shuffle_epi8(scales, get_scale_shuffle_k4(2*j+1)); - - const __m256i q5bits = _mm256_loadu_si256((const __m256i*)q5); q5 += 32; - - const __m256i q5l_0 = _mm256_and_si256(q5bits, m4); - const __m256i q5h_0 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_and_si256(hbits, hmask), bit++), 4); - const __m256i q5_0 = _mm256_add_epi8(q5l_0, q5h_0); - hmask = _mm256_slli_epi16(hmask, 1); - - const __m256i q5l_1 = _mm256_and_si256(_mm256_srli_epi16(q5bits, 4), m4); - const __m256i q5h_1 = _mm256_slli_epi16(_mm256_srli_epi16(_mm256_and_si256(hbits, hmask), bit++), 4); - const __m256i q5_1 = _mm256_add_epi8(q5l_1, q5h_1); - hmask = _mm256_slli_epi16(hmask, 1); - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - - __m256i p16_0 = _mm256_maddubs_epi16(q5_0, q8_0); - __m256i p16_1 = _mm256_maddubs_epi16(q5_1, q8_1); - - p16_0 = _mm256_madd_epi16(scale_0, p16_0); - p16_1 = _mm256_madd_epi16(scale_1, p16_1); - - sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_0, p16_1)); - - } - - __m256 vd = _mm256_set1_ps(d); - acc = _mm256_fmadd_ps(vd, _mm256_cvtepi32_ps(sumi), acc); - - } - - *s = hsum_float_8(acc) + summs; - -#elif defined __AVX__ - - const __m128i m4 = _mm_set1_epi8(0xF); - const __m128i mzero = _mm_setzero_si128(); - const __m128i mone = _mm_set1_epi8(1); - const __m128i m2 = _mm_set1_epi8(2); - - __m256 acc = _mm256_setzero_ps(); - - float summs = 0.f; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const uint8_t * restrict q5 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - memcpy(utmp, x[i].scales, 12); - utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); - const uint32_t uaux = utmp[1] & kmask1; - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[2] = uaux; - utmp[0] &= kmask1; - - const __m128i utmps = _mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0]); - const __m128i scales = _mm_cvtepu8_epi16(utmps); - const __m128i mins = _mm_cvtepu8_epi16(_mm_unpackhi_epi64(utmps, utmps)); - - const __m128i q8sums_0 = _mm_loadu_si128((const __m128i*)&y[i].bsums[0]); - const __m128i q8sums_1 = _mm_loadu_si128((const __m128i*)&y[i].bsums[8]); - const __m128i q8s = _mm_hadd_epi16(q8sums_0, q8sums_1); - const __m128i prod = _mm_madd_epi16(mins, q8s); - const __m128i hsum = _mm_hadd_epi32(_mm_hadd_epi32(prod, mzero), mzero); - summs += dmin * _mm_extract_epi32(hsum, 0); - - const __m128i hbits_0 = _mm_loadu_si128((const __m128i*)&x[i].qh[0]); - const __m128i hbits_1 = _mm_loadu_si128((const __m128i*)&x[i].qh[16]); - __m128i hmask = mone; - - __m128i sumi_0 = _mm_setzero_si128(); - __m128i sumi_1 = _mm_setzero_si128(); - - int bit = 0; - - __m128i shuffle = _mm_set1_epi16(0x0100); - for (int j = 0; j < QK_K/64; ++j) { - - const __m128i scale_0 = _mm_shuffle_epi8(scales, shuffle); - shuffle = _mm_add_epi16(shuffle, m2); - const __m128i scale_1 = _mm_shuffle_epi8(scales, shuffle); - shuffle = _mm_add_epi16(shuffle, m2); - - const __m128i q5bits_0 = _mm_loadu_si128((const __m128i*)q5); q5 += 16; - const __m128i q5bits_1 = _mm_loadu_si128((const __m128i*)q5); q5 += 16; - - __m128i q5l_0 = _mm_and_si128(q5bits_0, m4); - __m128i q5l_1 = _mm_and_si128(q5bits_1, m4); - __m128i q5h_0 = _mm_slli_epi16(_mm_srli_epi16(_mm_and_si128(hbits_0, hmask), bit), 4); - __m128i q5h_1 = _mm_slli_epi16(_mm_srli_epi16(_mm_and_si128(hbits_1, hmask), bit++), 4); - __m128i q5_0 = _mm_add_epi8(q5l_0, q5h_0); - __m128i q5_1 = _mm_add_epi8(q5l_1, q5h_1); - hmask = _mm_slli_epi16(hmask, 1); - - __m128i q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - __m128i q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - __m128i p16_0 = _mm_maddubs_epi16(q5_0, q8_0); - __m128i p16_1 = _mm_maddubs_epi16(q5_1, q8_1); - p16_0 = _mm_madd_epi16(scale_0, p16_0); - p16_1 = _mm_madd_epi16(scale_0, p16_1); - - q5l_0 = _mm_and_si128(_mm_srli_epi16(q5bits_0, 4), m4); - q5l_1 = _mm_and_si128(_mm_srli_epi16(q5bits_1, 4), m4); - q5h_0 = _mm_slli_epi16(_mm_srli_epi16(_mm_and_si128(hbits_0, hmask), bit), 4); - q5h_1 = _mm_slli_epi16(_mm_srli_epi16(_mm_and_si128(hbits_1, hmask), bit++), 4); - q5_0 = _mm_add_epi8(q5l_0, q5h_0); - q5_1 = _mm_add_epi8(q5l_1, q5h_1); - hmask = _mm_slli_epi16(hmask, 1); - - q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - __m128i p16_2 = _mm_maddubs_epi16(q5_0, q8_0); - __m128i p16_3 = _mm_maddubs_epi16(q5_1, q8_1); - p16_2 = _mm_madd_epi16(scale_1, p16_2); - p16_3 = _mm_madd_epi16(scale_1, p16_3); - - sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2)); - sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_1, p16_3)); - - } - - __m256 vd = _mm256_set1_ps(d); - __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0); - acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(sumi)), acc); - - } - - *s = hsum_float_8(acc) + summs; - -#elif defined __riscv_v_intrinsic - - const uint8_t * scales = (const uint8_t*)&utmp[0]; - const uint8_t * mins = (const uint8_t*)&utmp[2]; - - float sumf = 0; - float sums = 0.0; - - size_t vl; - - for (int i = 0; i < nb; ++i) { - - vl = 8; - - const uint8_t * restrict q5 = x[i].qs; - const uint8_t * restrict hm = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d; - - vint16mf2_t q8sums_0 = __riscv_vlse16_v_i16mf2(y[i].bsums, 4, vl); - vint16mf2_t q8sums_1 = __riscv_vlse16_v_i16mf2(y[i].bsums+1, 4, vl); - vint16mf2_t q8sums = __riscv_vadd_vv_i16mf2(q8sums_0, q8sums_1, vl); - - memcpy(utmp, x[i].scales, 12); - utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); - const uint32_t uaux = utmp[1] & kmask1; - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[2] = uaux; - utmp[0] &= kmask1; - - vuint8mf4_t mins8 = __riscv_vle8_v_u8mf4(mins, vl); - vint16mf2_t v_mins = __riscv_vreinterpret_v_u16mf2_i16mf2(__riscv_vzext_vf2_u16mf2(mins8, vl)); - vint32m1_t prod = __riscv_vwmul_vv_i32m1(q8sums, v_mins, vl); - - vint32m1_t sumi = __riscv_vredsum_vs_i32m1_i32m1(prod, __riscv_vmv_v_x_i32m1(0, 1), vl); - sumf -= dmin * __riscv_vmv_x_s_i32m1_i32(sumi); - - vl = 32; - int32_t aux32 = 0; - int is = 0; - - uint8_t m = 1; - vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); - vuint8m1_t vqh = __riscv_vle8_v_u8m1(hm, vl); - - for (int j = 0; j < QK_K/64; ++j) { - // load Q5 and Q8 - vuint8m1_t q5_x = __riscv_vle8_v_u8m1(q5, vl); - vint8m1_t q8_y1 = __riscv_vle8_v_i8m1(q8, vl); - vint8m1_t q8_y2 = __riscv_vle8_v_i8m1(q8+32, vl); - - // compute mask for addition - vint8m1_t q5_a = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(q5_x, 0x0F, vl)); - vuint8m1_t qh_m1 = __riscv_vand_vx_u8m1(vqh, m, vl); - vbool8_t vmask_1 = __riscv_vmsne_vx_u8m1_b8(qh_m1, 0, vl); - vint8m1_t q5_m1 = __riscv_vadd_vx_i8m1_mu(vmask_1, q5_a, q5_a, 16, vl); - m <<= 1; - - vint8m1_t q5_l = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vsrl_vx_u8m1(q5_x, 0x04, vl)); - vuint8m1_t qh_m2 = __riscv_vand_vx_u8m1(vqh, m, vl); - vbool8_t vmask_2 = __riscv_vmsne_vx_u8m1_b8(qh_m2, 0, vl); - vint8m1_t q5_m2 = __riscv_vadd_vx_i8m1_mu(vmask_2, q5_l, q5_l, 16, vl); - m <<= 1; - - vint16m2_t v0 = __riscv_vwmul_vv_i16m2(q5_m1, q8_y1, vl); - vint16m2_t v1 = __riscv_vwmul_vv_i16m2(q5_m2, q8_y2, vl); - - vint32m4_t vs1 = __riscv_vwmul_vx_i32m4(v0, scales[is++], vl); - vint32m4_t vs2 = __riscv_vwmul_vx_i32m4(v1, scales[is++], vl); - - vint32m1_t vacc1 = __riscv_vredsum_vs_i32m4_i32m1(vs1, vzero, vl); - vint32m1_t vacc2 = __riscv_vredsum_vs_i32m4_i32m1(vs2, vzero, vl); - - aux32 += __riscv_vmv_x_s_i32m1_i32(vacc1) + __riscv_vmv_x_s_i32m1_i32(vacc2); - q5 += 32; q8 += 64; - - } - - vfloat32m1_t vaux = __riscv_vfmul_vf_f32m1(__riscv_vfmv_v_f_f32m1(aux32, 1), d, 1); - sums += __riscv_vfmv_f_s_f32m1_f32(vaux); - - } - - *s = sumf+sums; - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0xF); - const vector signed char lowMask1 = vec_splats((int8_t)0x3f); - const vector signed char lowMask2 = vec_splats((int8_t)0x30); - const vector int v0 = vec_splats((int32_t)0); - const vector unsigned char v1 = vec_splats((unsigned char)0x1); - const vector unsigned char v2 = vec_splats((unsigned char)0x2); - const vector unsigned char v3 = vec_splats((unsigned char)0x3); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - - for (int i = 0; i < nb; ++i) { - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].dmin)); - vector float vdmin = vec_mul(vxmin, vyd); - - UNUSED(kmask1); - UNUSED(kmask2); - UNUSED(kmask3); - UNUSED(utmp); - - vector signed char u0 = (vector signed char)vec_xl_len(x[i].scales, 8); - vector signed char u1 = vec_and(vec_sr(u0, v2), lowMask2); - vector signed char u2 = (vector signed char)vec_xl_len(x[i].scales + 8, 4); - vector signed char u3 = vec_sr(u2, v4); - - vector signed char u30 = u1; - vector signed char u31 = (vector signed char)vec_mergeh((vector signed int)vec_and(u2, lowMask), (vector signed int)u3); - - u1 = vec_and(u0, lowMask1); - u2 = vec_or(u30, u31); - - vector signed char utmps = (vector signed char)vec_mergeh((vector signed int)u1, (vector signed int)u2); - - vector signed short q8ysums0 = vec_xl( 0, y[i].bsums); - vector signed short q8ysums1 = vec_xl(16, y[i].bsums); - - vector signed short vscales = vec_unpackh(utmps); - - vector signed short q5xmins = vec_unpackl(utmps); - vector signed short q5xmins0 = vec_mergeh(q5xmins, q5xmins); - vector signed short q5xmins1 = vec_mergel(q5xmins, q5xmins); - - vector signed int prod0 = vec_mule(q5xmins0, q8ysums0); - vector signed int prod1 = vec_mule(q5xmins1, q8ysums1); - vector signed int prod2 = vec_mulo(q5xmins0, q8ysums0); - vector signed int prod3 = vec_mulo(q5xmins1, q8ysums1); - - vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vdmin, vsumf0); - vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vdmin, vsumf1); - vsumf2 = vec_nmsub(vec_ctf(prod2, 0), vdmin, vsumf2); - vsumf3 = vec_nmsub(vec_ctf(prod3, 0), vdmin, vsumf3); - - vector signed char qxhs0 = (vector signed char)vec_xl( 0, x[i].qh); - vector signed char qxhs1 = (vector signed char)vec_xl(16, x[i].qh); - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - vector signed int vsumi2 = v0; - vector signed int vsumi3 = v0; - - const uint8_t * restrict q5 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - for (int j = 0; j < QK_K/64; ++j) { - __builtin_prefetch(q5, 0, 1); - __builtin_prefetch(q8, 0, 1); - - vector signed char qxs0 = (vector signed char)vec_xl( 0, q5); - vector signed char qxs1 = (vector signed char)vec_xl(16, q5); - q5 += 32; - - vector signed char qxs00 = vec_and(qxs0, lowMask); - vector signed char qxs01 = vec_sr(qxs0, v4); - vector signed char qxs10 = vec_and(qxs1, lowMask); - vector signed char qxs11 = vec_sr(qxs1, v4); - - vector signed char q5h00 = vec_sl(vec_and((vector signed char)v1, qxhs0), v4); - vector signed char q5h01 = vec_sl(vec_and((vector signed char)v2, qxhs0), v3); - vector signed char q5h10 = vec_sl(vec_and((vector signed char)v1, qxhs1), v4); - vector signed char q5h11 = vec_sl(vec_and((vector signed char)v2, qxhs1), v3); - qxhs0 = vec_sr(qxhs0, v2); - qxhs1 = vec_sr(qxhs1, v2); - - vector unsigned char q5x00 = (vector unsigned char)vec_or(q5h00, qxs00); - vector unsigned char q5x01 = (vector unsigned char)vec_or(q5h01, qxs01); - vector unsigned char q5x10 = (vector unsigned char)vec_or(q5h10, qxs10); - vector unsigned char q5x11 = (vector unsigned char)vec_or(q5h11, qxs11); - - vector signed char q8y00 = vec_xl( 0, q8); - vector signed char q8y10 = vec_xl(16, q8); - vector signed char q8y01 = vec_xl(32, q8); - vector signed char q8y11 = vec_xl(48, q8); - q8 += 64; - - vector signed int qv00 = vec_msum(q8y00, q5x00, v0); - vector signed int qv01 = vec_msum(q8y01, q5x01, v0); - vector signed int qv10 = vec_msum(q8y10, q5x10, v0); - vector signed int qv11 = vec_msum(q8y11, q5x11, v0); - - vector signed int vscales_h = vec_unpackh(vscales); - vector signed int vs0 = vec_splat(vscales_h, 0); - vector signed int vs1 = vec_splat(vscales_h, 1); - vscales = vec_sld(vscales, vscales, 12); - - vsumi0 = vec_add(vec_mul(qv00, vs0), vsumi0); - vsumi1 = vec_add(vec_mul(qv10, vs0), vsumi1); - vsumi2 = vec_add(vec_mul(qv01, vs1), vsumi2); - vsumi3 = vec_add(vec_mul(qv11, vs1), vsumi3); - } - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined __loongarch_asx - GGML_UNUSED(kmask1); - GGML_UNUSED(kmask2); - GGML_UNUSED(kmask3); - - const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); - const __m128i mzero = __lsx_vldi(0); - const __m256i mone = __lasx_xvreplgr2vr_b(1); - - __m256 acc = (__m256)__lasx_xvldi(0); - - float summs = 0.f; - - for (int i = 0; i < nb; ++i) { - - const uint8_t * restrict q5 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - memcpy(utmp, x[i].scales, 12); - utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); - const uint32_t uaux = utmp[1] & kmask1; - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[2] = uaux; - utmp[0] &= kmask1; - - const __m256i mins_and_scales = lasx_extu8_16(lsx_set_w(utmp[3], utmp[2], utmp[1], utmp[0])); - - const __m256i q8sums = __lasx_xvld((const __m256i*)y[i].bsums, 0); - const __m128i q8s = lsx_hadd_h(lasx_extracti128(q8sums, 0), lasx_extracti128(q8sums, 1)); - const __m128i prod = lsx_madd_h(lasx_extracti128(mins_and_scales, 1), q8s); - const __m128i hsum = lsx_hadd_w(lsx_hadd_w(prod, mzero), mzero); - summs += dmin * __lsx_vpickve2gr_w(hsum, 0); //TODO check - - const __m128i sc128 = lasx_extracti128(mins_and_scales, 0); - const __m256i scales = lasx_insertf128(sc128, sc128); - - const __m256i hbits = __lasx_xvld((const __m256i*)x[i].qh, 0); - __m256i hmask = mone; - - __m256i sumi = __lasx_xvldi(0); - - int bit = 0; - __m256i xvbit; - - for (int j = 0; j < QK_K/64; ++j) { - - const __m256i scale_0 = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+0)); - const __m256i scale_1 = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+1)); - - const __m256i q5bits = __lasx_xvld((const __m256i*)q5, 0); q5 += 32; - - xvbit = __lasx_xvreplgr2vr_h(bit++); - const __m256i q5l_0 = __lasx_xvand_v(q5bits, m4); - const __m256i q5h_0 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvand_v(hbits, hmask), xvbit), 4); - const __m256i q5_0 = __lasx_xvadd_b(q5l_0, q5h_0); - hmask = __lasx_xvslli_h(hmask, 1); - - xvbit = __lasx_xvreplgr2vr_h(bit++); - const __m256i q5l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q5bits, 4), m4); - const __m256i q5h_1 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvand_v(hbits, hmask), xvbit), 4); - const __m256i q5_1 = __lasx_xvadd_b(q5l_1, q5h_1); - hmask = __lasx_xvslli_h(hmask, 1); - - const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - - __m256i p16_0 = lasx_maddubs_h(q5_0, q8_0); - __m256i p16_1 = lasx_maddubs_h(q5_1, q8_1); - - p16_0 = lasx_madd_h(scale_0, p16_0); - p16_1 = lasx_madd_h(scale_1, p16_1); - - sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_1)); - - } - - __m256 vd = __lasx_xvreplfr2vr_s(d); - acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(sumi), acc); - - } - - *s = hsum_float_8(acc) + summs; - -#else - - const uint8_t * scales = (const uint8_t*)&utmp[0]; - const uint8_t * mins = (const uint8_t*)&utmp[2]; - - int8_t aux8[QK_K]; - int16_t aux16[8]; - float sums [8]; - int32_t aux32[8]; - memset(sums, 0, 8*sizeof(float)); - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const uint8_t * restrict q4 = x[i].qs; - const uint8_t * restrict hm = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - memset(aux32, 0, 8*sizeof(int32_t)); - int8_t * restrict a = aux8; - uint8_t m = 1; - for (int j = 0; j < QK_K/64; ++j) { - for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF); - for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0); - a += 32; m <<= 1; - for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] >> 4); - for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0); - a += 32; m <<= 1; - q4 += 32; - } - memcpy(utmp, x[i].scales, 12); - utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); - const uint32_t uaux = utmp[1] & kmask1; - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[2] = uaux; - utmp[0] &= kmask1; - - int sumi = 0; - for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2]; - a = aux8; - int is = 0; - for (int j = 0; j < QK_K/32; ++j) { - int32_t scale = scales[is++]; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - } - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; - const float dmin = GGML_FP16_TO_FP32(x[i].dmin) * y[i].d; - sumf -= dmin * sumi; - } - for (int l = 0; l < 8; ++l) sumf += sums[l]; - *s = sumf; -#endif -} - -void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q6_K * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#ifdef __ARM_NEON - float sum = 0; - - const uint8x16_t m4b = vdupq_n_u8(0xF); - const int32x4_t vzero = vdupq_n_s32(0); - //const int8x16_t m32s = vdupq_n_s8(32); - - const uint8x16_t mone = vdupq_n_u8(3); - - ggml_int8x16x4_t q6bytes; - ggml_uint8x16x4_t q6h; - - for (int i = 0; i < nb; ++i) { - - const float d_all = GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q6 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - const int8_t * restrict scale = x[i].scales; - - const ggml_int16x8x2_t q8sums = ggml_vld1q_s16_x2(y[i].bsums); - const int8x16_t scales = vld1q_s8(scale); - const ggml_int16x8x2_t q6scales = {{vmovl_s8(vget_low_s8(scales)), vmovl_s8(vget_high_s8(scales))}}; - - const int32x4_t prod = vaddq_s32(vaddq_s32(vmull_s16(vget_low_s16 (q8sums.val[0]), vget_low_s16 (q6scales.val[0])), - vmull_s16(vget_high_s16(q8sums.val[0]), vget_high_s16(q6scales.val[0]))), - vaddq_s32(vmull_s16(vget_low_s16 (q8sums.val[1]), vget_low_s16 (q6scales.val[1])), - vmull_s16(vget_high_s16(q8sums.val[1]), vget_high_s16(q6scales.val[1])))); - int32_t isum_mins = vaddvq_s32(prod); - - int32_t isum = 0; - - for (int j = 0; j < QK_K/128; ++j) { - - ggml_uint8x16x2_t qhbits = ggml_vld1q_u8_x2(qh); qh += 32; - ggml_uint8x16x4_t q6bits = ggml_vld1q_u8_x4(q6); q6 += 64; - ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); q8 += 64; - - q6h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits.val[0]), 4); - q6h.val[1] = vshlq_n_u8(vandq_u8(mone, qhbits.val[1]), 4); - uint8x16_t shifted = vshrq_n_u8(qhbits.val[0], 2); - q6h.val[2] = vshlq_n_u8(vandq_u8(mone, shifted), 4); - shifted = vshrq_n_u8(qhbits.val[1], 2); - q6h.val[3] = vshlq_n_u8(vandq_u8(mone, shifted), 4); - - //q6bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[0], m4b), q6h.val[0])), m32s); - //q6bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[1], m4b), q6h.val[1])), m32s); - //q6bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[2], m4b), q6h.val[2])), m32s); - //q6bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[3], m4b), q6h.val[3])), m32s); - q6bytes.val[0] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[0], m4b), q6h.val[0])); - q6bytes.val[1] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[1], m4b), q6h.val[1])); - q6bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[2], m4b), q6h.val[2])); - q6bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[3], m4b), q6h.val[3])); - - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + - vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + - vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + - vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; - - scale += 4; - - q8bytes = ggml_vld1q_s8_x4(q8); q8 += 64; - - shifted = vshrq_n_u8(qhbits.val[0], 4); - q6h.val[0] = vshlq_n_u8(vandq_u8(mone, shifted), 4); - shifted = vshrq_n_u8(qhbits.val[1], 4); - q6h.val[1] = vshlq_n_u8(vandq_u8(mone, shifted), 4); - shifted = vshrq_n_u8(qhbits.val[0], 6); - q6h.val[2] = vshlq_n_u8(vandq_u8(mone, shifted), 4); - shifted = vshrq_n_u8(qhbits.val[1], 6); - q6h.val[3] = vshlq_n_u8(vandq_u8(mone, shifted), 4); - - //q6bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[0], 4), q6h.val[0])), m32s); - //q6bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[1], 4), q6h.val[1])), m32s); - //q6bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[2], 4), q6h.val[2])), m32s); - //q6bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[3], 4), q6h.val[3])), m32s); - q6bytes.val[0] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[0], 4), q6h.val[0])); - q6bytes.val[1] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[1], 4), q6h.val[1])); - q6bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[2], 4), q6h.val[2])); - q6bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[3], 4), q6h.val[3])); - - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + - vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + - vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + - vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; - scale += 4; - } - //sum += isum * d_all * y[i].d; - sum += d_all * y[i].d * (isum - 32 * isum_mins); - - } - *s = sum; - -#elif defined __AVX2__ - - const __m256i m4 = _mm256_set1_epi8(0xF); - const __m256i m2 = _mm256_set1_epi8(3); - const __m256i m32s = _mm256_set1_epi8(32); - - __m256 acc = _mm256_setzero_ps(); - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q4 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - const __m128i scales = _mm_loadu_si128((const __m128i*)x[i].scales); - - __m256i sumi = _mm256_setzero_si256(); - - int is = 0; - - for (int j = 0; j < QK_K/128; ++j) { - - const __m128i scale_0 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 0)); - const __m128i scale_1 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 1)); - const __m128i scale_2 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 2)); - const __m128i scale_3 = _mm_shuffle_epi8(scales, get_scale_shuffle(is + 3)); - is += 4; - - const __m256i q4bits1 = _mm256_loadu_si256((const __m256i*)q4); q4 += 32; - const __m256i q4bits2 = _mm256_loadu_si256((const __m256i*)q4); q4 += 32; - const __m256i q4bitsH = _mm256_loadu_si256((const __m256i*)qh); qh += 32; - - const __m256i q4h_0 = _mm256_slli_epi16(_mm256_and_si256(q4bitsH, m2), 4); - const __m256i q4h_1 = _mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q4bitsH, 2), m2), 4); - const __m256i q4h_2 = _mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q4bitsH, 4), m2), 4); - const __m256i q4h_3 = _mm256_slli_epi16(_mm256_and_si256(_mm256_srli_epi16(q4bitsH, 6), m2), 4); - - const __m256i q4_0 = _mm256_or_si256(_mm256_and_si256(q4bits1, m4), q4h_0); - const __m256i q4_1 = _mm256_or_si256(_mm256_and_si256(q4bits2, m4), q4h_1); - const __m256i q4_2 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits1, 4), m4), q4h_2); - const __m256i q4_3 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits2, 4), m4), q4h_3); - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q8_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q8_3 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - - __m256i q8s_0 = _mm256_maddubs_epi16(m32s, q8_0); - __m256i q8s_1 = _mm256_maddubs_epi16(m32s, q8_1); - __m256i q8s_2 = _mm256_maddubs_epi16(m32s, q8_2); - __m256i q8s_3 = _mm256_maddubs_epi16(m32s, q8_3); - - __m256i p16_0 = _mm256_maddubs_epi16(q4_0, q8_0); - __m256i p16_1 = _mm256_maddubs_epi16(q4_1, q8_1); - __m256i p16_2 = _mm256_maddubs_epi16(q4_2, q8_2); - __m256i p16_3 = _mm256_maddubs_epi16(q4_3, q8_3); - - p16_0 = _mm256_sub_epi16(p16_0, q8s_0); - p16_1 = _mm256_sub_epi16(p16_1, q8s_1); - p16_2 = _mm256_sub_epi16(p16_2, q8s_2); - p16_3 = _mm256_sub_epi16(p16_3, q8s_3); - - p16_0 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_0), p16_0); - p16_1 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_1), p16_1); - p16_2 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_2), p16_2); - p16_3 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_3), p16_3); - - sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_0, p16_1)); - sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_2, p16_3)); - - } - - acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi), acc); - } - - *s = hsum_float_8(acc); - -#elif defined __AVX__ - - const __m128i m4 = _mm_set1_epi8(0xF); - const __m128i m3 = _mm_set1_epi8(3); - const __m128i m32s = _mm_set1_epi8(32); - const __m128i m2 = _mm_set1_epi8(2); - - __m256 acc = _mm256_setzero_ps(); - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q4 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - const __m128i scales = _mm_loadu_si128((const __m128i*)x[i].scales); - - __m128i sumi_0 = _mm_setzero_si128(); - __m128i sumi_1 = _mm_setzero_si128(); - - __m128i shuffle = _mm_set_epi64x(0x0101010101010101, 0x0000000000000000); - for (int j = 0; j < QK_K/128; ++j) { - - const __m128i q4bitsH_0 = _mm_loadu_si128((const __m128i*)qh); qh += 16; - const __m128i q4bitsH_1 = _mm_loadu_si128((const __m128i*)qh); qh += 16; - - const __m128i q4h_0 = _mm_slli_epi16(_mm_and_si128(q4bitsH_0, m3), 4); - const __m128i q4h_1 = _mm_slli_epi16(_mm_and_si128(q4bitsH_1, m3), 4); - const __m128i q4h_2 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH_0, 2), m3), 4); - const __m128i q4h_3 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH_1, 2), m3), 4); - const __m128i q4h_4 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH_0, 4), m3), 4); - const __m128i q4h_5 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH_1, 4), m3), 4); - const __m128i q4h_6 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH_0, 6), m3), 4); - const __m128i q4h_7 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH_1, 6), m3), 4); - - const __m128i q4bits1_0 = _mm_loadu_si128((const __m128i*)q4); q4 += 16; - const __m128i q4bits1_1 = _mm_loadu_si128((const __m128i*)q4); q4 += 16; - const __m128i q4bits2_0 = _mm_loadu_si128((const __m128i*)q4); q4 += 16; - const __m128i q4bits2_1 = _mm_loadu_si128((const __m128i*)q4); q4 += 16; - - const __m128i q4_0 = _mm_or_si128(_mm_and_si128(q4bits1_0, m4), q4h_0); - const __m128i q4_1 = _mm_or_si128(_mm_and_si128(q4bits1_1, m4), q4h_1); - const __m128i q4_2 = _mm_or_si128(_mm_and_si128(q4bits2_0, m4), q4h_2); - const __m128i q4_3 = _mm_or_si128(_mm_and_si128(q4bits2_1, m4), q4h_3); - const __m128i q4_4 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(q4bits1_0, 4), m4), q4h_4); - const __m128i q4_5 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(q4bits1_1, 4), m4), q4h_5); - const __m128i q4_6 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(q4bits2_0, 4), m4), q4h_6); - const __m128i q4_7 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(q4bits2_1, 4), m4), q4h_7); - - const __m128i q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_2 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_3 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_4 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_5 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_6 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - const __m128i q8_7 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; - - __m128i q8s_0 = _mm_maddubs_epi16(m32s, q8_0); - __m128i q8s_1 = _mm_maddubs_epi16(m32s, q8_1); - __m128i q8s_2 = _mm_maddubs_epi16(m32s, q8_2); - __m128i q8s_3 = _mm_maddubs_epi16(m32s, q8_3); - __m128i q8s_4 = _mm_maddubs_epi16(m32s, q8_4); - __m128i q8s_5 = _mm_maddubs_epi16(m32s, q8_5); - __m128i q8s_6 = _mm_maddubs_epi16(m32s, q8_6); - __m128i q8s_7 = _mm_maddubs_epi16(m32s, q8_7); - - __m128i p16_0 = _mm_maddubs_epi16(q4_0, q8_0); - __m128i p16_1 = _mm_maddubs_epi16(q4_1, q8_1); - __m128i p16_2 = _mm_maddubs_epi16(q4_2, q8_2); - __m128i p16_3 = _mm_maddubs_epi16(q4_3, q8_3); - __m128i p16_4 = _mm_maddubs_epi16(q4_4, q8_4); - __m128i p16_5 = _mm_maddubs_epi16(q4_5, q8_5); - __m128i p16_6 = _mm_maddubs_epi16(q4_6, q8_6); - __m128i p16_7 = _mm_maddubs_epi16(q4_7, q8_7); - - p16_0 = _mm_sub_epi16(p16_0, q8s_0); - p16_1 = _mm_sub_epi16(p16_1, q8s_1); - p16_2 = _mm_sub_epi16(p16_2, q8s_2); - p16_3 = _mm_sub_epi16(p16_3, q8s_3); - p16_4 = _mm_sub_epi16(p16_4, q8s_4); - p16_5 = _mm_sub_epi16(p16_5, q8s_5); - p16_6 = _mm_sub_epi16(p16_6, q8s_6); - p16_7 = _mm_sub_epi16(p16_7, q8s_7); - - const __m128i scale_0 = _mm_shuffle_epi8(scales, shuffle); - shuffle = _mm_add_epi8(shuffle, m2); - const __m128i scale_1 = _mm_shuffle_epi8(scales, shuffle); - shuffle = _mm_add_epi8(shuffle, m2); - const __m128i scale_2 = _mm_shuffle_epi8(scales, shuffle); - shuffle = _mm_add_epi8(shuffle, m2); - const __m128i scale_3 = _mm_shuffle_epi8(scales, shuffle); - shuffle = _mm_add_epi8(shuffle, m2); - - p16_0 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_0), p16_0); - p16_1 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_0, scale_0)), p16_1); - p16_2 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_1), p16_2); - p16_3 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_1, scale_1)), p16_3); - p16_4 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_2), p16_4); - p16_5 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_2, scale_2)), p16_5); - p16_6 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_3), p16_6); - p16_7 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_3, scale_3)), p16_7); - - sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2)); - sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_1, p16_3)); - sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_4, p16_6)); - sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_5, p16_7)); - - } - - __m256i sumi = MM256_SET_M128I(sumi_1, sumi_0); - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi)), acc); - } - - *s = hsum_float_8(acc); - -#elif defined __riscv_v_intrinsic - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - - const uint8_t * restrict q6 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - const int8_t * restrict scale = x[i].scales; - - size_t vl; - - vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); - - int sum_t = 0; - int is = 0; - - for (int j = 0; j < QK_K/128; ++j) { - - vl = 32; - - // load qh - vuint8m1_t qh_x = __riscv_vle8_v_u8m1(qh, vl); - - // load Q6 - vuint8m1_t q6_0 = __riscv_vle8_v_u8m1(q6, vl); - vuint8m1_t q6_1 = __riscv_vle8_v_u8m1(q6+32, vl); - - vuint8m1_t q6a_0 = __riscv_vand_vx_u8m1(q6_0, 0x0F, vl); - vuint8m1_t q6a_1 = __riscv_vand_vx_u8m1(q6_1, 0x0F, vl); - vuint8m1_t q6s_0 = __riscv_vsrl_vx_u8m1(q6_0, 0x04, vl); - vuint8m1_t q6s_1 = __riscv_vsrl_vx_u8m1(q6_1, 0x04, vl); - - vuint8m1_t qh_0 = __riscv_vand_vx_u8m1(qh_x, 0x03, vl); - vuint8m1_t qh_1 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(qh_x, 0x2, vl), 0x03 , vl); - vuint8m1_t qh_2 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(qh_x, 0x4, vl), 0x03 , vl); - vuint8m1_t qh_3 = __riscv_vand_vx_u8m1(__riscv_vsrl_vx_u8m1(qh_x, 0x6, vl), 0x03 , vl); - - vuint8m1_t qhi_0 = __riscv_vor_vv_u8m1(q6a_0, __riscv_vsll_vx_u8m1(qh_0, 0x04, vl), vl); - vuint8m1_t qhi_1 = __riscv_vor_vv_u8m1(q6a_1, __riscv_vsll_vx_u8m1(qh_1, 0x04, vl), vl); - vuint8m1_t qhi_2 = __riscv_vor_vv_u8m1(q6s_0, __riscv_vsll_vx_u8m1(qh_2, 0x04, vl), vl); - vuint8m1_t qhi_3 = __riscv_vor_vv_u8m1(q6s_1, __riscv_vsll_vx_u8m1(qh_3, 0x04, vl), vl); - - vint8m1_t a_0 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(qhi_0), 32, vl); - vint8m1_t a_1 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(qhi_1), 32, vl); - vint8m1_t a_2 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(qhi_2), 32, vl); - vint8m1_t a_3 = __riscv_vsub_vx_i8m1(__riscv_vreinterpret_v_u8m1_i8m1(qhi_3), 32, vl); - - // load Q8 and take product - vint16m2_t va_q_0 = __riscv_vwmul_vv_i16m2(a_0, __riscv_vle8_v_i8m1(q8, vl), vl); - vint16m2_t va_q_1 = __riscv_vwmul_vv_i16m2(a_1, __riscv_vle8_v_i8m1(q8+32, vl), vl); - vint16m2_t va_q_2 = __riscv_vwmul_vv_i16m2(a_2, __riscv_vle8_v_i8m1(q8+64, vl), vl); - vint16m2_t va_q_3 = __riscv_vwmul_vv_i16m2(a_3, __riscv_vle8_v_i8m1(q8+96, vl), vl); - - vl = 16; - - vint32m2_t vaux_0 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_0, 0), scale[is+0], vl); - vint32m2_t vaux_1 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_0, 1), scale[is+1], vl); - vint32m2_t vaux_2 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_1, 0), scale[is+2], vl); - vint32m2_t vaux_3 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_1, 1), scale[is+3], vl); - vint32m2_t vaux_4 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_2, 0), scale[is+4], vl); - vint32m2_t vaux_5 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_2, 1), scale[is+5], vl); - vint32m2_t vaux_6 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_3, 0), scale[is+6], vl); - vint32m2_t vaux_7 = __riscv_vwmul_vx_i32m2(__riscv_vget_v_i16m2_i16m1(va_q_3, 1), scale[is+7], vl); - - vint32m1_t isum0 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(vaux_0, vaux_1, vl), vzero, vl); - vint32m1_t isum1 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(vaux_2, vaux_3, vl), isum0, vl); - vint32m1_t isum2 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(vaux_4, vaux_5, vl), isum1, vl); - vint32m1_t isum3 = __riscv_vredsum_vs_i32m2_i32m1(__riscv_vadd_vv_i32m2(vaux_6, vaux_7, vl), isum2, vl); - - sum_t += __riscv_vmv_x_s_i32m1_i32(isum3); - - q6 += 64; qh += 32; q8 += 128; is=8; - - } - - sumf += d * sum_t; - - } - - *s = sumf; - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0xF); - const vector int v0 = vec_splats((int32_t)0); - const vector unsigned char v2 = vec_splats((unsigned char)0x2); - const vector unsigned char v3 = vec_splats((unsigned char)0x3); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - const vector unsigned char v6 = vec_splats((unsigned char)0x6); - const vector signed char off = vec_splats((signed char)0x20); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - - for (int i = 0; i < nb; ++i) { - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - vector signed int vsumi2 = v0; - vector signed int vsumi3 = v0; - vector signed int vsumi4 = v0; - vector signed int vsumi5 = v0; - vector signed int vsumi6 = v0; - vector signed int vsumi7 = v0; - - const uint8_t * restrict q6 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict qs = x[i].scales; - const int8_t * restrict q8 = y[i].qs; - - for (int j = 0; j < QK_K/128; ++j) { - __builtin_prefetch(q6, 0, 0); - __builtin_prefetch(qh, 0, 0); - __builtin_prefetch(q8, 0, 0); - - vector signed char qxs0 = (vector signed char)vec_xl( 0, q6); - vector signed char qxs1 = (vector signed char)vec_xl(16, q6); - vector signed char qxs2 = (vector signed char)vec_xl(32, q6); - vector signed char qxs3 = (vector signed char)vec_xl(48, q6); - q6 += 64; - - vector signed char qxs00 = vec_and(qxs0, lowMask); - vector signed char qxs01 = vec_sr(qxs0, v4); - vector signed char qxs10 = vec_and(qxs1, lowMask); - vector signed char qxs11 = vec_sr(qxs1, v4); - vector signed char qxs20 = vec_and(qxs2, lowMask); - vector signed char qxs21 = vec_sr(qxs2, v4); - vector signed char qxs30 = vec_and(qxs3, lowMask); - vector signed char qxs31 = vec_sr(qxs3, v4); - - vector signed char qxhs0 = (vector signed char)vec_xl( 0, qh); - vector signed char qxhs1 = (vector signed char)vec_xl(16, qh); - qh += 32; - - vector signed char qxh00 = vec_sl(vec_and((vector signed char)v3, qxhs0), v4); - vector signed char qxh01 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v4)), v4); - vector signed char qxh10 = vec_sl(vec_and((vector signed char)v3, qxhs1), v4); - vector signed char qxh11 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs1, v4)), v4); - vector signed char qxh20 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v2)), v4); - vector signed char qxh21 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v6)), v4); - vector signed char qxh30 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs1, v2)), v4); - vector signed char qxh31 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs1, v6)), v4); - - vector signed char q6x00 = vec_sub(vec_or(qxh00, qxs00), off); - vector signed char q6x01 = vec_sub(vec_or(qxh01, qxs01), off); - vector signed char q6x10 = vec_sub(vec_or(qxh10, qxs10), off); - vector signed char q6x11 = vec_sub(vec_or(qxh11, qxs11), off); - vector signed char q6x20 = vec_sub(vec_or(qxh20, qxs20), off); - vector signed char q6x21 = vec_sub(vec_or(qxh21, qxs21), off); - vector signed char q6x30 = vec_sub(vec_or(qxh30, qxs30), off); - vector signed char q6x31 = vec_sub(vec_or(qxh31, qxs31), off); - - vector signed char q8y00 = vec_xl( 0, q8); - vector signed char q8y10 = vec_xl( 16, q8); - vector signed char q8y20 = vec_xl( 32, q8); - vector signed char q8y30 = vec_xl( 48, q8); - vector signed char q8y01 = vec_xl( 64, q8); - vector signed char q8y11 = vec_xl( 80, q8); - vector signed char q8y21 = vec_xl( 96, q8); - vector signed char q8y31 = vec_xl(112, q8); - q8 += 128; - - vector signed short qv00 = vec_add(vec_mule(q6x00, q8y00), vec_mulo(q6x00, q8y00)); - vector signed short qv10 = vec_add(vec_mule(q6x10, q8y10), vec_mulo(q6x10, q8y10)); - vector signed short qv20 = vec_add(vec_mule(q6x20, q8y20), vec_mulo(q6x20, q8y20)); - vector signed short qv30 = vec_add(vec_mule(q6x30, q8y30), vec_mulo(q6x30, q8y30)); - vector signed short qv01 = vec_add(vec_mule(q6x01, q8y01), vec_mulo(q6x01, q8y01)); - vector signed short qv11 = vec_add(vec_mule(q6x11, q8y11), vec_mulo(q6x11, q8y11)); - vector signed short qv21 = vec_add(vec_mule(q6x21, q8y21), vec_mulo(q6x21, q8y21)); - vector signed short qv31 = vec_add(vec_mule(q6x31, q8y31), vec_mulo(q6x31, q8y31)); - - vector signed short vscales = vec_unpackh(vec_xl_len(qs, 8)); - qs += 8; - - vector signed short vs0 = vec_splat(vscales, 0); - vector signed short vs1 = vec_splat(vscales, 1); - vector signed short vs2 = vec_splat(vscales, 2); - vector signed short vs3 = vec_splat(vscales, 3); - vector signed short vs4 = vec_splat(vscales, 4); - vector signed short vs5 = vec_splat(vscales, 5); - vector signed short vs6 = vec_splat(vscales, 6); - vector signed short vs7 = vec_splat(vscales, 7); - - vsumi0 = vec_msum(qv00, vs0, vsumi0); - vsumi1 = vec_msum(qv01, vs4, vsumi1); - vsumi2 = vec_msum(qv10, vs1, vsumi2); - vsumi3 = vec_msum(qv11, vs5, vsumi3); - vsumi4 = vec_msum(qv20, vs2, vsumi4); - vsumi5 = vec_msum(qv21, vs6, vsumi5); - vsumi6 = vec_msum(qv30, vs3, vsumi6); - vsumi7 = vec_msum(qv31, vs7, vsumi7); - } - - vsumi0 = vec_add(vsumi0, vsumi4); - vsumi1 = vec_add(vsumi1, vsumi5); - vsumi2 = vec_add(vsumi2, vsumi6); - vsumi3 = vec_add(vsumi3, vsumi7); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined __loongarch_asx - - const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); - const __m256i m2 = __lasx_xvreplgr2vr_b(3); - const __m256i m32s = __lasx_xvreplgr2vr_b(32); - - __m256 acc = (__m256)__lasx_xvldi(0); - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q4 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - const __m128i scales = __lsx_vld((const __m128i*)x[i].scales, 0); - - __m256i sumi = __lasx_xvldi(0); - - int is = 0; - - for (int j = 0; j < QK_K/128; ++j) { - - const __m128i scale_0 = lsx_shuffle_b(scales, get_scale_shuffle(is + 0)); - const __m128i scale_1 = lsx_shuffle_b(scales, get_scale_shuffle(is + 1)); - const __m128i scale_2 = lsx_shuffle_b(scales, get_scale_shuffle(is + 2)); - const __m128i scale_3 = lsx_shuffle_b(scales, get_scale_shuffle(is + 3)); - is += 4; - - const __m256i q4bits1 = __lasx_xvld((const __m256i*)q4, 0); q4 += 32; - const __m256i q4bits2 = __lasx_xvld((const __m256i*)q4, 0); q4 += 32; - const __m256i q4bitsH = __lasx_xvld((const __m256i*)qh, 0); qh += 32; - - const __m256i q4h_0 = __lasx_xvslli_h(__lasx_xvand_v(q4bitsH, m2), 4); - const __m256i q4h_1 = __lasx_xvslli_h(__lasx_xvand_v(__lasx_xvsrli_h(q4bitsH, 2), m2), 4); - const __m256i q4h_2 = __lasx_xvslli_h(__lasx_xvand_v(__lasx_xvsrli_h(q4bitsH, 4), m2), 4); - const __m256i q4h_3 = __lasx_xvslli_h(__lasx_xvand_v(__lasx_xvsrli_h(q4bitsH, 6), m2), 4); - - const __m256i q4_0 = __lasx_xvor_v(__lasx_xvand_v(q4bits1, m4), q4h_0); - const __m256i q4_1 = __lasx_xvor_v(__lasx_xvand_v(q4bits2, m4), q4h_1); - const __m256i q4_2 = __lasx_xvor_v(__lasx_xvand_v(__lasx_xvsrli_h(q4bits1, 4), m4), q4h_2); - const __m256i q4_3 = __lasx_xvor_v(__lasx_xvand_v(__lasx_xvsrli_h(q4bits2, 4), m4), q4h_3); - - const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - const __m256i q8_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - const __m256i q8_3 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - - __m256i q8s_0 = lasx_maddubs_h(m32s, q8_0); - __m256i q8s_1 = lasx_maddubs_h(m32s, q8_1); - __m256i q8s_2 = lasx_maddubs_h(m32s, q8_2); - __m256i q8s_3 = lasx_maddubs_h(m32s, q8_3); - - __m256i p16_0 = lasx_maddubs_h(q4_0, q8_0); - __m256i p16_1 = lasx_maddubs_h(q4_1, q8_1); - __m256i p16_2 = lasx_maddubs_h(q4_2, q8_2); - __m256i p16_3 = lasx_maddubs_h(q4_3, q8_3); - - p16_0 = __lasx_xvsub_h(p16_0, q8s_0); - p16_1 = __lasx_xvsub_h(p16_1, q8s_1); - p16_2 = __lasx_xvsub_h(p16_2, q8s_2); - p16_3 = __lasx_xvsub_h(p16_3, q8s_3); - - p16_0 = lasx_madd_h(lasx_ext8_16(scale_0), p16_0); - p16_1 = lasx_madd_h(lasx_ext8_16(scale_1), p16_1); - p16_2 = lasx_madd_h(lasx_ext8_16(scale_2), p16_2); - p16_3 = lasx_madd_h(lasx_ext8_16(scale_3), p16_3); - - sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_1)); - sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_2, p16_3)); - } - - acc = __lasx_xvfmadd_s((__m256)__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), acc); - } - - *s = hsum_float_8(acc); - -#else - - int8_t aux8[QK_K]; - int16_t aux16[8]; - float sums [8]; - int32_t aux32[8]; - memset(sums, 0, 8*sizeof(float)); - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const uint8_t * restrict q4 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - memset(aux32, 0, 8*sizeof(int32_t)); - int8_t * restrict a = aux8; - for (int j = 0; j < QK_K; j += 128) { - for (int l = 0; l < 32; ++l) { - a[l + 0] = (int8_t)((q4[l + 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32; - a[l + 32] = (int8_t)((q4[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32; - a[l + 64] = (int8_t)((q4[l + 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32; - a[l + 96] = (int8_t)((q4[l + 32] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32; - } - a += 128; - q4 += 64; - qh += 32; - } - a = aux8; - int is = 0; - for (int j = 0; j < QK_K/16; ++j) { - int scale = x[i].scales[is++]; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - } - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; - } - for (int l = 0; l < 8; ++l) sumf += sums[l]; - *s = sumf; -#endif -} - -#if defined (__AVX__) || defined (__AVX2__) || defined (__ARM_NEON) || defined (__POWER9_VECTOR__) || defined(__loongarch_asx) -static const int8_t keven_signs_q2xs[1024] = { - 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, 1, - 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, 1, 1, 1, 1, -1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, -1, - 1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, -1, 1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, 1, -1, - 1, 1, -1, -1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 1, 1, -1, 1, -1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, 1, - 1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, 1, -1, 1, 1, 1, 1, -1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, -1, 1, 1, -1, - 1, 1, -1, 1, -1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, 1, - 1, 1, 1, -1, -1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, -1, 1, -1, 1, -1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, 1, - 1, 1, -1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, 1, 1, -1, -1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, -1, - 1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 1, 1, 1, -1, 1, 1, 1, -1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, 1, -1, 1, -1, - 1, 1, -1, 1, 1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, -1, 1, -1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, 1, 1, - 1, 1, 1, -1, 1, -1, 1, 1, -1, 1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, - 1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, 1, 1, -1, -1, -1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, -1, - 1, 1, 1, 1, -1, -1, 1, 1, -1, 1, 1, 1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, -1, -1, 1, 1, - 1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, -1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, -1, - 1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, 1, 1, 1, -1, 1, -1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, -1, - 1, 1, -1, -1, -1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, -1, 1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, 1, - 1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, -1, 1, 1, -1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 1, 1, 1, -1, -1, - 1, 1, -1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, 1, - 1, 1, 1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, 1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, 1, - 1, 1, -1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, -1, 1, 1, -1, -1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, -1, - 1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, 1, -1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, 1, -1, 1, - 1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1, 1, 1, -1, -1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, -1, - 1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, - 1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, 1, - 1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, 1, 1, -1, -1, -1, 1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, -1, 1, - 1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1, 1, -1, -1, -1, - 1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, 1, -1, -1, 1, 1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, -1, -1, -1, - 1, 1, -1, -1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, -1, 1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, 1, - 1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, -1, -1, 1, 1, -1, 1, 1, -1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, -1, - 1, 1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1, -1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, 1, - 1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, -1, -1, -1, -1, 1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, 1, - 1, 1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, 1, 1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, -1, -}; -#endif - -void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_iq2_xxs * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#if defined(__ARM_NEON) - - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - - uint32_t aux32[4]; - const uint8_t * aux8 = (const uint8_t *)aux32; - - ggml_int8x16x4_t q2u; - ggml_int8x16x4_t q2s; - ggml_int8x16x4_t q8b; - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint16_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - float sumf1 = 0, sumf2 = 0; - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - q8b = ggml_vld1q_s8_x4(q8); q8 += 64; - memcpy(aux32, q2, 4*sizeof(uint32_t)); q2 += 8; - q2u.val[0] = vcombine_s8(vld1_s8((const void *)(iq2xxs_grid + aux8[ 0])), vld1_s8((const void *)(iq2xxs_grid + aux8[ 1]))); - q2u.val[1] = vcombine_s8(vld1_s8((const void *)(iq2xxs_grid + aux8[ 2])), vld1_s8((const void *)(iq2xxs_grid + aux8[ 3]))); - q2u.val[2] = vcombine_s8(vld1_s8((const void *)(iq2xxs_grid + aux8[ 8])), vld1_s8((const void *)(iq2xxs_grid + aux8[ 9]))); - q2u.val[3] = vcombine_s8(vld1_s8((const void *)(iq2xxs_grid + aux8[10])), vld1_s8((const void *)(iq2xxs_grid + aux8[11]))); - q2s.val[0] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 7) & 127)))); - q2s.val[1] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 21) & 127)))); - q2s.val[2] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[3] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[3] >> 7) & 127)))); - q2s.val[3] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[3] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[3] >> 21) & 127)))); - q2u.val[0] = vmulq_s8(q2u.val[0], q2s.val[0]); - q2u.val[1] = vmulq_s8(q2u.val[1], q2s.val[1]); - q2u.val[2] = vmulq_s8(q2u.val[2], q2s.val[2]); - q2u.val[3] = vmulq_s8(q2u.val[3], q2s.val[3]); - const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[0], q8b.val[0]), q2u.val[1], q8b.val[1]); - const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[2], q8b.val[2]), q2u.val[3], q8b.val[3]); - sumf1 += vaddvq_s32(p1) * (0.5f + (aux32[1] >> 28)); - sumf2 += vaddvq_s32(p2) * (0.5f + (aux32[3] >> 28)); - } - sumf += d*(sumf1 + sumf2); - } - *s = 0.25f * sumf; - -#elif defined(__AVX2__) - - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - - uint32_t aux32[4]; - const uint8_t * aux8 = (const uint8_t *)aux32; - - __m256 accumf = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint16_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - __m256i sumi1 = _mm256_setzero_si256(); - __m256i sumi2 = _mm256_setzero_si256(); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - memcpy(aux32, q2, 4*sizeof(uint32_t)); q2 += 8; - const __m256i q2_1 = _mm256_set_epi64x(iq2xxs_grid[aux8[ 3]], iq2xxs_grid[aux8[ 2]], iq2xxs_grid[aux8[1]], iq2xxs_grid[aux8[0]]); - const __m256i q2_2 = _mm256_set_epi64x(iq2xxs_grid[aux8[11]], iq2xxs_grid[aux8[10]], iq2xxs_grid[aux8[9]], iq2xxs_grid[aux8[8]]); - const __m256i s2_1 = _mm256_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], - signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); - const __m256i s2_2 = _mm256_set_epi64x(signs64[(aux32[3] >> 21) & 127], signs64[(aux32[3] >> 14) & 127], - signs64[(aux32[3] >> 7) & 127], signs64[(aux32[3] >> 0) & 127]); - const __m256i q8s_1 = _mm256_sign_epi8(q8_1, s2_1); - const __m256i q8s_2 = _mm256_sign_epi8(q8_2, s2_2); - const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); - const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); - const uint16_t ls1 = aux32[1] >> 28; - const uint16_t ls2 = aux32[3] >> 28; - const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1)); - const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1)); - sumi1 = _mm256_add_epi32(sumi1, p1); - sumi2 = _mm256_add_epi32(sumi2, p2); - } - - accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); - - } - - *s = 0.125f * hsum_float_8(accumf); - -#elif defined(__AVX__) - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - - uint32_t aux32[4]; - const uint8_t * aux8 = (const uint8_t *)aux32; - - __m256 accumf = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint16_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - __m128i sumi1_0 = _mm_setzero_si128(); - __m128i sumi1_1 = _mm_setzero_si128(); - __m128i sumi2_0 = _mm_setzero_si128(); - __m128i sumi2_1 = _mm_setzero_si128(); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const __m128i q8_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - memcpy(aux32, q2, 4*sizeof(uint32_t)); q2 += 8; - const __m128i q2_1_0 = _mm_set_epi64x(iq2xxs_grid[aux8[1]], iq2xxs_grid[aux8[0]]); - const __m128i q2_1_1 = _mm_set_epi64x(iq2xxs_grid[aux8[3]], iq2xxs_grid[aux8[2]]); - const __m128i q2_2_0 = _mm_set_epi64x(iq2xxs_grid[aux8[9]], iq2xxs_grid[aux8[8]]); - const __m128i q2_2_1 = _mm_set_epi64x(iq2xxs_grid[aux8[11]], iq2xxs_grid[aux8[10]]); - const __m128i s2_1_0 = _mm_set_epi64x(signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); - const __m128i s2_1_1 = _mm_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127]); - const __m128i s2_2_0 = _mm_set_epi64x(signs64[(aux32[3] >> 7) & 127], signs64[(aux32[3] >> 0) & 127]); - const __m128i s2_2_1 = _mm_set_epi64x(signs64[(aux32[3] >> 21) & 127], signs64[(aux32[3] >> 14) & 127]); - const __m128i q8s_1_0 = _mm_sign_epi8(q8_1_0, s2_1_0); - const __m128i q8s_1_1 = _mm_sign_epi8(q8_1_1, s2_1_1); - const __m128i q8s_2_0 = _mm_sign_epi8(q8_2_0, s2_2_0); - const __m128i q8s_2_1 = _mm_sign_epi8(q8_2_1, s2_2_1); - const __m128i dot1_0 = _mm_maddubs_epi16(q2_1_0, q8s_1_0); - const __m128i dot1_1 = _mm_maddubs_epi16(q2_1_1, q8s_1_1); - const __m128i dot2_0 = _mm_maddubs_epi16(q2_2_0, q8s_2_0); - const __m128i dot2_1 = _mm_maddubs_epi16(q2_2_1, q8s_2_1); - const uint16_t ls1 = aux32[1] >> 28; - const uint16_t ls2 = aux32[3] >> 28; - const __m128i p1_0 = _mm_madd_epi16(dot1_0, _mm_set1_epi16(2*ls1+1)); - const __m128i p1_1 = _mm_madd_epi16(dot1_1, _mm_set1_epi16(2*ls1+1)); - const __m128i p2_0 = _mm_madd_epi16(dot2_0, _mm_set1_epi16(2*ls2+1)); - const __m128i p2_1 = _mm_madd_epi16(dot2_1, _mm_set1_epi16(2*ls2+1)); - sumi1_0 = _mm_add_epi32(sumi1_0, p1_0); - sumi1_1 = _mm_add_epi32(sumi1_1, p1_1); - sumi2_0 = _mm_add_epi32(sumi2_0, p2_0); - sumi2_1 = _mm_add_epi32(sumi2_1, p2_1); - } - - accumf = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_add_epi32(sumi1_1, sumi2_1), _mm_add_epi32(sumi1_0, sumi2_0)))), accumf); - - } - - *s = 0.125f * hsum_float_8(accumf); - -#elif defined(__POWER9_VECTOR__) - const vector int v0 = vec_splats((int32_t)0); - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - - for (int i = 0; i < nb; ++i) { - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - vector signed int vsumi2 = v0; - vector signed int vsumi3 = v0; - - const uint16_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - for (int j = 0; j < QK_K/32; j += 2) { - __builtin_prefetch(q2, 0, 1); - __builtin_prefetch(q8, 0, 1); - - uint32_t aux32[4]; - const uint8_t * aux8 = (const uint8_t *)aux32; - - memcpy(aux32, q2, 4*sizeof(uint32_t)); - q2 += 8; - - vector signed long long aux64x2_0 = {*(const int64_t *)(iq2xxs_grid + aux8[ 0]), *(const int64_t *)(iq2xxs_grid + aux8[ 1])}; - vector signed long long aux64x2_1 = {*(const int64_t *)(iq2xxs_grid + aux8[ 2]), *(const int64_t *)(iq2xxs_grid + aux8[ 3])}; - vector signed long long aux64x2_2 = {*(const int64_t *)(iq2xxs_grid + aux8[ 8]), *(const int64_t *)(iq2xxs_grid + aux8[ 9])}; - vector signed long long aux64x2_3 = {*(const int64_t *)(iq2xxs_grid + aux8[10]), *(const int64_t *)(iq2xxs_grid + aux8[11])}; - - vector signed long long vsigns0 = {*(const int64_t *)(signs64 + ((aux32[1] >> 0) & 127)), *(const int64_t *)(signs64 + ((aux32[1] >> 7) & 127))}; - vector signed long long vsigns1 = {*(const int64_t *)(signs64 + ((aux32[1] >> 14) & 127)), *(const int64_t *)(signs64 + ((aux32[1] >> 21) & 127))}; - vector signed long long vsigns2 = {*(const int64_t *)(signs64 + ((aux32[3] >> 0) & 127)), *(const int64_t *)(signs64 + ((aux32[3] >> 7) & 127))}; - vector signed long long vsigns3 = {*(const int64_t *)(signs64 + ((aux32[3] >> 14) & 127)), *(const int64_t *)(signs64 + ((aux32[3] >> 21) & 127))}; - - vector signed char q2x0 = (vector signed char)vec_mul((vector signed char)vsigns0, (vector signed char)aux64x2_0); - vector signed char q2x1 = (vector signed char)vec_mul((vector signed char)vsigns1, (vector signed char)aux64x2_1); - vector signed char q2x2 = (vector signed char)vec_mul((vector signed char)vsigns2, (vector signed char)aux64x2_2); - vector signed char q2x3 = (vector signed char)vec_mul((vector signed char)vsigns3, (vector signed char)aux64x2_3); - - vector signed char q8y0 = vec_xl( 0, q8); - vector signed char q8y1 = vec_xl(16, q8); - vector signed char q8y2 = vec_xl(32, q8); - vector signed char q8y3 = vec_xl(48, q8); - q8 += 64; - - vector signed short qv0 = vec_add(vec_mule(q2x0, q8y0), vec_mulo(q2x0, q8y0)); - vector signed short qv1 = vec_add(vec_mule(q2x1, q8y1), vec_mulo(q2x1, q8y1)); - vector signed short qv2 = vec_add(vec_mule(q2x2, q8y2), vec_mulo(q2x2, q8y2)); - vector signed short qv3 = vec_add(vec_mule(q2x3, q8y3), vec_mulo(q2x3, q8y3)); - - const uint16_t ls0 = aux32[1] >> 28; - const uint16_t ls1 = aux32[3] >> 28; - - vector signed short vscales01 = vec_splats((int16_t)(2*ls0+1)); - vector signed short vscales23 = vec_splats((int16_t)(2*ls1+1)); - - vsumi0 = vec_msum(qv0, vscales01, vsumi0); - vsumi1 = vec_msum(qv1, vscales01, vsumi1); - vsumi2 = vec_msum(qv2, vscales23, vsumi2); - vsumi3 = vec_msum(qv3, vscales23, vsumi3); - } - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = 0.125f * vec_extract(vsumf0, 0); - -#elif defined(__loongarch_asx) - - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - - uint32_t aux32[4]; - const uint8_t * aux8 = (const uint8_t *)aux32; - - __m256 accumf = (__m256)__lasx_xvldi(0); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint16_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - __m256i sumi1 = __lasx_xvldi(0); - __m256i sumi2 = __lasx_xvldi(0); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - memcpy(aux32, q2, 4*sizeof(uint32_t)); q2 += 8; - - const __m256i q2_1 = lasx_set_d(iq2xxs_grid[aux8[ 3]], iq2xxs_grid[aux8[ 2]], iq2xxs_grid[aux8[1]], iq2xxs_grid[aux8[0]]); - const __m256i q2_2 = lasx_set_d(iq2xxs_grid[aux8[11]], iq2xxs_grid[aux8[10]], iq2xxs_grid[aux8[9]], iq2xxs_grid[aux8[8]]); - const __m256i s2_1 = lasx_set_d(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], - signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); - const __m256i s2_2 = lasx_set_d(signs64[(aux32[3] >> 21) & 127], signs64[(aux32[3] >> 14) & 127], - signs64[(aux32[3] >> 7) & 127], signs64[(aux32[3] >> 0) & 127]); - const __m256i q8s_1 = __lasx_xvsigncov_b(s2_1, q8_1); - const __m256i q8s_2 = __lasx_xvsigncov_b(s2_2, q8_2); - const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); - const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); - const uint16_t ls1 = aux32[1] >> 28; - const uint16_t ls2 = aux32[3] >> 28; - const __m256i p1 = lasx_madd_h(dot1, __lasx_xvreplgr2vr_h(2*ls1+1)); - const __m256i p2 = lasx_madd_h(dot2, __lasx_xvreplgr2vr_h(2*ls2+1)); - sumi1 = __lasx_xvadd_w(sumi1, p1); - sumi2 = __lasx_xvadd_w(sumi2, p2); - } - - accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); - } - - *s = 0.125f * hsum_float_8(accumf); - -#else - - uint32_t aux32[2]; - const uint8_t * aux8 = (const uint8_t *)aux32; - - float sumf = 0.f; - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint16_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - int32_t bsum = 0; - for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { - memcpy(aux32, q2, 2*sizeof(uint32_t)); - q2 += 4; - const uint32_t ls = 2*(aux32[1] >> 28) + 1; - int32_t sumi = 0; - for (int l = 0; l < 4; ++l) { - const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]); - const uint8_t signs = ksigns_iq2xs[(aux32[1] >> 7*l) & 127]; - for (int j = 0; j < 8; ++j) { - sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1); - } - q8 += 8; - } - bsum += sumi * ls; - } - sumf += d * bsum; - } - *s = 0.125f * sumf; -#endif -} - -void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_iq2_xs * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#if defined(__ARM_NEON) - - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - - ggml_int8x16x4_t q2u; - ggml_int8x16x4_t q2s; - ggml_int8x16x4_t q8b; - - int32x4x4_t scales32; - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint16_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - const uint8x8_t scales8 = vld1_u8(x[i].scales); - const uint8x8_t scales_l = vand_u8(scales8, vdup_n_u8(0xf)); - const uint8x8_t scales_h = vshr_n_u8(scales8, 4); - uint8x16_t scales = vcombine_u8(vzip1_u8(scales_l, scales_h), vzip2_u8(scales_l, scales_h)); - scales = vaddq_u8(vshlq_n_u8(scales, 1), vdupq_n_u8(1)); - const uint16x8_t scales1 = vmovl_u8(vget_low_u8(scales)); - const uint16x8_t scales2 = vmovl_u8(vget_high_u8(scales)); - scales32.val[0] = vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(scales1))); - scales32.val[1] = vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(scales1))); - scales32.val[2] = vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(scales2))); - scales32.val[3] = vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(scales2))); - int32x4_t sumi = vdupq_n_s32(0); - for (int ib64 = 0; ib64 < QK_K/64; ++ib64) { - q8b = ggml_vld1q_s8_x4(q8); q8 += 64; - q2u.val[0] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[0] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[1] & 511)))); - q2u.val[1] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[2] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[3] & 511)))); - q2u.val[2] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[4] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[5] & 511)))); - q2u.val[3] = vcombine_s8(vld1_s8((const void *)(iq2xs_grid + (q2[6] & 511))), vld1_s8((const void *)(iq2xs_grid + (q2[7] & 511)))); - q2s.val[0] = vcombine_s8(vld1_s8((const void *)(signs64 + (q2[0] >> 9))), vld1_s8((const void *)(signs64 + (q2[1] >> 9)))); - q2s.val[1] = vcombine_s8(vld1_s8((const void *)(signs64 + (q2[2] >> 9))), vld1_s8((const void *)(signs64 + (q2[3] >> 9)))); - q2s.val[2] = vcombine_s8(vld1_s8((const void *)(signs64 + (q2[4] >> 9))), vld1_s8((const void *)(signs64 + (q2[5] >> 9)))); - q2s.val[3] = vcombine_s8(vld1_s8((const void *)(signs64 + (q2[6] >> 9))), vld1_s8((const void *)(signs64 + (q2[7] >> 9)))); - q2u.val[0] = vmulq_s8(q2u.val[0], q2s.val[0]); - q2u.val[1] = vmulq_s8(q2u.val[1], q2s.val[1]); - q2u.val[2] = vmulq_s8(q2u.val[2], q2s.val[2]); - q2u.val[3] = vmulq_s8(q2u.val[3], q2s.val[3]); - const int32x4_t p1 = ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[0], q8b.val[0]); - const int32x4_t p2 = ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[1], q8b.val[1]); - const int32x4_t p3 = ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[2], q8b.val[2]); - const int32x4_t p4 = ggml_vdotq_s32(vdupq_n_s32(0), q2u.val[3], q8b.val[3]); - const int32x4_t p = vpaddq_s32(vpaddq_s32(p1, p2), vpaddq_s32(p3, p4)); - sumi = vmlaq_s32(sumi, p, scales32.val[ib64]); - q2 += 8; - } - sumf += d*vaddvq_s32(sumi); - } - *s = 0.125f * sumf; - -#elif defined(__AVX2__) - - const __m256i mone = _mm256_set1_epi8(1); - static const char block_sign_shuffle_mask_1[32] = { - 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, - 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, - }; - static const char block_sign_shuffle_mask_2[32] = { - 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, - 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, - }; - static const uint8_t bit_selector_mask_bytes[32] = { - 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - }; - - const __m256i bit_selector_mask = _mm256_loadu_si256((const __m256i*)bit_selector_mask_bytes); - const __m256i block_sign_shuffle_1 = _mm256_loadu_si256((const __m256i*)block_sign_shuffle_mask_1); - const __m256i block_sign_shuffle_2 = _mm256_loadu_si256((const __m256i*)block_sign_shuffle_mask_2); - - static const uint8_t k_bit_helper[32] = { - 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, - 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, - }; - const __m256i bit_helper = _mm256_loadu_si256((const __m256i*)k_bit_helper); - const __m256i m511 = _mm256_set1_epi16(511); - const __m128i m4 = _mm_set1_epi8(0xf); - const __m128i m1 = _mm_set1_epi8(1); - - uint64_t aux64; - - // somewhat hacky, but gives a significant boost in performance - __m256i aux_gindex; - const uint16_t * gindex = (const uint16_t *)&aux_gindex; - - __m256 accumf = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint16_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - memcpy(&aux64, x[i].scales, 8); - __m128i stmp = _mm_set1_epi64x(aux64); - stmp = _mm_unpacklo_epi8(_mm_and_si128(stmp, m4), _mm_and_si128(_mm_srli_epi16(stmp, 4), m4)); - const __m128i scales = _mm_add_epi8(_mm_slli_epi16(stmp, 1), m1); - - __m256i sumi1 = _mm256_setzero_si256(); - __m256i sumi2 = _mm256_setzero_si256(); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 4) { - - const __m256i q2_data = _mm256_loadu_si256((const __m256i*)q2); q2 += 16; - aux_gindex = _mm256_and_si256(q2_data, m511); - - const __m256i partial_sign_bits = _mm256_srli_epi16(q2_data, 9); - const __m256i partial_sign_bits_upper = _mm256_srli_epi16(q2_data, 13); - const __m256i partial_sign_bits_for_counting = _mm256_xor_si256(partial_sign_bits, partial_sign_bits_upper); - - const __m256i odd_bits = _mm256_shuffle_epi8(bit_helper, partial_sign_bits_for_counting); - const __m256i full_sign_bits = _mm256_or_si256(partial_sign_bits, odd_bits); - - const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q8_3 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q8_4 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - - const __m256i q2_1 = _mm256_set_epi64x(iq2xs_grid[gindex[ 3]], iq2xs_grid[gindex[ 2]], - iq2xs_grid[gindex[ 1]], iq2xs_grid[gindex[ 0]]); - const __m256i q2_2 = _mm256_set_epi64x(iq2xs_grid[gindex[ 7]], iq2xs_grid[gindex[ 6]], - iq2xs_grid[gindex[ 5]], iq2xs_grid[gindex[ 4]]); - const __m256i q2_3 = _mm256_set_epi64x(iq2xs_grid[gindex[11]], iq2xs_grid[gindex[10]], - iq2xs_grid[gindex[ 9]], iq2xs_grid[gindex[ 8]]); - const __m256i q2_4 = _mm256_set_epi64x(iq2xs_grid[gindex[15]], iq2xs_grid[gindex[14]], - iq2xs_grid[gindex[13]], iq2xs_grid[gindex[12]]); - - const __m128i full_signs_l = _mm256_castsi256_si128(full_sign_bits); - const __m128i full_signs_h = _mm256_extractf128_si256(full_sign_bits, 1); - const __m256i full_signs_1 = MM256_SET_M128I(full_signs_l, full_signs_l); - const __m256i full_signs_2 = MM256_SET_M128I(full_signs_h, full_signs_h); - - __m256i signs; - signs = _mm256_shuffle_epi8(full_signs_1, block_sign_shuffle_1); - signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); - const __m256i q8s_1 = _mm256_sign_epi8(q8_1, _mm256_or_si256(signs, mone)); - - signs = _mm256_shuffle_epi8(full_signs_1, block_sign_shuffle_2); - signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); - const __m256i q8s_2 = _mm256_sign_epi8(q8_2, _mm256_or_si256(signs, mone)); - - signs = _mm256_shuffle_epi8(full_signs_2, block_sign_shuffle_1); - signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); - const __m256i q8s_3 = _mm256_sign_epi8(q8_3, _mm256_or_si256(signs, mone)); - - signs = _mm256_shuffle_epi8(full_signs_2, block_sign_shuffle_2); - signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); - const __m256i q8s_4 = _mm256_sign_epi8(q8_4, _mm256_or_si256(signs, mone)); - - const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); - const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); - const __m256i dot3 = _mm256_maddubs_epi16(q2_3, q8s_3); - const __m256i dot4 = _mm256_maddubs_epi16(q2_4, q8s_4); - - const __m256i sc1 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+0))); - const __m256i sc2 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+1))); - const __m256i sc3 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+2))); - const __m256i sc4 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+3))); - - sumi1 = _mm256_add_epi32(sumi1, _mm256_madd_epi16(dot1, sc1)); - sumi2 = _mm256_add_epi32(sumi2, _mm256_madd_epi16(dot2, sc2)); - sumi1 = _mm256_add_epi32(sumi1, _mm256_madd_epi16(dot3, sc3)); - sumi2 = _mm256_add_epi32(sumi2, _mm256_madd_epi16(dot4, sc4)); - } - - accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); - - } - - *s = 0.125f * hsum_float_8(accumf); - -#elif defined(__AVX__) - const __m128i mone = _mm_set1_epi8(1); - static const char block_sign_shuffle_mask_1[32] = { - 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, - 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, - }; - static const char block_sign_shuffle_mask_2[32] = { - 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, - 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, - }; - static const uint8_t bit_selector_mask_bytes[32] = { - 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - }; - - const __m128i bit_selector_mask_0 = _mm_loadu_si128((const __m128i*)bit_selector_mask_bytes); - const __m128i bit_selector_mask_1 = _mm_loadu_si128((const __m128i*)bit_selector_mask_bytes + 1); - const __m128i block_sign_shuffle_1_0 = _mm_loadu_si128((const __m128i*)block_sign_shuffle_mask_1); - const __m128i block_sign_shuffle_1_1 = _mm_loadu_si128((const __m128i*)block_sign_shuffle_mask_1 + 1); - const __m128i block_sign_shuffle_2_0 = _mm_loadu_si128((const __m128i*)block_sign_shuffle_mask_2); - const __m128i block_sign_shuffle_2_1 = _mm_loadu_si128((const __m128i*)block_sign_shuffle_mask_2 + 1); - - static const uint8_t k_bit_helper[32] = { - 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, - 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, - }; - const __m128i bit_helper_0 = _mm_loadu_si128((const __m128i*)k_bit_helper); - const __m128i bit_helper_1 = _mm_loadu_si128((const __m128i*)k_bit_helper + 1); - const __m128i m511 = _mm_set1_epi16(511); - const __m128i m4 = _mm_set1_epi8(0xf); - const __m128i m1 = _mm_set1_epi8(1); - - uint64_t aux64; - - // somewhat hacky, but gives a significant boost in performance - __m256i aux_gindex; - const uint16_t * gindex = (const uint16_t *)&aux_gindex; - - __m256 accumf = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint16_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - memcpy(&aux64, x[i].scales, 8); - __m128i stmp = _mm_set1_epi64x(aux64); - stmp = _mm_unpacklo_epi8(_mm_and_si128(stmp, m4), _mm_and_si128(_mm_srli_epi16(stmp, 4), m4)); - const __m128i scales = _mm_add_epi8(_mm_slli_epi16(stmp, 1), m1); - - __m128i sumi1_0 = _mm_setzero_si128(); - __m128i sumi1_1 = _mm_setzero_si128(); - __m128i sumi2_0 = _mm_setzero_si128(); - __m128i sumi2_1 = _mm_setzero_si128(); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 4) { - - const __m128i q2_data_0 = _mm_loadu_si128((const __m128i*)q2); - const __m128i q2_data_1 = _mm_loadu_si128((const __m128i*)q2 + 1); q2 += 16; - aux_gindex = MM256_SET_M128I(_mm_and_si128(q2_data_1, m511), _mm_and_si128(q2_data_0, m511)); - - const __m128i partial_sign_bits_0 = _mm_srli_epi16(q2_data_0, 9); - const __m128i partial_sign_bits_1 = _mm_srli_epi16(q2_data_1, 9); - const __m128i partial_sign_bits_upper_0 = _mm_srli_epi16(q2_data_0, 13); - const __m128i partial_sign_bits_upper_1 = _mm_srli_epi16(q2_data_1, 13); - const __m128i partial_sign_bits_for_counting_0 = _mm_xor_si128(partial_sign_bits_0, partial_sign_bits_upper_0); - const __m128i partial_sign_bits_for_counting_1 = _mm_xor_si128(partial_sign_bits_1, partial_sign_bits_upper_1); - - const __m128i odd_bits_0 = _mm_shuffle_epi8(bit_helper_0, partial_sign_bits_for_counting_0); - const __m128i odd_bits_1 = _mm_shuffle_epi8(bit_helper_1, partial_sign_bits_for_counting_1); - const __m128i full_sign_bits_0 = _mm_or_si128(partial_sign_bits_0, odd_bits_0); - const __m128i full_sign_bits_1 = _mm_or_si128(partial_sign_bits_1, odd_bits_1); - - const __m128i q8_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_3_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_3_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_4_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_4_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - - const __m128i q2_1_0 = _mm_set_epi64x(iq2xs_grid[gindex[1]], iq2xs_grid[gindex[0]]); - const __m128i q2_1_1 = _mm_set_epi64x(iq2xs_grid[gindex[3]], iq2xs_grid[gindex[2]]); - const __m128i q2_2_0 = _mm_set_epi64x(iq2xs_grid[gindex[5]], iq2xs_grid[gindex[4]]); - const __m128i q2_2_1 = _mm_set_epi64x(iq2xs_grid[gindex[7]], iq2xs_grid[gindex[6]]); - const __m128i q2_3_0 = _mm_set_epi64x(iq2xs_grid[gindex[9]], iq2xs_grid[gindex[8]]); - const __m128i q2_3_1 = _mm_set_epi64x(iq2xs_grid[gindex[11]], iq2xs_grid[gindex[10]]); - const __m128i q2_4_0 = _mm_set_epi64x(iq2xs_grid[gindex[13]], iq2xs_grid[gindex[12]]); - const __m128i q2_4_1 = _mm_set_epi64x(iq2xs_grid[gindex[15]], iq2xs_grid[gindex[14]]); - - // AVX2 full_signs_1 is full_sign_bits_0 here - // AVX2 full_signs_2 is full_sign_bits_1 here - __m128i signs_0, signs_1; - signs_0 = _mm_shuffle_epi8(full_sign_bits_0, block_sign_shuffle_1_0); - signs_1 = _mm_shuffle_epi8(full_sign_bits_0, block_sign_shuffle_1_1); - signs_0 = _mm_cmpeq_epi8(_mm_and_si128(signs_0, bit_selector_mask_0), bit_selector_mask_0); - signs_1 = _mm_cmpeq_epi8(_mm_and_si128(signs_1, bit_selector_mask_1), bit_selector_mask_1); - const __m128i q8s_1_0 = _mm_sign_epi8(q8_1_0, _mm_or_si128(signs_0, mone)); - const __m128i q8s_1_1 = _mm_sign_epi8(q8_1_1, _mm_or_si128(signs_1, mone)); - - signs_0 = _mm_shuffle_epi8(full_sign_bits_0, block_sign_shuffle_2_0); - signs_1 = _mm_shuffle_epi8(full_sign_bits_0, block_sign_shuffle_2_1); - signs_0 = _mm_cmpeq_epi8(_mm_and_si128(signs_0, bit_selector_mask_0), bit_selector_mask_0); - signs_1 = _mm_cmpeq_epi8(_mm_and_si128(signs_1, bit_selector_mask_1), bit_selector_mask_1); - const __m128i q8s_2_0 = _mm_sign_epi8(q8_2_0, _mm_or_si128(signs_0, mone)); - const __m128i q8s_2_1 = _mm_sign_epi8(q8_2_1, _mm_or_si128(signs_1, mone)); - - signs_0 = _mm_shuffle_epi8(full_sign_bits_1, block_sign_shuffle_1_0); - signs_1 = _mm_shuffle_epi8(full_sign_bits_1, block_sign_shuffle_1_1); - signs_0 = _mm_cmpeq_epi8(_mm_and_si128(signs_0, bit_selector_mask_0), bit_selector_mask_0); - signs_1 = _mm_cmpeq_epi8(_mm_and_si128(signs_1, bit_selector_mask_1), bit_selector_mask_1); - const __m128i q8s_3_0 = _mm_sign_epi8(q8_3_0, _mm_or_si128(signs_0, mone)); - const __m128i q8s_3_1 = _mm_sign_epi8(q8_3_1, _mm_or_si128(signs_1, mone)); - - signs_0 = _mm_shuffle_epi8(full_sign_bits_1, block_sign_shuffle_2_0); - signs_1 = _mm_shuffle_epi8(full_sign_bits_1, block_sign_shuffle_2_1); - signs_0 = _mm_cmpeq_epi8(_mm_and_si128(signs_0, bit_selector_mask_0), bit_selector_mask_0); - signs_1 = _mm_cmpeq_epi8(_mm_and_si128(signs_1, bit_selector_mask_1), bit_selector_mask_1); - const __m128i q8s_4_0 = _mm_sign_epi8(q8_4_0, _mm_or_si128(signs_0, mone)); - const __m128i q8s_4_1 = _mm_sign_epi8(q8_4_1, _mm_or_si128(signs_1, mone)); - - const __m128i dot1_0 = _mm_maddubs_epi16(q2_1_0, q8s_1_0); - const __m128i dot1_1 = _mm_maddubs_epi16(q2_1_1, q8s_1_1); - const __m128i dot2_0 = _mm_maddubs_epi16(q2_2_0, q8s_2_0); - const __m128i dot2_1 = _mm_maddubs_epi16(q2_2_1, q8s_2_1); - const __m128i dot3_0 = _mm_maddubs_epi16(q2_3_0, q8s_3_0); - const __m128i dot3_1 = _mm_maddubs_epi16(q2_3_1, q8s_3_1); - const __m128i dot4_0 = _mm_maddubs_epi16(q2_4_0, q8s_4_0); - const __m128i dot4_1 = _mm_maddubs_epi16(q2_4_1, q8s_4_1); - - __m128i sc_tmp = _mm_shuffle_epi8(scales, get_scale_shuffle(ib32+0)); - const __m128i sc1_0 = _mm_cvtepi8_epi16(sc_tmp); - const __m128i sc1_1 = _mm_cvtepi8_epi16(_mm_srli_si128(sc_tmp, 8)); - sc_tmp = _mm_shuffle_epi8(scales, get_scale_shuffle(ib32+1)); - const __m128i sc2_0 = _mm_cvtepi8_epi16(sc_tmp); - const __m128i sc2_1 = _mm_cvtepi8_epi16(_mm_srli_si128(sc_tmp, 8)); - sc_tmp = _mm_shuffle_epi8(scales, get_scale_shuffle(ib32+2)); - const __m128i sc3_0 = _mm_cvtepi8_epi16(sc_tmp); - const __m128i sc3_1 = _mm_cvtepi8_epi16(_mm_srli_si128(sc_tmp, 8)); - sc_tmp = _mm_shuffle_epi8(scales, get_scale_shuffle(ib32+3)); - const __m128i sc4_0 = _mm_cvtepi8_epi16(sc_tmp); - const __m128i sc4_1 = _mm_cvtepi8_epi16(_mm_srli_si128(sc_tmp, 8)); - - sumi1_0 = _mm_add_epi32(sumi1_0, _mm_madd_epi16(dot1_0, sc1_0)); - sumi1_1 = _mm_add_epi32(sumi1_1, _mm_madd_epi16(dot1_1, sc1_1)); - sumi2_0 = _mm_add_epi32(sumi2_0, _mm_madd_epi16(dot2_0, sc2_0)); - sumi2_1 = _mm_add_epi32(sumi2_1, _mm_madd_epi16(dot2_1, sc2_1)); - sumi1_0 = _mm_add_epi32(sumi1_0, _mm_madd_epi16(dot3_0, sc3_0)); - sumi1_1 = _mm_add_epi32(sumi1_1, _mm_madd_epi16(dot3_1, sc3_1)); - sumi2_0 = _mm_add_epi32(sumi2_0, _mm_madd_epi16(dot4_0, sc4_0)); - sumi2_1 = _mm_add_epi32(sumi2_1, _mm_madd_epi16(dot4_1, sc4_1)); - } - - accumf = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_add_epi32(sumi1_1, sumi2_1), _mm_add_epi32(sumi1_0, sumi2_0)))), accumf); - - } - - *s = 0.125f * hsum_float_8(accumf); - -#elif defined(__loongarch_asx) - - const __m256i mone = __lasx_xvreplgr2vr_b(1); - static const char block_sign_shuffle_mask_1[32] = { - 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, - 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, - }; - static const char block_sign_shuffle_mask_2[32] = { - 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, - 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, - }; - static const uint8_t bit_selector_mask_bytes[32] = { - 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - }; - - const __m256i bit_selector_mask = __lasx_xvld((const __m256i*)bit_selector_mask_bytes, 0); - const __m256i block_sign_shuffle_1 = __lasx_xvld((const __m256i*)block_sign_shuffle_mask_1, 0); - const __m256i block_sign_shuffle_2 = __lasx_xvld((const __m256i*)block_sign_shuffle_mask_2, 0); - - static const uint8_t k_bit_helper[32] = { - 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, - 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, - }; - const __m256i bit_helper = __lasx_xvld((const __m256i*)k_bit_helper, 0); - const __m256i m511 = __lasx_xvreplgr2vr_h(511); - const __m128i m4 = __lsx_vreplgr2vr_b(0xf); - const __m128i m1 = __lsx_vreplgr2vr_b(1); - - uint64_t aux64; - - // somewhat hacky, but gives a significant boost in performance - __m256i aux_gindex; - const uint16_t * gindex = (const uint16_t *)&aux_gindex; - - __m256 accumf = (__m256)__lasx_xvldi(0); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint16_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - memcpy(&aux64, x[i].scales, 8); - __m128i stmp = __lsx_vreplgr2vr_d(aux64); - stmp = __lsx_vilvl_b( __lsx_vand_v(__lsx_vsrli_h(stmp, 4), m4), __lsx_vand_v(stmp, m4)); - const __m128i scales = __lsx_vadd_b(__lsx_vslli_h(stmp, 1), m1); - - __m256i sumi1 = __lasx_xvldi(0); - __m256i sumi2 = __lasx_xvldi(0); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 4) { - - const __m256i q2_data = __lasx_xvld((const __m256i*)q2, 0); q2 += 16; - aux_gindex = __lasx_xvand_v(q2_data, m511); - - const __m256i partial_sign_bits = __lasx_xvsrli_h(q2_data, 9); - const __m256i partial_sign_bits_upper = __lasx_xvsrli_h(q2_data, 13); - const __m256i partial_sign_bits_for_counting = __lasx_xvxor_v(partial_sign_bits, partial_sign_bits_upper); - - const __m256i odd_bits = lasx_shuffle_b(bit_helper, partial_sign_bits_for_counting); - const __m256i full_sign_bits = __lasx_xvor_v(partial_sign_bits, odd_bits); - - const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - const __m256i q8_3 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - const __m256i q8_4 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - - const __m256i q2_1 = lasx_set_d(iq2xs_grid[gindex[ 3]], iq2xs_grid[gindex[ 2]], - iq2xs_grid[gindex[ 1]], iq2xs_grid[gindex[ 0]]); - const __m256i q2_2 = lasx_set_d(iq2xs_grid[gindex[ 7]], iq2xs_grid[gindex[ 6]], - iq2xs_grid[gindex[ 5]], iq2xs_grid[gindex[ 4]]); - const __m256i q2_3 = lasx_set_d(iq2xs_grid[gindex[11]], iq2xs_grid[gindex[10]], - iq2xs_grid[gindex[ 9]], iq2xs_grid[gindex[ 8]]); - const __m256i q2_4 = lasx_set_d(iq2xs_grid[gindex[15]], iq2xs_grid[gindex[14]], - iq2xs_grid[gindex[13]], iq2xs_grid[gindex[12]]); - - const __m128i full_signs_l = lasx_extracti128(full_sign_bits, 0); - const __m128i full_signs_h = lasx_extracti128(full_sign_bits, 1); - const __m256i full_signs_1 = lasx_insertf128(full_signs_l, full_signs_l); - const __m256i full_signs_2 = lasx_insertf128(full_signs_h, full_signs_h); - - __m256i signs; - signs = lasx_shuffle_b(full_signs_1, block_sign_shuffle_1); - signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); - const __m256i q8s_1 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_1); - - signs = lasx_shuffle_b(full_signs_1, block_sign_shuffle_2); - signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); - const __m256i q8s_2 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_2); - - signs = lasx_shuffle_b(full_signs_2, block_sign_shuffle_1); - signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); - const __m256i q8s_3 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_3); - - signs = lasx_shuffle_b(full_signs_2, block_sign_shuffle_2); - signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); - const __m256i q8s_4 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_4); - - const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); - const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); - const __m256i dot3 = lasx_maddubs_h(q2_3, q8s_3); - const __m256i dot4 = lasx_maddubs_h(q2_4, q8s_4); - - const __m256i sc1 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+0))); - const __m256i sc2 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+1))); - const __m256i sc3 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+2))); - const __m256i sc4 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+3))); - - sumi1 = __lasx_xvadd_w(sumi1, lasx_madd_h(dot1, sc1)); - sumi2 = __lasx_xvadd_w(sumi2, lasx_madd_h(dot2, sc2)); - sumi1 = __lasx_xvadd_w(sumi1, lasx_madd_h(dot3, sc3)); - sumi2 = __lasx_xvadd_w(sumi2, lasx_madd_h(dot4, sc4)); - } - - accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); - - } - - *s = 0.125f * hsum_float_8(accumf); -#elif defined(__POWER9_VECTOR__) - const vector int v0 = vec_splats((int32_t)0); - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - - for (int i = 0; i < nb; ++i) { - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - vector signed int vsumi2 = v0; - vector signed int vsumi3 = v0; - - const uint16_t * restrict q2 = x[i].qs; - const uint8_t * restrict sc = x[i].scales; - const int8_t * restrict q8 = y[i].qs; - - for (int j = 0; j < QK_K/64; ++j) { - __builtin_prefetch(q2, 0, 1); - __builtin_prefetch(q8, 0, 1); - - vector signed long long aux64x2_0 = {*(const int64_t *)(iq2xs_grid + (q2[0] & 511)), *(const int64_t *)(iq2xs_grid + (q2[1] & 511))}; - vector signed long long aux64x2_1 = {*(const int64_t *)(iq2xs_grid + (q2[2] & 511)), *(const int64_t *)(iq2xs_grid + (q2[3] & 511))}; - vector signed long long aux64x2_2 = {*(const int64_t *)(iq2xs_grid + (q2[4] & 511)), *(const int64_t *)(iq2xs_grid + (q2[5] & 511))}; - vector signed long long aux64x2_3 = {*(const int64_t *)(iq2xs_grid + (q2[6] & 511)), *(const int64_t *)(iq2xs_grid + (q2[7] & 511))}; - - vector signed long long vsigns0 = {*(const int64_t *)(signs64 + ((q2[0] >> 9))), *(const int64_t *)(signs64 + ((q2[1] >> 9)))}; - vector signed long long vsigns1 = {*(const int64_t *)(signs64 + ((q2[2] >> 9))), *(const int64_t *)(signs64 + ((q2[3] >> 9)))}; - vector signed long long vsigns2 = {*(const int64_t *)(signs64 + ((q2[4] >> 9))), *(const int64_t *)(signs64 + ((q2[5] >> 9)))}; - vector signed long long vsigns3 = {*(const int64_t *)(signs64 + ((q2[6] >> 9))), *(const int64_t *)(signs64 + ((q2[7] >> 9)))}; - q2 += 8; - - vector signed char q2x0 = (vector signed char)vec_mul((vector signed char)vsigns0, (vector signed char)aux64x2_0); - vector signed char q2x1 = (vector signed char)vec_mul((vector signed char)vsigns1, (vector signed char)aux64x2_1); - vector signed char q2x2 = (vector signed char)vec_mul((vector signed char)vsigns2, (vector signed char)aux64x2_2); - vector signed char q2x3 = (vector signed char)vec_mul((vector signed char)vsigns3, (vector signed char)aux64x2_3); - - vector signed char q8y0 = vec_xl( 0, q8); - vector signed char q8y1 = vec_xl(16, q8); - vector signed char q8y2 = vec_xl(32, q8); - vector signed char q8y3 = vec_xl(48, q8); - q8 += 64; - - vector signed short qv0 = vec_add(vec_mule(q2x0, q8y0), vec_mulo(q2x0, q8y0)); - vector signed short qv1 = vec_add(vec_mule(q2x1, q8y1), vec_mulo(q2x1, q8y1)); - vector signed short qv2 = vec_add(vec_mule(q2x2, q8y2), vec_mulo(q2x2, q8y2)); - vector signed short qv3 = vec_add(vec_mule(q2x3, q8y3), vec_mulo(q2x3, q8y3)); - - const uint16_t ls0 = (uint16_t)(sc[0] & 0xf); - const uint16_t ls1 = (uint16_t)(sc[0] >> 4); - const uint16_t ls2 = (uint16_t)(sc[1] & 0xf); - const uint16_t ls3 = (uint16_t)(sc[1] >> 4); - sc += 2; - - vector signed short vscales0 = vec_splats((int16_t)(2*ls0+1)); - vector signed short vscales1 = vec_splats((int16_t)(2*ls1+1)); - vector signed short vscales2 = vec_splats((int16_t)(2*ls2+1)); - vector signed short vscales3 = vec_splats((int16_t)(2*ls3+1)); - - vsumi0 = vec_msum(qv0, vscales0, vsumi0); - vsumi1 = vec_msum(qv1, vscales1, vsumi1); - vsumi2 = vec_msum(qv2, vscales2, vsumi2); - vsumi3 = vec_msum(qv3, vscales3, vsumi3); - } - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = 0.125f * vec_extract(vsumf0, 0); -#else - - float sumf = 0.f; - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint16_t * restrict q2 = x[i].qs; - const uint8_t * restrict sc = x[i].scales; - const int8_t * restrict q8 = y[i].qs; - int32_t bsum = 0; - for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { - const uint16_t ls1 = 2*(sc[ib32] & 0xf) + 1; - const uint16_t ls2 = 2*(sc[ib32] >> 4) + 1; - int32_t sumi = 0; - for (int l = 0; l < 2; ++l) { - const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511)); - const uint8_t signs = ksigns_iq2xs[q2[l] >> 9]; - for (int j = 0; j < 8; ++j) { - sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1); - } - q8 += 8; - } - bsum += sumi * ls1; - sumi = 0; - for (int l = 2; l < 4; ++l) { - const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511)); - const uint8_t signs = ksigns_iq2xs[q2[l] >> 9]; - for (int j = 0; j < 8; ++j) { - sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1); - } - q8 += 8; - } - bsum += sumi * ls2; - q2 += 4; - } - sumf += d * bsum; - } - *s = 0.125f * sumf; -#endif -} - -void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_iq2_s * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#if defined(__ARM_NEON) - - static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, - 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 - }; - - static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; - - const ggml_uint8x16x2_t mask1 = ggml_vld1q_u8_x2(k_mask1); - const uint8x16_t mask2 = vld1q_u8(k_mask2); - const uint8x16_t m1 = vdupq_n_u8(1); - const int32x4_t vzero = vdupq_n_s32(0); - - uint8x16x2_t vs; - ggml_int8x16x4_t q2s; - ggml_int8x16x4_t q8b; - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - - const uint8_t * restrict qs = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); - const int8_t * restrict q8 = y[i].qs; - - int sumi1 = 0, sumi2 = 0; - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - q8b = ggml_vld1q_s8_x4(q8); q8 += 64; - q2s.val[0] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[0] | ((qh[ib32+0] << 8) & 0x300)))), - vld1_s8((const int8_t *)(iq2s_grid + (qs[1] | ((qh[ib32+0] << 6) & 0x300))))); - q2s.val[1] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[2] | ((qh[ib32+0] << 4) & 0x300)))), - vld1_s8((const int8_t *)(iq2s_grid + (qs[3] | ((qh[ib32+0] << 2) & 0x300))))); - q2s.val[2] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[4] | ((qh[ib32+1] << 8) & 0x300)))), - vld1_s8((const int8_t *)(iq2s_grid + (qs[5] | ((qh[ib32+1] << 6) & 0x300))))); - q2s.val[3] = vcombine_s8(vld1_s8((const int8_t *)(iq2s_grid + (qs[6] | ((qh[ib32+1] << 4) & 0x300)))), - vld1_s8((const int8_t *)(iq2s_grid + (qs[7] | ((qh[ib32+1] << 2) & 0x300))))); - qs += 8; - - vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | ((uint32_t) signs[1] << 16))); - vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); - vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); - vs.val[0] = vceqq_u8(vs.val[0], mask2); - vs.val[1] = vceqq_u8(vs.val[1], mask2); - - q2s.val[0] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[0], m1)), q2s.val[0]); - q2s.val[1] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[1], m1)), q2s.val[1]); - - vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | ((uint32_t) signs[3] << 16))); - vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); - vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); - vs.val[0] = vceqq_u8(vs.val[0], mask2); - vs.val[1] = vceqq_u8(vs.val[1], mask2); - - signs += 4; - - q2s.val[2] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[0], m1)), q2s.val[2]); - q2s.val[3] = vmulq_s8(vreinterpretq_s8_u8(vorrq_u8(vs.val[1], m1)), q2s.val[3]); - - const int32x4_t p1 = ggml_vdotq_s32(vzero, q2s.val[0], q8b.val[0]); - const int32x4_t p2 = ggml_vdotq_s32(vzero, q2s.val[1], q8b.val[1]); - const int32x4_t p3 = ggml_vdotq_s32(vzero, q2s.val[2], q8b.val[2]); - const int32x4_t p4 = ggml_vdotq_s32(vzero, q2s.val[3], q8b.val[3]); - - sumi1 += vaddvq_s32(p1) * (1 + 2*(x[i].scales[ib32+0] & 0xf)); - sumi2 += vaddvq_s32(p2) * (1 + 2*(x[i].scales[ib32+0] >> 4)); - sumi1 += vaddvq_s32(p3) * (1 + 2*(x[i].scales[ib32+1] & 0xf)); - sumi2 += vaddvq_s32(p4) * (1 + 2*(x[i].scales[ib32+1] >> 4)); - } - sumf += d*(sumi1 + sumi2); - } - - *s = 0.125f * sumf; - -#elif defined(__AVX2__) - - static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, - 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 - }; - - static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - }; - - const __m128i m4 = _mm_set1_epi8(0xf); - const __m128i m1 = _mm_set1_epi8(1); - - const __m256i mask1 = _mm256_loadu_si256((const __m256i*)k_mask1); - const __m256i mask2 = _mm256_loadu_si256((const __m256i*)k_mask2); - - uint64_t aux64; - - __m256 accumf = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict qs = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); - const int8_t * restrict q8 = y[i].qs; - - memcpy(&aux64, x[i].scales, 8); - const __m128i scales8 = _mm_add_epi8(_mm_slli_epi16(_mm_and_si128(_mm_set_epi64x(aux64 >> 4, aux64), m4), 1), m1); - const __m256i scales16 = _mm256_cvtepi8_epi16(scales8); // 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 15 - - __m256i sumi1 = _mm256_setzero_si256(); - __m256i sumi2 = _mm256_setzero_si256(); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q2_1 = _mm256_set_epi64x(iq2s_grid[qs[3] | ((qh[ib32+0] << 2) & 0x300)], - iq2s_grid[qs[2] | ((qh[ib32+0] << 4) & 0x300)], - iq2s_grid[qs[1] | ((qh[ib32+0] << 6) & 0x300)], - iq2s_grid[qs[0] | ((qh[ib32+0] << 8) & 0x300)]); - const __m256i q2_2 = _mm256_set_epi64x(iq2s_grid[qs[7] | ((qh[ib32+1] << 2) & 0x300)], - iq2s_grid[qs[6] | ((qh[ib32+1] << 4) & 0x300)], - iq2s_grid[qs[5] | ((qh[ib32+1] << 6) & 0x300)], - iq2s_grid[qs[4] | ((qh[ib32+1] << 8) & 0x300)]); - qs += 8; - - __m256i aux256 = _mm256_set1_epi32(signs[0] | ((uint32_t) signs[1] << 16)); - aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); - const __m256i s2_1 = _mm256_cmpeq_epi8(aux256, mask2); - const __m256i q8s_1 = _mm256_sub_epi8(_mm256_xor_si256(s2_1, q8_1), s2_1); - - aux256 = _mm256_set1_epi32(signs[2] | ((uint32_t) signs[3] << 16)); - aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); - const __m256i s2_2 = _mm256_cmpeq_epi8(aux256, mask2); - const __m256i q8s_2 = _mm256_sub_epi8(_mm256_xor_si256(s2_2, q8_2), s2_2); - - signs += 4; - - const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); // blocks 2*ib32+0, 2*ib32+1 - const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); // blocks 2*ib32+2, 2*ib32+3 - - const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_shuffle_epi8(scales16, get_scale_shuffle_k4(ib32+0))); - const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_shuffle_epi8(scales16, get_scale_shuffle_k4(ib32+1))); - sumi1 = _mm256_add_epi32(sumi1, p1); - sumi2 = _mm256_add_epi32(sumi2, p2); - } - - accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); - - } - - *s = 0.125f * hsum_float_8(accumf); - -#elif defined(__AVX__) - static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, - 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 - }; - - static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - }; - - const __m128i m4 = _mm_set1_epi8(0xf); - const __m128i m1 = _mm_set1_epi8(1); - - const __m128i mask1_0 = _mm_loadu_si128((const __m128i*)k_mask1); - const __m128i mask1_1 = _mm_loadu_si128((const __m128i*)k_mask1 + 1); - const __m128i mask2_0 = _mm_loadu_si128((const __m128i*)k_mask2); - const __m128i mask2_1 = _mm_loadu_si128((const __m128i*)k_mask2 + 1); - - uint64_t aux64; - - __m256 accumf = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict qs = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); - const int8_t * restrict q8 = y[i].qs; - - memcpy(&aux64, x[i].scales, 8); - const __m128i scales8 = _mm_add_epi8(_mm_slli_epi16(_mm_and_si128(_mm_set_epi64x(aux64 >> 4, aux64), m4), 1), m1); - const __m128i scales16_0 = _mm_cvtepi8_epi16(scales8); - const __m128i scales16_1 = _mm_cvtepi8_epi16(_mm_srli_si128(scales8, 8)); - - __m128i sumi1_0 = _mm_setzero_si128(); - __m128i sumi1_1 = _mm_setzero_si128(); - __m128i sumi2_0 = _mm_setzero_si128(); - __m128i sumi2_1 = _mm_setzero_si128(); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const __m128i q8_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q2_1_0 = _mm_set_epi64x(iq2s_grid[qs[1] | ((qh[ib32+0] << 6) & 0x300)], - iq2s_grid[qs[0] | ((qh[ib32+0] << 8) & 0x300)]); - const __m128i q2_1_1 = _mm_set_epi64x(iq2s_grid[qs[3] | ((qh[ib32+0] << 2) & 0x300)], - iq2s_grid[qs[2] | ((qh[ib32+0] << 4) & 0x300)]); - const __m128i q2_2_0 = _mm_set_epi64x(iq2s_grid[qs[5] | ((qh[ib32+1] << 6) & 0x300)], - iq2s_grid[qs[4] | ((qh[ib32+1] << 8) & 0x300)]); - const __m128i q2_2_1 = _mm_set_epi64x(iq2s_grid[qs[7] | ((qh[ib32+1] << 2) & 0x300)], - iq2s_grid[qs[6] | ((qh[ib32+1] << 4) & 0x300)]); - qs += 8; - - __m128i aux128_0 = _mm_set1_epi32(signs[0] | ((uint32_t) signs[1] << 16)); - __m128i aux128_1 = aux128_0; - aux128_0 = _mm_and_si128(_mm_shuffle_epi8(aux128_0,mask1_0), mask2_0); - aux128_1 = _mm_and_si128(_mm_shuffle_epi8(aux128_1,mask1_1), mask2_1); - const __m128i s2_1_0 = _mm_cmpeq_epi8(aux128_0, mask2_0); - const __m128i s2_1_1 = _mm_cmpeq_epi8(aux128_1, mask2_1); - const __m128i q8s_1_0 = _mm_sub_epi8(_mm_xor_si128(s2_1_0, q8_1_0), s2_1_0); - const __m128i q8s_1_1 = _mm_sub_epi8(_mm_xor_si128(s2_1_1, q8_1_1), s2_1_1); - - aux128_0 = _mm_set1_epi32(signs[2] | ((uint32_t) signs[3] << 16)); - aux128_1 = aux128_0; - aux128_0 = _mm_and_si128(_mm_shuffle_epi8(aux128_0,mask1_0), mask2_0); - aux128_1 = _mm_and_si128(_mm_shuffle_epi8(aux128_1,mask1_1), mask2_1); - const __m128i s2_2_0 = _mm_cmpeq_epi8(aux128_0, mask2_0); - const __m128i s2_2_1 = _mm_cmpeq_epi8(aux128_1, mask2_1); - const __m128i q8s_2_0 = _mm_sub_epi8(_mm_xor_si128(s2_2_0, q8_2_0), s2_2_0); - const __m128i q8s_2_1 = _mm_sub_epi8(_mm_xor_si128(s2_2_1, q8_2_1), s2_2_1); - - signs += 4; - - const __m128i dot1_0 = _mm_maddubs_epi16(q2_1_0, q8s_1_0); - const __m128i dot1_1 = _mm_maddubs_epi16(q2_1_1, q8s_1_1); - const __m128i dot2_0 = _mm_maddubs_epi16(q2_2_0, q8s_2_0); - const __m128i dot2_1 = _mm_maddubs_epi16(q2_2_1, q8s_2_1); - - const __m128i p1_0 = _mm_madd_epi16(dot1_0, _mm_shuffle_epi8(scales16_0, _mm256_extractf128_si256(get_scale_shuffle_k4(ib32+0), 0))); - const __m128i p1_1 = _mm_madd_epi16(dot1_1, _mm_shuffle_epi8(scales16_1, _mm256_extractf128_si256(get_scale_shuffle_k4(ib32+0), 1))); - const __m128i p2_0 = _mm_madd_epi16(dot2_0, _mm_shuffle_epi8(scales16_0, _mm256_extractf128_si256(get_scale_shuffle_k4(ib32+1), 0))); - const __m128i p2_1 = _mm_madd_epi16(dot2_1, _mm_shuffle_epi8(scales16_1, _mm256_extractf128_si256(get_scale_shuffle_k4(ib32+1), 1))); - sumi1_0 = _mm_add_epi32(sumi1_0, p1_0); - sumi1_1 = _mm_add_epi32(sumi1_1, p1_1); - sumi2_0 = _mm_add_epi32(sumi2_0, p2_0); - sumi2_1 = _mm_add_epi32(sumi2_1, p2_1); - } - - accumf = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_add_epi32(sumi1_1, sumi2_1), _mm_add_epi32(sumi1_0, sumi2_0)))), accumf); - - } - - *s = 0.125f * hsum_float_8(accumf); - -#elif defined(__POWER9_VECTOR__) - static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, - 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 - }; - - static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; - - const vector int v0 = vec_splats((int32_t)0); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - - const vector unsigned char mask0 = vec_xl( 0, k_mask1); - const vector unsigned char mask1 = vec_xl(16, k_mask1); - const vector signed char mask2 = (vector signed char)vec_xl( 0, k_mask2); - - for (int i = 0; i < nb; ++i) { - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - vector signed int vsumi2 = v0; - vector signed int vsumi3 = v0; - - const uint8_t * restrict q2 = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); - const uint8_t * restrict sc = x[i].scales; - const int8_t * restrict q8 = y[i].qs; - - for (int j = 0; j < QK_K/32; j += 2) { - __builtin_prefetch(q2, 0, 1); - __builtin_prefetch(q8, 0, 1); - - vector signed long long aux64x2_0 = {*(const int64_t *)(iq2s_grid + (q2[0] | ((qh[0] << 8) & 0x300))), *(const int64_t *)(iq2s_grid + (q2[1] | ((qh[0] << 6) & 0x300)))}; - vector signed long long aux64x2_1 = {*(const int64_t *)(iq2s_grid + (q2[2] | ((qh[0] << 4) & 0x300))), *(const int64_t *)(iq2s_grid + (q2[3] | ((qh[0] << 2) & 0x300)))}; - vector signed long long aux64x2_2 = {*(const int64_t *)(iq2s_grid + (q2[4] | ((qh[1] << 8) & 0x300))), *(const int64_t *)(iq2s_grid + (q2[5] | ((qh[1] << 6) & 0x300)))}; - vector signed long long aux64x2_3 = {*(const int64_t *)(iq2s_grid + (q2[6] | ((qh[1] << 4) & 0x300))), *(const int64_t *)(iq2s_grid + (q2[7] | ((qh[1] << 2) & 0x300)))}; - q2 += 8; - qh += 2; - - vector signed char vsigns01 = (vector signed char)vec_splats(*(const uint32_t *)&signs[0]); - vector signed char vsigns23 = (vector signed char)vec_splats(*(const uint32_t *)&signs[2]); - signs += 4; - - vector signed char vsigns0 = vec_perm(vsigns01, vsigns01, mask0); - vector signed char vsigns1 = vec_perm(vsigns01, vsigns01, mask1); - vector signed char vsigns2 = vec_perm(vsigns23, vsigns23, mask0); - vector signed char vsigns3 = vec_perm(vsigns23, vsigns23, mask1); - - vsigns0 = (vector signed char)vec_cmpeq(vec_and(vsigns0, mask2), mask2); - vsigns1 = (vector signed char)vec_cmpeq(vec_and(vsigns1, mask2), mask2); - vsigns2 = (vector signed char)vec_cmpeq(vec_and(vsigns2, mask2), mask2); - vsigns3 = (vector signed char)vec_cmpeq(vec_and(vsigns3, mask2), mask2); - - vector signed char q2x0 = vec_sub(vec_xor(vsigns0, (vector signed char)aux64x2_0), vsigns0); - vector signed char q2x1 = vec_sub(vec_xor(vsigns1, (vector signed char)aux64x2_1), vsigns1); - vector signed char q2x2 = vec_sub(vec_xor(vsigns2, (vector signed char)aux64x2_2), vsigns2); - vector signed char q2x3 = vec_sub(vec_xor(vsigns3, (vector signed char)aux64x2_3), vsigns3); - - vector signed char q8y0 = vec_xl( 0, q8); - vector signed char q8y1 = vec_xl(16, q8); - vector signed char q8y2 = vec_xl(32, q8); - vector signed char q8y3 = vec_xl(48, q8); - q8 += 64; - - vector signed short qv0 = vec_add(vec_mule(q2x0, q8y0), vec_mulo(q2x0, q8y0)); - vector signed short qv1 = vec_add(vec_mule(q2x1, q8y1), vec_mulo(q2x1, q8y1)); - vector signed short qv2 = vec_add(vec_mule(q2x2, q8y2), vec_mulo(q2x2, q8y2)); - vector signed short qv3 = vec_add(vec_mule(q2x3, q8y3), vec_mulo(q2x3, q8y3)); - - const uint16_t ls0 = (uint16_t)(sc[0] & 0xf); - const uint16_t ls1 = (uint16_t)(sc[0] >> 4); - const uint16_t ls2 = (uint16_t)(sc[1] & 0xf); - const uint16_t ls3 = (uint16_t)(sc[1] >> 4); - sc += 2; - - vector signed short vscales0 = vec_splats((int16_t)(2*ls0+1)); - vector signed short vscales1 = vec_splats((int16_t)(2*ls1+1)); - vector signed short vscales2 = vec_splats((int16_t)(2*ls2+1)); - vector signed short vscales3 = vec_splats((int16_t)(2*ls3+1)); - - vsumi0 = vec_msum(qv0, vscales0, vsumi0); - vsumi1 = vec_msum(qv1, vscales1, vsumi1); - vsumi2 = vec_msum(qv2, vscales2, vsumi2); - vsumi3 = vec_msum(qv3, vscales3, vsumi3); - } - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = 0.125f * vec_extract(vsumf0, 0); - -#elif defined(__loongarch_asx) - - static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, - 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 - }; - - static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - }; - - - const __m128i m4 = __lsx_vreplgr2vr_b(0xf); - const __m128i m1 = __lsx_vreplgr2vr_b(1); - - const __m256i mask1 = __lasx_xvld((const __m256i*)k_mask1, 0); - const __m256i mask2 = __lasx_xvld((const __m256i*)k_mask2, 0); - uint64_t aux64; - - __m256 accumf = (__m256)__lasx_xvldi(0); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict qs = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); - const int8_t * restrict q8 = y[i].qs; - - __m128i tmp1; - memcpy(&aux64, x[i].scales, 8); - tmp1 = __lsx_vinsgr2vr_d(tmp1, aux64, 0); - tmp1 = __lsx_vinsgr2vr_d(tmp1, aux64 >> 4, 1); - const __m128i scales8 = __lsx_vadd_b(__lsx_vslli_h(__lsx_vand_v(tmp1, m4), 1), m1); - const __m256i scales16 = lasx_ext8_16(scales8); // 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 15 - - __m256i sumi1 = __lasx_xvldi(0); - __m256i sumi2 = __lasx_xvldi(0); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - const __m256i q2_1 = lasx_set_d(iq2s_grid[qs[3] | ((qh[ib32+0] << 2) & 0x300)], - iq2s_grid[qs[2] | ((qh[ib32+0] << 4) & 0x300)], - iq2s_grid[qs[1] | ((qh[ib32+0] << 6) & 0x300)], - iq2s_grid[qs[0] | ((qh[ib32+0] << 8) & 0x300)]); - const __m256i q2_2 = lasx_set_d(iq2s_grid[qs[7] | ((qh[ib32+1] << 2) & 0x300)], - iq2s_grid[qs[6] | ((qh[ib32+1] << 4) & 0x300)], - iq2s_grid[qs[5] | ((qh[ib32+1] << 6) & 0x300)], - iq2s_grid[qs[4] | ((qh[ib32+1] << 8) & 0x300)]); - qs += 8; - - __m256i aux256 = __lasx_xvreplgr2vr_w(signs[0] | ((uint32_t) signs[1] << 16)); - aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2); - const __m256i s2_1 = __lasx_xvseq_b(aux256, mask2); - const __m256i q8s_1 = __lasx_xvsub_b(__lasx_xvxor_v(s2_1, q8_1), s2_1); - - aux256 = __lasx_xvreplgr2vr_w(signs[2] | ((uint32_t) signs[3] << 16)); - aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2); - const __m256i s2_2 = __lasx_xvseq_b(aux256, mask2); - const __m256i q8s_2 = __lasx_xvsub_b(__lasx_xvxor_v(s2_2, q8_2), s2_2); - - signs += 4; - - const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); // blocks 2*ib32+0, 2*ib32+1 - const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); // blocks 2*ib32+2, 2*ib32+3 - - const __m256i p1 = lasx_madd_h(dot1, lasx_shuffle_b(scales16, get_scale_shuffle_k4(ib32+0))); - const __m256i p2 = lasx_madd_h(dot2, lasx_shuffle_b(scales16, get_scale_shuffle_k4(ib32+1))); - sumi1 = __lasx_xvadd_w(sumi1, p1); - sumi2 = __lasx_xvadd_w(sumi2, p2); - } - - accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); - } - - *s = 0.125f * hsum_float_8(accumf); - -#else - - float sumf = 0; - for (int i = 0; i < nb; i++) { - - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const int8_t * q8 = y[i].qs; - const uint8_t * qs = x[i].qs; - const uint8_t * qh = x[i].qh; - const uint8_t * signs = qs + QK_K/8; - - int bsum = 0; - for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { - int ls1 = 1 + 2*(x[i].scales[ib32] & 0xf); - int ls2 = 1 + 2*(x[i].scales[ib32] >> 4); - int sumi1 = 0, sumi2 = 0; - for (int l = 0; l < 2; ++l) { - const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300))); - for (int j = 0; j < 8; ++j) { - sumi1 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1); - } - q8 += 8; - } - for (int l = 2; l < 4; ++l) { - const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300))); - for (int j = 0; j < 8; ++j) { - sumi2 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1); - } - q8 += 8; - } - bsum += ls1 * sumi1 + ls2 * sumi2; - qs += 4; - signs += 4; - } - - sumf += d * bsum; - } - - *s = 0.125f * sumf; - -#endif - -} - -void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_iq3_xxs * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#if defined(__ARM_NEON) - - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - - uint32_t aux32[2]; - - ggml_int8x16x4_t q3s; - ggml_int8x16x4_t q8b; - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict q3 = x[i].qs; - const uint8_t * restrict gas = x[i].qs + QK_K/4; - const int8_t * restrict q8 = y[i].qs; - float sumf1 = 0, sumf2 = 0; - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - q8b = ggml_vld1q_s8_x4(q8); q8 += 64; - memcpy(aux32, gas, 2*sizeof(uint32_t)); gas += 2*sizeof(uint32_t); - const uint32x4_t aux32x4_0 = ggml_vld1q_u32(iq3xxs_grid[q3[ 0]], iq3xxs_grid[q3[ 1]], iq3xxs_grid[q3[ 2]], iq3xxs_grid[q3[ 3]]); - const uint32x4_t aux32x4_1 = ggml_vld1q_u32(iq3xxs_grid[q3[ 4]], iq3xxs_grid[q3[ 5]], iq3xxs_grid[q3[ 6]], iq3xxs_grid[q3[ 7]]); - const uint32x4_t aux32x4_2 = ggml_vld1q_u32(iq3xxs_grid[q3[ 8]], iq3xxs_grid[q3[ 9]], iq3xxs_grid[q3[10]], iq3xxs_grid[q3[11]]); - const uint32x4_t aux32x4_3 = ggml_vld1q_u32(iq3xxs_grid[q3[12]], iq3xxs_grid[q3[13]], iq3xxs_grid[q3[14]], iq3xxs_grid[q3[15]]); - q3 += 16; - q3s.val[0] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[0] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[0] >> 7) & 127)))); - q3s.val[1] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[0] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[0] >> 21) & 127)))); - q3s.val[2] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 7) & 127)))); - q3s.val[3] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 21) & 127)))); - q3s.val[0] = vmulq_s8(q3s.val[0], vreinterpretq_s8_u32(aux32x4_0)); - q3s.val[1] = vmulq_s8(q3s.val[1], vreinterpretq_s8_u32(aux32x4_1)); - q3s.val[2] = vmulq_s8(q3s.val[2], vreinterpretq_s8_u32(aux32x4_2)); - q3s.val[3] = vmulq_s8(q3s.val[3], vreinterpretq_s8_u32(aux32x4_3)); - const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[0], q8b.val[0]), q3s.val[1], q8b.val[1]); - const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[2], q8b.val[2]), q3s.val[3], q8b.val[3]); - sumf1 += vaddvq_s32(p1) * (0.5f + (aux32[0] >> 28)); - sumf2 += vaddvq_s32(p2) * (0.5f + (aux32[1] >> 28)); - } - sumf += d*(sumf1 + sumf2); - } - *s = 0.5f * sumf; - -#elif defined(__AVX2__) - - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - - uint32_t aux32[2]; - - __m256 accumf = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict q3 = x[i].qs; - const uint8_t * restrict gas = x[i].qs + QK_K/4; - const int8_t * restrict q8 = y[i].qs; - __m256i sumi1 = _mm256_setzero_si256(); - __m256i sumi2 = _mm256_setzero_si256(); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q2_1 = _mm256_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]], - iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); - q3 += 8; - const __m256i q2_2 = _mm256_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]], - iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); - q3 += 8; - memcpy(aux32, gas, 8); gas += 8; - const __m256i s2_1 = _mm256_set_epi64x(signs64[(aux32[0] >> 21) & 127], signs64[(aux32[0] >> 14) & 127], - signs64[(aux32[0] >> 7) & 127], signs64[(aux32[0] >> 0) & 127]); - const __m256i s2_2 = _mm256_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], - signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); - const __m256i q8s_1 = _mm256_sign_epi8(q8_1, s2_1); - const __m256i q8s_2 = _mm256_sign_epi8(q8_2, s2_2); - const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); - const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); - const uint16_t ls1 = aux32[0] >> 28; - const uint16_t ls2 = aux32[1] >> 28; - const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1)); - const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1)); - sumi1 = _mm256_add_epi32(sumi1, p1); - sumi2 = _mm256_add_epi32(sumi2, p2); - } - - accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); - - } - - *s = 0.25f * hsum_float_8(accumf); - -#elif defined(__AVX__) - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - - uint32_t aux32[2]; - - __m256 accumf = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict q3 = x[i].qs; - const uint8_t * restrict gas = x[i].qs + QK_K/4; - const int8_t * restrict q8 = y[i].qs; - __m128i sumi1_0 = _mm_setzero_si128(); - __m128i sumi1_1 = _mm_setzero_si128(); - __m128i sumi2_0 = _mm_setzero_si128(); - __m128i sumi2_1 = _mm_setzero_si128(); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const __m128i q8_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q2_1_0 = _mm_set_epi32(iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); - const __m128i q2_1_1 = _mm_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]]); - q3 += 8; - const __m128i q2_2_0 = _mm_set_epi32(iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); - const __m128i q2_2_1 = _mm_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]]); - q3 += 8; - memcpy(aux32, gas, 8); gas += 8; - const __m128i s2_1_0 = _mm_set_epi64x(signs64[(aux32[0] >> 7) & 127], signs64[(aux32[0] >> 0) & 127]); - const __m128i s2_1_1 = _mm_set_epi64x(signs64[(aux32[0] >> 21) & 127], signs64[(aux32[0] >> 14) & 127]); - const __m128i s2_2_0 = _mm_set_epi64x(signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); - const __m128i s2_2_1 = _mm_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127]); - const __m128i q8s_1_0 = _mm_sign_epi8(q8_1_0, s2_1_0); - const __m128i q8s_1_1 = _mm_sign_epi8(q8_1_1, s2_1_1); - const __m128i q8s_2_0 = _mm_sign_epi8(q8_2_0, s2_2_0); - const __m128i q8s_2_1 = _mm_sign_epi8(q8_2_1, s2_2_1); - const __m128i dot1_0 = _mm_maddubs_epi16(q2_1_0, q8s_1_0); - const __m128i dot1_1 = _mm_maddubs_epi16(q2_1_1, q8s_1_1); - const __m128i dot2_0 = _mm_maddubs_epi16(q2_2_0, q8s_2_0); - const __m128i dot2_1 = _mm_maddubs_epi16(q2_2_1, q8s_2_1); - const uint16_t ls1 = aux32[0] >> 28; - const uint16_t ls2 = aux32[1] >> 28; - const __m128i p1_0 = _mm_madd_epi16(dot1_0, _mm_set1_epi16(2*ls1+1)); - const __m128i p1_1 = _mm_madd_epi16(dot1_1, _mm_set1_epi16(2*ls1+1)); - const __m128i p2_0 = _mm_madd_epi16(dot2_0, _mm_set1_epi16(2*ls2+1)); - const __m128i p2_1 = _mm_madd_epi16(dot2_1, _mm_set1_epi16(2*ls2+1)); - sumi1_0 = _mm_add_epi32(sumi1_0, p1_0); - sumi1_1 = _mm_add_epi32(sumi1_1, p1_1); - sumi2_0 = _mm_add_epi32(sumi2_0, p2_0); - sumi2_1 = _mm_add_epi32(sumi2_1, p2_1); - } - - accumf = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_add_epi32(sumi1_1, sumi2_1), _mm_add_epi32(sumi1_0, sumi2_0)))), accumf); - - } - - *s = 0.25f * hsum_float_8(accumf); - -#elif defined(__POWER9_VECTOR__) - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - - const vector int v0 = vec_splats((int32_t)0); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - - for (int i = 0; i < nb; ++i) { - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - vector signed int vsumi2 = v0; - vector signed int vsumi3 = v0; - - const uint8_t * restrict q3 = x[i].qs; - const uint32_t * restrict signs = (const uint32_t *)(x[i].qs + QK_K/4); - const int8_t * restrict q8 = y[i].qs; - -#pragma GCC unroll 1 - for (int j = 0; j < QK_K/32; j += 2) { - __builtin_prefetch(q3, 0, 1); - __builtin_prefetch(q8, 0, 1); - - vector unsigned int aux32x4_0 = {iq3xxs_grid[q3[ 0]], iq3xxs_grid[q3[ 1]], iq3xxs_grid[q3[ 2]], iq3xxs_grid[q3[ 3]]}; - vector unsigned int aux32x4_1 = {iq3xxs_grid[q3[ 4]], iq3xxs_grid[q3[ 5]], iq3xxs_grid[q3[ 6]], iq3xxs_grid[q3[ 7]]}; - vector unsigned int aux32x4_2 = {iq3xxs_grid[q3[ 8]], iq3xxs_grid[q3[ 9]], iq3xxs_grid[q3[10]], iq3xxs_grid[q3[11]]}; - vector unsigned int aux32x4_3 = {iq3xxs_grid[q3[12]], iq3xxs_grid[q3[13]], iq3xxs_grid[q3[14]], iq3xxs_grid[q3[15]]}; - q3 += 16; - - vector unsigned long long aux64x2_0 = {(uint64_t)(signs64[(signs[0] >> 0) & 127]), (uint64_t)(signs64[(signs[0] >> 7) & 127])}; - vector unsigned long long aux64x2_1 = {(uint64_t)(signs64[(signs[0] >> 14) & 127]), (uint64_t)(signs64[(signs[0] >> 21) & 127])}; - vector unsigned long long aux64x2_2 = {(uint64_t)(signs64[(signs[1] >> 0) & 127]), (uint64_t)(signs64[(signs[1] >> 7) & 127])}; - vector unsigned long long aux64x2_3 = {(uint64_t)(signs64[(signs[1] >> 14) & 127]), (uint64_t)(signs64[(signs[1] >> 21) & 127])}; - - vector signed char q3x0 = vec_mul((vector signed char)aux64x2_0, (vector signed char)aux32x4_0); - vector signed char q3x1 = vec_mul((vector signed char)aux64x2_1, (vector signed char)aux32x4_1); - vector signed char q3x2 = vec_mul((vector signed char)aux64x2_2, (vector signed char)aux32x4_2); - vector signed char q3x3 = vec_mul((vector signed char)aux64x2_3, (vector signed char)aux32x4_3); - - vector signed char q8y0 = vec_xl( 0, q8); - vector signed char q8y1 = vec_xl(16, q8); - vector signed char q8y2 = vec_xl(32, q8); - vector signed char q8y3 = vec_xl(48, q8); - q8 += 64; - - vector signed short qv0 = vec_add(vec_mule(q3x0, q8y0), vec_mulo(q3x0, q8y0)); - vector signed short qv1 = vec_add(vec_mule(q3x1, q8y1), vec_mulo(q3x1, q8y1)); - vector signed short qv2 = vec_add(vec_mule(q3x2, q8y2), vec_mulo(q3x2, q8y2)); - vector signed short qv3 = vec_add(vec_mule(q3x3, q8y3), vec_mulo(q3x3, q8y3)); - - const uint16_t ls0 = (uint16_t)(signs[0] >> 28); - const uint16_t ls1 = (uint16_t)(signs[1] >> 28); - signs += 2; - - vector signed short vscales01 = (vector signed short)vec_splats((uint16_t)(2*ls0+1)); - vector signed short vscales23 = (vector signed short)vec_splats((uint16_t)(2*ls1+1)); - - vsumi0 = vec_msum(qv0, vscales01, vsumi0); - vsumi1 = vec_msum(qv1, vscales01, vsumi1); - vsumi2 = vec_msum(qv2, vscales23, vsumi2); - vsumi3 = vec_msum(qv3, vscales23, vsumi3); - } - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = 0.25f * vec_extract(vsumf0, 0); - -#elif defined(__loongarch_asx) - - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; - - uint32_t aux32[2]; - - __m256 accumf = (__m256)__lasx_xvldi(0); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict q3 = x[i].qs; - const uint8_t * restrict gas = x[i].qs + QK_K/4; - const int8_t * restrict q8 = y[i].qs; - __m256i sumi1 = __lasx_xvldi(0); - __m256i sumi2 = __lasx_xvldi(0); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - const __m256i q2_1 = lasx_set_w(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]], - iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); - q3 += 8; - const __m256i q2_2 = lasx_set_w(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]], - iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); - q3 += 8; - memcpy(aux32, gas, 8); gas += 8; - - const __m256i s2_1 = lasx_set_d(signs64[(aux32[0] >> 21) & 127], signs64[(aux32[0] >> 14) & 127], - signs64[(aux32[0] >> 7) & 127], signs64[(aux32[0] >> 0) & 127]); - const __m256i s2_2 = lasx_set_d(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], - signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); - const __m256i q8s_1 = __lasx_xvsigncov_b(s2_1, q8_1); - const __m256i q8s_2 = __lasx_xvsigncov_b(s2_2, q8_2); - const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); - const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); - const uint16_t ls1 = aux32[0] >> 28; - const uint16_t ls2 = aux32[1] >> 28; - - const __m256i p1 = lasx_madd_h(dot1, __lasx_xvreplgr2vr_h(2*ls1+1)); - const __m256i p2 = lasx_madd_h(dot2, __lasx_xvreplgr2vr_h(2*ls2+1)); - sumi1 = __lasx_xvadd_w(sumi1, p1); - sumi2 = __lasx_xvadd_w(sumi2, p2); - } - - accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); - } - - *s = 0.25f * hsum_float_8(accumf); - -#else - - uint32_t aux32; - - float sumf = 0.f; - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict q3 = x[i].qs; - const uint8_t * restrict gas = x[i].qs + QK_K/4; - const int8_t * restrict q8 = y[i].qs; - int32_t bsum = 0; - for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { - memcpy(&aux32, gas, sizeof(uint32_t)); gas += sizeof(uint32_t); - const uint32_t ls = 2*(aux32 >> 28) + 1; - int32_t sumi = 0; - for (int l = 0; l < 4; ++l) { - const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*l+0]); - const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*l+1]); - const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*l) & 127]; - for (int j = 0; j < 4; ++j) { - sumi += grid1[j] * q8[j+0] * (signs & kmask_iq2xs[j+0] ? -1 : 1); - sumi += grid2[j] * q8[j+4] * (signs & kmask_iq2xs[j+4] ? -1 : 1); - } - q8 += 8; - } - q3 += 8; - bsum += sumi * ls; - } - sumf += d * bsum; - } - *s = 0.25f * sumf; -#endif -} - -void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_iq3_s * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#if defined(__ARM_NEON) - - typedef union { - uint16x8_t vec_index; - uint16_t index[8]; - } vec_index_t; - - static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, - 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 - }; - - static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; - - static const int16_t k_shift[8] = {8, 7, 6, 5, 4, 3, 2, 1}; - - const ggml_uint8x16x2_t mask1 = ggml_vld1q_u8_x2(k_mask1); - const uint8x16_t mask2 = vld1q_u8(k_mask2); - - const int16x8_t hshift = vld1q_s16(k_shift); - const uint16x8_t m256 = vdupq_n_u16(256); - const uint8x16_t m1 = vdupq_n_u8(1); - - uint8x16x2_t vs; - ggml_int8x16x4_t q3s; - ggml_int8x16x4_t q8b; - vec_index_t idx; - - uint32_t scales32[2]; - const uint8_t * scales8 = (const uint8_t *)scales32; - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict qs = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const uint16_t * restrict signs = (const uint16_t *)x[i].signs; - const int8_t * restrict q8 = y[i].qs; - - memcpy(scales32, x[i].scales, 4); - scales32[1] = (((scales32[0] >> 4) & 0x0f0f0f0f) << 1) | 0x01010101; - scales32[0] = ((scales32[0] & 0x0f0f0f0f) << 1) | 0x01010101; - - int sumi1 = 0, sumi2 = 0; - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - q8b = ggml_vld1q_s8_x4(q8); q8 += 64; - - const uint8x16_t idx_l = vld1q_u8(qs); qs += 16; - idx.vec_index = vorrq_u16(vmovl_u8(vget_low_u8 (idx_l)), vandq_u16(vshlq_u16(vdupq_n_u16(qh[ib32+0]), hshift), m256)); - const uint32x4_t aux32x4_0 = ggml_vld1q_u32(iq3s_grid[idx.index[0]], iq3s_grid[idx.index[1]], - iq3s_grid[idx.index[2]], iq3s_grid[idx.index[3]]); - const uint32x4_t aux32x4_1 = ggml_vld1q_u32(iq3s_grid[idx.index[4]], iq3s_grid[idx.index[5]], - iq3s_grid[idx.index[6]], iq3s_grid[idx.index[7]]); - idx.vec_index = vorrq_u16(vmovl_u8(vget_high_u8(idx_l)), vandq_u16(vshlq_u16(vdupq_n_u16(qh[ib32+1]), hshift), m256)); - const uint32x4_t aux32x4_2 = ggml_vld1q_u32(iq3s_grid[idx.index[0]], iq3s_grid[idx.index[1]], - iq3s_grid[idx.index[2]], iq3s_grid[idx.index[3]]); - const uint32x4_t aux32x4_3 = ggml_vld1q_u32(iq3s_grid[idx.index[4]], iq3s_grid[idx.index[5]], - iq3s_grid[idx.index[6]], iq3s_grid[idx.index[7]]); - - - vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[0] | ((uint32_t) signs[1] << 16))); - vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); - vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); - vs.val[0] = vorrq_u8(vceqq_u8(vs.val[0], mask2), m1); - vs.val[1] = vorrq_u8(vceqq_u8(vs.val[1], mask2), m1); - - q3s.val[0] = vmulq_s8(vreinterpretq_s8_u8(vs.val[0]), vreinterpretq_s8_u32(aux32x4_0)); - q3s.val[1] = vmulq_s8(vreinterpretq_s8_u8(vs.val[1]), vreinterpretq_s8_u32(aux32x4_1)); - - vs.val[0] = vreinterpretq_u8_u32(vdupq_n_u32(signs[2] | ((uint32_t) signs[3] << 16))); - vs.val[1] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[1]), mask2); - vs.val[0] = vandq_u8(ggml_vqtbl1q_u8(vs.val[0], mask1.val[0]), mask2); - vs.val[0] = vorrq_u8(vceqq_u8(vs.val[0], mask2), m1); - vs.val[1] = vorrq_u8(vceqq_u8(vs.val[1], mask2), m1); - - signs += 4; - - q3s.val[2] = vmulq_s8(vreinterpretq_s8_u8(vs.val[0]), vreinterpretq_s8_u32(aux32x4_2)); - q3s.val[3] = vmulq_s8(vreinterpretq_s8_u8(vs.val[1]), vreinterpretq_s8_u32(aux32x4_3)); - - const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[0], q8b.val[0]), q3s.val[1], q8b.val[1]); - const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[2], q8b.val[2]), q3s.val[3], q8b.val[3]); - - sumi1 += vaddvq_s32(p1) * scales8[ib32/2+0]; - sumi2 += vaddvq_s32(p2) * scales8[ib32/2+4]; - } - sumf += d*(sumi1 + sumi2); - } - *s = sumf; - -#elif defined(__AVX2__) - - static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, - 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 - }; - - static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - }; - - const __m256i mask1 = _mm256_loadu_si256((const __m256i*)k_mask1); - const __m256i mask2 = _mm256_loadu_si256((const __m256i*)k_mask2); - - const __m256i idx_shift = _mm256_set_epi32(1, 2, 3, 4, 5, 6, 7, 8); - const __m256i idx_mask = _mm256_set1_epi32(256); - - typedef union { - __m256i vec[2]; - uint32_t index[16]; - } index_t; - - index_t idx; - - __m256 accumf = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict qs = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const uint16_t * restrict signs = (const uint16_t *)x[i].signs; - const int8_t * restrict q8 = y[i].qs; - __m256i sumi1 = _mm256_setzero_si256(); - __m256i sumi2 = _mm256_setzero_si256(); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i idx_l = _mm256_cvtepu8_epi16(_mm_loadu_si128((const __m128i *)qs)); qs += 16; - idx.vec[0] = _mm256_set1_epi32(qh[ib32+0]); - idx.vec[1] = _mm256_set1_epi32(qh[ib32+1]); - idx.vec[0] = _mm256_and_si256(_mm256_sllv_epi32(idx.vec[0], idx_shift), idx_mask); - idx.vec[1] = _mm256_and_si256(_mm256_sllv_epi32(idx.vec[1], idx_shift), idx_mask); - idx.vec[0] = _mm256_or_si256(idx.vec[0], _mm256_cvtepi16_epi32(_mm256_castsi256_si128(idx_l))); - idx.vec[1] = _mm256_or_si256(idx.vec[1], _mm256_cvtepi16_epi32(_mm256_extractf128_si256(idx_l, 1))); - - // At leat on my CPU (Ryzen 7950X), using _mm256_i32gather_epi32 is slower than _mm256_set_epi32. Strange. - //const __m256i q2_1 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[0], 4); - //const __m256i q2_2 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[1], 4); - const __m256i q2_1 = _mm256_set_epi32( - iq3s_grid[idx.index[7]], iq3s_grid[idx.index[6]], iq3s_grid[idx.index[5]], iq3s_grid[idx.index[4]], - iq3s_grid[idx.index[3]], iq3s_grid[idx.index[2]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]] - ); - const __m256i q2_2 = _mm256_set_epi32( - iq3s_grid[idx.index[15]], iq3s_grid[idx.index[14]], iq3s_grid[idx.index[13]], iq3s_grid[idx.index[12]], - iq3s_grid[idx.index[11]], iq3s_grid[idx.index[10]], iq3s_grid[idx.index[ 9]], iq3s_grid[idx.index[ 8]] - ); - - __m256i aux256 = _mm256_set1_epi32(signs[0] | (signs[1] << 16)); - aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); - const __m256i s2_1 = _mm256_cmpeq_epi8(aux256, mask2); - const __m256i q8s_1 = _mm256_sub_epi8(_mm256_xor_si256(s2_1, q8_1), s2_1); - - aux256 = _mm256_set1_epi32(signs[2] | (signs[3] << 16)); - aux256 = _mm256_and_si256(_mm256_shuffle_epi8(aux256,mask1), mask2); - const __m256i s2_2 = _mm256_cmpeq_epi8(aux256, mask2); - const __m256i q8s_2 = _mm256_sub_epi8(_mm256_xor_si256(s2_2, q8_2), s2_2); - - signs += 4; - - const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); - const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); - const uint16_t ls1 = x[i].scales[ib32/2] & 0xf; - const uint16_t ls2 = x[i].scales[ib32/2] >> 4; - const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1)); - const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1)); - sumi1 = _mm256_add_epi32(sumi1, p1); - sumi2 = _mm256_add_epi32(sumi2, p2); - } - - accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); - - } - - *s = hsum_float_8(accumf); - -#elif defined(__AVX__) - static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, - 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 - }; - - static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - }; - - const __m128i mask1_0 = _mm_loadu_si128((const __m128i*)k_mask1); - const __m128i mask1_1 = _mm_loadu_si128((const __m128i*)k_mask1 + 1); - const __m128i mask2_0 = _mm_loadu_si128((const __m128i*)k_mask2); - const __m128i mask2_1 = _mm_loadu_si128((const __m128i*)k_mask2 + 1); - - const __m128i idx_mul_0 = _mm_set_epi32(32, 64, 128, 256); - const __m128i idx_mul_1 = _mm_set_epi32(2, 4, 8, 16); - const __m128i idx_mask = _mm_set1_epi32(256); - - typedef union { - __m128i vec[4]; - uint32_t index[16]; - } index_t; - - index_t idx; - - __m256 accumf = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict qs = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const uint16_t * restrict signs = (const uint16_t *)x[i].signs; - const int8_t * restrict q8 = y[i].qs; - __m128i sumi1_0 = _mm_setzero_si128(); - __m128i sumi1_1 = _mm_setzero_si128(); - __m128i sumi2_0 = _mm_setzero_si128(); - __m128i sumi2_1 = _mm_setzero_si128(); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const __m128i q8_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i qs_tmp = _mm_loadu_si128((const __m128i *)qs); - const __m128i idx_l_0 = _mm_cvtepu8_epi16(qs_tmp); - const __m128i idx_l_1 = _mm_cvtepu8_epi16(_mm_srli_si128(qs_tmp, 8)); qs += 16; - idx.vec[0] = _mm_set1_epi32(qh[ib32+0]); - idx.vec[1] = idx.vec[0]; - idx.vec[2] = _mm_set1_epi32(qh[ib32+1]); - idx.vec[3] = idx.vec[2]; - - idx.vec[0] = _mm_and_si128(_mm_mullo_epi32(idx.vec[0], idx_mul_0), idx_mask); - idx.vec[1] = _mm_and_si128(_mm_mullo_epi32(idx.vec[1], idx_mul_1), idx_mask); - idx.vec[2] = _mm_and_si128(_mm_mullo_epi32(idx.vec[2], idx_mul_0), idx_mask); - idx.vec[3] = _mm_and_si128(_mm_mullo_epi32(idx.vec[3], idx_mul_1), idx_mask); - - idx.vec[0] = _mm_or_si128(idx.vec[0], _mm_cvtepi16_epi32(idx_l_0)); - idx.vec[1] = _mm_or_si128(idx.vec[1], _mm_cvtepi16_epi32(_mm_srli_si128(idx_l_0, 8))); - idx.vec[2] = _mm_or_si128(idx.vec[2], _mm_cvtepi16_epi32(idx_l_1)); - idx.vec[3] = _mm_or_si128(idx.vec[3], _mm_cvtepi16_epi32(_mm_srli_si128(idx_l_1, 8))); - - const __m128i q2_1_0 = _mm_set_epi32(iq3s_grid[idx.index[3]], iq3s_grid[idx.index[2]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]]); - const __m128i q2_1_1 = _mm_set_epi32(iq3s_grid[idx.index[7]], iq3s_grid[idx.index[6]], iq3s_grid[idx.index[5]], iq3s_grid[idx.index[4]]); - const __m128i q2_2_0 = _mm_set_epi32(iq3s_grid[idx.index[11]], iq3s_grid[idx.index[10]], iq3s_grid[idx.index[9]], iq3s_grid[idx.index[8]]); - const __m128i q2_2_1 = _mm_set_epi32(iq3s_grid[idx.index[15]], iq3s_grid[idx.index[14]], iq3s_grid[idx.index[13]], iq3s_grid[idx.index[12]]); - - __m128i aux128_0 = _mm_set1_epi32(signs[0] | (signs[1] << 16)); - __m128i aux128_1 = aux128_0; - aux128_0 = _mm_and_si128(_mm_shuffle_epi8(aux128_0,mask1_0), mask2_0); - aux128_1 = _mm_and_si128(_mm_shuffle_epi8(aux128_1,mask1_1), mask2_1); - const __m128i s2_1_0 = _mm_cmpeq_epi8(aux128_0, mask2_0); - const __m128i s2_1_1 = _mm_cmpeq_epi8(aux128_1, mask2_1); - const __m128i q8s_1_0 = _mm_sub_epi8(_mm_xor_si128(s2_1_0, q8_1_0), s2_1_0); - const __m128i q8s_1_1 = _mm_sub_epi8(_mm_xor_si128(s2_1_1, q8_1_1), s2_1_1); - - aux128_0 = _mm_set1_epi32(signs[2] | (signs[3] << 16)); - aux128_1 = aux128_0; - aux128_0 = _mm_and_si128(_mm_shuffle_epi8(aux128_0,mask1_0), mask2_0); - aux128_1 = _mm_and_si128(_mm_shuffle_epi8(aux128_1,mask1_1), mask2_1); - const __m128i s2_2_0 = _mm_cmpeq_epi8(aux128_0, mask2_0); - const __m128i s2_2_1 = _mm_cmpeq_epi8(aux128_1, mask2_1); - const __m128i q8s_2_0 = _mm_sub_epi8(_mm_xor_si128(s2_2_0, q8_2_0), s2_2_0); - const __m128i q8s_2_1 = _mm_sub_epi8(_mm_xor_si128(s2_2_1, q8_2_1), s2_2_1); - - signs += 4; - - const __m128i dot1_0 = _mm_maddubs_epi16(q2_1_0, q8s_1_0); - const __m128i dot1_1 = _mm_maddubs_epi16(q2_1_1, q8s_1_1); - const __m128i dot2_0 = _mm_maddubs_epi16(q2_2_0, q8s_2_0); - const __m128i dot2_1 = _mm_maddubs_epi16(q2_2_1, q8s_2_1); - const uint16_t ls1 = x[i].scales[ib32/2] & 0xf; - const uint16_t ls2 = x[i].scales[ib32/2] >> 4; - const __m128i p1_0 = _mm_madd_epi16(dot1_0, _mm_set1_epi16(2*ls1+1)); - const __m128i p1_1 = _mm_madd_epi16(dot1_1, _mm_set1_epi16(2*ls1+1)); - const __m128i p2_0 = _mm_madd_epi16(dot2_0, _mm_set1_epi16(2*ls2+1)); - const __m128i p2_1 = _mm_madd_epi16(dot2_1, _mm_set1_epi16(2*ls2+1)); - sumi1_0 = _mm_add_epi32(sumi1_0, p1_0); - sumi1_1 = _mm_add_epi32(sumi1_1, p1_1); - sumi2_0 = _mm_add_epi32(sumi2_0, p2_0); - sumi2_1 = _mm_add_epi32(sumi2_1, p2_1); - } - - accumf = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_add_epi32(sumi1_1, sumi2_1), _mm_add_epi32(sumi1_0, sumi2_0)))), accumf); - - } - - *s = hsum_float_8(accumf); - -#elif defined(__POWER9_VECTOR__) - static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, - 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 - }; - - static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; - - const vector int v0 = vec_splats((int32_t)0); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - - const vector unsigned char mask0 = vec_xl( 0, k_mask1); - const vector unsigned char mask1 = vec_xl(16, k_mask1); - const vector signed char mask2 = (vector signed char)vec_xl( 0, k_mask2); - - for (int i = 0; i < nb; ++i) { - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - const uint8_t * restrict q3 = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const uint16_t * restrict signs = (const uint16_t *)(x[i].signs); - const uint8_t * restrict sc = x[i].scales; - const int8_t * restrict q8 = y[i].qs; - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - vector signed int vsumi2 = v0; - vector signed int vsumi3 = v0; - - for (int j = 0; j < QK_K/32; j += 2) { - __builtin_prefetch(q3, 0, 1); - __builtin_prefetch(q8, 0, 1); - - vector unsigned int aux32x4_0 = {iq3s_grid[q3[ 0] | ((qh[0] << 8) & 256)], iq3s_grid[q3[ 1] | ((qh[0] << 7) & 256)], - iq3s_grid[q3[ 2] | ((qh[0] << 6) & 256)], iq3s_grid[q3[ 3] | ((qh[0] << 5) & 256)]}; - vector unsigned int aux32x4_1 = {iq3s_grid[q3[ 4] | ((qh[0] << 4) & 256)], iq3s_grid[q3[ 5] | ((qh[0] << 3) & 256)], - iq3s_grid[q3[ 6] | ((qh[0] << 2) & 256)], iq3s_grid[q3[ 7] | ((qh[0] << 1) & 256)]}; - vector unsigned int aux32x4_2 = {iq3s_grid[q3[ 8] | ((qh[1] << 8) & 256)], iq3s_grid[q3[ 9] | ((qh[1] << 7) & 256)], - iq3s_grid[q3[10] | ((qh[1] << 6) & 256)], iq3s_grid[q3[11] | ((qh[1] << 5) & 256)]}; - vector unsigned int aux32x4_3 = {iq3s_grid[q3[12] | ((qh[1] << 4) & 256)], iq3s_grid[q3[13] | ((qh[1] << 3) & 256)], - iq3s_grid[q3[14] | ((qh[1] << 2) & 256)], iq3s_grid[q3[15] | ((qh[1] << 1) & 256)]}; - q3 += 16; - qh += 2; - - vector signed char vsigns01 = (vector signed char)vec_splats(*(const uint32_t *)&signs[0]); - vector signed char vsigns02 = (vector signed char)vec_splats(*(const uint32_t *)&signs[2]); - signs += 4; - - vector signed char vsigns0 = vec_perm(vsigns01, vsigns01, mask0); - vector signed char vsigns1 = vec_perm(vsigns01, vsigns01, mask1); - vector signed char vsigns2 = vec_perm(vsigns02, vsigns02, mask0); - vector signed char vsigns3 = vec_perm(vsigns02, vsigns02, mask1); - - vsigns0 = (vector signed char)vec_cmpeq(vec_and(vsigns0, mask2), mask2); - vsigns1 = (vector signed char)vec_cmpeq(vec_and(vsigns1, mask2), mask2); - vsigns2 = (vector signed char)vec_cmpeq(vec_and(vsigns2, mask2), mask2); - vsigns3 = (vector signed char)vec_cmpeq(vec_and(vsigns3, mask2), mask2); - - vector signed char q3x0 = vec_sub(vec_xor(vsigns0, (vector signed char)aux32x4_0), vsigns0); - vector signed char q3x1 = vec_sub(vec_xor(vsigns1, (vector signed char)aux32x4_1), vsigns1); - vector signed char q3x2 = vec_sub(vec_xor(vsigns2, (vector signed char)aux32x4_2), vsigns2); - vector signed char q3x3 = vec_sub(vec_xor(vsigns3, (vector signed char)aux32x4_3), vsigns3); - - vector signed char q8y0 = vec_xl( 0, q8); - vector signed char q8y1 = vec_xl(16, q8); - vector signed char q8y2 = vec_xl(32, q8); - vector signed char q8y3 = vec_xl(48, q8); - q8 += 64; - - vector signed short qv0 = vec_add(vec_mule(q3x0, q8y0), vec_mulo(q3x0, q8y0)); - vector signed short qv1 = vec_add(vec_mule(q3x1, q8y1), vec_mulo(q3x1, q8y1)); - vector signed short qv2 = vec_add(vec_mule(q3x2, q8y2), vec_mulo(q3x2, q8y2)); - vector signed short qv3 = vec_add(vec_mule(q3x3, q8y3), vec_mulo(q3x3, q8y3)); - - const uint16_t ls0 = (uint16_t)(sc[0] & 0xf); - const uint16_t ls1 = (uint16_t)(sc[0] >> 4); - sc ++; - - vector signed short vscales01 = (vector signed short)vec_splats((uint16_t)(2*ls0+1)); - vector signed short vscales23 = (vector signed short)vec_splats((uint16_t)(2*ls1+1)); - - vsumi0 = vec_msum(qv0, vscales01, vsumi0); - vsumi1 = vec_msum(qv1, vscales01, vsumi1); - vsumi2 = vec_msum(qv2, vscales23, vsumi2); - vsumi3 = vec_msum(qv3, vscales23, vsumi3); - } - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined(__loongarch_asx) - - static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, - 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 - }; - - static const uint8_t k_mask2[32] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, - }; - - const __m256i mask1 = __lasx_xvld((const __m256i*)k_mask1, 0); - const __m256i mask2 = __lasx_xvld((const __m256i*)k_mask2, 0); - - __m256i idx_shift = lasx_set_w(1, 2, 3, 4, 5, 6, 7, 8); - const __m256i idx_mask = __lasx_xvreplgr2vr_w(256); - - typedef union { - __m256i vec[2]; - uint32_t index[16]; - } index_t; - - index_t idx; - - __m256 accumf = (__m256)__lasx_xvldi(0); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict qs = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const uint16_t * restrict signs = (const uint16_t *)x[i].signs; - const int8_t * restrict q8 = y[i].qs; - __m256i sumi1 = __lasx_xvldi(0); - __m256i sumi2 = __lasx_xvldi(0); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - const __m256i idx_l = lasx_extu8_16(__lsx_vld(qs, 0)); qs += 16; - idx.vec[0] = __lasx_xvreplgr2vr_w(qh[ib32+0]); - idx.vec[1] = __lasx_xvreplgr2vr_w(qh[ib32+1]); - idx.vec[0] = __lasx_xvand_v(__lasx_xvsll_w(idx.vec[0], idx_shift), idx_mask); - idx.vec[1] = __lasx_xvand_v(__lasx_xvsll_w(idx.vec[1], idx_shift), idx_mask); - idx.vec[0] = __lasx_xvor_v(idx.vec[0], lasx_ext16_32(lasx_extracti128(idx_l, 0))); - idx.vec[1] = __lasx_xvor_v(idx.vec[1], lasx_ext16_32(lasx_extracti128(idx_l, 1))); - - // At leat on my CPU (Ryzen 7950X), using _mm256_i32gather_epi32 is slower than _mm256_set_epi32. Strange. - //const __m256i q2_1 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[0], 4); - //const __m256i q2_2 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[1], 4); - const __m256i q2_1 = lasx_set_w( - iq3s_grid[idx.index[7]], iq3s_grid[idx.index[6]], iq3s_grid[idx.index[5]], iq3s_grid[idx.index[4]], - iq3s_grid[idx.index[3]], iq3s_grid[idx.index[2]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]] - ); - const __m256i q2_2 = lasx_set_w( - iq3s_grid[idx.index[15]], iq3s_grid[idx.index[14]], iq3s_grid[idx.index[13]], iq3s_grid[idx.index[12]], - iq3s_grid[idx.index[11]], iq3s_grid[idx.index[10]], iq3s_grid[idx.index[ 9]], iq3s_grid[idx.index[ 8]] - ); - - __m256i aux256 = __lasx_xvreplgr2vr_w(signs[0] | (signs[1] << 16)); - aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2); - const __m256i s2_1 = __lasx_xvseq_b(aux256, mask2); - const __m256i q8s_1 = __lasx_xvsub_b(__lasx_xvxor_v(s2_1, q8_1), s2_1); - - aux256 = __lasx_xvreplgr2vr_w(signs[2] | (signs[3] << 16)); - aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2); - const __m256i s2_2 = __lasx_xvseq_b(aux256, mask2); - const __m256i q8s_2 = __lasx_xvsub_b(__lasx_xvxor_v(s2_2, q8_2), s2_2); - - signs += 4; - - const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); - const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); - const uint16_t ls1 = x[i].scales[ib32/2] & 0xf; - const uint16_t ls2 = x[i].scales[ib32/2] >> 4; - const __m256i p1 = lasx_madd_h(dot1, __lasx_xvreplgr2vr_h(2*ls1+1)); - const __m256i p2 = lasx_madd_h(dot2, __lasx_xvreplgr2vr_h(2*ls2+1)); - sumi1 = __lasx_xvadd_w(sumi1, p1); - sumi2 = __lasx_xvadd_w(sumi2, p2); - } - - accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); - } - - *s = hsum_float_8(accumf); - -#else - - float sumf = 0.f; - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const uint8_t * restrict qs = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const uint8_t * restrict signs = x[i].signs; - const int8_t * restrict q8 = y[i].qs; - int32_t bsum = 0; - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { - const uint32_t ls1 = 2*(x[i].scales[ib32/2] & 0xf) + 1; - const uint32_t ls2 = 2*(x[i].scales[ib32/2] >> 4) + 1; - int32_t sumi = 0; - for (int l = 0; l < 4; ++l) { - const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+0] << (8-2*l)) & 256))); - const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+0] << (7-2*l)) & 256))); - for (int j = 0; j < 4; ++j) { - sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1); - sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1); - } - q8 += 8; - } - qs += 8; - signs += 4; - bsum += sumi * ls1; - sumi = 0; - for (int l = 0; l < 4; ++l) { - const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+1] << (8-2*l)) & 256))); - const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+1] << (7-2*l)) & 256))); - for (int j = 0; j < 4; ++j) { - sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1); - sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1); - } - q8 += 8; - } - qs += 8; - signs += 4; - bsum += sumi * ls2; - } - sumf += d * bsum; - } - *s = sumf; -#endif -} - -#if defined(__AVX2__) -static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) { - const __m256i ax = _mm256_sign_epi8(x, x); - const __m256i sy = _mm256_sign_epi8(y, x); - return _mm256_maddubs_epi16(ax, sy); -} -#elif defined(__loongarch_asx) -static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) { - const __m256i ax = __lasx_xvsigncov_b(x, x); - const __m256i sy = __lasx_xvsigncov_b(x, y); - __m256i tmp1, tmp2, tmp3; - tmp1 = __lasx_xvmulwev_h_bu_b(ax, sy); - tmp2 = __lasx_xvmulwod_h_bu_b(ax, sy); - tmp3 = __lasx_xvadd_h(tmp1, tmp2); - return __lasx_xvsat_h(tmp3, 15); -} -#endif - -void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_iq1_s * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#if defined __ARM_NEON - - ggml_int8x16x4_t q1b; - ggml_int8x16x4_t q8b; - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - - const int8_t * q8 = y[i].qs; - const uint8_t * qs = x[i].qs; - const uint16_t * qh = x[i].qh; - - int sumi1 = 0, sumi2 = 0, sumi3 = 0; - - for (int ib = 0; ib < QK_K/32; ib += 2) { - - q1b.val[0] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[0] | ((qh[ib+0] << 8) & 0x700)))), - vld1_s8((const int8_t *)(iq1s_grid + (qs[1] | ((qh[ib+0] << 5) & 0x700))))); - q1b.val[1] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[2] | ((qh[ib+0] << 2) & 0x700)))), - vld1_s8((const int8_t *)(iq1s_grid + (qs[3] | ((qh[ib+0] >> 1) & 0x700))))); - q1b.val[2] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[4] | ((qh[ib+1] << 8) & 0x700)))), - vld1_s8((const int8_t *)(iq1s_grid + (qs[5] | ((qh[ib+1] << 5) & 0x700))))); - q1b.val[3] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[6] | ((qh[ib+1] << 2) & 0x700)))), - vld1_s8((const int8_t *)(iq1s_grid + (qs[7] | ((qh[ib+1] >> 1) & 0x700))))); - qs += 8; - - q8b = ggml_vld1q_s8_x4(q8); q8 += 64; - - const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q1b.val[0], q8b.val[0]), q1b.val[1], q8b.val[1]); - const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q1b.val[2], q8b.val[2]), q1b.val[3], q8b.val[3]); - - const int ls1 = 2*((qh[ib+0] >> 12) & 7) + 1; - const int ls2 = 2*((qh[ib+1] >> 12) & 7) + 1; - sumi1 += vaddvq_s32(p1) * ls1; - sumi2 += vaddvq_s32(p2) * ls2; - sumi3 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * ls1 * (qh[ib+0] & 0x8000 ? -1 : 1) - + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * ls2 * (qh[ib+1] & 0x8000 ? -1 : 1); - - } - - sumf += y[i].d * GGML_FP16_TO_FP32(x[i].d) * (sumi1 + sumi2 + IQ1S_DELTA * sumi3); - } - - *s = sumf; - -#elif defined __AVX2__ - - __m256 accum = _mm256_setzero_ps(); - float accum1 = 0; - for (int i = 0; i < nb; ++i) { - - const int8_t * q8 = y[i].qs; - const uint8_t * qs = x[i].qs; - const uint16_t * qh = x[i].qh; - - __m256i sumi = _mm256_setzero_si256(); - int sumi1 = 0; - for (int ib = 0; ib < QK_K/32; ib += 2) { - const __m256i q1b_1 = _mm256_set_epi64x(iq1s_grid[qs[3] | ((qh[ib+0] >> 1) & 0x700)], iq1s_grid[qs[2] | ((qh[ib+0] << 2) & 0x700)], - iq1s_grid[qs[1] | ((qh[ib+0] << 5) & 0x700)], iq1s_grid[qs[0] | ((qh[ib+0] << 8) & 0x700)]); - const __m256i q1b_2 = _mm256_set_epi64x(iq1s_grid[qs[7] | ((qh[ib+1] >> 1) & 0x700)], iq1s_grid[qs[6] | ((qh[ib+1] << 2) & 0x700)], - iq1s_grid[qs[5] | ((qh[ib+1] << 5) & 0x700)], iq1s_grid[qs[4] | ((qh[ib+1] << 8) & 0x700)]); - qs += 8; - const __m256i q8b_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q8b_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - - const __m256i dot1 = mul_add_epi8(q1b_1, q8b_1); - const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2); - const int16_t ls1 = 2*((qh[ib+0] >> 12) & 7) + 1; - const int16_t ls2 = 2*((qh[ib+1] >> 12) & 7) + 1; - const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(ls1)); - const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(ls2)); - - sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p1, p2)); - sumi1 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * (qh[ib+0] & 0x8000 ? -1 : 1) * ls1 - + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * (qh[ib+1] & 0x8000 ? -1 : 1) * ls2; - } - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - accum = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(sumi), accum); - accum1 += d * sumi1; - - } - - *s = hsum_float_8(accum) + IQ1S_DELTA * accum1; - -#elif defined __AVX__ - __m256 accum = _mm256_setzero_ps(); - float accum1 = 0; - for (int i = 0; i < nb; ++i) { - - const int8_t * q8 = y[i].qs; - const uint8_t * qs = x[i].qs; - const uint16_t * qh = x[i].qh; - - __m128i sumi1_0 = _mm_setzero_si128(); - __m128i sumi1_1 = _mm_setzero_si128(); - int sumi1 = 0; - for (int ib = 0; ib < QK_K/32; ib += 2) { - const __m128i q1b_1_0 = _mm_set_epi64x(iq1s_grid[qs[1] | ((qh[ib+0] << 5) & 0x700)], iq1s_grid[qs[0] | ((qh[ib+0] << 8) & 0x700)]); - const __m128i q1b_1_1 = _mm_set_epi64x(iq1s_grid[qs[3] | ((qh[ib+0] >> 1) & 0x700)], iq1s_grid[qs[2] | ((qh[ib+0] << 2) & 0x700)]); - const __m128i q1b_2_0 = _mm_set_epi64x(iq1s_grid[qs[5] | ((qh[ib+1] << 5) & 0x700)], iq1s_grid[qs[4] | ((qh[ib+1] << 8) & 0x700)]); - const __m128i q1b_2_1 = _mm_set_epi64x(iq1s_grid[qs[7] | ((qh[ib+1] >> 1) & 0x700)], iq1s_grid[qs[6] | ((qh[ib+1] << 2) & 0x700)]); - qs += 8; - const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - - const __m128i dot1_0 = mul_add_epi8_sse(q1b_1_0, q8b_1_0); - const __m128i dot1_1 = mul_add_epi8_sse(q1b_1_1, q8b_1_1); - const __m128i dot2_0 = mul_add_epi8_sse(q1b_2_0, q8b_2_0); - const __m128i dot2_1 = mul_add_epi8_sse(q1b_2_1, q8b_2_1); - const int16_t ls1 = 2*((qh[ib+0] >> 12) & 7) + 1; - const int16_t ls2 = 2*((qh[ib+1] >> 12) & 7) + 1; - const __m128i p1_0 = _mm_madd_epi16(dot1_0, _mm_set1_epi16(ls1)); - const __m128i p1_1 = _mm_madd_epi16(dot1_1, _mm_set1_epi16(ls1)); - const __m128i p2_0 = _mm_madd_epi16(dot2_0, _mm_set1_epi16(ls2)); - const __m128i p2_1 = _mm_madd_epi16(dot2_1, _mm_set1_epi16(ls2)); - - sumi1_0 = _mm_add_epi32(sumi1_0, _mm_add_epi32(p1_0, p2_0)); - sumi1_1 = _mm_add_epi32(sumi1_1, _mm_add_epi32(p1_1, p2_1)); - sumi1 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * (qh[ib+0] & 0x8000 ? -1 : 1) * ls1 - + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * (qh[ib+1] & 0x8000 ? -1 : 1) * ls2; - } - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - accum = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(sumi1_1, sumi1_0))), accum); - accum1 += d * sumi1; - - } - - *s = hsum_float_8(accum) + IQ1S_DELTA * accum1; - -#elif defined(__POWER9_VECTOR__) - const vector unsigned char v0 = vec_splats((unsigned char)0x0); - const vector unsigned short vsign = vec_splats((unsigned short)0x8000); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - - for (int i = 0; i < nb; ++i) { - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - vector signed int vsumi0 = vec_splats((int32_t)0); - vector signed int vsumi1 = vec_splats((int32_t)0); - vector signed int vsumi2 = vec_splats((int32_t)0); - vector signed int vsumi3 = vec_splats((int32_t)0); - vector signed int vsumi8 = vec_splats((int32_t)0); - - const uint8_t * restrict q1 = x[i].qs; - const uint16_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - const int16_t * restrict qs = y[i].bsums; - - for (int j = 0; j < QK_K/32; j += 2) { - __builtin_prefetch(q1, 0, 1); - __builtin_prefetch(qh, 0, 1); - __builtin_prefetch(q8, 0, 1); - - vector signed long long aux64x2_0 = {*(const int64_t *)(iq1s_grid + (q1[0] | ((qh[0] << 8) & 0x700))), *(const int64_t *)(iq1s_grid + (q1[1] | ((qh[0] << 5) & 0x700)))}; - vector signed long long aux64x2_1 = {*(const int64_t *)(iq1s_grid + (q1[2] | ((qh[0] << 2) & 0x700))), *(const int64_t *)(iq1s_grid + (q1[3] | ((qh[0] >> 1) & 0x700)))}; - vector signed long long aux64x2_2 = {*(const int64_t *)(iq1s_grid + (q1[4] | ((qh[1] << 8) & 0x700))), *(const int64_t *)(iq1s_grid + (q1[5] | ((qh[1] << 5) & 0x700)))}; - vector signed long long aux64x2_3 = {*(const int64_t *)(iq1s_grid + (q1[6] | ((qh[1] << 2) & 0x700))), *(const int64_t *)(iq1s_grid + (q1[7] | ((qh[1] >> 1) & 0x700)))}; - q1 += 8; - - vector signed char q1x0 = (vector signed char)aux64x2_0; - vector signed char q1x1 = (vector signed char)aux64x2_1; - vector signed char q1x2 = (vector signed char)aux64x2_2; - vector signed char q1x3 = (vector signed char)aux64x2_3; - - vector signed char q8y0 = vec_xl( 0, q8); - vector signed char q8y1 = vec_xl(16, q8); - vector signed char q8y2 = vec_xl(32, q8); - vector signed char q8y3 = vec_xl(48, q8); - q8 += 64; - - vector signed short qv0 = vec_add(vec_mule(q1x0, q8y0), vec_mulo(q1x0, q8y0)); - vector signed short qv1 = vec_add(vec_mule(q1x1, q8y1), vec_mulo(q1x1, q8y1)); - vector signed short qv2 = vec_add(vec_mule(q1x2, q8y2), vec_mulo(q1x2, q8y2)); - vector signed short qv3 = vec_add(vec_mule(q1x3, q8y3), vec_mulo(q1x3, q8y3)); - - const uint16_t ls0 = (uint16_t)((qh[0] >> 12) & 7); - const uint16_t ls1 = (uint16_t)((qh[1] >> 12) & 7); - - vector signed short vscales01 = (vector signed short)vec_splats((uint16_t)(2*ls0+1)); - vector signed short vscales23 = (vector signed short)vec_splats((uint16_t)(2*ls1+1)); - vector signed short vscales = vec_sld(vscales23, vscales01, 8); - - vsumi0 = vec_msum(qv0, vscales01, vsumi0); - vsumi1 = vec_msum(qv1, vscales01, vsumi1); - vsumi2 = vec_msum(qv2, vscales23, vsumi2); - vsumi3 = vec_msum(qv3, vscales23, vsumi3); - - vector signed short q8ysums = vec_xl_len(qs, 8); - qs += 4; - q8ysums = vec_mergeh(q8ysums, (vector signed short)v0); - - vector signed short qxh = (vector signed short)vec_sld(vec_splats(qh[1]), vec_splats(qh[0]), 8); - qh += 2; - vector __bool short vsel = vec_cmpge(qxh, (vector signed short)v0); - - vector signed short q8ysum = vec_sel((vector signed short)vec_xor((vector unsigned short)q8ysums, vsign), q8ysums, vsel); - - vsumi8 = vec_add(vec_mule(q8ysum, vscales), vsumi8); - } - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - - vsumf0 = vec_madd(vec_ctf(vsumi8, 0), vec_mul(vd, vec_splats(IQ1S_DELTA)), vsumf0); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined(__loongarch_asx) - - __m256 accum = (__m256)__lasx_xvldi(0); - float accum1 = 0; - for (int i = 0; i < nb; ++i) { - - const int8_t * q8 = y[i].qs; - const uint8_t * qs = x[i].qs; - const uint16_t * qh = x[i].qh; - - __m256i sumi = __lasx_xvldi(0); - int sumi1 = 0; - for (int ib = 0; ib < QK_K/32; ib += 2) { - __m256i q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[0] | ((qh[ib+0] << 8) & 0x700)], 0); - q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[1] | ((qh[ib+0] << 5) & 0x700)], 1); - q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[2] | ((qh[ib+0] << 2) & 0x700)], 2); - q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[3] | ((qh[ib+0] >> 1) & 0x700)], 3); - - __m256i q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[4] | ((qh[ib+1] << 8) & 0x700)], 0); - q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[5] | ((qh[ib+1] << 5) & 0x700)], 1); - q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[6] | ((qh[ib+1] << 2) & 0x700)], 2); - q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[7] | ((qh[ib+1] >> 1) & 0x700)], 3); - - qs += 8; - const __m256i q8b_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - const __m256i q8b_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; - - const __m256i dot1 = mul_add_epi8(q1b_1, q8b_1); - const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2); - const int16_t ls1 = 2*((qh[ib+0] >> 12) & 7) + 1; - const int16_t ls2 = 2*((qh[ib+1] >> 12) & 7) + 1; - - __m256i tmp1, tmp5, tmp6; - tmp1 = __lasx_xvreplgr2vr_h(ls1); - tmp5 = __lasx_xvmulwev_w_h(dot1, tmp1); - tmp6 = __lasx_xvmulwod_w_h(dot1, tmp1); - const __m256i p1 = __lasx_xvadd_w(tmp5, tmp6); - - tmp1 = __lasx_xvreplgr2vr_h(ls2); - tmp5 = __lasx_xvmulwev_w_h(dot2, tmp1); - tmp6 = __lasx_xvmulwod_w_h(dot2, tmp1); - const __m256i p2 = __lasx_xvadd_w(tmp5, tmp6); - - sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p1, p2)); - sumi1 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * (qh[ib+0] & 0x8000 ? -1 : 1) * ls1 - + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * (qh[ib+1] & 0x8000 ? -1 : 1) * ls2; - } - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - accum = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), accum); - accum1 += d * sumi1; - } - - *s = hsum_float_8(accum) + IQ1S_DELTA * accum1; - -#else - - float sumf = 0; - for (int i = 0; i < nb; i++) { - - const int8_t * q8 = y[i].qs; - const uint8_t * qs = x[i].qs; - const uint16_t * qh = x[i].qh; - - int sumi = 0, sumi1 = 0; - for (int ib = 0; ib < QK_K/32; ++ib) { - const int ls = 2*((qh[ib] >> 12) & 7) + 1; - const int delta = qh[ib] & 0x8000 ? -1 : 1; - int lsum = 0; - for (int l = 0; l < 4; ++l) { - const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((qh[ib] >> 3*l) & 7) << 8))); - for (int j = 0; j < 8; ++j) { - lsum += q8[j] * grid[j]; - } - q8 += 8; - } - sumi += ls * lsum; - sumi1 += ls * delta * (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]); - qs += 4; - } - - sumf += GGML_FP16_TO_FP32(x[i].d) * y[i].d * (sumi + IQ1S_DELTA * sumi1); - } - - *s = sumf; - -#endif -} - -void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_iq1_m * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - - iq1m_scale_t scale; - -#if defined __ARM_NEON - const int32x4_t mask = vdupq_n_s32(0x7); - const int32x4_t mone = vdupq_n_s32(1); - const int32x4_t mzero = vdupq_n_s32(0); - - ggml_int8x16x4_t deltas; - deltas.val[0] = vcombine_s8(vdup_n_s8(+1), vdup_n_s8(+1)); - deltas.val[1] = vcombine_s8(vdup_n_s8(-1), vdup_n_s8(+1)); - deltas.val[2] = vcombine_s8(vdup_n_s8(+1), vdup_n_s8(-1)); - deltas.val[3] = vcombine_s8(vdup_n_s8(-1), vdup_n_s8(-1)); - - ggml_int8x16x4_t q1b; - ggml_int8x16x4_t q8b; - - uint32_t aux32; - const uint8_t * aux8 = (const uint8_t *)&aux32; - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - - const int8_t * q8 = y[i].qs; - const uint8_t * qs = x[i].qs; - const uint8_t * qh = x[i].qh; - const uint16_t * sc = (const uint16_t *)x[i].scales; - - scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); - - int32x4_t sumi1 = mzero; - int32x4_t sumi2 = mzero; - - for (int ib = 0; ib < QK_K/32; ib += 2) { - - q1b.val[0] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[0] | ((qh[0] << 8) & 0x700)))), - vld1_s8((const int8_t *)(iq1s_grid + (qs[1] | ((qh[0] << 4) & 0x700))))); - q1b.val[1] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[2] | ((qh[1] << 8) & 0x700)))), - vld1_s8((const int8_t *)(iq1s_grid + (qs[3] | ((qh[1] << 4) & 0x700))))); - q1b.val[2] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[4] | ((qh[2] << 8) & 0x700)))), - vld1_s8((const int8_t *)(iq1s_grid + (qs[5] | ((qh[2] << 4) & 0x700))))); - q1b.val[3] = vcombine_s8(vld1_s8((const int8_t *)(iq1s_grid + (qs[6] | ((qh[3] << 8) & 0x700)))), - vld1_s8((const int8_t *)(iq1s_grid + (qs[7] | ((qh[3] << 4) & 0x700))))); - - q8b = ggml_vld1q_s8_x4(q8); q8 += 64; - - const int32x4_t p1 = vpaddq_s32(ggml_vdotq_s32(mzero, q1b.val[0], q8b.val[0]), ggml_vdotq_s32(mzero, q1b.val[1], q8b.val[1])); - const int32x4_t p2 = vpaddq_s32(ggml_vdotq_s32(mzero, q1b.val[2], q8b.val[2]), ggml_vdotq_s32(mzero, q1b.val[3], q8b.val[3])); - const int32x4_t p12 = vpaddq_s32(p1, p2); - - const uint32_t * qh32 = (const uint32_t *)qh; // we are 4-byte aligned, so we can do that - aux32 = ((qh32[0] >> 3) & 0x01010101) | ((qh32[0] >> 6) & 0x02020202); - - const int32x4_t p3 = vpaddq_s32(ggml_vdotq_s32(mzero, deltas.val[aux8[0]], q8b.val[0]), ggml_vdotq_s32(mzero, deltas.val[aux8[1]], q8b.val[1])); - const int32x4_t p4 = vpaddq_s32(ggml_vdotq_s32(mzero, deltas.val[aux8[2]], q8b.val[2]), ggml_vdotq_s32(mzero, deltas.val[aux8[3]], q8b.val[3])); - const int32x4_t p34 = vpaddq_s32(p3, p4); - - int32x4_t scales_4 = ggml_vld1q_u32(sc[ib/2] >> 0, sc[ib/2] >> 3, sc[ib/2] >> 6, sc[ib/2] >> 9); - - scales_4 = vaddq_s32(vshlq_n_s32(vandq_s32(scales_4, mask), 1), mone); - - sumi1 = vmlaq_s32(sumi1, scales_4, p12); - sumi2 = vmlaq_s32(sumi2, scales_4, p34); - - qs += 8; qh += 4; - - } - - sumf += y[i].d * GGML_FP16_TO_FP32(scale.f16) * (vaddvq_s32(sumi1) + IQ1M_DELTA * vaddvq_s32(sumi2)); - } - - *s = sumf; - -#elif defined __AVX2__ - - const __m256i mask = _mm256_set1_epi16(0x7); - const __m256i mone = _mm256_set1_epi16(1); - - __m256 accum1 = _mm256_setzero_ps(); - __m256 accum2 = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - - const int8_t * q8 = y[i].qs; - const uint8_t * qs = x[i].qs; - const uint8_t * qh = x[i].qh; - const uint16_t * sc = (const uint16_t *)x[i].scales; - - scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); - - __m256i sumi1 = _mm256_setzero_si256(); - __m256i sumi2 = _mm256_setzero_si256(); - for (int ib = 0; ib < QK_K/32; ib += 2) { - const __m256i q1b_1 = _mm256_set_epi64x( - iq1s_grid[qs[3] | (((uint16_t)qh[1] << 4) & 0x700)], iq1s_grid[qs[2] | (((uint16_t)qh[1] << 8) & 0x700)], - iq1s_grid[qs[1] | (((uint16_t)qh[0] << 4) & 0x700)], iq1s_grid[qs[0] | (((uint16_t)qh[0] << 8) & 0x700)] - ); - const __m256i q1b_2 = _mm256_set_epi64x( - iq1s_grid[qs[7] | (((uint16_t)qh[3] << 4) & 0x700)], iq1s_grid[qs[6] | (((uint16_t)qh[3] << 8) & 0x700)], - iq1s_grid[qs[5] | (((uint16_t)qh[2] << 4) & 0x700)], iq1s_grid[qs[4] | (((uint16_t)qh[2] << 8) & 0x700)] - ); - const __m256i q8b_1 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - const __m256i q8b_2 = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; - - const __m256i dot1 = mul_add_epi8(q1b_1, q8b_1); - const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2); - - const __m256i delta1 = _mm256_set_epi64x(qh[1] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, - qh[1] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101, - qh[0] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, - qh[0] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101); - const __m256i delta2 = _mm256_set_epi64x(qh[3] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, - qh[3] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101, - qh[2] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, - qh[2] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101); - - const __m256i dot3 = mul_add_epi8(delta1, q8b_1); - const __m256i dot4 = mul_add_epi8(delta2, q8b_2); - - __m256i scale1 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 3), _mm_set1_epi16(sc[ib/2] >> 0)); - __m256i scale2 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 9), _mm_set1_epi16(sc[ib/2] >> 6)); - - scale1 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale1, mask), 1), mone); - scale2 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale2, mask), 1), mone); - const __m256i p1 = _mm256_madd_epi16(dot1, scale1); - const __m256i p2 = _mm256_madd_epi16(dot2, scale2); - const __m256i p3 = _mm256_madd_epi16(dot3, scale1); - const __m256i p4 = _mm256_madd_epi16(dot4, scale2); - - sumi1 = _mm256_add_epi32(sumi1, _mm256_add_epi32(p1, p2)); - sumi2 = _mm256_add_epi32(sumi2, _mm256_add_epi32(p3, p4)); - - qs += 8; qh += 4; - } - - const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(scale.f16)); - - accum1 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi1), accum1); - accum2 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi2), accum2); - } - - *s = hsum_float_8(accum1) + IQ1M_DELTA * hsum_float_8(accum2); - -#elif defined __AVX__ - const __m128i mask = _mm_set1_epi16(0x7); - const __m128i mone = _mm_set1_epi16(1); - - __m256 accum1 = _mm256_setzero_ps(); - __m256 accum2 = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - - const int8_t * q8 = y[i].qs; - const uint8_t * qs = x[i].qs; - const uint8_t * qh = x[i].qh; - const uint16_t * sc = (const uint16_t *)x[i].scales; - - scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); - - __m128i sumi1_0 = _mm_setzero_si128(); - __m128i sumi1_1 = _mm_setzero_si128(); - __m128i sumi2_0 = _mm_setzero_si128(); - __m128i sumi2_1 = _mm_setzero_si128(); - for (int ib = 0; ib < QK_K/32; ib += 2) { - const __m128i q1b_1_0 = _mm_set_epi64x( - iq1s_grid[qs[1] | (((uint16_t)qh[0] << 4) & 0x700)], iq1s_grid[qs[0] | (((uint16_t)qh[0] << 8) & 0x700)]); - const __m128i q1b_1_1 = _mm_set_epi64x( - iq1s_grid[qs[3] | (((uint16_t)qh[1] << 4) & 0x700)], iq1s_grid[qs[2] | (((uint16_t)qh[1] << 8) & 0x700)]); - const __m128i q1b_2_0 = _mm_set_epi64x( - iq1s_grid[qs[5] | (((uint16_t)qh[2] << 4) & 0x700)], iq1s_grid[qs[4] | (((uint16_t)qh[2] << 8) & 0x700)]); - const __m128i q1b_2_1 = _mm_set_epi64x( - iq1s_grid[qs[7] | (((uint16_t)qh[3] << 4) & 0x700)], iq1s_grid[qs[6] | (((uint16_t)qh[3] << 8) & 0x700)]); - const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - - const __m128i dot1_0 = mul_add_epi8_sse(q1b_1_0, q8b_1_0); - const __m128i dot1_1 = mul_add_epi8_sse(q1b_1_1, q8b_1_1); - const __m128i dot2_0 = mul_add_epi8_sse(q1b_2_0, q8b_2_0); - const __m128i dot2_1 = mul_add_epi8_sse(q1b_2_1, q8b_2_1); - - const __m128i delta1_0 = _mm_set_epi64x(qh[0] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, - qh[0] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101); - const __m128i delta1_1 = _mm_set_epi64x(qh[1] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, - qh[1] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101); - const __m128i delta2_0 = _mm_set_epi64x(qh[2] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, - qh[2] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101); - const __m128i delta2_1 = _mm_set_epi64x(qh[3] & 0x80 ? 0xffffffffffffffff : 0x0101010101010101, - qh[3] & 0x08 ? 0xffffffffffffffff : 0x0101010101010101); - - const __m128i dot3_0 = mul_add_epi8_sse(delta1_0, q8b_1_0); - const __m128i dot3_1 = mul_add_epi8_sse(delta1_1, q8b_1_1); - const __m128i dot4_0 = mul_add_epi8_sse(delta2_0, q8b_2_0); - const __m128i dot4_1 = mul_add_epi8_sse(delta2_1, q8b_2_1); - - __m128i scale1_0 = _mm_set1_epi16(sc[ib/2] >> 0); - __m128i scale1_1 = _mm_set1_epi16(sc[ib/2] >> 3); - __m128i scale2_0 = _mm_set1_epi16(sc[ib/2] >> 6); - __m128i scale2_1 = _mm_set1_epi16(sc[ib/2] >> 9); - - scale1_0 = _mm_add_epi16(_mm_slli_epi16(_mm_and_si128(scale1_0, mask), 1), mone); - scale1_1 = _mm_add_epi16(_mm_slli_epi16(_mm_and_si128(scale1_1, mask), 1), mone); - scale2_0 = _mm_add_epi16(_mm_slli_epi16(_mm_and_si128(scale2_0, mask), 1), mone); - scale2_1 = _mm_add_epi16(_mm_slli_epi16(_mm_and_si128(scale2_1, mask), 1), mone); - const __m128i p1_0 = _mm_madd_epi16(dot1_0, scale1_0); - const __m128i p1_1 = _mm_madd_epi16(dot1_1, scale1_1); - const __m128i p2_0 = _mm_madd_epi16(dot2_0, scale2_0); - const __m128i p2_1 = _mm_madd_epi16(dot2_1, scale2_1); - const __m128i p3_0 = _mm_madd_epi16(dot3_0, scale1_0); - const __m128i p3_1 = _mm_madd_epi16(dot3_1, scale1_1); - const __m128i p4_0 = _mm_madd_epi16(dot4_0, scale2_0); - const __m128i p4_1 = _mm_madd_epi16(dot4_1, scale2_1); - - sumi1_0 = _mm_add_epi32(sumi1_0, _mm_add_epi32(p1_0, p2_0)); - sumi1_1 = _mm_add_epi32(sumi1_1, _mm_add_epi32(p1_1, p2_1)); - sumi2_0 = _mm_add_epi32(sumi2_0, _mm_add_epi32(p3_0, p4_0)); - sumi2_1 = _mm_add_epi32(sumi2_1, _mm_add_epi32(p3_1, p4_1)); - - qs += 8; qh += 4; - } - - const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(scale.f16)); - - accum1 = _mm256_add_ps(_mm256_mul_ps(d, _mm256_cvtepi32_ps(MM256_SET_M128I(sumi1_1, sumi1_0))), accum1); - accum2 = _mm256_add_ps(_mm256_mul_ps(d, _mm256_cvtepi32_ps(MM256_SET_M128I(sumi2_1, sumi2_0))), accum2); - } - - *s = hsum_float_8(accum1) + IQ1M_DELTA * hsum_float_8(accum2); - -#else - - int sum1[2], sum2[2], delta[4]; - - float sumf = 0; - for (int i = 0; i < nb; i++) { - - const int8_t * q8 = y[i].qs; - const uint8_t * qs = x[i].qs; - const uint8_t * qh = x[i].qh; - const uint16_t * sc = (const uint16_t *)x[i].scales; - - scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); - - int sumi1 = 0, sumi2 = 0; - for (int ib = 0; ib < QK_K/32; ++ib) { - delta[0] = qh[0] & 0x08 ? -1 : 1; - delta[1] = qh[0] & 0x80 ? -1 : 1; - delta[2] = qh[1] & 0x08 ? -1 : 1; - delta[3] = qh[1] & 0x80 ? -1 : 1; - sum1[0] = sum1[1] = sum2[0] = sum2[1] = 0; - for (int l = 0; l < 4; ++l) { - const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((uint16_t)qh[l/2] << (8 - 4*(l%2))) & 0x700))); - int lsum1 = 0, lsum2 = 0; - for (int j = 0; j < 8; ++j) { - lsum1 += q8[j] * grid[j]; - lsum2 += q8[j]; - } - q8 += 8; - sum1[l/2] += lsum1; - sum2[l/2] += lsum2*delta[l]; - } - - const int ls1 = 2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1; - const int ls2 = 2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1; - - sumi1 += sum1[0] * ls1 + sum1[1] * ls2; - sumi2 += sum2[0] * ls1 + sum2[1] * ls2; - qs += 4; - qh += 2; - } - - sumf += GGML_FP16_TO_FP32(scale.f16) * y[i].d * (sumi1 + IQ1M_DELTA * sumi2); - } - - *s = sumf; - -#endif -} - -void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - assert(n % QK4_NL == 0); - static_assert(QK4_NL == QK8_0, "QK4_NL and QK8_0 must be the same"); - - const block_iq4_nl * restrict x = vx; - const block_q8_0 * restrict y = vy; - - const int nb = n / QK4_NL; - - int ib = 0; - float sumf = 0; - -#if defined __ARM_NEON - const int8x16_t values = vld1q_s8(kvalues_iq4nl); - const uint8x16_t m4b = vdupq_n_u8(0x0f); - uint8x16x2_t q4bits; - int8x16x4_t q4b; - int8x16x4_t q8b; - int32x4_t prod_1, prod_2; - - for (; ib + 1 < nb; ib += 2) { - - q4bits.val[0] = vld1q_u8(x[ib + 0].qs); - q4bits.val[1] = vld1q_u8(x[ib + 1].qs); - q8b.val[0] = vld1q_s8(y[ib + 0].qs); - q8b.val[1] = vld1q_s8(y[ib + 0].qs + 16); - q8b.val[2] = vld1q_s8(y[ib + 1].qs); - q8b.val[3] = vld1q_s8(y[ib + 1].qs + 16); - - q4b.val[0] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[0], m4b)); - q4b.val[1] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4)); - q4b.val[2] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[1], m4b)); - q4b.val[3] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[1], 4)); - - prod_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[0], q8b.val[0]), q4b.val[1], q8b.val[1]); - prod_2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[2], q8b.val[2]), q4b.val[3], q8b.val[3]); - - sumf += - GGML_FP16_TO_FP32(x[ib+0].d) * GGML_FP16_TO_FP32(y[ib + 0].d) * vaddvq_s32(prod_1) + - GGML_FP16_TO_FP32(x[ib+1].d) * GGML_FP16_TO_FP32(y[ib + 1].d) * vaddvq_s32(prod_2); - } - -#elif defined __AVX2__ - - const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl); - const __m128i m4b = _mm_set1_epi8(0x0f); - const __m256i mone = _mm256_set1_epi16(1); - - __m256 accum1 = _mm256_setzero_ps(); - __m256 accum2 = _mm256_setzero_ps(); - for (; ib + 1 < nb; ib += 2) { - const __m128i q4bits_1 = _mm_loadu_si128((const __m128i*)x[ib + 0].qs); - const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)x[ib + 1].qs); - const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)y[ib + 0].qs); - const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)y[ib + 1].qs); - const __m256i q4b_1 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)), - _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b))); - const __m256i q4b_2 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)), - _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b))); - const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); - const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); - const __m256i p_1 = _mm256_madd_epi16(p16_1, mone); - const __m256i p_2 = _mm256_madd_epi16(p16_2, mone); - accum1 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[ib + 0].d)*GGML_FP16_TO_FP32(x[ib + 0].d)), - _mm256_cvtepi32_ps(p_1), accum1); - accum2 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[ib + 1].d)*GGML_FP16_TO_FP32(x[ib + 1].d)), - _mm256_cvtepi32_ps(p_2), accum2); - } - - sumf = hsum_float_8(_mm256_add_ps(accum1, accum2)); - -#elif defined __AVX__ - const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl); - const __m128i m4b = _mm_set1_epi8(0x0f); - const __m128i mone = _mm_set1_epi16(1); - - __m256 accum1 = _mm256_setzero_ps(); - __m256 accum2 = _mm256_setzero_ps(); - for (; ib + 1 < nb; ib += 2) { - const __m128i q4bits_1 = _mm_loadu_si128((const __m128i *)x[ib + 0].qs); - const __m128i q4bits_2 = _mm_loadu_si128((const __m128i *)x[ib + 1].qs); - const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)y[ib + 0].qs); - const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)y[ib + 0].qs + 1); - const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs); - const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs + 1); - - const __m128i q4b_1_0 = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b)); - const __m128i q4b_1_1 = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)); - const __m128i q4b_2_0 = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b)); - const __m128i q4b_2_1 = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)); - const __m128i p16_1_0 = mul_add_epi8_sse(q4b_1_0, q8b_1_0); - const __m128i p16_1_1 = mul_add_epi8_sse(q4b_1_1, q8b_1_1); - const __m128i p16_2_0 = mul_add_epi8_sse(q4b_2_0, q8b_2_0); - const __m128i p16_2_1 = mul_add_epi8_sse(q4b_2_1, q8b_2_1); - const __m128i p_1_0 = _mm_madd_epi16(p16_1_0, mone); - const __m128i p_1_1 = _mm_madd_epi16(p16_1_1, mone); - const __m128i p_2_0 = _mm_madd_epi16(p16_2_0, mone); - const __m128i p_2_1 = _mm_madd_epi16(p16_2_1, mone); - accum1 = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[ib + 0].d)*GGML_FP16_TO_FP32(x[ib + 0].d)), - _mm256_cvtepi32_ps(MM256_SET_M128I(p_1_1, p_1_0))), accum1); - accum2 = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[ib + 1].d)*GGML_FP16_TO_FP32(x[ib + 1].d)), - _mm256_cvtepi32_ps(MM256_SET_M128I(p_2_1, p_2_0))), accum2); - } - - sumf = hsum_float_8(_mm256_add_ps(accum1, accum2)); - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0xF); - const vector signed int v0 = vec_splats((int32_t)0); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - - const vector signed char values = vec_xl( 0, kvalues_iq4nl); - -#pragma GCC unroll 4 - for (; ib < nb; ++ib) { - __builtin_prefetch(x[ib].qs, 0, 1); - __builtin_prefetch(y[ib].qs, 0, 1); - - - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d)); - vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d)); - vector float vd = vec_mul(vxd, vyd); - - vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs); - vector signed char q4x0 = vec_and(qxs, lowMask); - vector signed char q4x1 = vec_sr(qxs, v4); - - q4x0 = vec_perm(values, values, (vector unsigned char)q4x0); - q4x1 = vec_perm(values, values, (vector unsigned char)q4x1); - - vector signed char q8y0 = vec_xl( 0, y[ib].qs); - vector signed char q8y1 = vec_xl(16, y[ib].qs); - - vector signed short qv0 = vec_add(vec_mule(q4x0, q8y0), vec_mulo(q4x0, q8y0)); - vector signed short qv1 = vec_add(vec_mule(q4x1, q8y1), vec_mulo(q4x1, q8y1)); - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - - vsumi0 = vec_sum4s(qv0, vsumi0); - vsumi1 = vec_sum4s(qv1, vsumi1); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - } - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - sumf = vec_extract(vsumf0, 0); - -#elif defined (__loongarch_asx) - - const __m128i values128 = __lsx_vld((const __m128i*)kvalues_iq4nl, 0); - const __m128i m4b = __lsx_vreplgr2vr_b(0x0f); - const __m256i mone = __lasx_xvreplgr2vr_h(1); - - __m256 accum1 = (__m256)__lasx_xvldi(0); - __m256 accum2 = (__m256)__lasx_xvldi(0); - for (; ib + 1 < nb; ib += 2) { - const __m128i q4bits_1 = __lsx_vld((const __m128i*)x[ib + 0].qs, 0); - const __m128i q4bits_2 = __lsx_vld((const __m128i*)x[ib + 1].qs, 0); - const __m256i q8b_1 = __lasx_xvld((const __m256i *)y[ib + 0].qs, 0); - const __m256i q8b_2 = __lasx_xvld((const __m256i *)y[ib + 1].qs, 0); - const __m256i q4b_1 = lasx_insertf128(lsx_shuffle_b(values128, __lsx_vand_v(__lsx_vsrli_h(q4bits_1, 4), m4b)), - lsx_shuffle_b(values128, __lsx_vand_v(q4bits_1, m4b))); - const __m256i q4b_2 = lasx_insertf128(lsx_shuffle_b(values128, __lsx_vand_v(__lsx_vsrli_h(q4bits_2, 4), m4b)), - lsx_shuffle_b(values128, __lsx_vand_v(q4bits_2, m4b))); - const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); - const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); - const __m256i p_1 = lasx_madd_h(p16_1, mone); - const __m256i p_2 = lasx_madd_h(p16_2, mone); - accum1 = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[ib + 0].d)*GGML_FP16_TO_FP32(x[ib + 0].d)), - __lasx_xvffint_s_w(p_1), accum1); - accum2 = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[ib + 1].d)*GGML_FP16_TO_FP32(x[ib + 1].d)), - __lasx_xvffint_s_w(p_2), accum2); - } - - sumf = hsum_float_8(__lasx_xvfadd_s(accum1, accum2)); - -#endif - for (; ib < nb; ++ib) { - const float d = GGML_FP16_TO_FP32(y[ib].d)*GGML_FP16_TO_FP32(x[ib].d); - int sumi1 = 0, sumi2 = 0; - for (int j = 0; j < QK4_NL/2; ++j) { - sumi1 += y[ib].qs[j+ 0] * kvalues_iq4nl[x[ib].qs[j] & 0xf]; - sumi2 += y[ib].qs[j+QK4_NL/2] * kvalues_iq4nl[x[ib].qs[j] >> 4]; - } - sumf += d * (sumi1 + sumi2); - } - *s = sumf; -} - -void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - assert(n % QK_K == 0); - - const block_iq4_xs * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#if defined __ARM_NEON - const int8x16_t values = vld1q_s8(kvalues_iq4nl); - const uint8x16_t m4b = vdupq_n_u8(0x0f); - ggml_uint8x16x2_t q4bits; - ggml_int8x16x4_t q4b; - ggml_int8x16x4_t q8b; - int32x4_t prod_1, prod_2; - - float sumf = 0; - - for (int ibl = 0; ibl < nb; ++ibl) { - - const int8_t * q8 = y[ibl].qs; - const uint8_t * q4 = x[ibl].qs; - uint16_t h = x[ibl].scales_h; - - int sumi1 = 0, sumi2 = 0; - for (int ib = 0; ib < QK_K/64; ++ib) { - - q4bits = ggml_vld1q_u8_x2(q4); q4 += 32; - q8b = ggml_vld1q_s8_x4(q8); q8 += 64; - - q4b.val[0] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[0], m4b)); - q4b.val[1] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4)); - q4b.val[2] = ggml_vqtbl1q_s8(values, vandq_u8 (q4bits.val[1], m4b)); - q4b.val[3] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[1], 4)); - - prod_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[0], q8b.val[0]), q4b.val[1], q8b.val[1]); - prod_2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[2], q8b.val[2]), q4b.val[3], q8b.val[3]); - - int ls1 = ((x[ibl].scales_l[ib] & 0xf) | ((h << 4) & 0x30)) - 32; - int ls2 = ((x[ibl].scales_l[ib] >> 4) | ((h << 2) & 0x30)) - 32; - h >>= 4; - sumi1 += vaddvq_s32(prod_1) * ls1; - sumi2 += vaddvq_s32(prod_2) * ls2; - - } - - sumf += GGML_FP16_TO_FP32(x[ibl].d) * y[ibl].d * (sumi1 + sumi2); - } - - *s = sumf; - -#elif defined __AVX2__ - - const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl); - const __m128i m4b = _mm_set1_epi8(0x0f); - - __m256 accum = _mm256_setzero_ps(); - for (int ibl = 0; ibl < nb; ++ibl) { - const uint8_t * qs = x[ibl].qs; - const int8_t * q8 = y[ibl].qs; - uint16_t sh = x[ibl].scales_h; - __m256i sumi1 = _mm256_setzero_si256(); - __m256i sumi2 = _mm256_setzero_si256(); - for (int ib = 0; ib < QK_K/32; ib += 2) { - const __m128i q4bits_1 = _mm_loadu_si128((const __m128i*)qs); qs += 16; - const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)qs); qs += 16; - const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m256i q4b_1 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)), - _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b))); - const __m256i q4b_2 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)), - _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b))); - const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); - const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); - const int16_t ls1 = ((x[ibl].scales_l[ib/2] & 0xf) | ((sh << 4) & 0x30)) - 32; - const int16_t ls2 = ((x[ibl].scales_l[ib/2] >> 4) | ((sh << 2) & 0x30)) - 32; - sh >>= 4; - const __m256i p_1 = _mm256_madd_epi16(p16_1, _mm256_set1_epi16(ls1)); - const __m256i p_2 = _mm256_madd_epi16(p16_2, _mm256_set1_epi16(ls2)); - sumi1 = _mm256_add_epi32(p_1, sumi1); - sumi2 = _mm256_add_epi32(p_2, sumi2); - } - accum = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(x[ibl].d)*y[ibl].d), - _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accum); - } - - *s = hsum_float_8(accum); - -#elif defined __AVX__ - const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl); - const __m128i m4b = _mm_set1_epi8(0x0f); - - __m256 accum = _mm256_setzero_ps(); - for (int ibl = 0; ibl < nb; ++ibl) { - const uint8_t * qs = x[ibl].qs; - const int8_t * q8 = y[ibl].qs; - uint16_t sh = x[ibl].scales_h; - __m128i sumi1_0 = _mm_setzero_si128(); - __m128i sumi1_1 = _mm_setzero_si128(); - __m128i sumi2_0 = _mm_setzero_si128(); - __m128i sumi2_1 = _mm_setzero_si128(); - for (int ib = 0; ib < QK_K/32; ib += 2) { - const __m128i q4bits_1 = _mm_loadu_si128((const __m128i *)qs); qs += 16; - const __m128i q4bits_2 = _mm_loadu_si128((const __m128i *)qs); qs += 16; - const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)q8); q8 += 16; - const __m128i q4b_1_0 = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b)); - const __m128i q4b_1_1 = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)); - const __m128i q4b_2_0 = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b)); - const __m128i q4b_2_1 = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)); - const __m128i p16_1_0 = mul_add_epi8_sse(q4b_1_0, q8b_1_0); - const __m128i p16_1_1 = mul_add_epi8_sse(q4b_1_1, q8b_1_1); - const __m128i p16_2_0 = mul_add_epi8_sse(q4b_2_0, q8b_2_0); - const __m128i p16_2_1 = mul_add_epi8_sse(q4b_2_1, q8b_2_1); - const int16_t ls1 = ((x[ibl].scales_l[ib/2] & 0xf) | ((sh << 4) & 0x30)) - 32; - const int16_t ls2 = ((x[ibl].scales_l[ib/2] >> 4) | ((sh << 2) & 0x30)) - 32; - sh >>= 4; - const __m128i p_1_0 = _mm_madd_epi16(p16_1_0, _mm_set1_epi16(ls1)); - const __m128i p_1_1 = _mm_madd_epi16(p16_1_1, _mm_set1_epi16(ls1)); - const __m128i p_2_0 = _mm_madd_epi16(p16_2_0, _mm_set1_epi16(ls2)); - const __m128i p_2_1 = _mm_madd_epi16(p16_2_1, _mm_set1_epi16(ls2)); - sumi1_0 = _mm_add_epi32(p_1_0, sumi1_0); - sumi1_1 = _mm_add_epi32(p_1_1, sumi1_1); - sumi2_0 = _mm_add_epi32(p_2_0, sumi2_0); - sumi2_1 = _mm_add_epi32(p_2_1, sumi2_1); - } - __m128i sumi12_0 = _mm_add_epi32(sumi1_0, sumi2_0); - __m128i sumi12_1 = _mm_add_epi32(sumi1_1, sumi2_1); - accum = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(x[ibl].d)*y[ibl].d), - _mm256_cvtepi32_ps(MM256_SET_M128I(sumi12_1, sumi12_0))), accum); - } - - *s = hsum_float_8(accum); - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0xF); - const vector int v0 = vec_splats((int32_t)0); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - - const vector signed char values = vec_xl( 0, kvalues_iq4nl); - - for (int ibl = 0; ibl < nb; ++ibl) { - - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ibl].d)); - vector float vyd = vec_splats(y[ibl].d); - vector float vd = vec_mul(vxd, vyd); - - vector signed int vsumi0 = v0; - vector signed int vsumi1 = v0; - vector signed int vsumi2 = v0; - vector signed int vsumi3 = v0; - - uint16_t h = x[ibl].scales_h; - - const uint8_t * restrict q4 = x[ibl].qs; - const uint8_t * restrict sc = x[ibl].scales_l; - const int8_t * restrict q8 = y[ibl].qs; - - for (int ib = 0; ib < QK_K/64; ib ++ ) { - __builtin_prefetch(q4, 0, 1); - __builtin_prefetch(q8, 0, 1); - - vector signed char qxs0 = (vector signed char)vec_xl( 0, q4); - vector signed char qxs1 = (vector signed char)vec_xl(16, q4); - q4 += 32; - - vector signed char q4x00 = (vector signed char)vec_and(qxs0, lowMask); - vector signed char q4x01 = (vector signed char)vec_sr(qxs0, v4); - vector signed char q4x10 = (vector signed char)vec_and(qxs1, lowMask); - vector signed char q4x11 = (vector signed char)vec_sr(qxs1, v4); - - q4x00 = vec_perm(values, values, (vector unsigned char)q4x00); - q4x01 = vec_perm(values, values, (vector unsigned char)q4x01); - q4x10 = vec_perm(values, values, (vector unsigned char)q4x10); - q4x11 = vec_perm(values, values, (vector unsigned char)q4x11); - - vector signed char q8y0 = vec_xl( 0, q8); - vector signed char q8y1 = vec_xl(16, q8); - vector signed char q8y2 = vec_xl(32, q8); - vector signed char q8y3 = vec_xl(48, q8); - q8 += 64; - - vector signed short qv0 = vec_add(vec_mule(q4x00, q8y0), vec_mulo(q4x00, q8y0)); - vector signed short qv1 = vec_add(vec_mule(q4x01, q8y1), vec_mulo(q4x01, q8y1)); - vector signed short qv2 = vec_add(vec_mule(q4x10, q8y2), vec_mulo(q4x10, q8y2)); - vector signed short qv3 = vec_add(vec_mule(q4x11, q8y3), vec_mulo(q4x11, q8y3)); - - const uint16_t ls0 = (uint16_t)(((sc[0] & 0xf) | ((h << 4) & 0x30)) - 32); - const uint16_t ls1 = (uint16_t)(((sc[0] >> 4) | ((h << 2) & 0x30)) - 32); - h >>= 4; - sc ++; - - vector signed short vscales01 = vec_splats((int16_t)ls0); - vector signed short vscales23 = vec_splats((int16_t)ls1); - - vsumi0 = vec_msum(qv0, vscales01, vsumi0); - vsumi1 = vec_msum(qv1, vscales01, vsumi1); - vsumi2 = vec_msum(qv2, vscales23, vsumi2); - vsumi3 = vec_msum(qv3, vscales23, vsumi3); - } - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined(__loongarch_asx) - - const __m128i values128 = __lsx_vld((const __m128i*)kvalues_iq4nl, 0); - const __m128i m4b = __lsx_vreplgr2vr_b(0x0f); - - __m256 accum = (__m256)__lasx_xvldi(0); - __m256i tmp1; - __m128i tmp0, tmp2, tmp3, tmp4, mask_8f, mask; - - mask_8f = __lsx_vreplgr2vr_b(0x8f); - for (int ibl = 0; ibl < nb; ++ibl) { - const uint8_t * qs = x[ibl].qs; - const int8_t * q8 = y[ibl].qs; - uint16_t sh = x[ibl].scales_h; - __m256i sumi1 = __lasx_xvldi(0); - __m256i sumi2 = __lasx_xvldi(0); - __m128i zero = __lsx_vldi(0); - for (int ib = 0; ib < QK_K/32; ib += 2) { - const __m128i q4bits_1 = __lsx_vld((const __m128i*)qs, 0); qs += 16; - const __m128i q4bits_2 = __lsx_vld((const __m128i*)qs, 0); qs += 16; - const __m256i q8b_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - const __m256i q8b_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; - tmp2 = __lsx_vand_v(__lsx_vand_v(__lsx_vsrli_h(q4bits_1, 4), m4b), mask_8f); - tmp0 = __lsx_vori_b(tmp2, 0x10); - mask = __lsx_vsle_b(zero, tmp2); - tmp3 = __lsx_vand_v(tmp0, mask); - tmp3 = __lsx_vshuf_b(values128, zero, tmp3); - - tmp2 = __lsx_vand_v(__lsx_vand_v(q4bits_1, m4b), mask_8f); - tmp0 = __lsx_vori_b(tmp2, 0x10); - mask = __lsx_vsle_b(zero, tmp2); - tmp4 = __lsx_vand_v(tmp0, mask); - tmp4 = __lsx_vshuf_b(values128, zero, tmp4); - - const __m256i q4b_1 = lasx_insertf128(tmp3, tmp4); - - tmp2 = __lsx_vand_v(__lsx_vand_v(__lsx_vsrli_h(q4bits_2, 4), m4b), mask_8f); - tmp0 = __lsx_vori_b(tmp2, 0x10); - mask = __lsx_vsle_b(zero, tmp2); - tmp3 = __lsx_vand_v(tmp0, mask); - tmp3 = __lsx_vshuf_b(values128, zero, tmp3); - - tmp2 = __lsx_vand_v(__lsx_vand_v(q4bits_2, m4b), mask_8f); - tmp0 = __lsx_vori_b(tmp2, 0x10); - mask = __lsx_vsle_b(zero, tmp2); - tmp4 = __lsx_vand_v(tmp0, mask); - tmp4 = __lsx_vshuf_b(values128, zero, tmp4); - - const __m256i q4b_2 = lasx_insertf128(tmp3, tmp4); - - const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); - const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); - const int16_t ls1 = ((x[ibl].scales_l[ib/2] & 0xf) | ((sh << 4) & 0x30)) - 32; - const int16_t ls2 = ((x[ibl].scales_l[ib/2] >> 4) | ((sh << 2) & 0x30)) - 32; - sh >>= 4; - __m256i tmp5, tmp6; - tmp1 = __lasx_xvreplgr2vr_h(ls1); - tmp5 = __lasx_xvmulwev_w_h(p16_1, tmp1); - tmp6 = __lasx_xvmulwod_w_h(p16_1, tmp1); - const __m256i p_1 = __lasx_xvadd_w(tmp5, tmp6); - tmp1 = __lasx_xvreplgr2vr_h(ls2); - tmp5 = __lasx_xvmulwev_w_h(p16_2, tmp1); - tmp6 = __lasx_xvmulwod_w_h(p16_2, tmp1); - const __m256i p_2 = __lasx_xvadd_w(tmp5, tmp6); - sumi1 = __lasx_xvadd_w(p_1, sumi1); - sumi2 = __lasx_xvadd_w(p_2, sumi2); - } - accum = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ibl].d)*y[ibl].d), - __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accum); - } - - *s = hsum_float_8(accum); - -#else - float sumf = 0; - for (int ibl = 0; ibl < nb; ++ibl) { - const float d4d8 = GGML_FP16_TO_FP32(x[ibl].d) * y[ibl].d; - uint16_t h = x[ibl].scales_h; - const uint8_t * qs = x[ibl].qs; - const int8_t * q8 = y[ibl].qs; - for (int ib = 0; ib < QK_K/32; ib += 2) { - const uint8_t ls1 = (x[ibl].scales_l[ib/2] & 0xf) | ((h << 4) & 0x30); - const uint8_t ls2 = (x[ibl].scales_l[ib/2] >> 4) | ((h << 2) & 0x30); - h >>= 4; - const float d1 = d4d8*(ls1 - 32); - const float d2 = d4d8*(ls2 - 32); - int sumi1 = 0, sumi2 = 0; - for (int j = 0; j < 16; ++j) { - sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf]; - sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >> 4]; - } - sumf += d1 * (sumi1 + sumi2); - qs += 16; - q8 += 32; - sumi1 = sumi2 = 0; - for (int j = 0; j < 16; ++j) { - sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf]; - sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >> 4]; - } - sumf += d2 * (sumi1 + sumi2); - qs += 16; - q8 += 32; - } - } - *s = sumf; -#endif -} - // ================================ IQ2 quantization ============================================= typedef struct { @@ -14249,12 +3770,6 @@ size_t quantize_iq3_xxs(const float * restrict src, void * restrict dst, int64_t return nrow * nblock * sizeof(block_iq3_xxs); } -void quantize_row_iq3_xxs(const float * restrict x, void * restrict vy, int64_t k) { - assert(k % QK_K == 0); - block_iq3_xxs * restrict y = vy; - quantize_row_iq3_xxs_ref(x, y, k); -} - void quantize_row_iq3_xxs_ref(const float * restrict x, block_iq3_xxs * restrict y, int64_t k) { assert(k % QK_K == 0); quantize_row_iq3_xxs_impl(256, x, y, k, NULL); @@ -14465,12 +3980,6 @@ size_t quantize_iq3_s(const float * restrict src, void * restrict dst, int64_t n return nrow * nblock * sizeof(block_iq3_s); } -void quantize_row_iq3_s(const float * restrict x, void * restrict vy, int64_t k) { - assert(k % QK_K == 0); - block_iq3_s * restrict y = vy; - quantize_row_iq3_s_ref(x, y, k); -} - void quantize_row_iq3_s_ref(const float * restrict x, block_iq3_s * restrict y, int64_t k) { assert(k % QK_K == 0); quantize_iq3_s(x, y, 1, k, NULL); @@ -15194,7 +4703,8 @@ size_t quantize_iq4_nl(const float * restrict src, void * restrict dst, int64_t return nrow * nblock * sizeof(block_iq4_nl); } -void quantize_row_iq4_nl(const float * restrict x, void * restrict vy, int64_t k) { +//void quantize_row_iq4_nl_ref(const float * restrict x, void * restrict vy, int64_t k) { +void quantize_row_iq4_nl_ref(const float * restrict x, block_iq4_nl * restrict y, int64_t k) { GGML_ASSERT(k%QK4_NL == 0); int64_t nblock = k/QK4_NL; uint8_t L[QK4_NL]; @@ -15202,18 +4712,13 @@ void quantize_row_iq4_nl(const float * restrict x, void * restrict vy, int64_t k uint16_t unused_h; uint8_t * unused_l = NULL; float scale; - block_iq4_nl * iq4 = (block_iq4_nl *)vy; + block_iq4_nl * iq4 = y; for (int ibl = 0; ibl < nblock; ++ibl) { quantize_row_iq4_nl_impl(QK4_NL, 32, x + QK4_NL*ibl, &iq4[ibl].d, iq4[ibl].qs, &unused_h, unused_l, &scale, weight, L, kvalues_iq4nl, NULL, -1); } } -void quantize_row_iq4_nl_ref(const float * restrict x, block_iq4_nl * restrict y, int64_t k) { - assert(k % QK4_NL == 0); - quantize_row_iq4_nl(x, y, k); -} - size_t quantize_iq4_xs(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { GGML_ASSERT(n_per_row%QK_K == 0); int64_t nblock = n_per_row/QK_K; @@ -15234,12 +4739,6 @@ size_t quantize_iq4_xs(const float * restrict src, void * restrict dst, int64_t return nrow * nblock * sizeof(block_iq4_xs); } -void quantize_row_iq4_xs(const float * restrict x, void * restrict vy, int64_t k) { - assert(k % QK_K == 0); - block_iq4_xs * restrict y = vy; - quantize_row_iq4_xs_ref(x, y, k); -} - void quantize_row_iq4_xs_ref(const float * restrict x, block_iq4_xs * restrict y, int64_t k) { assert(k % QK_K == 0); quantize_iq4_xs(x, y, 1, k, NULL); @@ -15432,11 +4931,7 @@ void quantize_row_iq2_s_ref(const float * restrict x, block_iq2_s * restrict y, quantize_iq2_s(x, y, 1, k, NULL); } -void quantize_row_iq2_s(const float * restrict x, void * restrict vy, int64_t k) { - assert(k % QK_K == 0); - block_iq2_s * restrict y = vy; - quantize_row_iq2_s_ref(x, y, k); -} +// =============================== data validation static bool validate_float(float f, size_t i) { if (isinf(f)) { diff --git a/ggml/src/ggml-quants.h b/ggml/src/ggml-quants.h index df9c4b24a..d09173e11 100644 --- a/ggml/src/ggml-quants.h +++ b/ggml/src/ggml-quants.h @@ -11,136 +11,89 @@ extern "C" { #endif +// NOTE: these functions are defined as GGML_API because they used by the CPU backend + // Quantization -void quantize_row_q4_0_ref(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int64_t k); -void quantize_row_q4_1_ref(const float * GGML_RESTRICT x, block_q4_1 * GGML_RESTRICT y, int64_t k); -void quantize_row_q5_0_ref(const float * GGML_RESTRICT x, block_q5_0 * GGML_RESTRICT y, int64_t k); -void quantize_row_q5_1_ref(const float * GGML_RESTRICT x, block_q5_1 * GGML_RESTRICT y, int64_t k); -void quantize_row_q8_0_ref(const float * GGML_RESTRICT x, block_q8_0 * GGML_RESTRICT y, int64_t k); -void quantize_row_q8_1_ref(const float * GGML_RESTRICT x, block_q8_1 * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_q4_0_ref(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_q4_1_ref(const float * GGML_RESTRICT x, block_q4_1 * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_q5_0_ref(const float * GGML_RESTRICT x, block_q5_0 * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_q5_1_ref(const float * GGML_RESTRICT x, block_q5_1 * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_q8_0_ref(const float * GGML_RESTRICT x, block_q8_0 * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_q8_1_ref(const float * GGML_RESTRICT x, block_q8_1 * GGML_RESTRICT y, int64_t k); -void quantize_row_q2_K_ref(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int64_t k); -void quantize_row_q3_K_ref(const float * GGML_RESTRICT x, block_q3_K * GGML_RESTRICT y, int64_t k); -void quantize_row_q4_K_ref(const float * GGML_RESTRICT x, block_q4_K * GGML_RESTRICT y, int64_t k); -void quantize_row_q5_K_ref(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int64_t k); -void quantize_row_q6_K_ref(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int64_t k); -void quantize_row_q8_K_ref(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_q2_K_ref(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_q3_K_ref(const float * GGML_RESTRICT x, block_q3_K * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_q4_K_ref(const float * GGML_RESTRICT x, block_q4_K * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_q5_K_ref(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_q6_K_ref(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_q8_K_ref(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int64_t k); -void quantize_row_tq1_0_ref(const float * GGML_RESTRICT x, block_tq1_0 * GGML_RESTRICT y, int64_t k); -void quantize_row_tq2_0_ref(const float * GGML_RESTRICT x, block_tq2_0 * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_tq1_0_ref(const float * GGML_RESTRICT x, block_tq1_0 * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_tq2_0_ref(const float * GGML_RESTRICT x, block_tq2_0 * GGML_RESTRICT y, int64_t k); -void quantize_row_iq3_xxs_ref(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int64_t k); -void quantize_row_iq4_nl_ref (const float * GGML_RESTRICT x, block_iq4_nl * GGML_RESTRICT y, int64_t k); -void quantize_row_iq4_xs_ref (const float * GGML_RESTRICT x, block_iq4_xs * GGML_RESTRICT y, int64_t k); -void quantize_row_iq3_s_ref (const float * GGML_RESTRICT x, block_iq3_s * GGML_RESTRICT y, int64_t k); -void quantize_row_iq2_s_ref (const float * GGML_RESTRICT x, block_iq2_s * GGML_RESTRICT y, int64_t k); - -void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_q4_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_q5_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_q5_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); - -void quantize_row_q2_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_q3_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_q4_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_q5_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); - -void quantize_row_tq1_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_tq2_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); - -void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_iq4_nl (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_iq4_xs (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_iq3_s (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); -void quantize_row_iq2_s (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_iq3_xxs_ref(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_iq4_nl_ref (const float * GGML_RESTRICT x, block_iq4_nl * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_iq4_xs_ref (const float * GGML_RESTRICT x, block_iq4_xs * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_iq3_s_ref (const float * GGML_RESTRICT x, block_iq3_s * GGML_RESTRICT y, int64_t k); +GGML_API void quantize_row_iq2_s_ref (const float * GGML_RESTRICT x, block_iq2_s * GGML_RESTRICT y, int64_t k); // Dequantization -void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_q4_1(const block_q4_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_q5_0(const block_q5_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_q5_1(const block_q5_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_q8_0(const block_q8_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -//void dequantize_row_q8_1(const block_q8_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_q4_1(const block_q4_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_q5_0(const block_q5_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_q5_1(const block_q5_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_q8_0(const block_q8_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +//GGML_API void dequantize_row_q8_1(const block_q8_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_q2_K(const block_q2_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_q3_K(const block_q3_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_q4_K(const block_q4_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_q5_K(const block_q5_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_q6_K(const block_q6_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_q8_K(const block_q8_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_q2_K(const block_q2_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_q3_K(const block_q3_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_q4_K(const block_q4_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_q5_K(const block_q5_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_q6_K(const block_q6_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_q8_K(const block_q8_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_tq1_0(const block_tq1_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_tq2_0(const block_tq2_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_tq1_0(const block_tq1_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_tq2_0(const block_tq2_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_iq2_s (const block_iq2_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_iq1_s (const block_iq1_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_iq1_m (const block_iq1_m * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_iq4_nl (const block_iq4_nl * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_iq4_xs (const block_iq4_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); -void dequantize_row_iq3_s (const block_iq3_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); - -// Dot product -void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); - -void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); - -void ggml_vec_dot_tq1_0_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_tq2_0_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); - -void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_iq2_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_iq1_m_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_iq4_nl_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_iq4_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); +GGML_API void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_iq2_s (const block_iq2_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_iq1_s (const block_iq1_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_iq1_m (const block_iq1_m * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_iq4_nl (const block_iq4_nl * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_iq4_xs (const block_iq4_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); +GGML_API void dequantize_row_iq3_s (const block_iq3_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k); // Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization") -size_t quantize_iq2_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_iq2_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_iq2_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_iq3_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_iq1_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_iq1_m (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_iq4_nl (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_iq4_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_iq3_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_iq2_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_iq2_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_iq2_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_iq3_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_iq1_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_iq1_m (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_iq4_nl (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_iq4_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_iq3_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_tq1_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_tq2_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_tq1_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_tq2_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_q2_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_q3_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_q4_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_q5_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_q6_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_q4_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_q4_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_q5_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_q5_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -size_t quantize_q8_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_q2_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_q3_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_q4_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_q5_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_q6_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_q4_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_q4_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_q5_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_q5_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); +GGML_API size_t quantize_q8_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix); -void iq2xs_init_impl(enum ggml_type type); -void iq2xs_free_impl(enum ggml_type type); -void iq3xs_init_impl(int grid_size); -void iq3xs_free_impl(int grid_size); +GGML_API void iq2xs_init_impl(enum ggml_type type); +GGML_API void iq2xs_free_impl(enum ggml_type type); +GGML_API void iq3xs_init_impl(int grid_size); +GGML_API void iq3xs_free_impl(int grid_size); #ifdef __cplusplus } diff --git a/ggml/src/ggml-rpc/CMakeLists.txt b/ggml/src/ggml-rpc/CMakeLists.txt new file mode 100644 index 000000000..f5acb8ec2 --- /dev/null +++ b/ggml/src/ggml-rpc/CMakeLists.txt @@ -0,0 +1,9 @@ +message(STATUS "Using RPC backend") + +ggml_add_backend_library(ggml-rpc + ggml-rpc.cpp + ) + +if (WIN32) + target_link_libraries(ggml-rpc PRIVATE ws2_32) +endif() diff --git a/ggml/src/ggml-rpc.cpp b/ggml/src/ggml-rpc/ggml-rpc.cpp similarity index 98% rename from ggml/src/ggml-rpc.cpp rename to ggml/src/ggml-rpc/ggml-rpc.cpp index 2778009e4..431082426 100644 --- a/ggml/src/ggml-rpc.cpp +++ b/ggml/src/ggml-rpc/ggml-rpc.cpp @@ -671,7 +671,7 @@ static ggml_backend_i ggml_backend_rpc_interface = { /* .event_wait = */ NULL, }; -GGML_API ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint) { +ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint) { static std::mutex mutex; std::lock_guard lock(mutex); // NOTE: buffer types are allocated and never freed; this is by design @@ -718,7 +718,7 @@ ggml_backend_t ggml_backend_rpc_init(const char * endpoint) { return backend; } -GGML_API bool ggml_backend_is_rpc(ggml_backend_t backend) { +bool ggml_backend_is_rpc(ggml_backend_t backend) { return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_rpc_guid()); } @@ -730,7 +730,7 @@ static void get_device_memory(const std::shared_ptr & sock, size_t * f *total = response.total_mem; } -GGML_API void ggml_backend_rpc_get_device_memory(const char * endpoint, size_t * free, size_t * total) { +void ggml_backend_rpc_get_device_memory(const char * endpoint, size_t * free, size_t * total) { auto sock = get_socket(endpoint); if (sock == nullptr) { *free = 0; @@ -1296,13 +1296,6 @@ static ggml_backend_buffer_type_t ggml_backend_rpc_device_get_buffer_type(ggml_b UNUSED(dev); } -static ggml_backend_buffer_t ggml_backend_rpc_device_buffer_from_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) { - return ggml_backend_cpu_buffer_from_ptr(ptr, size); - - UNUSED(dev); - UNUSED(max_tensor_size); -} - static bool ggml_backend_rpc_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { UNUSED(dev); UNUSED(op); @@ -1328,7 +1321,7 @@ static const struct ggml_backend_device_i ggml_backend_rpc_device_i = { /* .init_backend = */ ggml_backend_rpc_device_init, /* .get_buffer_type = */ ggml_backend_rpc_device_get_buffer_type, /* .get_host_buffer_type = */ NULL, - /* .buffer_from_host_ptr = */ ggml_backend_rpc_device_buffer_from_ptr, + /* .buffer_from_host_ptr = */ NULL, /* .supports_op = */ ggml_backend_rpc_device_supports_op, /* .supports_buft = */ ggml_backend_rpc_device_supports_buft, /* .offload_op = */ NULL, @@ -1376,8 +1369,9 @@ static const struct ggml_backend_reg_i ggml_backend_rpc_reg_i = { ggml_backend_reg_t ggml_backend_rpc_reg(void) { static struct ggml_backend_reg ggml_backend_rpc_reg = { - /* .iface = */ ggml_backend_rpc_reg_i, - /* .context = */ NULL, + /* .api_version = */ GGML_BACKEND_API_VERSION, + /* .iface = */ ggml_backend_rpc_reg_i, + /* .context = */ NULL, }; return &ggml_backend_rpc_reg; @@ -1408,3 +1402,5 @@ ggml_backend_dev_t ggml_backend_rpc_add_device(const char * endpoint) { return dev; } + +GGML_BACKEND_DL_IMPL(ggml_backend_rpc_reg) diff --git a/ggml/src/ggml-sycl/CMakeLists.txt b/ggml/src/ggml-sycl/CMakeLists.txt new file mode 100644 index 000000000..3579a311a --- /dev/null +++ b/ggml/src/ggml-sycl/CMakeLists.txt @@ -0,0 +1,84 @@ +if (NOT GGML_SYCL_TARGET MATCHES "^(INTEL|NVIDIA|AMD)$") + message(FATAL_ERROR "Invalid backend chosen, supported options are INTEL, NVIDIA, or AMD") +endif() + +check_cxx_compiler_flag("-fsycl" SUPPORTS_SYCL) + +if (DEFINED ENV{ONEAPI_ROOT}) + message(STATUS "Using oneAPI Release SYCL compiler (icpx).") +elseif(SUPPORTS_SYCL) + message(WARNING "Using open-source SYCL compiler (clang++). Didn't detect ENV {ONEAPI_ROOT}. + If you expected the oneAPI Release compiler, please install oneAPI & source it, like: + source /opt/intel/oneapi/setvars.sh") +else() + message(FATAL_ERROR, "C++ compiler lacks SYCL support.") +endif() +message(STATUS "SYCL found") +#todo: AOT + +ggml_add_backend_library(ggml-sycl + ggml-sycl.cpp + ../../include/ggml-sycl.h + ) + +if (GGML_SYCL_F16) + if (GGML_SYCL_TARGET STREQUAL "AMD") + message(WARNING "AMD target does not entirely support FP16 in the SYCL backend.") + endif() + add_compile_definitions(GGML_SYCL_F16) +endif() + +set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing -fsycl") + +if (GGML_SYCL_TARGET STREQUAL "NVIDIA") + add_compile_definitions(GGML_SYCL_WARP_SIZE=32) +elseif (GGML_SYCL_TARGET STREQUAL "AMD") + # INFO: Allowed Sub_group_sizes are not consistent through all + # hip targets. For example, 64 is used for certain models, but the backend + # does not support it. + # Target archs tested working: gfx1030, gfx1031, (Only tested sub_group_size = 32) + add_compile_definitions(GGML_SYCL_WARP_SIZE=32) +else() + add_compile_definitions(GGML_SYCL_WARP_SIZE=16) +endif() + +file(GLOB GGML_HEADERS_SYCL "*.hpp") +file(GLOB GGML_SOURCES_SYCL "*.cpp") +target_sources(ggml-sycl PRIVATE ${GGML_HEADERS_SYCL} ${GGML_SOURCES_SYCL}) + +find_package(DNNL) +message("-- DNNL found:" ${DNNL_FOUND}) + +if (GGML_SYCL_TARGET STREQUAL "INTEL") + add_compile_definitions(GGML_SYCL_DNNL=${DNNL_FOUND}) +else() + add_compile_definitions(GGML_SYCL_DNNL=0) +endif() + +if (${DNNL_FOUND} AND GGML_SYCL_TARGET STREQUAL "INTEL") + target_link_libraries(ggml-sycl PRIVATE DNNL::dnnl) +endif() + +if (WIN32) + find_package(IntelSYCL REQUIRED) + find_package(MKL REQUIRED) + target_link_libraries(ggml-sycl PRIVATE IntelSYCL::SYCL_CXX MKL::MKL MKL::MKL_SYCL) +else() + if (GGML_SYCL_TARGET STREQUAL "INTEL") + target_link_libraries(ggml-sycl PRIVATE sycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread) + elseif (GGML_SYCL_TARGET STREQUAL "NVIDIA") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=nvptx64-nvidia-cuda") + add_compile_definitions(GGML_SYCL_NVIDIA) + target_link_libraries(ggml-sycl PRIVATE sycl pthread m dl onemkl_blas_cublas) + elseif (GGML_SYCL_TARGET STREQUAL "AMD") + if (NOT GGML_SYCL_DEVICE_ARCH) + message(ERROR "Can't enable SYCL hip backend, GGML_SYCL_DEVICE_ARCH has not been set.") + endif() + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=amdgcn-amd-amdhsa") + target_link_libraries(ggml-sycl PRIVATE sycl pthread m dl onemkl) + endif() + + if (GGML_SYCL_DEVICE_ARCH) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Xsycl-target-backend --offload-arch=${GGML_SYCL_DEVICE_ARCH}") + endif() +endif() diff --git a/ggml/src/ggml-sycl/backend.hpp b/ggml/src/ggml-sycl/backend.hpp index d21b5f8dd..85748a5b4 100644 --- a/ggml/src/ggml-sycl/backend.hpp +++ b/ggml/src/ggml-sycl/backend.hpp @@ -26,5 +26,8 @@ #include "softmax.hpp" #include "tsembd.hpp" #include "im2col.hpp" +#include "wkv6.hpp" +#include "outprod.hpp" +#include "element_wise.hpp" #endif // GGML_SYCL_BACKEND_HPP diff --git a/ggml/src/ggml-sycl/common.cpp b/ggml/src/ggml-sycl/common.cpp index cf5291b31..97ab2003c 100644 --- a/ggml/src/ggml-sycl/common.cpp +++ b/ggml/src/ggml-sycl/common.cpp @@ -62,3 +62,43 @@ int64_t downsample_sycl_global_range(int64_t accumulate_block_num, int64_t block } return sycl_down_blk_size; } + +void ggml_sycl_op_flatten(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const ggml_sycl_op_flatten_t op) try { + const int64_t nrows0 = ggml_nrows(src0); + + const bool use_src1 = src1 != nullptr; + const int64_t nrows1 = use_src1 ? ggml_nrows(src1) : 1; + + GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT); + GGML_ASSERT( dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT); + + ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; + ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr; + ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; + + // dd = data device + float * src0_ddf = (float *) src0->data; + float * src1_ddf = use_src1 ? (float *) src1->data : nullptr; + float * dst_ddf = (float *) dst->data; + + ggml_sycl_pool_alloc src0_f(ctx.pool()); + ggml_sycl_pool_alloc src1_f(ctx.pool()); + ggml_sycl_pool_alloc dst_f(ctx.pool()); + + ggml_sycl_set_device(ctx.device); + queue_ptr main_stream = ctx.stream(); + // GGML_SYCL_DEBUG("ctx.device=%d, main_stream=%p src0_on_device=%d, src1_on_device=%d, dst_on_device=%d\n", + // ctx.device, main_stream, src0_on_device, src1_on_device, dst_on_device); + + // do the computation + op(ctx, src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream); + // print_ggml_tensor("tensor", dst); +} +catch (sycl::exception const &exc) { + + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp index bc0faa867..4549fa5e9 100644 --- a/ggml/src/ggml-sycl/common.hpp +++ b/ggml/src/ggml-sycl/common.hpp @@ -404,4 +404,262 @@ static __dpct_inline__ Tp* get_pointer(sycl::local_accessor acc) { int64_t downsample_sycl_global_range(int64_t accumulate_block_num, int64_t block_size); +typedef void (*ggml_sycl_op_flatten_t)(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const queue_ptr &main_stream); + +template +static void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst_t * dst, + int ne0, int ne1, int ne2, int ne3, + int ne10, int ne11, int ne12, int ne13, + /*int s0, */ int s1, int s2, int s3, + /*int s10,*/ int s11, int s12, int s13, + const sycl::nd_item<3> &item_ct1) { + const int i0s = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + const int i1 = (item_ct1.get_local_range(1) * item_ct1.get_group(1) + + item_ct1.get_local_id(1)); + const int i2 = (item_ct1.get_local_range(0) * item_ct1.get_group(0) + + item_ct1.get_local_id(0)) / + ne3; + const int i3 = (item_ct1.get_local_range(0) * item_ct1.get_group(0) + + item_ct1.get_local_id(0)) % + ne3; + + if (i0s >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) { + return; + } + + const int i11 = i1 % ne11; + const int i12 = i2 % ne12; + const int i13 = i3 % ne13; + + const size_t i_src0 = i3*s3 + i2*s2 + i1*s1; + const size_t i_src1 = i13*s13 + i12*s12 + i11*s11; + const size_t i_dst = i_src0; + + const src0_t * src0_row = src0 + i_src0; + const src1_t * src1_row = src1 + i_src1; + dst_t * dst_row = dst + i_dst; + + for (int i0 = i0s; i0 < ne0; + i0 += item_ct1.get_local_range(2) * item_ct1.get_group_range(2)) { + const int i10 = i0 % ne10; + dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]); + } +} + +template +static void k_bin_bcast_unravel(const src0_t * src0, const src1_t * src1, dst_t * dst, + int ne0, int ne1, int ne2, int ne3, + int ne10, int ne11, int ne12, int ne13, + /*int s0, */ int s1, int s2, int s3, + /*int s10,*/ int s11, int s12, int s13, + const sycl::nd_item<3> &item_ct1) { + + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + const int i3 = i/(ne2*ne1*ne0); + const int i2 = (i/(ne1*ne0)) % ne2; + const int i1 = (i/ne0) % ne1; + const int i0 = i % ne0; + + if (i0 >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) { + return; + } + + const int i11 = i1 % ne11; + const int i12 = i2 % ne12; + const int i13 = i3 % ne13; + + const size_t i_src0 = i3*s3 + i2*s2 + i1*s1; + const size_t i_src1 = i13*s13 + i12*s12 + i11*s11; + const size_t i_dst = i_src0; + + const src0_t * src0_row = src0 + i_src0; + const src1_t * src1_row = src1 + i_src1; + dst_t * dst_row = dst + i_dst; + + const int i10 = i0 % ne10; + dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]); +} + + +template +struct bin_bcast_sycl { + template + void operator()(ggml_backend_sycl_context & ctx, + const struct ggml_tensor *src0, + const struct ggml_tensor *src1, struct ggml_tensor *dst, + const src0_t *src0_dd, const src1_t *src1_dd, dst_t *dst_dd, + queue_ptr stream) { + + GGML_TENSOR_BINARY_OP_LOCALS + + int nr0 = ne10/ne0; + int nr1 = ne11/ne1; + int nr2 = ne12/ne2; + int nr3 = ne13/ne3; + + int nr[4] = { nr0, nr1, nr2, nr3 }; + + // collapse dimensions until first broadcast dimension + int64_t cne0[] = {ne0, ne1, ne2, ne3}; + int64_t cne1[] = {ne10, ne11, ne12, ne13}; + size_t cnb0[] = {nb0, nb1, nb2, nb3}; + size_t cnb1[] = {nb10, nb11, nb12, nb13}; + auto collapse = [](int64_t cne[]) { + cne[0] *= cne[1]; + cne[1] = cne[2]; + cne[2] = cne[3]; + cne[3] = 1; + }; + + auto collapse_nb = [](size_t cnb[], int64_t cne[]) { + cnb[1] *= cne[1]; + cnb[2] *= cne[2]; + cnb[3] *= cne[3]; + }; + + for (int i = 0; i < 4; i++) { + if (nr[i] != 1) { + break; + } + if (i > 0) { + collapse_nb(cnb0, cne0); + collapse_nb(cnb1, cne1); + collapse(cne0); + collapse(cne1); + } + } + { + int64_t ne0 = cne0[0]; + int64_t ne1 = cne0[1]; + int64_t ne2 = cne0[2]; + int64_t ne3 = cne0[3]; + + int64_t ne10 = cne1[0]; + int64_t ne11 = cne1[1]; + int64_t ne12 = cne1[2]; + int64_t ne13 = cne1[3]; + + size_t nb0 = cnb0[0]; + size_t nb1 = cnb0[1]; + size_t nb2 = cnb0[2]; + size_t nb3 = cnb0[3]; + + size_t nb10 = cnb1[0]; + size_t nb11 = cnb1[1]; + size_t nb12 = cnb1[2]; + size_t nb13 = cnb1[3]; + + size_t s0 = nb0 / sizeof(dst_t); + size_t s1 = nb1 / sizeof(dst_t); + size_t s2 = nb2 / sizeof(dst_t); + size_t s3 = nb3 / sizeof(dst_t); + + size_t s10 = nb10 / sizeof(src1_t); + size_t s11 = nb11 / sizeof(src1_t); + size_t s12 = nb12 / sizeof(src1_t); + size_t s13 = nb13 / sizeof(src1_t); + + GGML_ASSERT(s0 == 1); + GGML_ASSERT(s10 == 1); + + const int block_size = 128; + + int64_t hne0 = std::max(ne0/2LL, 1LL); + + sycl::range<3> block_dims(1, 1, 1); + block_dims[2] = std::min(hne0, block_size); + block_dims[1] = std::min( + ne1, block_size / (unsigned int)block_dims[2]); + block_dims[0] = std::min( + std::min( + ne2 * ne3, block_size / (unsigned int)block_dims[2] / + (unsigned int)block_dims[1]), + 64U); + + sycl::range<3> block_nums( + (ne2 * ne3 + block_dims[0] - 1) / block_dims[0], + (ne1 + block_dims[1] - 1) / block_dims[1], + (hne0 + block_dims[2] - 1) / block_dims[2]); + + if (block_nums[0] > 65535) { + // this is the maximum number of blocks in z direction, fallback to 1D grid kernel + int block_num = (ne0*ne1*ne2*ne3 + block_size - 1) / block_size; + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, block_num) * + sycl::range<3>(1, 1, block_size), + sycl::range<3>(1, 1, block_size)), + [=](sycl::nd_item<3> item_ct1) { + k_bin_bcast_unravel( + src0_dd, src1_dd, dst_dd, ne0, ne1, ne2, ne3, + ne10, ne11, ne12, ne13, s1, s2, s3, s11, s12, + s13, item_ct1); + }); + } + } else { + /* + DPCT1049:16: The work-group size passed to the SYCL kernel may + exceed the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if + needed. + */ + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + k_bin_bcast(src0_dd, src1_dd, dst_dd, ne0, ne1, + ne2, ne3, ne10, ne11, ne12, ne13, + s1, s2, s3, s11, s12, s13, + item_ct1); + }); + } + } + } +}; + +template +inline void ggml_sycl_op_bin_bcast(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const queue_ptr &main_stream) { + + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + op()(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); + } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { + op()(ctx, src0, src1, dst, (const sycl::half *)src0_dd, src1_dd, + (sycl::half *)dst_dd, main_stream); + } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) { + op()(ctx, src0, src1, dst, (const sycl::half *)src0_dd, src1_dd, dst_dd, + main_stream); + } else if (src0->type == GGML_TYPE_I32 && dst->type == GGML_TYPE_I32) { + op()(ctx, src0, src1, dst, (const int32_t *)src0_dd, (const int32_t *)src1_dd, (int32_t *)dst_dd, + main_stream); + } else if (src0->type == GGML_TYPE_I16 && dst->type == GGML_TYPE_I16) { + op()(ctx, src0, src1, dst, (const int16_t *)src0_dd, (const int16_t *)src1_dd, (int16_t *)dst_dd, + main_stream); + } else { + fprintf(stderr, "%s: unsupported types: dst: %s, src0: %s, src1: %s\n", __func__, + ggml_type_name(dst->type), ggml_type_name(src0->type), ggml_type_name(src1->type)); + GGML_ABORT("fatal error"); + } +} + + +void ggml_sycl_op_flatten(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const ggml_sycl_op_flatten_t op); + #endif // GGML_SYCL_COMMON_HPP diff --git a/ggml/src/ggml-sycl/concat.cpp b/ggml/src/ggml-sycl/concat.cpp index 632eedb9d..c90c452d8 100644 --- a/ggml/src/ggml-sycl/concat.cpp +++ b/ggml/src/ggml-sycl/concat.cpp @@ -106,6 +106,7 @@ static void concat_f32_sycl(const float *x, const float *y, float *dst, concat_f32_dim1(x, y, dst, ne0, ne01, item_ct1); }); break; + // dim >=2 will be dispatched to the default path default: stream->parallel_for( sycl::nd_range<3>(gridDim * diff --git a/ggml/src/ggml-sycl/dpct/helper.hpp b/ggml/src/ggml-sycl/dpct/helper.hpp index fe4a8f744..d1b5dd87c 100644 --- a/ggml/src/ggml-sycl/dpct/helper.hpp +++ b/ggml/src/ggml-sycl/dpct/helper.hpp @@ -15,6 +15,7 @@ #include #include +#include #include #include @@ -1688,9 +1689,14 @@ namespace dpct auto data_a = get_memory(a); auto data_b = get_memory(b); auto data_c = get_memory(c); - oneapi::mkl::blas::column_major::gemm( - q, a_trans, b_trans, m, n, k, alpha_value, data_a, lda, - data_b, ldb, beta_value, data_c, ldc); +#ifdef GGML_SYCL_NVIDIA + oneapi::mkl::blas::column_major::gemm(oneapi::mkl::backend_selector{ q }, + a_trans, b_trans, m, n, k, alpha_value, data_a, lda, data_b, ldb, + beta_value, data_c, ldc); +#else + oneapi::mkl::blas::column_major::gemm(q, a_trans, b_trans, m, n, k, alpha_value, data_a, lda, data_b, ldb, + beta_value, data_c, ldc); +#endif } template @@ -1753,14 +1759,22 @@ namespace dpct matrix_info->ld_info[2] = ldc; matrix_info->groupsize_info = batch_size; +#ifdef GGML_SYCL_NVIDIA sycl::event e = oneapi::mkl::blas::column_major::gemm_batch( - q, matrix_info->transpose_info, matrix_info->transpose_info + 1, - matrix_info->size_info, matrix_info->size_info + 1, - matrix_info->size_info + 2, matrix_info->value_info, - reinterpret_cast(a), matrix_info->ld_info, - reinterpret_cast(b), matrix_info->ld_info + 1, - matrix_info->value_info + 1, reinterpret_cast(c), + oneapi::mkl::backend_selector{ q }, matrix_info->transpose_info, + matrix_info->transpose_info + 1, matrix_info->size_info, matrix_info->size_info + 1, + matrix_info->size_info + 2, matrix_info->value_info, reinterpret_cast(a), + matrix_info->ld_info, reinterpret_cast(b), matrix_info->ld_info + 1, + matrix_info->value_info + 1, reinterpret_cast(c), matrix_info->ld_info + 2, 1, + &(matrix_info->groupsize_info)); +#else + sycl::event e = oneapi::mkl::blas::column_major::gemm_batch( + q, matrix_info->transpose_info, matrix_info->transpose_info + 1, matrix_info->size_info, + matrix_info->size_info + 1, matrix_info->size_info + 2, matrix_info->value_info, + reinterpret_cast(a), matrix_info->ld_info, reinterpret_cast(b), + matrix_info->ld_info + 1, matrix_info->value_info + 1, reinterpret_cast(c), matrix_info->ld_info + 2, 1, &(matrix_info->groupsize_info)); +#endif q.submit([&](sycl::handler &cgh) { @@ -1782,10 +1796,16 @@ namespace dpct auto data_a = get_memory(a); auto data_b = get_memory(b); auto data_c = get_memory(c); +#ifdef GGML_SYCL_NVIDIA oneapi::mkl::blas::column_major::gemm_batch( - q, a_trans, b_trans, m, n, k, alpha_value, data_a, lda, - stride_a, data_b, ldb, stride_b, beta_value, - data_c, ldc, stride_c, batch_size); + oneapi::mkl::backend_selector{ q }, a_trans, b_trans, m, n, k, + alpha_value, data_a, lda, stride_a, data_b, ldb, stride_b, beta_value, data_c, ldc, stride_c, + batch_size); +#else + oneapi::mkl::blas::column_major::gemm_batch(q, a_trans, b_trans, m, n, k, alpha_value, data_a, lda, + stride_a, data_b, ldb, stride_b, beta_value, data_c, ldc, + stride_c, batch_size); +#endif } } // namespace detail @@ -1830,31 +1850,10 @@ namespace dpct : id); } - template - sycl::vec extract_and_sign_or_zero_extend4(T val) - { - return sycl::vec(val) - .template as, int8_t, uint8_t>, 4>>() - .template convert(); - } - - template - using dot_product_acc_t = - std::conditional_t && std::is_unsigned_v, - uint32_t, int32_t>; - template inline auto dp4a(T1 a, T2 b, T3 c) { - dot_product_acc_t res = c; - auto va = extract_and_sign_or_zero_extend4(a); - auto vb = extract_and_sign_or_zero_extend4(b); - res += va[0] * vb[0]; - res += va[1] * vb[1]; - res += va[2] * vb[2]; - res += va[3] * vb[3]; - return res; + return syclcompat::dp4a(a, b, c); } struct sub_sat diff --git a/ggml/src/ggml-sycl/element_wise.cpp b/ggml/src/ggml-sycl/element_wise.cpp new file mode 100644 index 000000000..e5cd736eb --- /dev/null +++ b/ggml/src/ggml-sycl/element_wise.cpp @@ -0,0 +1,1011 @@ +#include "common.hpp" +#include "element_wise.hpp" + +void acc_f32(const float * x, const float * y, float * dst, const int ne, + const int ne10, const int ne11, const int ne12, + const int nb1, const int nb2, int offset, const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + if (i >= ne) { + return; + } + int src1_idx = i - offset; + int oz = src1_idx / nb2; + int oy = (src1_idx - (oz * nb2)) / nb1; + int ox = src1_idx % nb1; + if (src1_idx >= 0 && ox < ne10 && oy < ne11 && oz < ne12) { + dst[i] = x[i] + y[ox + oy * ne10 + oz * ne10 * ne11]; + } else { + dst[i] = x[i]; + } +} + +void gelu_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const float GELU_COEF_A = 0.044715f; + const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + + float xi = x[i]; + dst[i] = 0.5f * xi * + (1.0f + + sycl::tanh(SQRT_2_OVER_PI * xi * (1.0f + GELU_COEF_A * xi * xi))); +} + +void silu_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = x[i] / (1.0f + sycl::native::exp(-x[i])); +} + +void gelu_quick_f32(const float *x, float *dst, int k, + const sycl::nd_item<3> &item_ct1) { + const float GELU_QUICK_COEF = -1.702f; + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + if (i >= k) { + return; + } + dst[i] = x[i] * (1.0f / (1.0f + sycl::native::exp(GELU_QUICK_COEF * x[i]))); +} + +void tanh_f32(const float *x, float *dst, int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + if (i >= k) { + return; + } + dst[i] = sycl::tanh((float)(x[i])); +} + +void relu_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = sycl::fmax((float)(x[i]), (float)0); +} + +void sigmoid_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = 1.0f / (1.0f + sycl::native::exp(-x[i])); +} + +void sqrt_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = sycl::sqrt(x[i]); +} + +void sin_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = sycl::sin(x[i]); +} + +void cos_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = sycl::cos(x[i]); +} + +void hardsigmoid_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = sycl::fmin(1.0f, sycl::fmax(0.0f, (x[i] + 3.0f) / 6.0f)); +} + +void hardswish_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = x[i] * sycl::fmin(1.0f, sycl::fmax(0.0f, (x[i] + 3.0f) / 6.0f)); +} + +void exp_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = sycl::exp(x[i]); +} + +void log_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + float xi = x[i]; + if (xi <= 0) { + dst[i] = -INFINITY; + } else { + dst[i] = sycl::log(xi); + } +} + +void neg_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = -x[i]; +} + +void step_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = x[i] > 0.0f; +} + +void leaky_relu_f32(const float *x, float *dst, const int k, const float negative_slope, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + if (i >= k) { + return; + } + dst[i] = sycl::fmax((float)(x[i]), (float)0) + + sycl::fmin((float)(x[i]), 0.0f) * negative_slope; +} + +void sqr_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = x[i] * x[i]; +} + +void upscale_f32(const float *x, float *dst, const int nb00, const int nb01, + const int nb02, const int nb03, const int ne10, const int ne11, + const int ne12, const int ne13, const float sf0, const float sf1, + const float sf2, const float sf3, const sycl::nd_item<1> &item_ct1) { + int index = item_ct1.get_local_id(0) + + item_ct1.get_group(0) * item_ct1.get_local_range(0); + if (index >= ne10 * ne11 * ne12 * ne13) { + return; + } + // operation + int i10 = index % ne10; + int i11 = (index / ne10) % ne11; + int i12 = (index / (ne10 * ne11)) % ne12; + int i13 = (index / (ne10 * ne11 * ne12)) % ne13; + + int i00 = i10 / sf0; + int i01 = i11 / sf1; + int i02 = i12 / sf2; + int i03 = i13 / sf3; + + dst[index] = *(float *)((char *)x + i03 * nb03 + i02 * nb02 + i01 * nb01 + i00 * nb00); +} + +void pad_f32(const float *x, float *dst, const int ne0, const int ne00, const int ne01, const int ne02, + const sycl::nd_item<3> &item_ct1) { + int nidx = item_ct1.get_local_id(2) + + item_ct1.get_group(2) * item_ct1.get_local_range(2); + if (nidx >= ne0) { + return; + } + + // operation + int offset_dst = nidx + item_ct1.get_group(1) * ne0 + + item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1); + if (nidx < ne00 && item_ct1.get_group(1) < ne01 && + item_ct1.get_group(0) < ne02) { + int offset_src = nidx + item_ct1.get_group(1) * ne00 + + item_ct1.get_group(0) * ne00 * ne01; + dst[offset_dst] = x[offset_src]; + } else { + dst[offset_dst] = 0.0f; + } +} + + + +void acc_f32_sycl(const float *x, const float *y, float *dst, + const int n_elements, const int ne10, const int ne11, + const int ne12, const int nb1, const int nb2, + const int offset, queue_ptr stream) { + int num_blocks = (n_elements + SYCL_ACC_BLOCK_SIZE - 1) / SYCL_ACC_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + acc_f32(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset, + item_ct1); + }); +} + +void gelu_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_GELU_BLOCK_SIZE - 1) / SYCL_GELU_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + gelu_f32(x, dst, k, item_ct1); + }); +} + +void silu_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_SILU_BLOCK_SIZE - 1) / SYCL_SILU_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_SILU_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_SILU_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + silu_f32(x, dst, k, item_ct1); + }); +} + +void gelu_quick_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_GELU_BLOCK_SIZE - 1) / SYCL_GELU_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + gelu_quick_f32(x, dst, k, item_ct1); + }); +} + +void tanh_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_TANH_BLOCK_SIZE - 1) / SYCL_TANH_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_TANH_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_TANH_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + tanh_f32(x, dst, k, item_ct1); + }); +} + +void relu_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_RELU_BLOCK_SIZE - 1) / SYCL_RELU_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + relu_f32(x, dst, k, item_ct1); + }); +} + +void hardsigmoid_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_HARDSIGMOID_BLOCK_SIZE - 1) / SYCL_HARDSIGMOID_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + hardsigmoid_f32(x, dst, k, item_ct1); + }); +} + +void hardswish_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_HARDSWISH_BLOCK_SIZE - 1) / SYCL_HARDSWISH_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + hardswish_f32(x, dst, k, item_ct1); + }); +} + +void exp_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_EXP_BLOCK_SIZE - 1) / SYCL_EXP_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_EXP_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_EXP_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + exp_f32(x, dst, k, item_ct1); + }); +} + +void log_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_EXP_BLOCK_SIZE - 1) / SYCL_EXP_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_EXP_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_EXP_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + log_f32(x, dst, k, item_ct1); + }); +} + +void neg_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_NEG_BLOCK_SIZE - 1) / SYCL_NEG_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_NEG_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_NEG_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + neg_f32(x, dst, k, item_ct1); + }); +} + +void step_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_NEG_BLOCK_SIZE - 1) / SYCL_NEG_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_NEG_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_NEG_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + step_f32(x, dst, k, item_ct1); + }); +} + +void sigmoid_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_SIGMOID_BLOCK_SIZE - 1) / SYCL_SIGMOID_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_SIGMOID_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_SIGMOID_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + sigmoid_f32(x, dst, k, item_ct1); + }); +} + +void sqrt_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_SQRT_BLOCK_SIZE - 1) / SYCL_SQRT_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_SQRT_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_SQRT_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + sqrt_f32(x, dst, k, item_ct1); + }); +} + +void sin_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_SIN_BLOCK_SIZE - 1) / SYCL_SIN_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_SIN_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_SIN_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + sin_f32(x, dst, k, item_ct1); + }); +} + +void cos_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_SIN_BLOCK_SIZE - 1) / SYCL_SIN_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_SIN_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_SIN_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + cos_f32(x, dst, k, item_ct1); + }); +} + +void leaky_relu_f32_sycl(const float *x, float *dst, const int k, + const float negative_slope, + queue_ptr stream) { + const int num_blocks = (k + SYCL_RELU_BLOCK_SIZE - 1) / SYCL_RELU_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + leaky_relu_f32(x, dst, k, negative_slope, item_ct1); + }); +} + +void sqr_f32_sycl(const float *x, float *dst, const int k, + queue_ptr stream) { + const int num_blocks = (k + SYCL_SQR_BLOCK_SIZE - 1) / SYCL_SQR_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_SQR_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_SQR_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + sqr_f32(x, dst, k, item_ct1); + }); +} + +void upscale_f32_sycl(const float *x, float *dst, const int nb00, const int nb01, + const int nb02, const int nb03, const int ne10, const int ne11, + const int ne12, const int ne13, const float sf0, const float sf1, + const float sf2, const float sf3, queue_ptr stream) { + int dst_size = ne10 * ne11 * ne12 * ne13; + int num_blocks = (dst_size + SYCL_UPSCALE_BLOCK_SIZE - 1) / SYCL_UPSCALE_BLOCK_SIZE; + sycl::range<1> gridDim(num_blocks * SYCL_UPSCALE_BLOCK_SIZE); + stream->parallel_for( + sycl::nd_range<1>(gridDim, sycl::range<1>(SYCL_UPSCALE_BLOCK_SIZE)), + [=](sycl::nd_item<1> item_ct1) { + upscale_f32(x, dst, nb00, nb01, nb02, nb03, ne10, ne11, ne12, ne13, sf0, sf1, sf2, sf3, item_ct1); + }); +} + +void pad_f32_sycl(const float *x, float *dst, const int ne00, + const int ne01, const int ne02, const int ne0, + const int ne1, const int ne2, queue_ptr stream) { + int num_blocks = (ne0 + SYCL_PAD_BLOCK_SIZE - 1) / SYCL_PAD_BLOCK_SIZE; + sycl::range<3> gridDim(ne2, ne1, num_blocks); + stream->parallel_for( + sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + pad_f32(x, dst, ne0, ne00, ne01, ne02, item_ct1); + }); +} + +inline void ggml_sycl_op_silu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + silu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_gelu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + gelu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} +inline void ggml_sycl_op_gelu_quick(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + gelu_quick_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_tanh(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + tanh_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_relu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + relu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_hardsigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + hardsigmoid_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_hardswish(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + hardswish_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_exp(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + exp_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_log(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + log_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_sigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + sigmoid_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_sqrt(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + sqrt_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_sin(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + sin_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_cos(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + cos_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_step(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + step_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_neg(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + neg_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_leaky_relu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + float negative_slope; + memcpy(&negative_slope, dst->op_params, sizeof(float)); + + leaky_relu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), negative_slope, main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_sqr(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + sqr_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_upscale(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + const float sf0 = (float)dst->ne[0]/src0->ne[0]; + const float sf1 = (float)dst->ne[1]/src0->ne[1]; + const float sf2 = (float)dst->ne[2]/src0->ne[2]; + const float sf3 = (float)dst->ne[3]/src0->ne[3]; + + upscale_f32_sycl(src0_dd, dst_dd, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3], + dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], sf0, sf1, sf2, sf3, + main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_pad(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors + + pad_f32_sycl(src0_dd, dst_dd, + src0->ne[0], src0->ne[1], src0->ne[2], + dst->ne[0], dst->ne[1], dst->ne[2], main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_acc(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + GGML_ASSERT(dst->ne[3] == 1); // just 3D tensors supported + + int nb1 = dst->op_params[0] / 4; // 4 bytes of float32 + int nb2 = dst->op_params[1] / 4; // 4 bytes of float32 + // int nb3 = dst->op_params[2] / 4; // 4 bytes of float32 - unused + int offset = dst->op_params[3] / 4; // offset in bytes + + acc_f32_sycl(src0_dd, src1_dd, dst_dd, ggml_nelements(dst), src1->ne[0], src1->ne[1], src1->ne[2], nb1, nb2, offset, main_stream); + + (void) dst; +} + +inline void ggml_sycl_op_add(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const queue_ptr &main_stream) { + + ggml_sycl_op_bin_bcast>(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); +} + +inline void ggml_sycl_op_sub(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const queue_ptr &main_stream) { + + ggml_sycl_op_bin_bcast>(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); +} + +inline void ggml_sycl_op_mul(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const queue_ptr &main_stream) { + + ggml_sycl_op_bin_bcast>(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); +} + +inline void ggml_sycl_op_div(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const queue_ptr &main_stream) { + + ggml_sycl_op_bin_bcast>(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); +} + + +void ggml_sycl_sqrt(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sqrt); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_sin(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sin); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_cos(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_cos); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_acc(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_acc); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_gelu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_gelu); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_silu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_silu); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_gelu_quick(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_gelu_quick); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_tanh(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_tanh); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_relu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_relu); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_sigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sigmoid); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_hardsigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_hardsigmoid); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_hardswish(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_hardswish); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + + +void ggml_sycl_exp(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_exp); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_log(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_log); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_neg(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_neg); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_step(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_step); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_leaky_relu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_leaky_relu); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_sqr(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sqr); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_upscale(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_upscale); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_pad(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_pad); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + + + +void ggml_sycl_add(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_add); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_sub(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sub); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_mul(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_mul); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + +void ggml_sycl_div(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_div); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} diff --git a/ggml/src/ggml-sycl/element_wise.hpp b/ggml/src/ggml-sycl/element_wise.hpp new file mode 100644 index 000000000..8152edf58 --- /dev/null +++ b/ggml/src/ggml-sycl/element_wise.hpp @@ -0,0 +1,76 @@ +#ifndef GGML_SYCL_ELEMENTWISE_HPP +#define GGML_SYCL_ELEMENTWISE_HPP + +#include "common.hpp" + +static __dpct_inline__ float op_repeat(const float a, const float b) { + return b; + GGML_UNUSED(a); +} + +static __dpct_inline__ float op_add(const float a, const float b) { + return a + b; +} + +static __dpct_inline__ float op_sub(const float a, const float b) { + return a - b; +} + +static __dpct_inline__ float op_mul(const float a, const float b) { + return a * b; +} + +static __dpct_inline__ float op_div(const float a, const float b) { + return a / b; +} + + +void ggml_sycl_sqrt(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_sin(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_cos(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_acc(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_gelu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_silu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_gelu_quick(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_tanh(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_relu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_sigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_hardsigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_hardswish(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_exp(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_log(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_neg(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_step(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_leaky_relu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_sqr(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_upscale(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_pad(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_add(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_sub(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_mul(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +void ggml_sycl_div(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +#endif // GGML_SYCL_ELEMENTWISE_HPP diff --git a/ggml/src/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp similarity index 80% rename from ggml/src/ggml-sycl.cpp rename to ggml/src/ggml-sycl/ggml-sycl.cpp index a62c67f4f..135efb521 100644 --- a/ggml/src/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -47,7 +47,7 @@ static ggml_sycl_device_info ggml_sycl_init() { info.device_count = dpct::dev_mgr::instance().device_count(); if (info.device_count == 0) { - fprintf(stderr, "%s: failed to initialize " GGML_SYCL_NAME ": %s\n", __func__); + GGML_LOG_ERROR("%s: failed to initialize " GGML_SYCL_NAME ": %s\n", __func__); return info; } @@ -55,16 +55,16 @@ static ggml_sycl_device_info ggml_sycl_init() { int64_t total_vram = 0; #if defined(GGML_SYCL_FORCE_MMQ) - fprintf(stderr, "%s: GGML_SYCL_FORCE_MMQ: yes\n", __func__); + GGML_LOG_INFO("%s: GGML_SYCL_FORCE_MMQ: yes\n", __func__); #else - fprintf(stderr, "%s: GGML_SYCL_FORCE_MMQ: no\n", __func__); + GGML_LOG_INFO("%s: GGML_SYCL_FORCE_MMQ: no\n", __func__); #endif #if defined(SYCL_USE_XMX) - fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__); + GGML_LOG_INFO("%s: SYCL_USE_XMX: yes\n", __func__); #else - fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__); + GGML_LOG_INFO("%s: SYCL_USE_XMX: no\n", __func__); #endif - fprintf(stderr, "%s: found %d " GGML_SYCL_NAME " devices:\n", __func__, info.device_count); + GGML_LOG_INFO("%s: found %d " GGML_SYCL_NAME " devices:\n", __func__, info.device_count); for (int i = 0; i < info.device_count; ++i) { info.devices[i].vmm = 0; @@ -110,7 +110,7 @@ void print_device_detail(int id, sycl::device &device, std::string device_type) auto global_mem_size = prop.get_global_mem_size()/1000000; - fprintf(stderr, "|%2d|%19s|%39s|%7s|%7d|%8d|%5d|%6luM|%21s|\n", id, device_type.c_str(), + GGML_LOG_INFO("|%2d|%19s|%39s|%7s|%7d|%8d|%5d|%6luM|%21s|\n", id, device_type.c_str(), name.c_str(), version.c_str(), prop.get_max_compute_units(), prop.get_max_work_group_size(), prop.get_max_sub_group_size(), global_mem_size, device.get_info().c_str()); @@ -120,19 +120,30 @@ void ggml_backend_sycl_print_sycl_devices() { GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_print_sycl_devices\n"); int device_count = dpct::dev_mgr::instance().device_count(); std::map DeviceNums; - fprintf(stderr, "found %d SYCL devices:\n", device_count); - fprintf(stderr, "| | | | |Max | |Max |Global | |\n"); - fprintf(stderr, "| | | | |compute|Max work|sub |mem | |\n"); - fprintf(stderr, "|ID| Device Type| Name|Version|units |group |group|size | Driver version|\n"); - fprintf(stderr, "|--|-------------------|---------------------------------------|-------|-------|--------|-----|-------|---------------------|\n"); + GGML_LOG_INFO("Found %d SYCL devices:\n", device_count); + + GGML_LOG_INFO( + "| | | | " + " |Max | |Max |Global | |\n"); + GGML_LOG_INFO( + "| | | | " + " |compute|Max work|sub |mem | |\n"); + GGML_LOG_INFO( + "|ID| Device Type| " + "Name|Version|units |group |group|size | Driver version|\n"); + GGML_LOG_INFO( + "|--|-------------------|---------------------------------------|------" + "-|-------|--------|-----|-------|---------------------|\n"); + for (int id = 0; id < device_count; ++id) { - sycl::device device = dpct::dev_mgr::instance().get_device(id); - sycl::backend backend = device.get_backend(); - std::string backend_type = get_device_backend_and_type(device); - int type_id=DeviceNums[backend_type]++; - std::stringstream device_type; - device_type << "[" << backend_type << ":" << std::to_string(type_id) << "]"; - print_device_detail(id, device, device_type.str()); + sycl::device device = dpct::dev_mgr::instance().get_device(id); + sycl::backend backend = device.get_backend(); + std::string backend_type = get_device_backend_and_type(device); + int type_id = DeviceNums[backend_type]++; + std::stringstream device_type; + device_type << "[" << backend_type << ":" << std::to_string(type_id) + << "]"; + print_device_detail(id, device, device_type.str()); } } @@ -154,15 +165,14 @@ static void ggml_check_sycl() try { static bool initialized = false; if (!initialized) { - fprintf(stderr, "[SYCL] call ggml_check_sycl\n"); + GGML_LOG_INFO("[SYCL] call ggml_check_sycl\n"); g_ggml_sycl_debug = get_sycl_env("GGML_SYCL_DEBUG", 0); - - fprintf(stderr, "%s: GGML_SYCL_DEBUG: %d\n", __func__, g_ggml_sycl_debug); + GGML_LOG_INFO("%s: GGML_SYCL_DEBUG: %d\n", __func__, g_ggml_sycl_debug); #if defined(GGML_SYCL_F16) - fprintf(stderr, "%s: GGML_SYCL_F16: yes\n", __func__); + GGML_LOG_INFO("%s: GGML_SYCL_F16: yes\n", __func__); #else - fprintf(stderr, "%s: GGML_SYCL_F16: no\n", __func__); + GGML_LOG_INFO("%s: GGML_SYCL_F16: no\n", __func__); #endif /* NOT REMOVE, keep it for next optimize for XMX. @@ -180,9 +190,10 @@ static void ggml_check_sycl() try { return; } GGML_ASSERT(g_all_sycl_device_count <= GGML_SYCL_MAX_DEVICES); - ggml_backend_sycl_print_sycl_devices(); + initialized = true; g_sycl_loaded = true; + ggml_backend_sycl_print_sycl_devices(); } } catch (sycl::exception const &exc) { @@ -205,7 +216,7 @@ inline void check_allow_gpu_index(const int device_index) { __func__, device_index, ggml_sycl_info().device_count - 1); - fprintf(stderr, "%s\n", error_buf); + GGML_LOG_ERROR("%s\n", error_buf); assert(false); } } @@ -475,8 +486,8 @@ ggml_backend_sycl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, SYCL_CHECK(CHECK_TRY_ERROR(dev_ptr = (void *)sycl::malloc_device( size, *stream))); if (!dev_ptr) { - fprintf(stderr, "%s: can't malloc %lu Bytes memory on device", __func__, size); - return nullptr; + GGML_LOG_ERROR("%s: can't allocate %lu Bytes of memory on device\n", __func__, size); + return nullptr; } ggml_backend_sycl_buffer_context * ctx = new ggml_backend_sycl_buffer_context(buft_ctx->device, dev_ptr, buft_ctx->stream); return ggml_backend_buffer_init(buft, ggml_backend_sycl_buffer_interface, ctx, size); @@ -752,7 +763,7 @@ ggml_backend_sycl_split_buffer_init_tensor(ggml_backend_buffer_t buffer, size, *stream))); if (!buf) { char err_buf[1024]; - snprintf(err_buf, 1023, "%s: can't malloc %lu Bytes memory on device", __func__, size); + snprintf(err_buf, 1023, "%s: can't allocate %lu Bytes of memory on device\n", __func__, size); throw std::runtime_error(err_buf); } // set padding to 0 to avoid possible NaN values @@ -1142,17 +1153,18 @@ struct ggml_sycl_pool_leg : public ggml_sycl_pool { CHECK_TRY_ERROR(ptr = (void *)sycl::malloc_device( look_ahead_size, *qptr))); if (!ptr) { - fprintf(stderr, "%s: can't malloc %lu Bytes memory on device", __func__, look_ahead_size); + GGML_LOG_ERROR("%s: can't allocate %lu Bytes of memory on device/GPU\n", __func__, look_ahead_size); return nullptr; } *actual_size = look_ahead_size; pool_size += look_ahead_size; - #ifdef DEBUG_SYCL_MALLOC - fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, id, nnz, +#ifdef DEBUG_SYCL_MALLOC + GGML_LOG_DEBUG("%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, id, nnz, (uint32_t)(max_size/1024/1024), (uint32_t)(g_sycl_pool_size[id]/1024/1024), (uint32_t)(size/1024/1024)); - #endif +#endif + // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg look_ahead_size=%lu, return %p\n", look_ahead_size, ptr); return ptr; } @@ -1166,7 +1178,7 @@ struct ggml_sycl_pool_leg : public ggml_sycl_pool { return; } } - fprintf(stderr, "WARNING: sycl buffer pool full, increase MAX_sycl_BUFFERS\n"); + GGML_LOG_WARN("WARNING: sycl buffer pool full, increase MAX_sycl_BUFFERS\n"); SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, *qptr))); pool_size -= size; } @@ -1194,272 +1206,8 @@ typedef void (*ggml_sycl_op_mul_mat_t)( float *dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, const int64_t src1_padded_row_size, const queue_ptr &stream); -typedef void (*ggml_sycl_op_flatten_t)(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream); -static __dpct_inline__ float op_repeat(const float a, const float b) { - return b; - GGML_UNUSED(a); -} -static __dpct_inline__ float op_add(const float a, const float b) { - return a + b; -} - -static __dpct_inline__ float op_mul(const float a, const float b) { - return a * b; -} - -static __dpct_inline__ float op_div(const float a, const float b) { - return a / b; -} - -template -static void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst_t * dst, - int ne0, int ne1, int ne2, int ne3, - int ne10, int ne11, int ne12, int ne13, - /*int s0, */ int s1, int s2, int s3, - /*int s10,*/ int s11, int s12, int s13, - const sycl::nd_item<3> &item_ct1) { - const int i0s = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - const int i1 = (item_ct1.get_local_range(1) * item_ct1.get_group(1) + - item_ct1.get_local_id(1)); - const int i2 = (item_ct1.get_local_range(0) * item_ct1.get_group(0) + - item_ct1.get_local_id(0)) / - ne3; - const int i3 = (item_ct1.get_local_range(0) * item_ct1.get_group(0) + - item_ct1.get_local_id(0)) % - ne3; - - if (i0s >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) { - return; - } - - const int i11 = i1 % ne11; - const int i12 = i2 % ne12; - const int i13 = i3 % ne13; - - const size_t i_src0 = i3*s3 + i2*s2 + i1*s1; - const size_t i_src1 = i13*s13 + i12*s12 + i11*s11; - const size_t i_dst = i_src0; - - const src0_t * src0_row = src0 + i_src0; - const src1_t * src1_row = src1 + i_src1; - dst_t * dst_row = dst + i_dst; - - for (int i0 = i0s; i0 < ne0; - i0 += item_ct1.get_local_range(2) * item_ct1.get_group_range(2)) { - const int i10 = i0 % ne10; - dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]); - } -} - -template -static void k_bin_bcast_unravel(const src0_t * src0, const src1_t * src1, dst_t * dst, - int ne0, int ne1, int ne2, int ne3, - int ne10, int ne11, int ne12, int ne13, - /*int s0, */ int s1, int s2, int s3, - /*int s10,*/ int s11, int s12, int s13, - const sycl::nd_item<3> &item_ct1) { - - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - - const int i3 = i/(ne2*ne1*ne0); - const int i2 = (i/(ne1*ne0)) % ne2; - const int i1 = (i/ne0) % ne1; - const int i0 = i % ne0; - - if (i0 >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) { - return; - } - - const int i11 = i1 % ne11; - const int i12 = i2 % ne12; - const int i13 = i3 % ne13; - - const size_t i_src0 = i3*s3 + i2*s2 + i1*s1; - const size_t i_src1 = i13*s13 + i12*s12 + i11*s11; - const size_t i_dst = i_src0; - - const src0_t * src0_row = src0 + i_src0; - const src1_t * src1_row = src1 + i_src1; - dst_t * dst_row = dst + i_dst; - - const int i10 = i0 % ne10; - dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]); -} - -static void acc_f32(const float * x, const float * y, float * dst, const int ne, - const int ne10, const int ne11, const int ne12, - const int nb1, const int nb2, int offset, const sycl::nd_item<3> &item_ct1) { - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - if (i >= ne) { - return; - } - int src1_idx = i - offset; - int oz = src1_idx / nb2; - int oy = (src1_idx - (oz * nb2)) / nb1; - int ox = src1_idx % nb1; - if (src1_idx >= 0 && ox < ne10 && oy < ne11 && oz < ne12) { - dst[i] = x[i] + y[ox + oy * ne10 + oz * ne10 * ne11]; - } else { - dst[i] = x[i]; - } -} - -static void gelu_f32(const float * x, float * dst, const int k, - const sycl::nd_item<3> &item_ct1) { - const float GELU_COEF_A = 0.044715f; - const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - - if (i >= k) { - return; - } - - float xi = x[i]; - dst[i] = 0.5f * xi * - (1.0f + - sycl::tanh(SQRT_2_OVER_PI * xi * (1.0f + GELU_COEF_A * xi * xi))); -} - -static void silu_f32(const float * x, float * dst, const int k, - const sycl::nd_item<3> &item_ct1) { - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - - if (i >= k) { - return; - } - dst[i] = x[i] / (1.0f + sycl::native::exp(-x[i])); -} - -static void gelu_quick_f32(const float *x, float *dst, int k, - const sycl::nd_item<3> &item_ct1) { - const float GELU_QUICK_COEF = -1.702f; - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - if (i >= k) { - return; - } - dst[i] = x[i] * (1.0f / (1.0f + sycl::native::exp(GELU_QUICK_COEF * x[i]))); -} - -static void tanh_f32(const float *x, float *dst, int k, - const sycl::nd_item<3> &item_ct1) { - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - if (i >= k) { - return; - } - dst[i] = sycl::tanh((float)(x[i])); -} - -static void relu_f32(const float * x, float * dst, const int k, - const sycl::nd_item<3> &item_ct1) { - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - - if (i >= k) { - return; - } - dst[i] = sycl::fmax((float)(x[i]), (float)0); -} - -static void hardsigmoid_f32(const float * x, float * dst, const int k, - const sycl::nd_item<3> &item_ct1) { - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - - if (i >= k) { - return; - } - dst[i] = sycl::fmin(1.0f, sycl::fmax(0.0f, (x[i] + 3.0f) / 6.0f)); -} - -static void hardswish_f32(const float * x, float * dst, const int k, - const sycl::nd_item<3> &item_ct1) { - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - - if (i >= k) { - return; - } - dst[i] = x[i] * sycl::fmin(1.0f, sycl::fmax(0.0f, (x[i] + 3.0f) / 6.0f)); -} - -static void leaky_relu_f32(const float *x, float *dst, const int k, const float negative_slope, - const sycl::nd_item<3> &item_ct1) { - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - if (i >= k) { - return; - } - dst[i] = sycl::fmax((float)(x[i]), (float)0) + - sycl::fmin((float)(x[i]), 0.0f) * negative_slope; -} - -static void sqr_f32(const float * x, float * dst, const int k, - const sycl::nd_item<3> &item_ct1) { - const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - - if (i >= k) { - return; - } - dst[i] = x[i] * x[i]; -} - -static void upscale_f32(const float *x, float *dst, const int nb00, const int nb01, - const int nb02, const int nb03, const int ne10, const int ne11, - const int ne12, const int ne13, const float sf0, const float sf1, - const float sf2, const float sf3, const sycl::nd_item<1> &item_ct1) { - int index = item_ct1.get_local_id(0) + - item_ct1.get_group(0) * item_ct1.get_local_range(0); - if (index >= ne10 * ne11 * ne12 * ne13) { - return; - } - // operation - int i10 = index % ne10; - int i11 = (index / ne10) % ne11; - int i12 = (index / (ne10 * ne11)) % ne12; - int i13 = (index / (ne10 * ne11 * ne12)) % ne13; - - int i00 = i10 / sf0; - int i01 = i11 / sf1; - int i02 = i12 / sf2; - int i03 = i13 / sf3; - - dst[index] = *(float *)((char *)x + i03 * nb03 + i02 * nb02 + i01 * nb01 + i00 * nb00); -} - -static void pad_f32(const float *x, float *dst, const int ne0, const int ne00, const int ne01, const int ne02, - const sycl::nd_item<3> &item_ct1) { - int nidx = item_ct1.get_local_id(2) + - item_ct1.get_group(2) * item_ct1.get_local_range(2); - if (nidx >= ne0) { - return; - } - - // operation - int offset_dst = nidx + item_ct1.get_group(1) * ne0 + - item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1); - if (nidx < ne00 && item_ct1.get_group(1) < ne01 && - item_ct1.get_group(0) < ne02) { - int offset_src = nidx + item_ct1.get_group(1) * ne00 + - item_ct1.get_group(0) * ne00 * ne01; - dst[offset_dst] = x[offset_src]; - } else { - dst[offset_dst] = 0.0f; - } -} template static void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int kx, const int kx_padded, @@ -2148,297 +1896,6 @@ static void get_rows_sycl_float(ggml_backend_sycl_context & ctx, const ggml_tens (void) dst; } -template -struct bin_bcast_sycl { - template - void operator()(ggml_backend_sycl_context & ctx, - const struct ggml_tensor *src0, - const struct ggml_tensor *src1, struct ggml_tensor *dst, - const src0_t *src0_dd, const src1_t *src1_dd, dst_t *dst_dd, - queue_ptr stream) { - - GGML_TENSOR_BINARY_OP_LOCALS - - int nr0 = ne10/ne0; - int nr1 = ne11/ne1; - int nr2 = ne12/ne2; - int nr3 = ne13/ne3; - - int nr[4] = { nr0, nr1, nr2, nr3 }; - - // collapse dimensions until first broadcast dimension - int64_t cne0[] = {ne0, ne1, ne2, ne3}; - int64_t cne1[] = {ne10, ne11, ne12, ne13}; - size_t cnb0[] = {nb0, nb1, nb2, nb3}; - size_t cnb1[] = {nb10, nb11, nb12, nb13}; - auto collapse = [](int64_t cne[]) { - cne[0] *= cne[1]; - cne[1] = cne[2]; - cne[2] = cne[3]; - cne[3] = 1; - }; - - auto collapse_nb = [](size_t cnb[], int64_t cne[]) { - cnb[1] *= cne[1]; - cnb[2] *= cne[2]; - cnb[3] *= cne[3]; - }; - - for (int i = 0; i < 4; i++) { - if (nr[i] != 1) { - break; - } - if (i > 0) { - collapse_nb(cnb0, cne0); - collapse_nb(cnb1, cne1); - collapse(cne0); - collapse(cne1); - } - } - { - int64_t ne0 = cne0[0]; - int64_t ne1 = cne0[1]; - int64_t ne2 = cne0[2]; - int64_t ne3 = cne0[3]; - - int64_t ne10 = cne1[0]; - int64_t ne11 = cne1[1]; - int64_t ne12 = cne1[2]; - int64_t ne13 = cne1[3]; - - size_t nb0 = cnb0[0]; - size_t nb1 = cnb0[1]; - size_t nb2 = cnb0[2]; - size_t nb3 = cnb0[3]; - - size_t nb10 = cnb1[0]; - size_t nb11 = cnb1[1]; - size_t nb12 = cnb1[2]; - size_t nb13 = cnb1[3]; - - size_t s0 = nb0 / sizeof(dst_t); - size_t s1 = nb1 / sizeof(dst_t); - size_t s2 = nb2 / sizeof(dst_t); - size_t s3 = nb3 / sizeof(dst_t); - - size_t s10 = nb10 / sizeof(src1_t); - size_t s11 = nb11 / sizeof(src1_t); - size_t s12 = nb12 / sizeof(src1_t); - size_t s13 = nb13 / sizeof(src1_t); - - GGML_ASSERT(s0 == 1); - GGML_ASSERT(s10 == 1); - - const int block_size = 128; - - int64_t hne0 = std::max(ne0/2LL, 1LL); - - sycl::range<3> block_dims(1, 1, 1); - block_dims[2] = std::min(hne0, block_size); - block_dims[1] = std::min( - ne1, block_size / (unsigned int)block_dims[2]); - block_dims[0] = std::min( - std::min( - ne2 * ne3, block_size / (unsigned int)block_dims[2] / - (unsigned int)block_dims[1]), - 64U); - - sycl::range<3> block_nums( - (ne2 * ne3 + block_dims[0] - 1) / block_dims[0], - (ne1 + block_dims[1] - 1) / block_dims[1], - (hne0 + block_dims[2] - 1) / block_dims[2]); - - if (block_nums[0] > 65535) { - // this is the maximum number of blocks in z direction, fallback to 1D grid kernel - int block_num = (ne0*ne1*ne2*ne3 + block_size - 1) / block_size; - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, block_num) * - sycl::range<3>(1, 1, block_size), - sycl::range<3>(1, 1, block_size)), - [=](sycl::nd_item<3> item_ct1) { - k_bin_bcast_unravel( - src0_dd, src1_dd, dst_dd, ne0, ne1, ne2, ne3, - ne10, ne11, ne12, ne13, s1, s2, s3, s11, s12, - s13, item_ct1); - }); - } - } else { - /* - DPCT1049:16: The work-group size passed to the SYCL kernel may - exceed the limit. To get the device limit, query - info::device::max_work_group_size. Adjust the work-group size if - needed. - */ - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - k_bin_bcast(src0_dd, src1_dd, dst_dd, ne0, ne1, - ne2, ne3, ne10, ne11, ne12, ne13, - s1, s2, s3, s11, s12, s13, - item_ct1); - }); - } - } - } -}; - -static void acc_f32_sycl(const float *x, const float *y, float *dst, - const int n_elements, const int ne10, const int ne11, - const int ne12, const int nb1, const int nb2, - const int offset, queue_ptr stream) { - int num_blocks = (n_elements + SYCL_ACC_BLOCK_SIZE - 1) / SYCL_ACC_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - acc_f32(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset, - item_ct1); - }); -} - -static void gelu_f32_sycl(const float *x, float *dst, const int k, - queue_ptr stream) { - const int num_blocks = (k + SYCL_GELU_BLOCK_SIZE - 1) / SYCL_GELU_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - gelu_f32(x, dst, k, item_ct1); - }); -} - -static void silu_f32_sycl(const float *x, float *dst, const int k, - queue_ptr stream) { - const int num_blocks = (k + SYCL_SILU_BLOCK_SIZE - 1) / SYCL_SILU_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_SILU_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_SILU_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - silu_f32(x, dst, k, item_ct1); - }); -} - -static void gelu_quick_f32_sycl(const float *x, float *dst, const int k, - queue_ptr stream) { - const int num_blocks = (k + SYCL_GELU_BLOCK_SIZE - 1) / SYCL_GELU_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - gelu_quick_f32(x, dst, k, item_ct1); - }); -} - -static void tanh_f32_sycl(const float *x, float *dst, const int k, - queue_ptr stream) { - const int num_blocks = (k + SYCL_TANH_BLOCK_SIZE - 1) / SYCL_TANH_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_TANH_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_TANH_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - tanh_f32(x, dst, k, item_ct1); - }); -} - -static void relu_f32_sycl(const float *x, float *dst, const int k, - queue_ptr stream) { - const int num_blocks = (k + SYCL_RELU_BLOCK_SIZE - 1) / SYCL_RELU_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - relu_f32(x, dst, k, item_ct1); - }); -} - -static void hardsigmoid_f32_sycl(const float *x, float *dst, const int k, - queue_ptr stream) { - const int num_blocks = (k + SYCL_HARDSIGMOID_BLOCK_SIZE - 1) / SYCL_HARDSIGMOID_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_HARDSIGMOID_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - hardsigmoid_f32(x, dst, k, item_ct1); - }); -} - -static void hardswish_f32_sycl(const float *x, float *dst, const int k, - queue_ptr stream) { - const int num_blocks = (k + SYCL_HARDSWISH_BLOCK_SIZE - 1) / SYCL_HARDSWISH_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_HARDSWISH_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - hardswish_f32(x, dst, k, item_ct1); - }); -} - -static void leaky_relu_f32_sycl(const float *x, float *dst, const int k, - const float negative_slope, - queue_ptr stream) { - const int num_blocks = (k + SYCL_RELU_BLOCK_SIZE - 1) / SYCL_RELU_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - leaky_relu_f32(x, dst, k, negative_slope, item_ct1); - }); -} - -static void sqr_f32_sycl(const float *x, float *dst, const int k, - queue_ptr stream) { - const int num_blocks = (k + SYCL_SQR_BLOCK_SIZE - 1) / SYCL_SQR_BLOCK_SIZE; - stream->parallel_for( - sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * - sycl::range<3>(1, 1, SYCL_SQR_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_SQR_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - sqr_f32(x, dst, k, item_ct1); - }); -} - -static void upscale_f32_sycl(const float *x, float *dst, const int nb00, const int nb01, - const int nb02, const int nb03, const int ne10, const int ne11, - const int ne12, const int ne13, const float sf0, const float sf1, - const float sf2, const float sf3, queue_ptr stream) { - int dst_size = ne10 * ne11 * ne12 * ne13; - int num_blocks = (dst_size + SYCL_UPSCALE_BLOCK_SIZE - 1) / SYCL_UPSCALE_BLOCK_SIZE; - sycl::range<1> gridDim(num_blocks * SYCL_UPSCALE_BLOCK_SIZE); - stream->parallel_for( - sycl::nd_range<1>(gridDim, sycl::range<1>(SYCL_UPSCALE_BLOCK_SIZE)), - [=](sycl::nd_item<1> item_ct1) { - upscale_f32(x, dst, nb00, nb01, nb02, nb03, ne10, ne11, ne12, ne13, sf0, sf1, sf2, sf3, item_ct1); - }); -} - -static void pad_f32_sycl(const float *x, float *dst, const int ne00, - const int ne01, const int ne02, const int ne0, - const int ne1, const int ne2, queue_ptr stream) { - int num_blocks = (ne0 + SYCL_PAD_BLOCK_SIZE - 1) / SYCL_PAD_BLOCK_SIZE; - sycl::range<3> gridDim(ne2, ne1, num_blocks); - stream->parallel_for( - sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - pad_f32(x, dst, ne0, ne00, ne01, ne02, item_ct1); - }); -} static void quantize_row_q8_1_sycl(const float *x, void *vy, const int kx, const int ky, const int kx_padded, @@ -2816,6 +2273,58 @@ static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols, } } +static void argmax_f32_i32_sycl(const float *x, int *dst, const int ncols, + const int nrows, queue_ptr stream) { + const sycl::range<3> block_dims(1, 1, SYCL_ARGMAX_BLOCK_SIZE); + const sycl::range<3> block_nums(1, nrows, 1); + const size_t shared_mem = 256 * sizeof(float); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor shared_data( + sycl::range<1>(shared_mem/sizeof(float)), cgh); + sycl::local_accessor shared_indices( + sycl::range<1>(shared_mem/sizeof(float)), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + const int tid = item_ct1.get_local_id(2); + const int row = item_ct1.get_global_id(1); + + float max_val = -INFINITY; + int max_idx = -1; + + for (int col = tid; col < ncols; col += 256) { + float val = x[row * ncols + col]; + if (val > max_val) { + max_val = val; + max_idx = col; + } + } + + shared_data[tid] = max_val; + shared_indices[tid] = max_idx; + item_ct1.barrier(sycl::access::fence_space::local_space); + + for (int stride = 256/2; stride > 0; stride >>= 1) { + if (tid < stride) { + float val1 = shared_data[tid]; + float val2 = shared_data[tid + stride]; + if (val2 > val1) { + shared_data[tid] = val2; + shared_indices[tid] = shared_indices[tid + stride]; + } + } + item_ct1.barrier(sycl::access::fence_space::local_space); + } + + + if (tid == 0) { + dst[row] = shared_indices[0]; + } + }); + }); +} static void diag_mask_inf_f32_sycl(const float *x, float *dst, const int ncols_x, const int nrows_x, const int rows_per_channel, const int n_past, @@ -2940,39 +2449,12 @@ static void ggml_sycl_op_get_rows(ggml_backend_sycl_context & ctx, const ggml_te break; default: // TODO: k-quants - fprintf(stderr, "%s: unsupported type: %s\n", __func__, ggml_type_name(src0->type)); + GGML_LOG_ERROR("%s: unsupported type: %s\n", __func__, ggml_type_name(src0->type)); GGML_ABORT("fatal error"); break; } } -template -inline void ggml_sycl_op_bin_bcast(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const float *src0_dd, const float *src1_dd, - float *dst_dd, - const queue_ptr &main_stream) { - - if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - op()(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); - } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { - op()(ctx, src0, src1, dst, (const sycl::half *)src0_dd, src1_dd, - (sycl::half *)dst_dd, main_stream); - } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) { - op()(ctx, src0, src1, dst, (const sycl::half *)src0_dd, src1_dd, dst_dd, - main_stream); - } else if (src0->type == GGML_TYPE_I32 && dst->type == GGML_TYPE_I32) { - op()(ctx, src0, src1, dst, (const int32_t *)src0_dd, (const int32_t *)src1_dd, (int32_t *)dst_dd, - main_stream); - } else if (src0->type == GGML_TYPE_I16 && dst->type == GGML_TYPE_I16) { - op()(ctx, src0, src1, dst, (const int16_t *)src0_dd, (const int16_t *)src1_dd, (int16_t *)dst_dd, - main_stream); - } else { - fprintf(stderr, "%s: unsupported types: dst: %s, src0: %s, src1: %s\n", __func__, - ggml_type_name(dst->type), ggml_type_name(src0->type), ggml_type_name(src1->type)); - GGML_ABORT("fatal error"); - } -} static void ggml_sycl_op_repeat(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, @@ -2986,230 +2468,6 @@ static void ggml_sycl_op_repeat(ggml_backend_sycl_context & ctx, const ggml_tens (void) src1_d; } -inline void ggml_sycl_op_add(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream) { - - ggml_sycl_op_bin_bcast>(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); -} - -inline void ggml_sycl_op_acc(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - GGML_ASSERT(dst->ne[3] == 1); // just 3D tensors supported - - int nb1 = dst->op_params[0] / 4; // 4 bytes of float32 - int nb2 = dst->op_params[1] / 4; // 4 bytes of float32 - // int nb3 = dst->op_params[2] / 4; // 4 bytes of float32 - unused - int offset = dst->op_params[3] / 4; // offset in bytes - - acc_f32_sycl(src0_dd, src1_dd, dst_dd, ggml_nelements(dst), src1->ne[0], src1->ne[1], src1->ne[2], nb1, nb2, offset, main_stream); - - (void) dst; -} - -inline void ggml_sycl_op_mul(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream) { - - ggml_sycl_op_bin_bcast>(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); -} - -inline void ggml_sycl_op_div(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream) { - - ggml_sycl_op_bin_bcast>(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); -} - -inline void ggml_sycl_op_gelu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - gelu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -inline void ggml_sycl_op_silu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - silu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -inline void ggml_sycl_op_gelu_quick(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const float *src0_dd, const float *src1_dd, - float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - gelu_quick_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -inline void ggml_sycl_op_tanh(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - tanh_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -inline void ggml_sycl_op_relu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - relu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -static void ggml_sycl_op_hardsigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const float *src0_dd, const float *src1_dd, - float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - hardsigmoid_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -static void ggml_sycl_op_hardswish(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const float *src0_dd, const float *src1_dd, - float *dst_dd, const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - hardswish_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -inline void ggml_sycl_op_leaky_relu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const float *src0_dd, const float *src1_dd, - float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - float negative_slope; - memcpy(&negative_slope, dst->op_params, sizeof(float)); - - leaky_relu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), negative_slope, main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -inline void ggml_sycl_op_sqr(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - sqr_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -inline void ggml_sycl_op_upscale(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const float *src0_dd, const float *src1_dd, - float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F32); - - const float sf0 = (float)dst->ne[0]/src0->ne[0]; - const float sf1 = (float)dst->ne[1]/src0->ne[1]; - const float sf2 = (float)dst->ne[2]/src0->ne[2]; - const float sf3 = (float)dst->ne[3]/src0->ne[3]; - - upscale_f32_sycl(src0_dd, dst_dd, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3], - dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], sf0, sf1, sf2, sf3, - main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} - -inline void ggml_sycl_op_pad(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F32); - GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors - - pad_f32_sycl(src0_dd, dst_dd, - src0->ne[0], src0->ne[1], src0->ne[2], - dst->ne[0], dst->ne[1], dst->ne[2], main_stream); - - (void) src1; - (void) dst; - (void) src1_dd; -} inline void ggml_sycl_op_mul_mat_sycl( ggml_backend_sycl_context & ctx, @@ -3315,12 +2573,17 @@ inline void ggml_sycl_op_mul_mat_sycl( const float alpha = 1.0f; const float beta = 0.0f; #if !GGML_SYCL_DNNL +# ifdef GGML_SYCL_NVIDIA SYCL_CHECK(CHECK_TRY_ERROR(oneapi::mkl::blas::column_major::gemm( - *stream, oneapi::mkl::transpose::trans, - oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10, - dpct::get_value(&alpha, *stream), src0_ddf_i, ne00, - src1_ddf1_i, ne10, dpct::get_value(&beta, *stream), + oneapi::mkl::backend_selector{ *stream }, oneapi::mkl::transpose::trans, + oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10, dpct::get_value(&alpha, *stream), src0_ddf_i, + ne00, src1_ddf1_i, ne10, dpct::get_value(&beta, *stream), dst_dd_i, ldc))); +# else + SYCL_CHECK(CHECK_TRY_ERROR(oneapi::mkl::blas::column_major::gemm( + *stream, oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10, + dpct::get_value(&alpha, *stream), src0_ddf_i, ne00, src1_ddf1_i, ne10, dpct::get_value(&beta, *stream), dst_dd_i, ldc))); +# endif #else auto dnnl_stream = ctx.stream_dnnl(stream); DnnlGemmWrapper::row_gemm(dnnl_stream, false, true, src1_ncols, row_diff, ne10, src1_ddf1_i, DnnlGemmWrapper::to_dt(), @@ -3379,6 +2642,23 @@ static void ggml_sycl_op_pool2d(ggml_backend_sycl_context & ctx, const ggml_tens (void) src1_dd; } +inline void ggml_sycl_op_sum(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const queue_ptr &main_stream) { + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + const int64_t ne = ggml_nelements(src0); + + sum_rows_f32_sycl(src0_dd, dst_dd, ne, 1, main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + inline void ggml_sycl_op_sum_rows(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, @@ -3419,6 +2699,25 @@ inline void ggml_sycl_op_argsort(ggml_backend_sycl_context & ctx, const ggml_ten (void) src1_dd; } +inline void ggml_sycl_op_argmax(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_I32); + + const int64_t ncols = src0->ne[0]; + const int64_t nrows = ggml_nrows(src0); + + argmax_f32_i32_sycl(src0_dd, (int *)dst_dd, ncols, nrows, main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + inline void ggml_sycl_op_diag_mask_inf(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, @@ -3489,46 +2788,6 @@ inline void ggml_sycl_op_clamp(ggml_backend_sycl_context & ctx, const ggml_tenso (void) src1_dd; } -static void ggml_sycl_op_flatten(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, - const ggml_tensor *src1, ggml_tensor *dst, - const ggml_sycl_op_flatten_t op) try { - const int64_t nrows0 = ggml_nrows(src0); - - const bool use_src1 = src1 != nullptr; - const int64_t nrows1 = use_src1 ? ggml_nrows(src1) : 1; - - GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT); - GGML_ASSERT( dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT); - - ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr; - ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - - // dd = data device - float * src0_ddf = (float *) src0->data; - float * src1_ddf = use_src1 ? (float *) src1->data : nullptr; - float * dst_ddf = (float *) dst->data; - - ggml_sycl_pool_alloc src0_f(ctx.pool()); - ggml_sycl_pool_alloc src1_f(ctx.pool()); - ggml_sycl_pool_alloc dst_f(ctx.pool()); - - ggml_sycl_set_device(ctx.device); - queue_ptr main_stream = ctx.stream(); - // GGML_SYCL_DEBUG("ctx.device=%d, main_stream=%p src0_on_device=%d, src1_on_device=%d, dst_on_device=%d\n", - // ctx.device, main_stream, src0_on_device, src1_on_device, dst_on_device); - - // do the computation - op(ctx, src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream); - // print_ggml_tensor("tensor", dst); -} -catch (sycl::exception const &exc) { - - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - static void ggml_sycl_set_peer_access(const int n_tokens, int main_device) { static bool peer_access_enabled = false; @@ -3908,115 +3167,24 @@ static void ggml_sycl_get_rows(ggml_backend_sycl_context & ctx, const ggml_tenso GGML_SYCL_DEBUG("call %s done\n", __func__); } -static void ggml_sycl_add(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_add); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_acc(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_acc); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_mul(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_mul); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_div(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_div); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_gelu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_gelu); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_silu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_silu); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_gelu_quick(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_gelu_quick); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_tanh(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_tanh); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_relu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_relu); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_hardsigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_hardsigmoid); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_hardswish(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_hardswish); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_leaky_relu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_leaky_relu); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_sqr(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sqr); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - static void ggml_sycl_norm(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_norm); GGML_SYCL_DEBUG("call %s done\n", __func__); } -static void ggml_sycl_group_norm(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_group_norm); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_upscale(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_upscale); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - -static void ggml_sycl_pad(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_pad); - GGML_SYCL_DEBUG("call %s done\n", __func__); -} - - static void ggml_sycl_rms_norm(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_rms_norm); GGML_SYCL_DEBUG("call %s done\n", __func__); } +static void ggml_sycl_group_norm(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_group_norm); + GGML_SYCL_DEBUG("call %s done\n", __func__); +} + static void ggml_sycl_mul_mat_vec_p021(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst) try { @@ -4296,8 +3464,15 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor use_dequantize_mul_mat_vec = use_dequantize_mul_mat_vec && !use_mul_mat_vec_q; if (!split && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { - // KQ single-batch - ggml_sycl_mul_mat_vec_p021(ctx, src0, src1, dst); + // TODO: Refactor and cleanup of mul mat dispatching. + if (src0->ne[3] == 1 && src1->ne[3] == 1) { + // KQ single-batch + // mmv p021 was specific for these dimensions + ggml_sycl_mul_mat_vec_p021(ctx, src0, src1, dst); + } else { + // The kernel from the if path is faster for that specific case, but does not support all mul mats. + ggml_sycl_mul_mat_batched_sycl(ctx, src0, src1, dst); + } } else if (!split && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { // KQV single-batch ggml_sycl_mul_mat_vec_nc(ctx, src0, src1, dst); @@ -4592,7 +3767,7 @@ static void ggml_sycl_cpy(ggml_backend_sycl_context & ctx, const ggml_tensor *sr } else if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_I32) { ggml_cpy_i32_i32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else { - fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__, + GGML_LOG_ERROR("%s: unsupported type combination (%s to %s)\n", __func__, ggml_type_name(src0->type), ggml_type_name(src1->type)); GGML_ABORT("fatal error"); } @@ -4632,6 +3807,11 @@ static void ggml_sycl_im2col(ggml_backend_sycl_context & ctx, const ggml_tensor ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_im2col); } +static void ggml_sycl_sum(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(ggml_is_contiguous(src0)); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sum); +} + static void ggml_sycl_sum_rows(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_ASSERT(ggml_is_contiguous(src0)); ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sum_rows); @@ -4642,6 +3822,11 @@ static void ggml_sycl_argsort(ggml_backend_sycl_context & ctx, const ggml_tensor ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_argsort); } +static void ggml_sycl_argmax(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(ggml_is_contiguous(src0)); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_argmax); +} + static void ggml_sycl_nop(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { (void) src0; (void) src1; @@ -4657,7 +3842,7 @@ void ggml_sycl_set_main_device(const int main_device) try { dpct::device_info prop; SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( prop, dpct::dev_mgr::instance().get_device(main_device)))); - fprintf(stderr, "Using device %d (%s) as main device\n", + GGML_LOG_INFO("Using device %d (%s) as main device\n", main_device, prop.get_name()); } } @@ -4673,6 +3858,9 @@ bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct ggml_tens ggml_sycl_func_t func; switch (tensor->op) { + case GGML_OP_ARGMAX: + func = ggml_sycl_argmax; + break; case GGML_OP_CONV_TRANSPOSE_1D: func = ggml_sycl_op_conv_transpose_1d; break; @@ -4686,19 +3874,32 @@ bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct ggml_tens func = ggml_sycl_dup; break; case GGML_OP_ADD: + case GGML_OP_ADD1: // TODO: more efficient implementation func = ggml_sycl_add; break; + case GGML_OP_SUB: + func = ggml_sycl_sub; + break; case GGML_OP_ACC: func = ggml_sycl_acc; break; case GGML_OP_MUL: func = ggml_sycl_mul; break; + case GGML_OP_LOG: + func = ggml_sycl_log; + break; case GGML_OP_DIV: func = ggml_sycl_div; break; case GGML_OP_UNARY: switch (ggml_get_unary_op(tensor)) { + case GGML_UNARY_OP_NEG: + func = ggml_sycl_neg; + break; + case GGML_UNARY_OP_STEP: + func = ggml_sycl_step; + break; case GGML_UNARY_OP_GELU: func = ggml_sycl_gelu; break; @@ -4714,12 +3915,18 @@ bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct ggml_tens case GGML_UNARY_OP_RELU: func = ggml_sycl_relu; break; + case GGML_UNARY_OP_SIGMOID: + func = ggml_sycl_sigmoid; + break; case GGML_UNARY_OP_HARDSIGMOID: func = ggml_sycl_hardsigmoid; break; case GGML_UNARY_OP_HARDSWISH: func = ggml_sycl_hardswish; break; + case GGML_UNARY_OP_EXP: + func = ggml_sycl_exp; + break; default: return false; } @@ -4757,12 +3964,24 @@ bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct ggml_tens } func = ggml_sycl_mul_mat_id; break; + case GGML_OP_OUT_PROD: + func = ggml_sycl_op_out_prod; + break; case GGML_OP_SCALE: func = ggml_sycl_scale; break; case GGML_OP_SQR: func = ggml_sycl_sqr; break; + case GGML_OP_SQRT: + func = ggml_sycl_sqrt; + break; + case GGML_OP_SIN: + func = ggml_sycl_sin; + break; + case GGML_OP_COS: + func = ggml_sycl_cos; + break; case GGML_OP_CLAMP: func = ggml_sycl_clamp; break; @@ -4794,6 +4013,9 @@ bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct ggml_tens case GGML_OP_POOL_2D: func = ggml_sycl_pool2d; break; + case GGML_OP_SUM: + func = ggml_sycl_sum; + break; case GGML_OP_SUM_ROWS: func = ggml_sycl_sum_rows; break; @@ -4803,6 +4025,9 @@ bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct ggml_tens case GGML_OP_TIMESTEP_EMBEDDING: func = ggml_sycl_op_timestep_embedding; break; + case GGML_OP_RWKV_WKV6: + func = ggml_sycl_op_rwkv_wkv6; + break; default: return false; } @@ -4964,7 +4189,7 @@ static ggml_status ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_ #endif bool ok = ggml_sycl_compute_forward(*sycl_ctx, node); if (!ok) { - fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); + GGML_LOG_ERROR("%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); } GGML_ASSERT(ok); } @@ -5125,13 +4350,17 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g } break; case GGML_OP_UNARY: switch (ggml_get_unary_op(op)) { + case GGML_UNARY_OP_NEG: + case GGML_UNARY_OP_STEP: case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_SILU: case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_SIGMOID: case GGML_UNARY_OP_HARDSIGMOID: case GGML_UNARY_OP_HARDSWISH: case GGML_UNARY_OP_GELU_QUICK: case GGML_UNARY_OP_TANH: + case GGML_UNARY_OP_EXP: return ggml_is_contiguous(op->src[0]); default: return false; @@ -5168,6 +4397,8 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g } return true; } break; + case GGML_OP_OUT_PROD: + return op->type == GGML_TYPE_F32 && op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && op->ne[2] == 1 && op->ne[3] == 1; case GGML_OP_GET_ROWS: { switch (op->src[0]->type) { @@ -5213,10 +4444,10 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g case GGML_OP_CONCAT: { ggml_type src0_type = op->src[0]->type; - int dim = op->op_params[0]; - return ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]) && src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16 && dim == 2; + return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16; } break; case GGML_OP_DUP: + case GGML_OP_ARGMAX: case GGML_OP_NONE: case GGML_OP_RESHAPE: case GGML_OP_REPEAT: @@ -5225,11 +4456,17 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g case GGML_OP_TRANSPOSE: case GGML_OP_NORM: case GGML_OP_ADD: + case GGML_OP_ADD1: + case GGML_OP_LOG: + case GGML_OP_SUB: case GGML_OP_MUL: case GGML_OP_DIV: case GGML_OP_RMS_NORM: case GGML_OP_SCALE: case GGML_OP_SQR: + case GGML_OP_SQRT: + case GGML_OP_SIN: + case GGML_OP_COS: case GGML_OP_CLAMP: return true; case GGML_OP_CONT: @@ -5243,6 +4480,7 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g // TODO: add support for the new F32 operations return op->src[0]->type == GGML_TYPE_F16; case GGML_OP_POOL_2D: + case GGML_OP_SUM: case GGML_OP_SUM_ROWS: case GGML_OP_ARGSORT: case GGML_OP_ACC: @@ -5251,6 +4489,7 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g case GGML_OP_PAD: case GGML_OP_LEAKY_RELU: case GGML_OP_TIMESTEP_EMBEDDING: + case GGML_OP_RWKV_WKV6: return true; default: return false; @@ -5268,9 +4507,23 @@ static bool ggml_backend_sycl_device_supports_buft(ggml_backend_dev_t dev, ggml_ return buft_ctx->device == sycl_ctx->device; } +static int64_t get_op_batch_size(const ggml_tensor * op) { + switch (op->op) { + case GGML_OP_GET_ROWS: + return 0; + case GGML_OP_MUL_MAT: + return op->ne[1]; + case GGML_OP_MUL_MAT_ID: + case GGML_OP_ROPE: + return op->ne[2]; + default: + return ggml_nrows(op); + } +} + static bool ggml_backend_sycl_device_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) { const int min_batch_size = 32; - return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS && op->op != GGML_OP_MUL_MAT_ID; + return get_op_batch_size(op) >= min_batch_size; GGML_UNUSED(dev); } @@ -5408,16 +4661,17 @@ ggml_backend_reg_t ggml_backend_sycl_reg() { dev_ctx->description = prop.get_name(); ggml_backend_dev_t dev = new ggml_backend_device { - /* .interface = */ ggml_backend_sycl_device_interface, - /* .reg = */ ®, - /* .context = */ dev_ctx + /* .iface = */ ggml_backend_sycl_device_interface, + /* .reg = */ ®, + /* .context = */ dev_ctx }; ctx->devices.push_back(dev); } reg = ggml_backend_reg { - /* .interface = */ ggml_backend_sycl_reg_interface, - /* .context = */ ctx + /* .api_version = */ GGML_BACKEND_API_VERSION, + /* .iface = */ ggml_backend_sycl_reg_interface, + /* .context = */ ctx }; } @@ -5435,7 +4689,7 @@ ggml_backend_t ggml_backend_sycl_init(int device) { ggml_backend_sycl_context * ctx = new ggml_backend_sycl_context(device); if (ctx == nullptr) { - fprintf(stderr, "%s: error: failed to allocate context\n", __func__); + GGML_LOG_ERROR("%s: error: failed to allocate context\n", __func__); return nullptr; }; @@ -5449,3 +4703,4 @@ ggml_backend_t ggml_backend_sycl_init(int device) { return sycl_backend; } +GGML_BACKEND_DL_IMPL(ggml_backend_sycl_reg) diff --git a/ggml/src/ggml-sycl/norm.cpp b/ggml/src/ggml-sycl/norm.cpp index b3159b9d1..72d8fdb87 100644 --- a/ggml/src/ggml-sycl/norm.cpp +++ b/ggml/src/ggml-sycl/norm.cpp @@ -8,7 +8,6 @@ static void norm_f32(const float* x, float* dst, const int ncols, const float ep const int nthreads = item_ct1.get_local_range(2); const int nwarps = nthreads / WARP_SIZE; - assert(nwarps % WARP_SIZE == 0); sycl::float2 mean_var = sycl::float2(0.f, 0.f); for (int col = tid; col < ncols; col += block_size) { @@ -55,7 +54,6 @@ static void group_norm_f32(const float* x, float* dst, const int group_size, con int end = start + group_size; const int nthreads = item_ct1.get_local_range(2); const int nwarps = nthreads / WARP_SIZE; - assert(nwarps % WARP_SIZE == 0); start += item_ct1.get_local_id(2); int nreduce = nwarps / WARP_SIZE; @@ -144,7 +142,6 @@ static void rms_norm_f32(const float* x, float* dst, const int ncols, const floa const int tid = item_ct1.get_local_id(2); const int nthreads = item_ct1.get_local_range(2); const int nwarps = nthreads / WARP_SIZE; - assert(nwarps % WARP_SIZE == 0); float tmp = 0.0f; // partial sum for thread in warp for (int col = tid; col < ncols; col += block_size) { @@ -202,6 +199,7 @@ static void norm_f32_sycl(const float* x, float* dst, const int ncols, } else { const int work_group_size = ggml_sycl_info().max_work_group_sizes[device]; + assert(work_group_size % (WARP_SIZE * WARP_SIZE) == 0); const sycl::range<3> block_dims(1, 1, work_group_size); /* DPCT1049:17: The work-group size passed to the SYCL kernel may exceed @@ -244,6 +242,7 @@ static void group_norm_f32_sycl(const float* x, float* dst, } else { const int work_group_size = ggml_sycl_info().max_work_group_sizes[device]; + assert(work_group_size % (WARP_SIZE * WARP_SIZE) == 0); const sycl::range<3> block_dims(1, 1, work_group_size); /* DPCT1049:18: The work-group size passed to the SYCL kernel may exceed @@ -290,6 +289,7 @@ static void rms_norm_f32_sycl(const float* x, float* dst, const int ncols, } else { const int work_group_size = ggml_sycl_info().max_work_group_sizes[device]; + assert(work_group_size % (WARP_SIZE * WARP_SIZE) == 0); const sycl::range<3> block_dims(1, 1, work_group_size); /* DPCT1049:19: The work-group size passed to the SYCL kernel may exceed diff --git a/ggml/src/ggml-sycl/outprod.cpp b/ggml/src/ggml-sycl/outprod.cpp new file mode 100644 index 000000000..ef9af0b76 --- /dev/null +++ b/ggml/src/ggml-sycl/outprod.cpp @@ -0,0 +1,56 @@ +#include +#include +#include "outprod.hpp" + + +void ggml_sycl_op_out_prod(ggml_backend_sycl_context& ctx, const ggml_tensor* src0, + const ggml_tensor* src1, ggml_tensor* dst) { + + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguous(dst)); + + GGML_TENSOR_BINARY_OP_LOCALS + + // Get SYCL queue + dpct::queue_ptr stream = ctx.stream(); + + // Dimension checks + GGML_ASSERT(ne01 == ne11); // Inner dimensions must match + GGML_ASSERT(ne0 == ne00); // Output rows match src0 rows + GGML_ASSERT(ne1 == ne10); // Output cols match src1 cols + + // Get data pointers + const float* src0_d = (const float*)src0->data; + const float* src1_d = (const float*)src1->data; + float* dst_d = (float*)dst->data; + + // GEMM parameters + const float alpha = 1.0f; + const float beta = 0.0f; + + // Handle transposition of src1 + const bool src1_T = ggml_is_transposed(src1); + const oneapi::mkl::transpose src1_op = + src1_T ? oneapi::mkl::transpose::nontrans : oneapi::mkl::transpose::trans; + const int64_t ldb = (src1_T ? nb10 : nb11) / sizeof(float); + + try { + // Perform matrix multiplication using oneMKL GEMM +#ifdef GGML_SYCL_NVIDIA + oneapi::mkl::blas::column_major::gemm(oneapi::mkl::backend_selector{ *stream }, + oneapi::mkl::transpose::nontrans, src1_op, ne0, ne1, ne01, alpha, src0_d, + ne00, src1_d, ldb, beta, dst_d, ne0); +#else + oneapi::mkl::blas::column_major::gemm(*stream, oneapi::mkl::transpose::nontrans, src1_op, ne0, ne1, ne01, alpha, + src0_d, ne00, src1_d, ldb, beta, dst_d, ne0); +#endif + } + catch (sycl::exception const& exc) { + std::cerr << exc.what() << std::endl; + GGML_ASSERT(false); + } +} diff --git a/ggml/src/ggml-sycl/outprod.hpp b/ggml/src/ggml-sycl/outprod.hpp new file mode 100644 index 000000000..9c042738a --- /dev/null +++ b/ggml/src/ggml-sycl/outprod.hpp @@ -0,0 +1,11 @@ +#ifndef GGML_SYCL_OUTPROD_HPP +#define GGML_SYCL_OUTPROD_HPP + +#include "common.hpp" + +void ggml_sycl_op_out_prod(ggml_backend_sycl_context& ctx, const ggml_tensor* src0, + const ggml_tensor* src1, ggml_tensor* dst); + + +#endif // GGML_SYCL_OUTPROD_HPP + diff --git a/ggml/src/ggml-sycl/presets.hpp b/ggml/src/ggml-sycl/presets.hpp index 340ab8e93..af1890727 100644 --- a/ggml/src/ggml-sycl/presets.hpp +++ b/ggml/src/ggml-sycl/presets.hpp @@ -25,6 +25,11 @@ #define SYCL_RELU_BLOCK_SIZE 256 #define SYCL_HARDSIGMOID_BLOCK_SIZE 256 #define SYCL_HARDSWISH_BLOCK_SIZE 256 +#define SYCL_EXP_BLOCK_SIZE 256 +#define SYCL_NEG_BLOCK_SIZE 256 +#define SYCL_SIGMOID_BLOCK_SIZE 256 +#define SYCL_SQRT_BLOCK_SIZE 256 +#define SYCL_SIN_BLOCK_SIZE 256 #define SYCL_SQR_BLOCK_SIZE 256 #define SYCL_CPY_BLOCK_SIZE 32 #define SYCL_SCALE_BLOCK_SIZE 256 @@ -41,6 +46,7 @@ #define SYCL_ACC_BLOCK_SIZE 256 #define SYCL_IM2COL_BLOCK_SIZE 256 #define SYCL_POOL2D_BLOCK_SIZE 256 +#define SYCL_ARGMAX_BLOCK_SIZE 256 #define SYCL_CONV_TRANPOSE_1D_BLOCK_SIZE 256 #define SYCL_TIMESTEP_EMBEDDING_BLOCK_SIZE 256 diff --git a/ggml/src/ggml-sycl/vecdotq.hpp b/ggml/src/ggml-sycl/vecdotq.hpp index d2dccade2..c5942008a 100644 --- a/ggml/src/ggml-sycl/vecdotq.hpp +++ b/ggml/src/ggml-sycl/vecdotq.hpp @@ -968,8 +968,8 @@ vec_dot_iq3_xxs_q8_1(const void *__restrict__ vbq, grid1[0] ^ signs[0], signs[0], std::minus<>()); const int grid_h = dpct::vectorized_binary( grid2[0] ^ signs[1], signs[1], std::minus<>()); - sumi = dpct::dp4a(grid_l, *((int *)q8 + 0), sumi); - sumi = dpct::dp4a(grid_h, *((int *)q8 + 1), sumi); + sumi = dpct::dp4a(grid_l, *((const int *)q8 + 0), sumi); + sumi = dpct::dp4a(grid_h, *((const int *)q8 + 1), sumi); q8 += 8; aux32 >>= 7; } @@ -1009,8 +1009,8 @@ vec_dot_iq3_s_q8_1(const void *__restrict__ vbq, grid1[0] ^ signs0, signs0, std::minus<>()); const int grid_h = dpct::vectorized_binary( grid2[0] ^ signs1, signs1, std::minus<>()); - sumi = dpct::dp4a(grid_l, *((int *)q8 + 0), sumi); - sumi = dpct::dp4a(grid_h, *((int *)q8 + 1), sumi); + sumi = dpct::dp4a(grid_l, *((const int *)q8 + 0), sumi); + sumi = dpct::dp4a(grid_h, *((const int *)q8 + 1), sumi); q8 += 8; } const float d = diff --git a/ggml/src/ggml-sycl/wkv6.cpp b/ggml/src/ggml-sycl/wkv6.cpp new file mode 100644 index 000000000..4c737f4bf --- /dev/null +++ b/ggml/src/ggml-sycl/wkv6.cpp @@ -0,0 +1,138 @@ +#include +#include "wkv6.hpp" + +constexpr int WKV_BLOCK_SIZE = 64; // Matching CUDA_WKV_BLOCK_SIZE + +// Helper function for the main kernel +static void rwkv_wkv_f32_kernel( + const int B, const int T, const int C, const int H, + const float* k, const float* v, const float* r, + const float* tf, const float* td, const float* s, + float* dst, const sycl::nd_item<3>& item_ct1, float* shared_mem) { + + const int tid = item_ct1.get_local_id(2); + const int bid = item_ct1.get_group(2); + + const int head_size = WKV_BLOCK_SIZE; + const int batch_i = bid / H; + const int head_i = bid % H; + const int state_size = C * head_size; + const int n_seq_tokens = T / B; + + // Set up shared memory pointers + float* _k = shared_mem; + float* _r = _k + head_size; + float* _tf = _r + head_size; + float* _td = _tf + head_size; + + // Local state array + float state[WKV_BLOCK_SIZE]; + + // Load initial state + #pragma unroll + for (int i = 0; i < head_size; i++) { + state[i] = s[batch_i * state_size + head_i * head_size * head_size + i * head_size + tid]; + } + + // Sync threads before shared memory operations + item_ct1.barrier(sycl::access::fence_space::local_space); + + // Load time-mixing parameters + _tf[tid] = tf[head_i * head_size + tid]; + item_ct1.barrier(sycl::access::fence_space::local_space); + + // Main sequence processing loop + for (int t = batch_i * n_seq_tokens * C + head_i * head_size + tid; + t < (batch_i + 1) * n_seq_tokens * C + head_i * head_size + tid; + t += C) { + + item_ct1.barrier(sycl::access::fence_space::local_space); + + // Load current timestep data to shared memory + _k[tid] = k[t]; + _r[tid] = r[t]; + _td[tid] = td[t]; + + item_ct1.barrier(sycl::access::fence_space::local_space); + + const float _v = v[t]; + float y = 0; + + // Process in chunks of 4 for better vectorization + sycl::float4 k4, r4, tf4, td4, s4, kv4; + #pragma unroll + for (int j = 0; j < head_size; j += 4) { + // Load data in vec4 chunks + k4 = sycl::float4(_k[j], _k[j+1], _k[j+2], _k[j+3]); + r4 = sycl::float4(_r[j], _r[j+1], _r[j+2], _r[j+3]); + tf4 = sycl::float4(_tf[j], _tf[j+1], _tf[j+2], _tf[j+3]); + td4 = sycl::float4(_td[j], _td[j+1], _td[j+2], _td[j+3]); + s4 = sycl::float4(state[j], state[j+1], state[j+2], state[j+3]); + + // Compute key-value product + sycl::float4 kv4 = k4 * _v; + + // Accumulate weighted sum + y += sycl::dot(r4, tf4 * kv4 + s4); + + // Update state + s4 = s4 * td4 + kv4; + + // Store updated state + state[j] = s4.x(); + state[j+1] = s4.y(); + state[j+2] = s4.z(); + state[j+3] = s4.w(); + } + + dst[t] = y; + } + + // Save final state + #pragma unroll + for (int i = 0; i < head_size; i++) { + dst[T * C + batch_i * state_size + head_i * head_size * head_size + i * head_size + tid] = state[i]; + } +} + +void ggml_sycl_op_rwkv_wkv6(ggml_backend_sycl_context& ctx, const ggml_tensor* src0, + const ggml_tensor* src1, ggml_tensor* dst) { + + const float* k_d = (const float*)dst->src[0]->data; + const float* v_d = (const float*)dst->src[1]->data; + const float* r_d = (const float*)dst->src[2]->data; + const float* tf_d = (const float*)dst->src[3]->data; + const float* td_d = (const float*)dst->src[4]->data; + const float* s_d = (const float*)dst->src[5]->data; + float* dst_d = (float*)dst->data; + + const int64_t B = dst->src[5]->ne[1]; + const int64_t T = dst->src[0]->ne[3]; + const int64_t C = dst->ne[0]; + const int64_t H = dst->src[0]->ne[2]; + + GGML_ASSERT(dst->src[5]->type == GGML_TYPE_F32); + GGML_ASSERT(C % H == 0); + GGML_ASSERT(C / H == WKV_BLOCK_SIZE); // The current sycl kernel is designed for RWKV6, HEAD_SIZE == 64 + + dpct::queue_ptr stream = ctx.stream(); + + // Calculate execution configuration + const size_t shared_mem_size = WKV_BLOCK_SIZE * 4 * sizeof(float); // For k, r, tf, td + sycl::range<3> block_dims(1, 1, C / H); + sycl::range<3> grid_dims(1, 1, B * H); + + // Submit kernel + stream->submit([&](sycl::handler& cgh) { + sycl::local_accessor shared_mem_acc(shared_mem_size, cgh); + + cgh.parallel_for( + sycl::nd_range<3>(grid_dims * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + rwkv_wkv_f32_kernel( + B, T, C, H, k_d, v_d, r_d, tf_d, td_d, s_d, dst_d, + item_ct1, shared_mem_acc.get_pointer() + ); + }); + }); +} diff --git a/ggml/src/ggml-sycl/wkv6.hpp b/ggml/src/ggml-sycl/wkv6.hpp new file mode 100644 index 000000000..ddfa3377b --- /dev/null +++ b/ggml/src/ggml-sycl/wkv6.hpp @@ -0,0 +1,10 @@ +#ifndef GGML_SYCL_WKV6_HPP +#define GGML_SYCL_WKV6_HPP + +#include "common.hpp" + +void ggml_sycl_op_rwkv_wkv6(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor * dst); + + +#endif // GGML_SYCL_WKV6_HPP diff --git a/ggml/src/ggml-threading.cpp b/ggml/src/ggml-threading.cpp new file mode 100644 index 000000000..25a19eedb --- /dev/null +++ b/ggml/src/ggml-threading.cpp @@ -0,0 +1,12 @@ +#include "ggml-threading.h" +#include + +std::mutex ggml_critical_section_mutex; + +void ggml_critical_section_start() { + ggml_critical_section_mutex.lock(); +} + +void ggml_critical_section_end(void) { + ggml_critical_section_mutex.unlock(); +} diff --git a/ggml/src/ggml-threading.h b/ggml/src/ggml-threading.h new file mode 100644 index 000000000..ce975d880 --- /dev/null +++ b/ggml/src/ggml-threading.h @@ -0,0 +1,12 @@ +#pragma once + +#ifdef __cplusplus +extern "C" { +#endif + +void ggml_critical_section_start(void); +void ggml_critical_section_end(void); + +#ifdef __cplusplus +} +#endif diff --git a/ggml/src/ggml-vulkan/CMakeLists.txt b/ggml/src/ggml-vulkan/CMakeLists.txt new file mode 100644 index 000000000..ae0485e04 --- /dev/null +++ b/ggml/src/ggml-vulkan/CMakeLists.txt @@ -0,0 +1,78 @@ +find_package(Vulkan COMPONENTS glslc REQUIRED) + +if (Vulkan_FOUND) + message(STATUS "Vulkan found") + + ggml_add_backend_library(ggml-vulkan + ggml-vulkan.cpp + ../../include/ggml-vulkan.h + ) + + target_link_libraries(ggml-vulkan PRIVATE Vulkan::Vulkan) + target_include_directories(ggml-vulkan PRIVATE ${CMAKE_CURRENT_BINARY_DIR}) + + # Workaround to the "can't dereference invalidated vector iterator" bug in clang-cl debug build + # Posssibly relevant: https://stackoverflow.com/questions/74748276/visual-studio-no-displays-the-correct-length-of-stdvector + if (MSVC AND CMAKE_CXX_COMPILER_ID STREQUAL "Clang") + add_compile_definitions(_ITERATOR_DEBUG_LEVEL=0) + endif() + + if (GGML_VULKAN_CHECK_RESULTS) + add_compile_definitions(GGML_VULKAN_CHECK_RESULTS) + endif() + + if (GGML_VULKAN_DEBUG) + add_compile_definitions(GGML_VULKAN_DEBUG) + endif() + + if (GGML_VULKAN_MEMORY_DEBUG) + add_compile_definitions(GGML_VULKAN_MEMORY_DEBUG) + endif() + + if (GGML_VULKAN_SHADER_DEBUG_INFO) + add_compile_definitions(GGML_VULKAN_SHADER_DEBUG_INFO) + endif() + + if (GGML_VULKAN_PERF) + add_compile_definitions(GGML_VULKAN_PERF) + endif() + + if (GGML_VULKAN_VALIDATE) + add_compile_definitions(GGML_VULKAN_VALIDATE) + endif() + + if (GGML_VULKAN_RUN_TESTS) + add_compile_definitions(GGML_VULKAN_RUN_TESTS) + endif() + + add_subdirectory(vulkan-shaders) + + set (_ggml_vk_genshaders_cmd vulkan-shaders-gen) + set (_ggml_vk_header ${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan-shaders.hpp) + set (_ggml_vk_source ${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan-shaders.cpp) + set (_ggml_vk_input_dir ${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders) + set (_ggml_vk_output_dir ${CMAKE_CURRENT_BINARY_DIR}/vulkan-shaders.spv) + + file(GLOB _ggml_vk_shader_deps "${_ggml_vk_input_dir}/*.comp") + + add_custom_command( + OUTPUT ${_ggml_vk_header} + ${_ggml_vk_source} + + COMMAND ${_ggml_vk_genshaders_cmd} + --glslc ${Vulkan_GLSLC_EXECUTABLE} + --input-dir ${_ggml_vk_input_dir} + --output-dir ${_ggml_vk_output_dir} + --target-hpp ${_ggml_vk_header} + --target-cpp ${_ggml_vk_source} + --no-clean + + DEPENDS ${_ggml_vk_shader_deps} + COMMENT "Generate vulkan shaders" + ) + + target_sources(ggml-vulkan PRIVATE ${_ggml_vk_source} ${_ggml_vk_header}) + +else() + message(WARNING "Vulkan not found") +endif() diff --git a/ggml/src/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp similarity index 75% rename from ggml/src/ggml-vulkan.cpp rename to ggml/src/ggml-vulkan/ggml-vulkan.cpp index 83c37ea9c..c7ac0e8f7 100644 --- a/ggml/src/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -106,6 +106,15 @@ struct vk_matmul_pipeline_struct { typedef std::shared_ptr vk_matmul_pipeline; +struct vk_matmul_pipeline2 { + vk_matmul_pipeline2() { + f16acc = std::make_shared(); + f32acc = std::make_shared(); + } + vk_matmul_pipeline f32acc; + vk_matmul_pipeline f16acc; +}; + struct vk_device_struct; typedef std::shared_ptr vk_device; typedef std::weak_ptr vk_device_ref; @@ -149,23 +158,27 @@ struct vk_device_struct { std::string name; uint64_t max_memory_allocation_size; bool fp16; + bool pipeline_robustness; vk::Device device; uint32_t vendor_id; vk_queue compute_queue; vk_queue transfer_queue; bool single_queue; uint32_t subgroup_size; + uint32_t shader_core_count; bool uma; + bool coopmat2; size_t idx; vk_matmul_pipeline pipeline_matmul_f32; vk_matmul_pipeline pipeline_matmul_f32_f16; - vk_matmul_pipeline pipeline_matmul_f16; - vk_matmul_pipeline pipeline_matmul_f16_f32; + vk_matmul_pipeline2 pipeline_matmul_f16; + vk_matmul_pipeline2 pipeline_matmul_f16_f32; vk_pipeline pipeline_matmul_split_k_reduce; - vk_matmul_pipeline pipeline_dequant_mul_mat_mat[GGML_TYPE_COUNT]; + vk_matmul_pipeline2 pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_COUNT]; + vk_matmul_pipeline2 pipeline_dequant_mul_mat_mat[GGML_TYPE_COUNT]; vk_matmul_pipeline pipeline_matmul_id_f32; vk_matmul_pipeline pipeline_matmul_id_f16; @@ -183,9 +196,10 @@ struct vk_device_struct { vk_pipeline pipeline_get_rows[GGML_TYPE_COUNT]; vk_pipeline pipeline_get_rows_f32[GGML_TYPE_COUNT]; vk_pipeline pipeline_acc_f32; - vk_pipeline pipeline_add_f32, pipeline_add_f16_f32_f16; - vk_pipeline pipeline_mul_f32; - vk_pipeline pipeline_div_f32; + vk_pipeline pipeline_add_f32, pipeline_add_f32_norepeat; + vk_pipeline pipeline_add_f16_f32_f16, pipeline_add_f16_f32_f16_norepeat; + vk_pipeline pipeline_mul_f32, pipeline_mul_f32_norepeat; + vk_pipeline pipeline_div_f32, pipeline_div_f32_norepeat; vk_pipeline pipeline_concat_f32, pipeline_concat_f16, pipeline_concat_i32; vk_pipeline pipeline_upscale_f32; vk_pipeline pipeline_scale_f32; @@ -196,6 +210,7 @@ struct vk_device_struct { vk_pipeline pipeline_pad_f32; vk_pipeline pipeline_repeat_f32; vk_pipeline pipeline_cpy_f32_f32, pipeline_cpy_f32_f16, pipeline_cpy_f16_f16; + vk_pipeline pipeline_contig_cpy_f32_f32, pipeline_contig_cpy_f32_f16, pipeline_contig_cpy_f16_f16; vk_pipeline pipeline_norm_f32; vk_pipeline pipeline_group_norm_f32; vk_pipeline pipeline_rms_norm_f32; @@ -207,6 +222,7 @@ struct vk_device_struct { vk_pipeline pipeline_tanh_f32; vk_pipeline pipeline_diag_mask_inf_f32; vk_pipeline pipeline_soft_max_f32, pipeline_soft_max_f32_f16; + vk_pipeline pipeline_soft_max_f32_wg512, pipeline_soft_max_f32_f16_wg512; vk_pipeline pipeline_rope_norm_f32, pipeline_rope_norm_f16; vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16; vk_pipeline pipeline_argsort_f32; @@ -215,6 +231,14 @@ struct vk_device_struct { vk_pipeline pipeline_timestep_embedding_f32; vk_pipeline pipeline_pool2d_f32; + // [2][2][2] is for {f16acc,f32acc}x{large,small_rows}x{unaligned, aligned} + vk_pipeline pipeline_flash_attn_f32_f16_D64[GGML_TYPE_COUNT][2][2][2]; + vk_pipeline pipeline_flash_attn_f32_f16_D80[GGML_TYPE_COUNT][2][2][2]; + vk_pipeline pipeline_flash_attn_f32_f16_D96[GGML_TYPE_COUNT][2][2][2]; + vk_pipeline pipeline_flash_attn_f32_f16_D112[GGML_TYPE_COUNT][2][2][2]; + vk_pipeline pipeline_flash_attn_f32_f16_D128[GGML_TYPE_COUNT][2][2][2]; + vk_pipeline pipeline_flash_attn_f32_f16_D256[GGML_TYPE_COUNT][2][2][2]; + std::unordered_map pipelines; std::unordered_map pipeline_descriptor_set_requirements; @@ -326,6 +350,40 @@ struct vk_mat_vec_id_push_constants { uint32_t nei0; uint32_t ne11; }; +struct vk_flash_attn_push_constants { + uint32_t N; + uint32_t KV; + + uint32_t ne1; + uint32_t ne2; + uint32_t ne3; + + uint32_t neq2; + uint32_t neq3; + uint32_t nek2; + uint32_t nek3; + uint32_t nev2; + uint32_t nev3; + uint32_t nem1; + + uint32_t nb02; + uint32_t nb03; + uint32_t nb12; + uint32_t nb13; + uint32_t nb22; + uint32_t nb23; + uint32_t nb31; + + float scale; + float max_bias; + float logit_softcap; + + uint32_t mask; + uint32_t n_head_log2; + float m0; + float m1; +}; + struct vk_op_push_constants { uint32_t KX; uint32_t KY; @@ -339,7 +397,45 @@ struct vk_op_unary_push_constants { uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13; uint32_t d_offset; float param1; float param2; + uint32_t ne0_012mp; uint32_t ne0_012L; + uint32_t ne0_01mp; uint32_t ne0_01L; + uint32_t ne0_0mp; uint32_t ne0_0L; + uint32_t ne1_012mp; uint32_t ne1_012L; + uint32_t ne1_01mp; uint32_t ne1_01L; + uint32_t ne1_0mp; uint32_t ne1_0L; }; +static_assert(sizeof(vk_op_unary_push_constants) <= 128, "sizeof(vk_op_unary_push_constants) must be <= 128"); + +// See https://gmplib.org/~tege/divcnst-pldi94.pdf figure 4.1. +// Precompute mp (m' in the paper) and L such that division +// can be computed using a multiply (high 32b of 64b result) +// and a shift: +// +// n/d = (mulhi(n, mp) + n) >> L; +void init_fastdiv_values(uint32_t d, uint32_t &mp, uint32_t &L) +{ + // compute L = ceil(log2(d)); + L = 0; + while (L < 32 && (uint32_t{1} << L) < d) { + L++; + } + + mp = (uint32_t)((uint64_t{1} << 32) * ((uint64_t{1} << L) - d) / d + 1); +} + +template void init_pushconst_fastdiv(T &p) { + static_assert(!std::is_const::value, "unexpected type"); +} + +template <> void init_pushconst_fastdiv(vk_op_unary_push_constants &p) { + // Compute magic values to divide by these six numbers. + init_fastdiv_values(p.ne02*p.ne01*p.ne00, p.ne0_012mp, p.ne0_012L); + init_fastdiv_values(p.ne01*p.ne00, p.ne0_01mp, p.ne0_01L); + init_fastdiv_values(p.ne00, p.ne0_0mp, p.ne0_0L); + init_fastdiv_values(p.ne12*p.ne11*p.ne10, p.ne1_012mp, p.ne1_012L); + init_fastdiv_values(p.ne11*p.ne10, p.ne1_01mp, p.ne1_01L); + init_fastdiv_values(p.ne10, p.ne1_0mp, p.ne1_0L); +} struct vk_op_binary_push_constants { uint32_t ne; @@ -377,6 +473,7 @@ struct vk_op_soft_max_push_constants { float m0; float m1; uint32_t n_head_log2; + uint32_t nrows_x; }; struct vk_op_argsort_push_constants { @@ -641,7 +738,7 @@ static uint32_t compile_count = 0; static std::mutex compile_count_mutex; static std::condition_variable compile_count_cond; -static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipeline, const std::string name, size_t spv_size, const void* spv_data, const std::string entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, std::vector specialization_constants, uint32_t align) { +static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipeline, const std::string name, size_t spv_size, const void* spv_data, const std::string entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, std::vector specialization_constants, uint32_t align, bool disable_robustness) { VK_LOG_DEBUG("ggml_vk_create_pipeline(" << device->name << ", " << name << ", " << entrypoint << ", " << parameter_count << ", " << push_constant_size << ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " << align << ")"); GGML_ASSERT(parameter_count > 0); GGML_ASSERT(wg_denoms[0] > 0 && wg_denoms[1] > 0 && wg_denoms[2] > 0); // NOLINT @@ -711,6 +808,15 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin vk::PipelineCreateFlags(), pipeline_shader_create_info, pipeline->layout); + + vk::PipelineRobustnessCreateInfoEXT rci; + + if (device->pipeline_robustness && disable_robustness) { + rci.storageBuffers = vk::PipelineRobustnessBufferBehaviorEXT::eDisabled; + rci.uniformBuffers = vk::PipelineRobustnessBufferBehaviorEXT::eDisabled; + compute_pipeline_create_info.setPNext(&rci); + } + pipeline->pipeline = device->device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value; { @@ -722,6 +828,12 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin std::lock_guard guard(compile_count_mutex); assert(compile_count > 0); compile_count--; + + // "Progress bar" for shader compiles + static uint32_t total_compile_count = 0; + if ((total_compile_count++ % 10) == 0) { + std::cerr << "."; + } } compile_count_cond.notify_all(); } @@ -1047,7 +1159,6 @@ static vk_buffer ggml_vk_create_buffer(vk_device& device, size_t size, vk::Memor return buf; } - buf->size = size; vk::BufferCreateInfo buffer_create_info{ vk::BufferCreateFlags(), size, @@ -1075,7 +1186,6 @@ static vk_buffer ggml_vk_create_buffer(vk_device& device, size_t size, vk::Memor if (memory_type_index == UINT32_MAX) { device->device.destroyBuffer(buf->buffer); - buf->size = 0; throw vk::OutOfDeviceMemoryError("No suitable memory type found"); } @@ -1092,13 +1202,11 @@ static vk_buffer ggml_vk_create_buffer(vk_device& device, size_t size, vk::Memor } catch (const vk::SystemError& e) { device->device.destroyBuffer(buf->buffer); - buf->size = 0; throw e; } } else { // Out of Host/Device memory, clean up buffer device->device.destroyBuffer(buf->buffer); - buf->size = 0; throw e; } } @@ -1111,6 +1219,7 @@ static vk_buffer ggml_vk_create_buffer(vk_device& device, size_t size, vk::Memor device->device.bindBufferMemory(buf->buffer, buf->device_memory, 0); buf->device = device; + buf->size = size; #ifdef GGML_VULKAN_MEMORY_DEBUG device->memory_logger->log_allocation(buf, size); @@ -1200,41 +1309,129 @@ static void ggml_vk_wait_events(vk_context& ctx, std::vector&& events ); } +// number of rows/cols for flash attention shader +static constexpr uint32_t flash_attention_num_small_rows = 32; +static std::array fa_rows_cols(uint32_t D, uint32_t clamp, ggml_type type, bool small_rows) { + GGML_UNUSED(clamp); + + // small rows, large cols + if (small_rows) { + return {flash_attention_num_small_rows, 128}; + } + // small cols to reduce register count + if (ggml_is_quantized(type) || D == 256) { + return {64, 32}; + } + return {64, 64}; +}; + + static void ggml_vk_load_shaders(vk_device& device) { VK_LOG_DEBUG("ggml_vk_load_shaders(" << device->name << ")"); + std::cerr << "ggml_vulkan: Compiling shaders"; + + // some shaders require the subgroup size to be 16 or larger + const uint32_t subgroup_size_16 = std::max(device->subgroup_size, 16u); + // mulmat - std::initializer_list warptile_l = { 128, 128, 128, 16, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size }; - std::initializer_list warptile_m = { 128, 64, 64, 16, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size }; - std::initializer_list warptile_s = { std::max(device->subgroup_size, 16u), 32, 32, 16, 32, 32, 2, 2, 2, device->subgroup_size }; + std::vector l_warptile, m_warptile, s_warptile, + l_warptile_mmq, m_warptile_mmq, s_warptile_mmq, + l_warptile_mmq_k, m_warptile_mmq_k, s_warptile_mmq_k, + l_warptile_mmqid, m_warptile_mmqid, s_warptile_mmqid; + std::array l_wg_denoms, m_wg_denoms, s_wg_denoms, + l_mmq_wg_denoms, m_mmq_wg_denoms, s_mmq_wg_denoms, + l_mmq_wg_denoms_k, m_mmq_wg_denoms_k, s_mmq_wg_denoms_k, + l_mmqid_wg_denoms, m_mmqid_wg_denoms, s_mmqid_wg_denoms; - std::initializer_list warptile_mmq_l = { 128, 128, 128, 32, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size }; - std::initializer_list warptile_mmq_m = { 128, 64, 64, 32, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size }; - std::initializer_list warptile_mmq_s = { std::max(device->subgroup_size, 16u), 32, 32, 32, 32, 32, 2, 2, 2, device->subgroup_size }; + uint32_t l_align, m_align, s_align; + if (device->coopmat2) { + // spec constants and tile sizes for non-quant matmul/matmul_id + l_warptile = { 256, 128, 256, 64 }; + m_warptile = { 256, 128, 128, 64 }; + s_warptile = { 128, 32, 16, 64 }; + l_wg_denoms = {128, 256, 1 }; + m_wg_denoms = {128, 128, 1 }; + s_wg_denoms = { 32, 16, 1 }; - std::array l_wg_denoms = {128, 128, 1 }; - std::array m_wg_denoms = { 64, 64, 1 }; - std::array s_wg_denoms = { 32, 32, 1 }; + // spec constants and tile sizes for quant matmul (non-Qi_K) + l_warptile_mmq = { 256, 128, 256, 64 }; + m_warptile_mmq = { 256, 128, 128, 64 }; + s_warptile_mmq = { 256, 128, 128, 64 }; + l_mmq_wg_denoms = { 128, 256, 1 }; + m_mmq_wg_denoms = { 128, 128, 1 }; + s_mmq_wg_denoms = { 128, 128, 1 }; - uint32_t l_align = 128; - uint32_t m_align = 64; - uint32_t s_align = 32; + // spec constants and tile sizes for quant matmul (Qi_K) + l_warptile_mmq_k = { 256, 128, 512, 16 }; + m_warptile_mmq_k = { 256, 128, 256, 16 }; + s_warptile_mmq_k = { 256, 32, 128, 64 }; + l_mmq_wg_denoms_k = { 128, 512, 1 }; + m_mmq_wg_denoms_k = { 128, 256, 1 }; + s_mmq_wg_denoms_k = { 32, 128, 1 }; + + // spec constants and tile sizes for quant matmul_id + l_warptile_mmqid = { 256, 128, 128, 16 }; + m_warptile_mmqid = { 256, 128, 64, 16 }; + s_warptile_mmqid = { 256, 64, 64, 16 }; + l_mmqid_wg_denoms = { 128, 128, 1 }; + m_mmqid_wg_denoms = { 128, 64, 1 }; + s_mmqid_wg_denoms = { 64, 64, 1 }; + + l_align = 128; + m_align = 64; + s_align = 32; + } else { + l_warptile = { 128, 128, 128, 16, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size }; + m_warptile = { 128, 64, 64, 16, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size }; + s_warptile = { subgroup_size_16, 32, 32, 16, 32, 32, 2, 2, 2, device->subgroup_size }; + l_warptile_mmq = { 128, 128, 128, 32, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size }; + m_warptile_mmq = { 128, 64, 64, 32, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size }; + s_warptile_mmq = { subgroup_size_16, 32, 32, 32, 32, 32, 2, 2, 2, device->subgroup_size }; + l_mmq_wg_denoms = l_wg_denoms = {128, 128, 1 }; + m_mmq_wg_denoms = m_wg_denoms = { 64, 64, 1 }; + s_mmq_wg_denoms = s_wg_denoms = { 32, 32, 1 }; + l_align = 128; + m_align = 64; + s_align = 32; + + // Fallback to smaller sizes if there's not enough shared memory. Given the current shaders + // and tile sizes, this should handle 16KB, 32KB, and 48KB+. + // This logic doesn't explicitly account for the 12KB row_ids in the mul_mat_mat_id shaders. + // But the numbers happen to work out for 32KB shared memory size that when using the medium + // size there's enough room for everything, and we assert for this. + uint32_t shmem_needed = (l_warptile[1] + l_warptile[2]) * (l_warptile[3] + 1) * sizeof(float); + if (shmem_needed > device->properties.limits.maxComputeSharedMemorySize) { + l_warptile = m_warptile; + l_wg_denoms = m_wg_denoms; + shmem_needed = (l_warptile[1] + l_warptile[2]) * (l_warptile[3] + 1) * sizeof(float); + GGML_ASSERT(shmem_needed <= device->properties.limits.maxComputeSharedMemorySize); + } + if (device->properties.limits.maxComputeSharedMemorySize >= 32768) { + // assert mul_mat_mat_id shaders will fit. + GGML_ASSERT(shmem_needed + 3072*4 <= device->properties.limits.maxComputeSharedMemorySize); + } + + shmem_needed = (l_warptile_mmq[1] + l_warptile_mmq[2]) * (l_warptile_mmq[3] + 1) * sizeof(float); + if (shmem_needed > device->properties.limits.maxComputeSharedMemorySize) { + if (device->properties.limits.maxComputeSharedMemorySize == 32768) { + l_warptile_mmq = m_warptile_mmq; + l_mmq_wg_denoms = m_mmq_wg_denoms; + } else { + l_warptile_mmq = s_warptile_mmq; + l_mmq_wg_denoms = s_mmq_wg_denoms; + } + shmem_needed = (l_warptile_mmq[1] + l_warptile_mmq[2]) * (l_warptile_mmq[3] + 1) * sizeof(float); + GGML_ASSERT(shmem_needed <= device->properties.limits.maxComputeSharedMemorySize); + } + if (device->properties.limits.maxComputeSharedMemorySize >= 32768) { + // assert mul_mat_mat_id shaders will fit. + GGML_ASSERT(shmem_needed + 3072*4 <= device->properties.limits.maxComputeSharedMemorySize); + } + } device->pipeline_matmul_f32 = std::make_shared(); device->pipeline_matmul_f32_f16 = std::make_shared(); - device->pipeline_matmul_f16_f32 = std::make_shared(); - device->pipeline_matmul_f16 = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K] = std::make_shared(); - device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL] = std::make_shared(); device->pipeline_matmul_id_f32 = std::make_shared(); device->pipeline_matmul_id_f16_f32 = std::make_shared(); @@ -1252,7 +1449,7 @@ static void ggml_vk_load_shaders(vk_device& device) { device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL] = std::make_shared(); std::vector> compiles; - auto const &ggml_vk_create_pipeline = [&](vk_device& device, vk_pipeline& pipeline, const std::string &name, size_t spv_size, const void* spv_data, const std::string &entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, std::vector&& specialization_constants, uint32_t align) { + auto const &ggml_vk_create_pipeline = [&](vk_device& device, vk_pipeline& pipeline, const std::string &name, size_t spv_size, const void* spv_data, const std::string &entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, const std::vector& specialization_constants, uint32_t align, bool disable_robustness = false) { { // wait until fewer than N compiles are in progress uint32_t N = std::max(1u, std::thread::hardware_concurrency()); @@ -1262,459 +1459,249 @@ static void ggml_vk_load_shaders(vk_device& device) { } compile_count++; } - compiles.push_back(std::async(ggml_vk_create_pipeline_func, std::ref(device), std::ref(pipeline), name, spv_size, spv_data, entrypoint, parameter_count, push_constant_size, wg_denoms, specialization_constants, align)); + compiles.push_back(std::async(ggml_vk_create_pipeline_func, std::ref(device), std::ref(pipeline), name, spv_size, spv_data, entrypoint, parameter_count, push_constant_size, wg_denoms, specialization_constants, align, disable_robustness)); }; +#if defined(VK_NV_cooperative_matrix2) + if (device->coopmat2) { + + auto const &fa_wg_denoms = [&](uint32_t D, uint32_t clamp, ggml_type type, bool small_rows) -> std::array { + return {fa_rows_cols(D, clamp, type, small_rows)[0], 1, 1}; + }; + + auto const &fa_spec_constants = [&](uint32_t D, uint32_t clamp, ggml_type type, bool small_rows) -> std::vector { + // For large number of rows, 128 invocations seems to work best. + // For small number of rows (e.g. N==1), 256 works better. But matrix granularity for 256 is 32, so we + // can't use 256 for D==80. + uint32_t wg_size = (small_rows && (D % 32) == 0) ? 256 : 128; + auto rows_cols = fa_rows_cols(D, clamp, type, small_rows); + return {wg_size, rows_cols[0], rows_cols[1], (D), clamp}; + }; + +#define CREATE_FA2(TYPE, NAMELC, D) \ + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D[TYPE][0][0][0], "flash_attn_f32_f16_D" #D "_f16acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc_cm2_len, flash_attn_f32_f16_ ## NAMELC ## _f16acc_cm2_data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(D,1,TYPE,false), fa_spec_constants(D,1,TYPE,false), 1); \ + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D[TYPE][0][0][1], "flash_attn_f32_f16_D" #D "_aligned_f16acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc_cm2_len, flash_attn_f32_f16_ ## NAMELC ## _f16acc_cm2_data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(D,0,TYPE,false), fa_spec_constants(D,0,TYPE,false), fa_rows_cols(D,0,TYPE,false)[1]); \ + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D[TYPE][1][0][0], "flash_attn_f32_f16_D" #D "_f32acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _cm2_len, flash_attn_f32_f16_ ## NAMELC ## _cm2_data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(D,1,TYPE,false), fa_spec_constants(D,1,TYPE,false), 1); \ + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D[TYPE][1][0][1], "flash_attn_f32_f16_D" #D "_aligned_f32acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _cm2_len, flash_attn_f32_f16_ ## NAMELC ## _cm2_data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(D,0,TYPE,false), fa_spec_constants(D,0,TYPE,false), fa_rows_cols(D,0,TYPE,false)[1]); \ + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D[TYPE][0][1][0], "flash_attn_f32_f16_D" #D "_f16acc_smallrows" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc_cm2_len, flash_attn_f32_f16_ ## NAMELC ## _f16acc_cm2_data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(D,1,TYPE,true), fa_spec_constants(D,1,TYPE,true), 1); \ + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D[TYPE][0][1][1], "flash_attn_f32_f16_D" #D "_aligned_f16acc_smallrows" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc_cm2_len, flash_attn_f32_f16_ ## NAMELC ## _f16acc_cm2_data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(D,0,TYPE,true), fa_spec_constants(D,0,TYPE,true), fa_rows_cols(D,0,TYPE,true)[1]); \ + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D[TYPE][1][1][0], "flash_attn_f32_f16_D" #D "_f32acc_smallrows" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _cm2_len, flash_attn_f32_f16_ ## NAMELC ## _cm2_data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(D,1,TYPE,true), fa_spec_constants(D,1,TYPE,true), 1); \ + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_f32_f16_D ## D[TYPE][1][1][1], "flash_attn_f32_f16_D" #D "_aligned_f32acc_smallrows" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _cm2_len, flash_attn_f32_f16_ ## NAMELC ## _cm2_data, "main", 5, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(D,0,TYPE,true), fa_spec_constants(D,0,TYPE,true), fa_rows_cols(D,0,TYPE,true)[1]); \ + +#define CREATE_FA(TYPE, NAMELC) \ + CREATE_FA2(TYPE, NAMELC, 64) \ + CREATE_FA2(TYPE, NAMELC, 80) \ + CREATE_FA2(TYPE, NAMELC, 96) \ + CREATE_FA2(TYPE, NAMELC, 112) \ + CREATE_FA2(TYPE, NAMELC, 128) \ + CREATE_FA2(TYPE, NAMELC, 256) + + CREATE_FA(GGML_TYPE_F16, f16) + CREATE_FA(GGML_TYPE_Q4_0, q4_0) + CREATE_FA(GGML_TYPE_Q4_1, q4_1) + CREATE_FA(GGML_TYPE_Q5_0, q5_0) + CREATE_FA(GGML_TYPE_Q5_1, q5_1) + CREATE_FA(GGML_TYPE_Q8_0, q8_0) + // K dequants currently disabled because D dimension is rounded up to 256 and runs inefficiently + //CREATE_FA(GGML_TYPE_Q2_K, q2_k) + //CREATE_FA(GGML_TYPE_Q3_K, q3_k) + //CREATE_FA(GGML_TYPE_Q4_K, q4_k) + //CREATE_FA(GGML_TYPE_Q5_K, q5_k) + //CREATE_FA(GGML_TYPE_Q6_K, q6_k) + CREATE_FA(GGML_TYPE_IQ4_NL, iq4_nl) +#undef CREATE_FA + + // Create 6 variants, {s,m,l}x{unaligned,aligned} +#define CREATE_MM(PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _cm2_len, NAMELC ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _cm2_len, NAMELC ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _cm2_len, NAMELC ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _cm2_len, NAMELC ## _aligned ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## _aligned ## F16ACC ## _cm2_len, NAMELC ## _aligned ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _cm2_len, NAMELC ## _aligned ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align); \ + + // Create 2 variants, {f16,f32} accumulator +#define CREATE_MM2(PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT) \ + CREATE_MM(PIPELINE_NAME . f16acc, NAMELC, _f16acc, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT) \ + CREATE_MM(PIPELINE_NAME . f32acc, NAMELC, , WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT) \ + + CREATE_MM(pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3) + CREATE_MM(pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3) + + CREATE_MM2(pipeline_matmul_f16, matmul_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 3) + CREATE_MM2(pipeline_matmul_f16_f32, matmul_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 3) + CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q4_0].f16acc, matmul_q4_0_f16, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) + CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q4_1].f16acc, matmul_q4_1_f16, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) + CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q5_0].f16acc, matmul_q5_0_f16, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) + CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q5_1].f16acc, matmul_q5_1_f16, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) + CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q8_0].f16acc, matmul_q8_0_f16, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) + CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q2_K].f16acc, matmul_q2_k_f16, _f16acc, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3) + CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q3_K].f16acc, matmul_q3_k_f16, _f16acc, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3) + CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q4_K].f16acc, matmul_q4_k_f16, _f16acc, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3) + CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q5_K].f16acc, matmul_q5_k_f16, _f16acc, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3) + CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q6_K].f16acc, matmul_q6_k_f16, _f16acc, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3) + CREATE_MM(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ4_NL].f16acc, matmul_iq4_nl_f16, _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) + + CREATE_MM(pipeline_matmul_id_f32, matmul_id_f32_f32, , wg_denoms, warptile, vk_mat_mat_id_push_constants, 4) + CREATE_MM(pipeline_matmul_id_f16, matmul_id_f16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, 4) + CREATE_MM(pipeline_matmul_id_f16_f32, matmul_id_f16_f32, , wg_denoms, warptile, vk_mat_mat_id_push_constants, 4) + + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_q4_0_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4) + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_q4_1_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4) + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_q5_0_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4) + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_q5_1_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4) + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_q8_0_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4) + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_q2_k_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4) + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_q3_k_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4) + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_q4_k_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4) + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_q5_k_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4) + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K], matmul_id_q6_k_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4) + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL], matmul_id_iq4_nl_f32, , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4) +#undef CREATE_MM +#undef CREATE_MM2 + } else +#endif if (device->fp16) { - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_f32_len, matmul_f32_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_f32_len, matmul_f32_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_f32_len, matmul_f32_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_f32_aligned_len, matmul_f32_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_f32_aligned_len, matmul_f32_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_f32_aligned_len, matmul_f32_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + // Create 6 variants, {s,m,l}x{unaligned,aligned} +#define CREATE_MM(PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _len, NAMELC ## _aligned ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## _aligned ## F16ACC ## _len, NAMELC ## _aligned ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _len, NAMELC ## _aligned ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align); \ - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->l, "matmul_f32_f16_l", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->m, "matmul_f32_f16_m", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->s, "matmul_f32_f16_s", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->a_l, "matmul_f32_f16_aligned_l", matmul_f32_f16_aligned_len, matmul_f32_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->a_m, "matmul_f32_f16_aligned_m", matmul_f32_f16_aligned_len, matmul_f32_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->a_s, "matmul_f32_f16_aligned_s", matmul_f32_f16_aligned_len, matmul_f32_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + CREATE_MM(pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_matmul_f16.f32acc, matmul_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_matmul_f16_f32.f32acc, matmul_f16_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->a_l, "matmul_f16_aligned_l", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->a_m, "matmul_f16_aligned_m", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->a_s, "matmul_f16_aligned_s", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f32acc, matmul_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f32acc, matmul_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f32acc, matmul_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1].f32acc, matmul_q5_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f32acc, matmul_q8_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->l, "matmul_f16_f32_l", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->m, "matmul_f16_f32_m", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->s, "matmul_f16_f32_s", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->a_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->a_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->a_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f32acc, matmul_q2_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f32acc, matmul_q3_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f32acc, matmul_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f32acc, matmul_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f32acc, matmul_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f32acc, matmul_iq4_nl_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->l, "matmul_q4_0_f32_l", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->m, "matmul_q4_0_f32_m", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->s, "matmul_q4_0_f32_s", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + // If there's not enough shared memory for row_ids and the result tile, don't create these pipelines. + if (device->properties.limits.maxComputeSharedMemorySize >= 32768) { + CREATE_MM(pipeline_matmul_id_f32, matmul_id_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4); + CREATE_MM(pipeline_matmul_id_f16, matmul_id_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 4); + CREATE_MM(pipeline_matmul_id_f16_f32, matmul_id_f16_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_1_f32_l", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_1_f32_m", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_1_f32_s", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_1_f32_aligned_l", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_1_f32_aligned_m", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_1_f32_aligned_s", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_q5_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_q8_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->l, "matmul_q5_0_f32_l", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->m, "matmul_q5_0_f32_m", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->s, "matmul_q5_0_f32_s", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_l, "matmul_q5_0_f32_aligned_l", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_m, "matmul_q5_0_f32_aligned_m", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_s, "matmul_q5_0_f32_aligned_s", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->l, "matmul_q5_1_f32_l", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->m, "matmul_q5_1_f32_m", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->s, "matmul_q5_1_f32_s", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_l, "matmul_q5_1_f32_aligned_l", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_m, "matmul_q5_1_f32_aligned_m", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_s, "matmul_q5_1_f32_aligned_s", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->l, "matmul_q8_0_f32_l", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->m, "matmul_q8_0_f32_m", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->s, "matmul_q8_0_f32_s", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_l, "matmul_q8_0_f32_aligned_l", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_m, "matmul_q8_0_f32_aligned_m", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_s, "matmul_q8_0_f32_aligned_s", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->l, "matmul_q2_k_f32_l", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->m, "matmul_q2_k_f32_m", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->s, "matmul_q2_k_f32_s", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_l, "matmul_q2_k_f32_aligned_l", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_m, "matmul_q2_k_f32_aligned_m", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_s, "matmul_q2_k_f32_aligned_s", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->l, "matmul_q3_k_f32_l", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->m, "matmul_q3_k_f32_m", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->s, "matmul_q3_k_f32_s", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_l, "matmul_q3_k_f32_aligned_l", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_m, "matmul_q3_k_f32_aligned_m", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_s, "matmul_q3_k_f32_aligned_s", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->l, "matmul_q4_k_f32_l", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->m, "matmul_q4_k_f32_m", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->s, "matmul_q4_k_f32_s", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_l, "matmul_q4_k_f32_aligned_l", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_m, "matmul_q4_k_f32_aligned_m", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_s, "matmul_q4_k_f32_aligned_s", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->l, "matmul_q5_k_f32_l", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->m, "matmul_q5_k_f32_m", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->s, "matmul_q5_k_f32_s", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_l, "matmul_q5_k_f32_aligned_l", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_m, "matmul_q5_k_f32_aligned_m", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_s, "matmul_q5_k_f32_aligned_s", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->l, "matmul_q6_k_f32_l", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->m, "matmul_q6_k_f32_m", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->s, "matmul_q6_k_f32_s", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_l, "matmul_q6_k_f32_aligned_l", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_m, "matmul_q6_k_f32_aligned_m", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_s, "matmul_q6_k_f32_aligned_s", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->l, "matmul_iq4_nl_f32_l", matmul_iq4_nl_f32_len, matmul_iq4_nl_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->m, "matmul_iq4_nl_f32_m", matmul_iq4_nl_f32_len, matmul_iq4_nl_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->s, "matmul_iq4_nl_f32_s", matmul_iq4_nl_f32_len, matmul_iq4_nl_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_l, "matmul_iq4_nl_f32_aligned_l", matmul_iq4_nl_f32_aligned_len, matmul_iq4_nl_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_m, "matmul_iq4_nl_f32_aligned_m", matmul_iq4_nl_f32_aligned_len, matmul_iq4_nl_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_s, "matmul_iq4_nl_f32_aligned_s", matmul_iq4_nl_f32_aligned_len, matmul_iq4_nl_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->l, "matmul_id_f32_l", matmul_id_f32_f32_len, matmul_id_f32_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->m, "matmul_id_f32_m", matmul_id_f32_f32_len, matmul_id_f32_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->s, "matmul_id_f32_s", matmul_id_f32_f32_len, matmul_id_f32_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->a_l, "matmul_id_f32_aligned_l", matmul_id_f32_f32_aligned_len, matmul_id_f32_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->a_m, "matmul_id_f32_aligned_m", matmul_id_f32_f32_aligned_len, matmul_id_f32_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->a_s, "matmul_id_f32_aligned_s", matmul_id_f32_f32_aligned_len, matmul_id_f32_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->l, "matmul_id_f16_l", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->m, "matmul_id_f16_m", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->s, "matmul_id_f16_s", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->a_l, "matmul_id_f16_aligned_l", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->a_m, "matmul_id_f16_aligned_m", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->a_s, "matmul_id_f16_aligned_s", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->l, "matmul_id_f16_f32_l", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->m, "matmul_id_f16_f32_m", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->s, "matmul_id_f16_f32_s", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->a_l, "matmul_id_f16_f32_aligned_l", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->a_m, "matmul_id_f16_f32_aligned_m", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->a_s, "matmul_id_f16_f32_aligned_s", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->l, "matmul_id_q4_1_f32_l", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->m, "matmul_id_q4_1_f32_m", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->s, "matmul_id_q4_1_f32_s", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_l, "matmul_id_q4_1_f32_aligned_l", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_m, "matmul_id_q4_1_f32_aligned_m", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_s, "matmul_id_q4_1_f32_aligned_s", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->l, "matmul_id_q5_0_f32_l", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->m, "matmul_id_q5_0_f32_m", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->s, "matmul_id_q5_0_f32_s", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_l, "matmul_id_q5_0_f32_aligned_l", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_m, "matmul_id_q5_0_f32_aligned_m", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_s, "matmul_id_q5_0_f32_aligned_s", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->l, "matmul_id_q5_1_f32_l", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->m, "matmul_id_q5_1_f32_m", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->s, "matmul_id_q5_1_f32_s", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_l, "matmul_id_q5_1_f32_aligned_l", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_m, "matmul_id_q5_1_f32_aligned_m", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_s, "matmul_id_q5_1_f32_aligned_s", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->l, "matmul_id_q8_0_f32_l", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->m, "matmul_id_q8_0_f32_m", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->s, "matmul_id_q8_0_f32_s", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_l, "matmul_id_q8_0_f32_aligned_l", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_m, "matmul_id_q8_0_f32_aligned_m", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_s, "matmul_id_q8_0_f32_aligned_s", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->l, "matmul_id_q2_k_f32_l", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->m, "matmul_id_q2_k_f32_m", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->s, "matmul_id_q2_k_f32_s", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_l, "matmul_id_q2_k_f32_aligned_l", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_m, "matmul_id_q2_k_f32_aligned_m", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_s, "matmul_id_q2_k_f32_aligned_s", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->l, "matmul_id_q3_k_f32_l", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->m, "matmul_id_q3_k_f32_m", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->s, "matmul_id_q3_k_f32_s", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_l, "matmul_id_q3_k_f32_aligned_l", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_m, "matmul_id_q3_k_f32_aligned_m", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_s, "matmul_id_q3_k_f32_aligned_s", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->l, "matmul_id_q4_k_f32_l", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->m, "matmul_id_q4_k_f32_m", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->s, "matmul_id_q4_k_f32_s", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_l, "matmul_id_q4_k_f32_aligned_l", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_m, "matmul_id_q4_k_f32_aligned_m", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_s, "matmul_id_q4_k_f32_aligned_s", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->l, "matmul_id_q5_k_f32_l", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->m, "matmul_id_q5_k_f32_m", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->s, "matmul_id_q5_k_f32_s", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_l, "matmul_id_q5_k_f32_aligned_l", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_m, "matmul_id_q5_k_f32_aligned_m", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_s, "matmul_id_q5_k_f32_aligned_s", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->l, "matmul_id_q6_k_f32_l", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->m, "matmul_id_q6_k_f32_m", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->s, "matmul_id_q6_k_f32_s", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_l, "matmul_id_q6_k_f32_aligned_l", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_m, "matmul_id_q6_k_f32_aligned_m", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_s, "matmul_id_q6_k_f32_aligned_s", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->l, "matmul_id_iq4_nl_f32_l", matmul_id_iq4_nl_f32_len, matmul_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->m, "matmul_id_iq4_nl_f32_m", matmul_id_iq4_nl_f32_len, matmul_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->s, "matmul_id_iq4_nl_f32_s", matmul_id_iq4_nl_f32_len, matmul_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_l, "matmul_id_iq4_nl_f32_aligned_l", matmul_id_iq4_nl_f32_aligned_len, matmul_id_iq4_nl_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_m, "matmul_id_iq4_nl_f32_aligned_m", matmul_id_iq4_nl_f32_aligned_len, matmul_id_iq4_nl_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_s, "matmul_id_iq4_nl_f32_aligned_s", matmul_id_iq4_nl_f32_aligned_len, matmul_id_iq4_nl_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_q2_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_q3_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K], matmul_id_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL], matmul_id_iq4_nl_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + } +#undef CREATE_MM } else { - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_f32_fp32_len, matmul_f32_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_f32_fp32_len, matmul_f32_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_f32_fp32_len, matmul_f32_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_f32_aligned_fp32_len, matmul_f32_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_f32_aligned_fp32_len, matmul_f32_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_f32_aligned_fp32_len, matmul_f32_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + // Create 6 variants, {s,m,l}x{unaligned,aligned} +#define CREATE_MM(PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _fp32_len, NAMELC ## _aligned ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## _aligned ## F16ACC ## _fp32_len, NAMELC ## _aligned ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _fp32_len, NAMELC ## _aligned ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align); \ - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->l, "matmul_f32_f16_l", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->m, "matmul_f32_f16_m", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->s, "matmul_f32_f16_s", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->a_l, "matmul_f32_f16_aligned_l", matmul_f32_f16_aligned_fp32_len, matmul_f32_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->a_m, "matmul_f32_f16_aligned_m", matmul_f32_f16_aligned_fp32_len, matmul_f32_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f32_f16->a_s, "matmul_f32_f16_aligned_s", matmul_f32_f16_aligned_fp32_len, matmul_f32_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + CREATE_MM(pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_matmul_f16.f32acc, matmul_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_matmul_f16_f32.f32acc, matmul_f16_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->a_l, "matmul_f16_aligned_l", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->a_m, "matmul_f16_aligned_m", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16->a_s, "matmul_f16_aligned_s", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f32acc, matmul_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f32acc, matmul_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f32acc, matmul_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1].f32acc, matmul_q5_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f32acc, matmul_q8_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->l, "matmul_f16_f32_l", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->m, "matmul_f16_f32_m", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->s, "matmul_f16_f32_s", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->a_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->a_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_f16_f32->a_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f32acc, matmul_q2_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f32acc, matmul_q3_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f32acc, matmul_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f32acc, matmul_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f32acc, matmul_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); + CREATE_MM(pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f32acc, matmul_iq4_nl_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->l, "matmul_q4_0_f32_l", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->m, "matmul_q4_0_f32_m", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->s, "matmul_q4_0_f32_s", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + // If there's not enough shared memory for row_ids and the result tile, don't create these pipelines. + if (device->properties.limits.maxComputeSharedMemorySize >= 32768) { + CREATE_MM(pipeline_matmul_id_f32, matmul_id_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4); + CREATE_MM(pipeline_matmul_id_f16, matmul_id_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 4); + CREATE_MM(pipeline_matmul_id_f16_f32, matmul_id_f16_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_1_f32_l", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_1_f32_m", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_1_f32_s", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_1_f32_aligned_l", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_1_f32_aligned_m", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_1_f32_aligned_s", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_q5_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_q8_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->l, "matmul_q5_0_f32_l", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->m, "matmul_q5_0_f32_m", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->s, "matmul_q5_0_f32_s", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_l, "matmul_q5_0_f32_aligned_l", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_m, "matmul_q5_0_f32_aligned_m", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_s, "matmul_q5_0_f32_aligned_s", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->l, "matmul_q5_1_f32_l", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->m, "matmul_q5_1_f32_m", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->s, "matmul_q5_1_f32_s", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_l, "matmul_q5_1_f32_aligned_l", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_m, "matmul_q5_1_f32_aligned_m", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_s, "matmul_q5_1_f32_aligned_s", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->l, "matmul_q8_0_f32_l", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->m, "matmul_q8_0_f32_m", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->s, "matmul_q8_0_f32_s", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_l, "matmul_q8_0_f32_aligned_l", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_m, "matmul_q8_0_f32_aligned_m", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_s, "matmul_q8_0_f32_aligned_s", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->l, "matmul_q2_k_f32_l", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->m, "matmul_q2_k_f32_m", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->s, "matmul_q2_k_f32_s", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_l, "matmul_q2_k_f32_aligned_l", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_m, "matmul_q2_k_f32_aligned_m", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_s, "matmul_q2_k_f32_aligned_s", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->l, "matmul_q3_k_f32_l", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->m, "matmul_q3_k_f32_m", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->s, "matmul_q3_k_f32_s", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_l, "matmul_q3_k_f32_aligned_l", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_m, "matmul_q3_k_f32_aligned_m", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_s, "matmul_q3_k_f32_aligned_s", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->l, "matmul_q4_k_f32_l", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->m, "matmul_q4_k_f32_m", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->s, "matmul_q4_k_f32_s", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_l, "matmul_q4_k_f32_aligned_l", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_m, "matmul_q4_k_f32_aligned_m", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_s, "matmul_q4_k_f32_aligned_s", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->l, "matmul_q5_k_f32_l", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->m, "matmul_q5_k_f32_m", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->s, "matmul_q5_k_f32_s", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_l, "matmul_q5_k_f32_aligned_l", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_m, "matmul_q5_k_f32_aligned_m", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_s, "matmul_q5_k_f32_aligned_s", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->l, "matmul_q6_k_f32_l", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->m, "matmul_q6_k_f32_m", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->s, "matmul_q6_k_f32_s", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_l, "matmul_q6_k_f32_aligned_l", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_m, "matmul_q6_k_f32_aligned_m", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_s, "matmul_q6_k_f32_aligned_s", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->l, "matmul_iq4_nl_f32_l", matmul_iq4_nl_f32_fp32_len, matmul_iq4_nl_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->m, "matmul_iq4_nl_f32_m", matmul_iq4_nl_f32_fp32_len, matmul_iq4_nl_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->s, "matmul_iq4_nl_f32_s", matmul_iq4_nl_f32_fp32_len, matmul_iq4_nl_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_l, "matmul_iq4_nl_f32_aligned_l", matmul_iq4_nl_f32_aligned_fp32_len, matmul_iq4_nl_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_m, "matmul_iq4_nl_f32_aligned_m", matmul_iq4_nl_f32_aligned_fp32_len, matmul_iq4_nl_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_s, "matmul_iq4_nl_f32_aligned_s", matmul_iq4_nl_f32_aligned_fp32_len, matmul_iq4_nl_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->l, "matmul_id_f32_l", matmul_id_f32_f32_fp32_len, matmul_id_f32_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->m, "matmul_id_f32_m", matmul_id_f32_f32_fp32_len, matmul_id_f32_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->s, "matmul_id_f32_s", matmul_id_f32_f32_fp32_len, matmul_id_f32_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->a_l, "matmul_id_f32_aligned_l", matmul_id_f32_f32_aligned_fp32_len, matmul_id_f32_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->a_m, "matmul_id_f32_aligned_m", matmul_id_f32_f32_aligned_fp32_len, matmul_id_f32_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->a_s, "matmul_id_f32_aligned_s", matmul_id_f32_f32_aligned_fp32_len, matmul_id_f32_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->l, "matmul_id_f16_l", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->m, "matmul_id_f16_m", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->s, "matmul_id_f16_s", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->a_l, "matmul_id_f16_aligned_l", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->a_m, "matmul_id_f16_aligned_m", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16->a_s, "matmul_id_f16_aligned_s", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->l, "matmul_id_f16_f32_l", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->m, "matmul_id_f16_f32_m", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->s, "matmul_id_f16_f32_s", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->a_l, "matmul_id_f16_f32_aligned_l", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->a_m, "matmul_id_f16_f32_aligned_m", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f16_f32->a_s, "matmul_id_f16_f32_aligned_s", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->l, "matmul_id_q4_1_f32_l", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->m, "matmul_id_q4_1_f32_m", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->s, "matmul_id_q4_1_f32_s", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_l, "matmul_id_q4_1_f32_aligned_l", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_m, "matmul_id_q4_1_f32_aligned_m", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_s, "matmul_id_q4_1_f32_aligned_s", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->l, "matmul_id_q5_0_f32_l", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->m, "matmul_id_q5_0_f32_m", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->s, "matmul_id_q5_0_f32_s", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_l, "matmul_id_q5_0_f32_aligned_l", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_m, "matmul_id_q5_0_f32_aligned_m", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_s, "matmul_id_q5_0_f32_aligned_s", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->l, "matmul_id_q5_1_f32_l", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->m, "matmul_id_q5_1_f32_m", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->s, "matmul_id_q5_1_f32_s", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_l, "matmul_id_q5_1_f32_aligned_l", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_m, "matmul_id_q5_1_f32_aligned_m", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_s, "matmul_id_q5_1_f32_aligned_s", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->l, "matmul_id_q8_0_f32_l", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->m, "matmul_id_q8_0_f32_m", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->s, "matmul_id_q8_0_f32_s", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_l, "matmul_id_q8_0_f32_aligned_l", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_m, "matmul_id_q8_0_f32_aligned_m", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_s, "matmul_id_q8_0_f32_aligned_s", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->l, "matmul_id_q2_k_f32_l", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->m, "matmul_id_q2_k_f32_m", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->s, "matmul_id_q2_k_f32_s", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_l, "matmul_id_q2_k_f32_aligned_l", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_m, "matmul_id_q2_k_f32_aligned_m", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_s, "matmul_id_q2_k_f32_aligned_s", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->l, "matmul_id_q3_k_f32_l", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->m, "matmul_id_q3_k_f32_m", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->s, "matmul_id_q3_k_f32_s", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_l, "matmul_id_q3_k_f32_aligned_l", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_m, "matmul_id_q3_k_f32_aligned_m", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_s, "matmul_id_q3_k_f32_aligned_s", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->l, "matmul_id_q4_k_f32_l", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->m, "matmul_id_q4_k_f32_m", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->s, "matmul_id_q4_k_f32_s", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_l, "matmul_id_q4_k_f32_aligned_l", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_m, "matmul_id_q4_k_f32_aligned_m", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_s, "matmul_id_q4_k_f32_aligned_s", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->l, "matmul_id_q5_k_f32_l", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->m, "matmul_id_q5_k_f32_m", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->s, "matmul_id_q5_k_f32_s", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_l, "matmul_id_q5_k_f32_aligned_l", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_m, "matmul_id_q5_k_f32_aligned_m", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_s, "matmul_id_q5_k_f32_aligned_s", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->l, "matmul_id_q6_k_f32_l", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->m, "matmul_id_q6_k_f32_m", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->s, "matmul_id_q6_k_f32_s", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_l, "matmul_id_q6_k_f32_aligned_l", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_m, "matmul_id_q6_k_f32_aligned_m", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_s, "matmul_id_q6_k_f32_aligned_s", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->l, "matmul_id_iq4_nl_f32_l", matmul_id_iq4_nl_f32_fp32_len, matmul_id_iq4_nl_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->m, "matmul_id_iq4_nl_f32_m", matmul_id_iq4_nl_f32_fp32_len, matmul_id_iq4_nl_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->s, "matmul_id_iq4_nl_f32_s", matmul_id_iq4_nl_f32_fp32_len, matmul_id_iq4_nl_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_l, "matmul_id_iq4_nl_f32_aligned_l", matmul_id_iq4_nl_f32_aligned_fp32_len, matmul_id_iq4_nl_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_m, "matmul_id_iq4_nl_f32_aligned_m", matmul_id_iq4_nl_f32_aligned_fp32_len, matmul_id_iq4_nl_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_s, "matmul_id_iq4_nl_f32_aligned_s", matmul_id_iq4_nl_f32_aligned_fp32_len, matmul_id_iq4_nl_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_q2_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_q3_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K], matmul_id_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL], matmul_id_iq4_nl_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4); + } +#undef CREATE_MM } // mul mat vec - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f32_f32", mul_mat_vec_f32_f32_f32_len, mul_mat_vec_f32_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32_f32", mul_mat_vec_f16_f32_f32_len, mul_mat_vec_f16_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32_f32", mul_mat_vec_q4_0_f32_f32_len, mul_mat_vec_q4_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32_f32", mul_mat_vec_q4_1_f32_f32_len, mul_mat_vec_q4_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32_f32", mul_mat_vec_q5_0_f32_f32_len, mul_mat_vec_q5_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32_f32", mul_mat_vec_q5_1_f32_f32_len, mul_mat_vec_q5_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32_f32", mul_mat_vec_q8_0_f32_f32_len, mul_mat_vec_q8_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f32_f32", mul_mat_vec_q2_k_f32_f32_len, mul_mat_vec_q2_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f32_f32", mul_mat_vec_q3_k_f32_f32_len, mul_mat_vec_q3_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f32_f32", mul_mat_vec_q4_k_f32_f32_len, mul_mat_vec_q4_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f32_f32", mul_mat_vec_q5_k_f32_f32_len, mul_mat_vec_q5_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f32_f32", mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f32_f32", mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + // computing two rows per workgroup is a benefit for Q4_0 -> Q5_1, but not for Q8_0. + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f32_f32", mul_mat_vec_f32_f32_f32_len, mul_mat_vec_f32_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32_f32", mul_mat_vec_f16_f32_f32_len, mul_mat_vec_f16_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32_f32", mul_mat_vec_q4_0_f32_f32_len, mul_mat_vec_q4_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32_f32", mul_mat_vec_q4_1_f32_f32_len, mul_mat_vec_q4_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32_f32", mul_mat_vec_q5_0_f32_f32_len, mul_mat_vec_q5_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32_f32", mul_mat_vec_q5_1_f32_f32_len, mul_mat_vec_q5_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32_f32", mul_mat_vec_q8_0_f32_f32_len, mul_mat_vec_q8_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size, 1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f32_f32", mul_mat_vec_q2_k_f32_f32_len, mul_mat_vec_q2_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f32_f32", mul_mat_vec_q3_k_f32_f32_len, mul_mat_vec_q3_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f32_f32", mul_mat_vec_q4_k_f32_f32_len, mul_mat_vec_q4_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f32_f32", mul_mat_vec_q5_k_f32_f32_len, mul_mat_vec_q5_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f32_f32", mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f32_f32", mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f16_f32", mul_mat_vec_f32_f16_f32_len, mul_mat_vec_f32_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f16_f32", mul_mat_vec_f16_f16_f32_len, mul_mat_vec_f16_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f16_f32", mul_mat_vec_q4_0_f16_f32_len, mul_mat_vec_q4_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f16_f32", mul_mat_vec_q4_1_f16_f32_len, mul_mat_vec_q4_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f16_f32", mul_mat_vec_q5_0_f16_f32_len, mul_mat_vec_q5_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f16_f32", mul_mat_vec_q5_1_f16_f32_len, mul_mat_vec_q5_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f16_f32", mul_mat_vec_q8_0_f16_f32_len, mul_mat_vec_q8_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f16_f32", mul_mat_vec_q2_k_f16_f32_len, mul_mat_vec_q2_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f16_f32", mul_mat_vec_q3_k_f16_f32_len, mul_mat_vec_q3_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f16_f32", mul_mat_vec_q4_k_f16_f32_len, mul_mat_vec_q4_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f16_f32", mul_mat_vec_q5_k_f16_f32_len, mul_mat_vec_q5_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f16_f32", mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f16_f32", mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f16_f32", mul_mat_vec_f32_f16_f32_len, mul_mat_vec_f32_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f16_f32", mul_mat_vec_f16_f16_f32_len, mul_mat_vec_f16_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f16_f32", mul_mat_vec_q4_0_f16_f32_len, mul_mat_vec_q4_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f16_f32", mul_mat_vec_q4_1_f16_f32_len, mul_mat_vec_q4_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f16_f32", mul_mat_vec_q5_0_f16_f32_len, mul_mat_vec_q5_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f16_f32", mul_mat_vec_q5_1_f16_f32_len, mul_mat_vec_q5_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f16_f32", mul_mat_vec_q8_0_f16_f32_len, mul_mat_vec_q8_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size, 1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f16_f32", mul_mat_vec_q2_k_f16_f32_len, mul_mat_vec_q2_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f16_f32", mul_mat_vec_q3_k_f16_f32_len, mul_mat_vec_q3_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f16_f32", mul_mat_vec_q4_k_f16_f32_len, mul_mat_vec_q4_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f16_f32", mul_mat_vec_q5_k_f16_f32_len, mul_mat_vec_q5_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f16_f32", mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f16_f32", mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32", mul_mat_vec_id_f32_f32_len, mul_mat_vec_id_f32_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32", mul_mat_vec_id_f16_f32_len, mul_mat_vec_id_f16_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", mul_mat_vec_id_q4_0_f32_len, mul_mat_vec_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", mul_mat_vec_id_q4_1_f32_len, mul_mat_vec_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", mul_mat_vec_id_q5_0_f32_len, mul_mat_vec_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32", mul_mat_vec_id_q5_1_f32_len, mul_mat_vec_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32", mul_mat_vec_id_q8_0_f32_len, mul_mat_vec_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_k_f32", mul_mat_vec_id_q2_k_f32_len, mul_mat_vec_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_k_f32", mul_mat_vec_id_q3_k_f32_len, mul_mat_vec_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32", mul_mat_vec_id_q4_k_f32_len, mul_mat_vec_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32", mul_mat_vec_id_q5_k_f32_len, mul_mat_vec_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32", mul_mat_vec_id_q6_k_f32_len, mul_mat_vec_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_id_iq4_nl_f32", mul_mat_vec_id_iq4_nl_f32_len, mul_mat_vec_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32", mul_mat_vec_id_f32_f32_len, mul_mat_vec_id_f32_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32", mul_mat_vec_id_f16_f32_len, mul_mat_vec_id_f16_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", mul_mat_vec_id_q4_0_f32_len, mul_mat_vec_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", mul_mat_vec_id_q4_1_f32_len, mul_mat_vec_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", mul_mat_vec_id_q5_0_f32_len, mul_mat_vec_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32", mul_mat_vec_id_q5_1_f32_len, mul_mat_vec_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32", mul_mat_vec_id_q8_0_f32_len, mul_mat_vec_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size, 1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_k_f32", mul_mat_vec_id_q2_k_f32_len, mul_mat_vec_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_k_f32", mul_mat_vec_id_q3_k_f32_len, mul_mat_vec_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32", mul_mat_vec_id_q4_k_f32_len, mul_mat_vec_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32", mul_mat_vec_id_q5_k_f32_len, mul_mat_vec_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32", mul_mat_vec_id_q6_k_f32_len, mul_mat_vec_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_id_iq4_nl_f32", mul_mat_vec_id_iq4_nl_f32_len, mul_mat_vec_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); // dequant shaders ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16", dequant_f32_len, dequant_f32_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); @@ -1749,7 +1736,7 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q8_0], "get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ4_NL], "get_rows_iq4_nl_f32", get_rows_iq4_nl_f32_len, get_rows_iq4_nl_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256 * 4, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_mul_mat_vec_p021_f16_f32, "mul_mat_vec_p021_f16_f32", mul_mat_vec_p021_f16_f32_len, mul_mat_vec_p021_f16_f32_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 7 * sizeof(uint32_t), {1, 1, 1}, {}, 1); @@ -1762,13 +1749,21 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_f16, "cpy_f32_f16", cpy_f32_f16_len, cpy_f32_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_cpy_f16_f16, "cpy_f16_f16", cpy_f16_f16_len, cpy_f16_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_add_f32, "add_f32", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_add_f16_f32_f16, "add_f16_f32_f16", add_f16_f32_f16_len, add_f16_f32_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f32_f32, "contig_cpy_f32_f32", contig_cpy_f32_f32_len, contig_cpy_f32_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f32_f16, "contig_cpy_f32_f16", contig_cpy_f32_f16_len, contig_cpy_f32_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f16_f16, "contig_cpy_f16_f16", contig_cpy_f16_f16_len, contig_cpy_f16_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_add_f32, "add_f32", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {0}, 1); + ggml_vk_create_pipeline(device, device->pipeline_add_f32_norepeat, "add_f32_norepeat", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {1}, 1); + ggml_vk_create_pipeline(device, device->pipeline_add_f16_f32_f16, "add_f16_f32_f16", add_f16_f32_f16_len, add_f16_f32_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {0}, 1); + ggml_vk_create_pipeline(device, device->pipeline_add_f16_f32_f16_norepeat, "add_f16_f32_f16_norepeat", add_f16_f32_f16_len, add_f16_f32_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {1}, 1); ggml_vk_create_pipeline(device, device->pipeline_acc_f32, "acc_f32", acc_f32_len, acc_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_mul_f32, "mul_f32", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_div_f32, "div_f32", div_f32_len, div_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_mul_f32, "mul_f32", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {0}, 1); + ggml_vk_create_pipeline(device, device->pipeline_mul_f32_norepeat, "mul_f32_norepeat", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {1}, 1); + ggml_vk_create_pipeline(device, device->pipeline_div_f32, "div_f32", div_f32_len, div_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {0}, 1); + ggml_vk_create_pipeline(device, device->pipeline_div_f32_norepeat, "div_f32_norepeat", div_f32_len, div_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {1}, 1); ggml_vk_create_pipeline(device, device->pipeline_concat_f32, "concat_f32", concat_f32_len, concat_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_concat_f16, "concat_f16", concat_f16_len, concat_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); @@ -1797,8 +1792,10 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_wg512, "soft_max_f32_wg512", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 512 }, 1); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16_wg512, "soft_max_f32_f16_wg512", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 512 }, 1); ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f32, "rope_norm_f32", rope_norm_f32_len, rope_norm_f32_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f16, "rope_norm_f16", rope_norm_f16_len, rope_norm_f16_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); @@ -1820,6 +1817,7 @@ static void ggml_vk_load_shaders(vk_device& device) { for (auto &c : compiles) { c.wait(); } + std::cerr << "Done!" << std::endl; } static vk_device ggml_vk_get_device(size_t idx) { @@ -1849,12 +1847,28 @@ static vk_device ggml_vk_get_device(size_t idx) { device->physical_device = physical_devices[dev_num]; const std::vector ext_props = device->physical_device.enumerateDeviceExtensionProperties(); + bool fp16_storage = false; + bool fp16_compute = false; bool maintenance4_support = false; + bool sm_builtins = false; + bool pipeline_robustness = false; + bool coopmat2_support = false; // Check if maintenance4 is supported for (const auto& properties : ext_props) { if (strcmp("VK_KHR_maintenance4", properties.extensionName) == 0) { maintenance4_support = true; + } else if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) { + fp16_storage = true; + } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) { + fp16_compute = true; + } else if (strcmp("VK_NV_shader_sm_builtins", properties.extensionName) == 0) { + sm_builtins = true; + } else if (strcmp("VK_EXT_pipeline_robustness", properties.extensionName) == 0) { + pipeline_robustness = true; + } else if (strcmp("VK_NV_cooperative_matrix2", properties.extensionName) == 0 && + !getenv("GGML_VULKAN_DISABLE_COOPMAT2")) { + coopmat2_support = true; } } @@ -1862,11 +1876,29 @@ static vk_device ggml_vk_get_device(size_t idx) { vk::PhysicalDeviceMaintenance3Properties props3; vk::PhysicalDeviceMaintenance4Properties props4; vk::PhysicalDeviceSubgroupProperties subgroup_props; + vk::PhysicalDeviceShaderSMBuiltinsPropertiesNV sm_props; props2.pNext = &props3; props3.pNext = &subgroup_props; + + VkBaseOutStructure * last_struct = (VkBaseOutStructure *)&subgroup_props; + if (maintenance4_support) { - subgroup_props.pNext = &props4; + last_struct->pNext = (VkBaseOutStructure *)&props4; + last_struct = (VkBaseOutStructure *)&props4; } + if (sm_builtins) { + last_struct->pNext = (VkBaseOutStructure *)&sm_props; + last_struct = (VkBaseOutStructure *)&sm_props; + } + +#if defined(VK_NV_cooperative_matrix2) + vk::PhysicalDeviceCooperativeMatrix2PropertiesNV coopmat2_props; + if (coopmat2_support) { + last_struct->pNext = (VkBaseOutStructure *)&coopmat2_props; + last_struct = (VkBaseOutStructure *)&coopmat2_props; + } +#endif + device->physical_device.getProperties2(&props2); device->properties = props2.properties; @@ -1883,16 +1915,10 @@ static vk_device ggml_vk_get_device(size_t idx) { device->vendor_id = device->properties.vendorID; device->subgroup_size = subgroup_props.subgroupSize; device->uma = device->properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu; - - bool fp16_storage = false; - bool fp16_compute = false; - - for (const auto& properties : ext_props) { - if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) { - fp16_storage = true; - } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) { - fp16_compute = true; - } + if (sm_builtins) { + device->shader_core_count = sm_props.shaderSMCount; + } else { + device->shader_core_count = 0; } const char* GGML_VK_DISABLE_F16 = getenv("GGML_VK_DISABLE_F16"); @@ -1937,10 +1963,112 @@ static vk_device ggml_vk_get_device(size_t idx) { vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES; vk11_features.pNext = &vk12_features; + last_struct = (VkBaseOutStructure *)&vk12_features; + + VkPhysicalDevicePipelineRobustnessFeaturesEXT pl_robustness_features; + pl_robustness_features.pNext = nullptr; + pl_robustness_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_PIPELINE_ROBUSTNESS_FEATURES_EXT; + pl_robustness_features.pipelineRobustness = VK_FALSE; + + if (pipeline_robustness) { + last_struct->pNext = (VkBaseOutStructure *)&pl_robustness_features; + last_struct = (VkBaseOutStructure *)&pl_robustness_features; + device_extensions.push_back("VK_EXT_pipeline_robustness"); + } + +#if defined(VK_NV_cooperative_matrix2) + VkPhysicalDeviceCooperativeMatrix2FeaturesNV coopmat2_features {}; + coopmat2_features.pNext = nullptr; + coopmat2_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_COOPERATIVE_MATRIX_2_FEATURES_NV; + if (coopmat2_support) { + last_struct->pNext = (VkBaseOutStructure *)&coopmat2_features; + last_struct = (VkBaseOutStructure *)&coopmat2_features; + device_extensions.push_back("VK_NV_cooperative_matrix2"); + } +#endif + vkGetPhysicalDeviceFeatures2(device->physical_device, &device_features2); device->fp16 = device->fp16 && vk12_features.shaderFloat16; + device->pipeline_robustness = pl_robustness_features.pipelineRobustness; + + if (coopmat2_support) { +#if defined(VK_NV_cooperative_matrix2) + if (coopmat2_features.cooperativeMatrixWorkgroupScope && + coopmat2_features.cooperativeMatrixFlexibleDimensions && + coopmat2_features.cooperativeMatrixReductions && + coopmat2_features.cooperativeMatrixConversions && + coopmat2_features.cooperativeMatrixPerElementOperations && + coopmat2_features.cooperativeMatrixTensorAddressing && + coopmat2_features.cooperativeMatrixBlockLoads && + vk12_features.bufferDeviceAddress) { + + std::vector flexible_dimensions; + uint32_t count = 0; + + PFN_vkGetPhysicalDeviceCooperativeMatrixFlexibleDimensionsPropertiesNV + _vkGetPhysicalDeviceCooperativeMatrixFlexibleDimensionsPropertiesNV = + (PFN_vkGetPhysicalDeviceCooperativeMatrixFlexibleDimensionsPropertiesNV) + vk_instance.instance.getProcAddr("vkGetPhysicalDeviceCooperativeMatrixFlexibleDimensionsPropertiesNV"); + + _vkGetPhysicalDeviceCooperativeMatrixFlexibleDimensionsPropertiesNV(device->physical_device, &count, nullptr); + + VkCooperativeMatrixFlexibleDimensionsPropertiesNV empty_prop {}; + empty_prop.sType = VK_STRUCTURE_TYPE_COOPERATIVE_MATRIX_FLEXIBLE_DIMENSIONS_PROPERTIES_NV; + flexible_dimensions.resize(count, empty_prop); + + _vkGetPhysicalDeviceCooperativeMatrixFlexibleDimensionsPropertiesNV(device->physical_device, &count, flexible_dimensions.data()); + + bool found_fp16_128 = false, + found_fp16_256 = false, + found_fp32_128 = false, + found_fp32_256 = false; + // need to support fp16*fp16 with fp16/fp32 accumulator, for workgroupsize 128 + // with 32x16x16 and 256 with 32x32x16. + for (auto &prop : flexible_dimensions) { + if (prop.saturatingAccumulation == VK_FALSE && + prop.scope == VK_SCOPE_WORKGROUP_KHR && + prop.AType == VK_COMPONENT_TYPE_FLOAT16_KHR && + prop.BType == VK_COMPONENT_TYPE_FLOAT16_KHR) { + + if (prop.workgroupInvocations == 128 && + prop.MGranularity <= 32 && + prop.NGranularity <= 16 && + prop.KGranularity <= 16) { + if (prop.CType == VK_COMPONENT_TYPE_FLOAT16_KHR && + prop.ResultType == VK_COMPONENT_TYPE_FLOAT16_KHR) { + found_fp16_128 = true; + } + if (prop.CType == VK_COMPONENT_TYPE_FLOAT32_KHR && + prop.ResultType == VK_COMPONENT_TYPE_FLOAT32_KHR) { + found_fp32_128 = true; + } + } + if (prop.workgroupInvocations == 256 && + prop.MGranularity <= 32 && + prop.NGranularity <= 32 && + prop.KGranularity <= 16) { + if (prop.CType == VK_COMPONENT_TYPE_FLOAT16_KHR && + prop.ResultType == VK_COMPONENT_TYPE_FLOAT16_KHR) { + found_fp16_256 = true; + } + if (prop.CType == VK_COMPONENT_TYPE_FLOAT32_KHR && + prop.ResultType == VK_COMPONENT_TYPE_FLOAT32_KHR) { + found_fp32_256 = true; + } + } + } + } + if (found_fp16_128 && found_fp16_256 && + found_fp32_128 && found_fp32_256 && + coopmat2_props.cooperativeMatrixFlexibleDimensionsMaxDimension >= 512) { + device->coopmat2 = true; + } + } +#endif + } + if (!vk11_features.storageBuffer16BitAccess) { std::cerr << "ggml_vulkan: device " << GGML_VK_NAME << idx << " does not support 16-bit storage." << std::endl; throw std::runtime_error("Unsupported device"); @@ -2063,10 +2191,11 @@ static void ggml_vk_print_gpu_info(size_t idx) { fp16 = fp16 && vk12_features.shaderFloat16; std::string device_name = props2.properties.deviceName.data(); - std::cerr << GGML_VK_NAME << idx << ": " << device_name << " (" << driver_props.driverName << ") | uma: " << uma << " | fp16: " << fp16 << " | warp size: " << subgroup_size << std::endl; + GGML_LOG_DEBUG("ggml_vulkan: %zu = %s (%s) | uma: %d | fp16: %d | warp size: %zu\n", + idx, device_name.c_str(), driver_props.driverName.data(), uma, fp16, subgroup_size); if (props2.properties.deviceType == vk::PhysicalDeviceType::eCpu) { - std::cerr << "ggml_vulkan: Warning: Device type is CPU. This is probably not the device you want." << std::endl; + GGML_LOG_DEBUG("ggml_vulkan: Warning: Device type is CPU. This is probably not the device you want.\n"); } } @@ -2121,8 +2250,7 @@ void ggml_vk_instance_init() { }; validation_features.setPNext(nullptr); instance_create_info.setPNext(&validation_features); - - std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; + GGML_LOG_DEBUG("ggml_vulkan: Validation layers enabled\n"); } vk_instance.instance = vk::createInstance(instance_create_info); @@ -2236,8 +2364,7 @@ void ggml_vk_instance_init() { vk_instance.device_indices.push_back(0); } } - - std::cerr << "ggml_vulkan: Found " << vk_instance.device_indices.size() << " Vulkan devices:" << std::endl; + GGML_LOG_DEBUG("ggml_vulkan: Found %zu Vulkan devices:\n", vk_instance.device_indices.size()); for (size_t i = 0; i < vk_instance.device_indices.size(); i++) { ggml_vk_print_gpu_info(i); @@ -2293,7 +2420,7 @@ static vk_pipeline ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type return ctx->device->pipeline_dequant[type]; } -static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type) { +static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type, ggml_prec prec) { VK_LOG_DEBUG("ggml_vk_get_mul_mat_mat_pipeline(" << ggml_type_name(src0_type) << ", " << ggml_type_name(src1_type) << ")"); if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) { return ctx->device->pipeline_matmul_f32; @@ -2301,14 +2428,23 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) { return ctx->device->pipeline_matmul_f32_f16; } - if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) { - return ctx->device->pipeline_matmul_f16_f32; - } - if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) { - return ctx->device->pipeline_matmul_f16; + if (prec == GGML_PREC_DEFAULT && ctx->device->coopmat2) { + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) { + return ctx->device->pipeline_matmul_f16_f32.f16acc; + } + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) { + return ctx->device->pipeline_matmul_f16.f16acc; + } + } else { + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) { + return ctx->device->pipeline_matmul_f16_f32.f32acc; + } + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) { + return ctx->device->pipeline_matmul_f16.f32acc; + } } - if (src1_type != GGML_TYPE_F32) { + if (src1_type != GGML_TYPE_F32 && !ctx->device->coopmat2) { return nullptr; } @@ -2329,7 +2465,11 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte return nullptr; } - return ctx->device->pipeline_dequant_mul_mat_mat[src0_type]; + if (ctx->device->coopmat2) { + assert(src1_type == GGML_TYPE_F16); + return ctx->device->pipeline_dequant_mul_mat_mat_f16[src0_type].f16acc; + } + return ctx->device->pipeline_dequant_mul_mat_mat[src0_type].f32acc; } static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type) { @@ -2958,15 +3098,25 @@ static void ggml_vk_buffer_memset(vk_buffer& dst, size_t offset, uint32_t c, siz dst->device->device.resetFences({ dst->device->fence }); } -static uint32_t ggml_vk_guess_split_k(int m, int n, int k) { +static uint32_t ggml_vk_guess_split_k(ggml_backend_vk_context * ctx, int m, int n, int k, const vk_pipeline& pipeline) { VK_LOG_DEBUG("ggml_vk_guess_split_k(" << m << ", " << n << ", " << k << ")"); - // if (k > 128 && (m < 128 || n < 128) && m > 2 && n > 2) { - // return 4; - // } - return 1; + uint32_t split_k = 1; + if (ctx->device->shader_core_count != 0 && m >= (int)pipeline->wg_denoms[0] && n >= (int)pipeline->wg_denoms[1]) { + // If k is 'large' and the SMs will fill less than halfway, use split_k. + uint32_t m_tiles = CEIL_DIV(m, pipeline->wg_denoms[0]); + uint32_t n_tiles = CEIL_DIV(n, pipeline->wg_denoms[1]); + if (k >= 2048 && m_tiles * n_tiles < ctx->device->shader_core_count / 2) { + split_k = ctx->device->shader_core_count / (m_tiles * n_tiles); + // Clamp to 2 or 4 + split_k = std::min(split_k, 4u); + if (split_k == 3) { + split_k = 2; + } + } + } - GGML_UNUSED(m); GGML_UNUSED(n); GGML_UNUSED(k); + return split_k; } static vk_pipeline ggml_vk_guess_matmul_pipeline_amd(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, bool aligned) { @@ -3003,6 +3153,16 @@ static vk_pipeline ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, break; } + if (ctx->device->coopmat2) { + if ((m % mmp->l->wg_denoms[0]) == 0 && (n % mmp->l->wg_denoms[1]) == 0) { + return aligned ? mmp->a_l : mmp->l; + } + if ((m % mmp->m->wg_denoms[0]) == 0 && (n % mmp->m->wg_denoms[1]) == 0) { + return aligned ? mmp->a_m : mmp->m; + } + return aligned ? mmp->a_s : mmp->s; + } + if (m <= 32 || n <= 32) { return aligned ? mmp->a_s : mmp->s; } @@ -3064,18 +3224,34 @@ static bool ggml_vk_dim01_contiguous(const ggml_tensor * tensor) { tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; } -static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, ggml_type from, ggml_type to) { - if (from == GGML_TYPE_F32 && to == GGML_TYPE_F32) { - return ctx->device->pipeline_cpy_f32_f32; +static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src, const ggml_tensor * dst, ggml_type to) { + + // Choose "contiguous copy" shader if src/dst are contiguous + bool contig = ggml_is_contiguous(src) && (!dst || ggml_is_contiguous(dst)); + + if (src->type == GGML_TYPE_F32 && to == GGML_TYPE_F32) { + if (contig) { + return ctx->device->pipeline_contig_cpy_f32_f32; + } else { + return ctx->device->pipeline_cpy_f32_f32; + } } - if (from == GGML_TYPE_F32 && to == GGML_TYPE_F16) { - return ctx->device->pipeline_cpy_f32_f16; + if (src->type == GGML_TYPE_F32 && to == GGML_TYPE_F16) { + if (contig) { + return ctx->device->pipeline_contig_cpy_f32_f16; + } else { + return ctx->device->pipeline_cpy_f32_f16; + } } - if (from == GGML_TYPE_F16 && to == GGML_TYPE_F16) { - return ctx->device->pipeline_cpy_f16_f16; + if (src->type == GGML_TYPE_F16 && to == GGML_TYPE_F16) { + if (contig) { + return ctx->device->pipeline_contig_cpy_f16_f16; + } else { + return ctx->device->pipeline_cpy_f16_f16; + } } - std::cerr << "Missing CPY op for types: " << ggml_type_name(from) << " " << ggml_type_name(to) << std::endl; + std::cerr << "Missing CPY op for types: " << ggml_type_name(src->type) << " " << ggml_type_name(to) << std::endl; GGML_ABORT("fatal error"); } @@ -3085,16 +3261,26 @@ static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context& const int tensor_type_size = ggml_type_size(tensor->type); const uint32_t ne = ggml_nelements(tensor); + std::array elements; - const vk_op_unary_push_constants pc = { + if (ne > 262144) { + elements = { 512, 512, CEIL_DIV(ne, 262144) }; + } else if (ne > 512) { + elements = { 512, CEIL_DIV(ne, 512), 1 }; + } else { + elements = { ne, 1, 1 }; + } + + vk_op_unary_push_constants pc = { (uint32_t)ne, (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3], (uint32_t)tensor->nb[0] / tensor_type_size, (uint32_t)tensor->nb[1] / tensor_type_size, (uint32_t)tensor->nb[2] / tensor_type_size, (uint32_t)tensor->nb[3] / tensor_type_size, (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->ne[2], (uint32_t)tensor->ne[3], 1 , (uint32_t)tensor->ne[0] , (uint32_t)(tensor->ne[0] * tensor->ne[1]) , (uint32_t)(tensor->ne[0] * tensor->ne[1] * tensor->ne[2]), 0, 0.0f, 0.0f, }; + init_pushconst_fastdiv(pc); ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, sizeof(vk_op_unary_push_constants), &pc, { ne, 1, 1 }); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, sizeof(vk_op_unary_push_constants), &pc, elements); } static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { @@ -3141,18 +3327,20 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub } const bool x_non_contig = !ggml_vk_dim01_contiguous(src0); - const bool y_non_contig = !ggml_vk_dim01_contiguous(src1); + // Reformat and convert to fp16 if src1 is non-contiguous, or for coopmat2 for better perf + const bool y_non_contig = (ctx->device->coopmat2 && src1->type == GGML_TYPE_F32) || + !ggml_vk_dim01_contiguous(src1); const bool y_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig; - vk_matmul_pipeline mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, src0->type, y_non_contig ? GGML_TYPE_F16 : src1->type); + vk_matmul_pipeline mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, src0->type, y_non_contig ? GGML_TYPE_F16 : src1->type, (ggml_prec)dst->op_params[0]); const bool qx_needs_dequant = mmp == nullptr || x_non_contig; const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig; - if (mmp == nullptr) { + if (qx_needs_dequant) { // Fall back to dequant + f16 mulmat - mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, GGML_TYPE_F16, y_f32_kernel ? GGML_TYPE_F32 : GGML_TYPE_F16); + mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, GGML_TYPE_F16, y_f32_kernel ? GGML_TYPE_F32 : GGML_TYPE_F16, (ggml_prec)dst->op_params[0]); } // Not implemented @@ -3165,10 +3353,10 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, ne11)); const bool aligned = ne10 == kpad && ne01 > 8 && ne11 > 8; - const uint32_t split_k = ggml_vk_guess_split_k(ne01, ne11, ne10); - vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, ne11, aligned); + const uint32_t split_k = ggml_vk_guess_split_k(ctx, ne01, ne11, ne10, pipeline); + const uint64_t qx_sz = ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type); const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); const uint64_t x_sz = !qx_needs_dequant ? qx_sz : sizeof(ggml_fp16_t) * x_ne; @@ -3179,12 +3367,12 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub vk_pipeline to_fp16_vk_1 = nullptr; if (x_non_contig) { - to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, GGML_TYPE_F16); + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0, nullptr, GGML_TYPE_F16); } else { to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type); } if (y_non_contig) { - to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, GGML_TYPE_F16); + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1, nullptr, GGML_TYPE_F16); } else { to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); } @@ -3194,7 +3382,7 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub if (dryrun) { const uint64_t x_sz_upd = x_sz * ne02 * ne03; const uint64_t y_sz_upd = y_sz * ne12 * ne13; - const uint64_t split_k_size = split_k > 1 ? d_sz * ne12 * ne13 * 4 : 0; + const uint64_t split_k_size = split_k > 1 ? d_sz * ne12 * ne13 * split_k : 0; if ( (qx_needs_dequant && x_sz_upd > ctx->device->max_memory_allocation_size) || (qy_needs_dequant && y_sz_upd > ctx->device->max_memory_allocation_size) || @@ -3364,10 +3552,10 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context& vk_pipeline to_fp16_vk_0 = nullptr; vk_pipeline to_fp16_vk_1 = nullptr; if (x_non_contig) { - to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, src0->type); + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0, nullptr, src0->type); } if (y_non_contig) { - to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, src1->type); + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1, nullptr, src1->type); } else { to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); } @@ -3461,7 +3649,7 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context& if (ne01 > max_groups_x) { groups_z = 64; - groups_x /= groups_z; + groups_x = CEIL_DIV(groups_x, groups_z); } // compute @@ -3633,9 +3821,19 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) { VK_LOG_DEBUG("ggml_vk_mul_mat(" << src0 << ", " << src1 << ", " << dst << ")"); - if (src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && dst->ne[1] == 1) { + if (src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && dst->ne[1] == 1 && + // detect 0213 permutation, and batch size of 1 + src0->nb[0] <= src0->nb[2] && + src0->nb[2] <= src0->nb[1] && + src0->nb[1] <= src0->nb[3] && + src1->nb[0] <= src1->nb[2] && + src1->nb[2] <= src1->nb[1] && + src1->nb[1] <= src1->nb[3] && + src0->ne[3] == 1 && + src1->ne[3] == 1) { ggml_vk_mul_mat_vec_p021_f16_f32(ctx, subctx, src0, src1, dst, dryrun); - } else if (src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && dst->ne[1] == 1) { + } else if (src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && dst->ne[1] == 1 && + !ggml_is_permuted(src0) && !ggml_is_permuted(src1)) { ggml_vk_mul_mat_vec_nc_f16_f32(ctx, subctx, src0, src1, dst, dryrun); } else if (dst->ne[1] == 1 && (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) { ggml_vk_mul_mat_vec_q_f16(ctx, subctx, src0, src1, dst, dryrun); @@ -3711,7 +3909,7 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context& const bool qx_needs_dequant = mmp == nullptr || x_non_contig; const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig; - if (mmp == nullptr) { + if (qx_needs_dequant) { GGML_ABORT("fatal error"); } @@ -3738,12 +3936,12 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context& vk_pipeline to_fp16_vk_1 = nullptr; if (x_non_contig) { - to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, GGML_TYPE_F16); + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0, nullptr, GGML_TYPE_F16); } else { to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type); } if (y_non_contig) { - to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, GGML_TYPE_F16); + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1, nullptr, GGML_TYPE_F16); } else { to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); } @@ -3931,10 +4129,10 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte vk_pipeline to_fp16_vk_0 = nullptr; vk_pipeline to_fp16_vk_1 = nullptr; if (x_non_contig) { - to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, src0->type); + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0, nullptr, src0->type); } if (y_non_contig) { - to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, src1->type); + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1, nullptr, src1->type); } else { to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); } @@ -4028,7 +4226,7 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte if (ne01 > max_groups_x) { groups_z = 64; - groups_x /= groups_z; + groups_x = CEIL_DIV(groups_x, groups_z); } // compute @@ -4053,6 +4251,167 @@ static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context& subctx } } +static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * q, const ggml_tensor * k, const ggml_tensor * v, const ggml_tensor * mask, ggml_tensor * dst, bool dryrun = false) { + VK_LOG_DEBUG("ggml_vk_flash_attn((" << q << ", name=" << q->name << ", type=" << q->type << ", ne0=" << q->ne[0] << ", ne1=" << q->ne[1] << ", ne2=" << q->ne[2] << ", ne3=" << q->ne[3] << ", nb0=" << q->nb[0] << ", nb1=" << q->nb[1] << ", nb2=" << q->nb[2] << ", nb3=" << q->nb[3]; + std::cerr << "), (" << k << ", name=" << k->name << ", type=" << k->type << ", ne0=" << k->ne[0] << ", ne1=" << k->ne[1] << ", ne2=" << k->ne[2] << ", ne3=" << k->ne[3] << ", nb0=" << k->nb[0] << ", nb1=" << k->nb[1] << ", nb2=" << k->nb[2] << ", nb3=" << k->nb[3]; + std::cerr << "), (" << v << ", name=" << v->name << ", type=" << v->type << ", ne0=" << v->ne[0] << ", ne1=" << v->ne[1] << ", ne2=" << v->ne[2] << ", ne3=" << v->ne[3] << ", nb0=" << v->nb[0] << ", nb1=" << v->nb[1] << ", nb2=" << v->nb[2] << ", nb3=" << v->nb[3]; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3]; + std::cerr << "), " << (dryrun ? "dryrun" : "") << ")"); + + GGML_TENSOR_LOCALS(int64_t, neq, q, ne) + GGML_TENSOR_LOCALS(size_t, nbq, q, nb) + GGML_TENSOR_LOCALS(int64_t, nek, k, ne) + GGML_TENSOR_LOCALS(size_t, nbk, k, nb) + GGML_TENSOR_LOCALS(int64_t, nev, v, ne) + GGML_TENSOR_LOCALS(size_t, nbv, v, nb) + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) + GGML_TENSOR_LOCALS(size_t, nb, dst, nb) + + const uint32_t nem1 = mask ? mask->ne[1] : 0; + const uint32_t nbm1 = mask ? mask->nb[1] : 0; + + const uint32_t D = neq0; + const uint32_t N = neq1; + const uint32_t KV = nek1; + + GGML_ASSERT(ne0 == D); + GGML_ASSERT(ne2 == N); + + // input tensor rows must be contiguous + GGML_ASSERT(nbq0 == ggml_type_size(q->type)); + GGML_ASSERT(nbk0 == ggml_type_size(k->type)); + GGML_ASSERT(nbv0 == ggml_type_size(v->type)); + + GGML_ASSERT(neq0 == D); + GGML_ASSERT(nek0 == D); + GGML_ASSERT(nev0 == D); + + GGML_ASSERT(neq1 == N); + GGML_ASSERT(nev0 == D); + + GGML_ASSERT(nev1 == nek1); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + assert(dst->type == GGML_TYPE_F32); + assert(q->type == GGML_TYPE_F32); + assert(k->type == v->type); + + vk_pipeline *pipelines; + // XXX TODO other backends may be changing accumulator precision to default to f32 soon + bool f32acc = dst->op_params[3] == GGML_PREC_F32; + bool small_rows = N <= flash_attention_num_small_rows; + switch (D) { + case 64: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D64[k->type][f32acc][small_rows][0]; break; + case 80: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D80[k->type][f32acc][small_rows][0]; break; + case 96: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D96[k->type][f32acc][small_rows][0]; break; + case 112: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D112[k->type][f32acc][small_rows][0]; break; + case 128: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D128[k->type][f32acc][small_rows][0]; break; + case 256: pipelines = &ctx->device->pipeline_flash_attn_f32_f16_D256[k->type][f32acc][small_rows][0]; break; + default: + assert(!"unsupported D value"); + return; + } + assert(pipelines); + + bool aligned = (KV % pipelines[1]->align) == 0; + vk_pipeline pipeline = pipelines[aligned]; + assert(pipeline); + + if (dryrun) { + // Request descriptor sets + ggml_pipeline_request_descriptor_sets(ctx->device, pipeline, 1); + return; + } + + float scale = 1.0f; + float max_bias = 0.0f; + float logit_softcap = 0.0f; + + memcpy(&scale, (const float *) dst->op_params + 0, sizeof(float)); + memcpy(&max_bias, (const float *) dst->op_params + 1, sizeof(float)); + memcpy(&logit_softcap, (const float *) dst->op_params + 2, sizeof(float)); + + if (logit_softcap != 0) { + scale /= logit_softcap; + } + + const uint32_t n_head_kv = neq2; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv)); + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + ggml_vk_sync_buffers(subctx); + + vk_buffer d_Q, d_K, d_V, d_D, d_M; + uint64_t q_buf_offset, k_buf_offset, v_buf_offset, d_buf_offset, m_buf_offset; + + bool Q_uma = false, K_uma = false, V_uma = false, D_uma = false, M_uma = false; + + if (ctx->device->uma) { + ggml_vk_host_get(ctx->device, q->data, d_Q, q_buf_offset); + ggml_vk_host_get(ctx->device, k->data, d_K, q_buf_offset); + ggml_vk_host_get(ctx->device, v->data, d_V, q_buf_offset); + ggml_vk_host_get(ctx->device, dst->data, d_D, q_buf_offset); + Q_uma = d_Q != nullptr; + K_uma = d_K != nullptr; + V_uma = d_V != nullptr; + D_uma = d_D != nullptr; + if (mask) { + ggml_vk_host_get(ctx->device, mask->data, d_M, q_buf_offset); + M_uma = d_M != nullptr; + } + } + + + ggml_backend_vk_buffer_context * d_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context; + ggml_backend_vk_buffer_context * q_buf_ctx = (ggml_backend_vk_buffer_context *)q->buffer->context; + ggml_backend_vk_buffer_context * k_buf_ctx = (ggml_backend_vk_buffer_context *)k->buffer->context; + ggml_backend_vk_buffer_context * v_buf_ctx = (ggml_backend_vk_buffer_context *)v->buffer->context; + + if (!Q_uma) { + d_Q = q_buf_ctx->dev_buffer; + q_buf_offset = vk_tensor_offset(q) + q->view_offs; + } + if (!K_uma) { + d_K = k_buf_ctx->dev_buffer; + k_buf_offset = vk_tensor_offset(k) + k->view_offs; + } + if (!V_uma) { + d_V = v_buf_ctx->dev_buffer; + v_buf_offset = vk_tensor_offset(v) + v->view_offs; + } + if (!D_uma) { + d_D = d_buf_ctx->dev_buffer; + d_buf_offset = vk_tensor_offset(dst) + dst->view_offs; + } + + if (!M_uma) { + d_M = d_Q; + m_buf_offset = q_buf_offset; + if (mask) { + ggml_backend_vk_buffer_context * m_buf_ctx = (ggml_backend_vk_buffer_context*)mask->buffer->context; + d_M = m_buf_ctx->dev_buffer; + m_buf_offset = vk_tensor_offset(mask) + mask->view_offs; + } + } + + const vk_flash_attn_push_constants pc = { N, KV, (uint32_t)ne1, (uint32_t)ne2, (uint32_t)ne3, (uint32_t)neq2, (uint32_t)neq3, (uint32_t)nek2, (uint32_t)nek3, (uint32_t)nev2, (uint32_t)nev3, nem1, (uint32_t)nbq2, (uint32_t)nbq3, (uint32_t)nbk2, (uint32_t)nbk3, (uint32_t)nbv2, (uint32_t)nbv3, nbm1, scale, max_bias, logit_softcap, mask != nullptr, n_head_log2, m0, m1 }; + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, + { + vk_subbuffer{d_Q, q_buf_offset, VK_WHOLE_SIZE}, + vk_subbuffer{d_K, k_buf_offset, VK_WHOLE_SIZE}, + vk_subbuffer{d_V, v_buf_offset, VK_WHOLE_SIZE}, + vk_subbuffer{d_M, m_buf_offset, VK_WHOLE_SIZE}, + vk_subbuffer{d_D, d_buf_offset, VK_WHOLE_SIZE}, + }, + sizeof(vk_flash_attn_push_constants), &pc, { (uint32_t)neq1, (uint32_t)neq2, (uint32_t)neq3 }); +} + static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op) { switch (op) { case GGML_OP_GET_ROWS: @@ -4071,20 +4430,20 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return nullptr; case GGML_OP_ADD: if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_add_f32; + return ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_add_f32_norepeat : ctx->device->pipeline_add_f32; } if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) { - return ctx->device->pipeline_add_f16_f32_f16; + return ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_add_f16_f32_f16_norepeat : ctx->device->pipeline_add_f16_f32_f16; } return nullptr; case GGML_OP_MUL: if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_mul_f32; + return ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_mul_f32_norepeat : ctx->device->pipeline_mul_f32; } return nullptr; case GGML_OP_DIV: if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_div_f32; + return ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_div_f32_norepeat : ctx->device->pipeline_div_f32; } return nullptr; case GGML_OP_CONCAT: @@ -4141,7 +4500,7 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const case GGML_OP_CPY: case GGML_OP_CONT: case GGML_OP_DUP: - return ggml_vk_get_cpy_pipeline(ctx, src0->type, dst->type); + return ggml_vk_get_cpy_pipeline(ctx, src0, dst, dst->type); case GGML_OP_NORM: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { return ctx->device->pipeline_norm_f32; @@ -4197,10 +4556,10 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_soft_max_f32; + return src0->ne[0] > 1024 ? ctx->device->pipeline_soft_max_f32_wg512 : ctx->device->pipeline_soft_max_f32; } if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_soft_max_f32_f16; + return src0->ne[0] > 1024 ? ctx->device->pipeline_soft_max_f32_f16_wg512 : ctx->device->pipeline_soft_max_f32_f16; } return nullptr; case GGML_OP_ROPE: @@ -4274,7 +4633,6 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) { case GGML_OP_DIV: case GGML_OP_CONCAT: case GGML_OP_UPSCALE: - case GGML_OP_SCALE: case GGML_OP_SQR: case GGML_OP_SIN: case GGML_OP_COS: @@ -4288,7 +4646,7 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) { } template -static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op, const PC&& pc, bool dryrun = false) { +static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op, PC&& pc, bool dryrun = false) { VK_LOG_DEBUG("ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; if (src1 != nullptr) { std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; @@ -4328,6 +4686,8 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co const uint64_t ned3 = dst->ne[3]; const uint64_t ned = ned0 * ned1; + init_pushconst_fastdiv(pc); + vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, src2, dst, op); if (pipeline == nullptr) { @@ -4473,7 +4833,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co const uint32_t OH = is_2D ? dst->ne[2] : 1; const uint32_t OW = dst->ne[1]; - const uint32_t batch = src1->ne[3]; + const uint32_t batch = src1->ne[is_2D ? 3 : 2]; elements = { OW * KW * KH, OH, batch * IC }; } break; @@ -4847,6 +5207,7 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx, scale, max_bias, m0, m1, n_head_log2, + nrows_x, }, dryrun); } @@ -4918,7 +5279,7 @@ static void ggml_vk_im2col(ggml_backend_vk_context * ctx, vk_context& subctx, co const uint32_t OW = dst->ne[1]; const uint32_t offset_delta = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32 - const uint32_t batch_offset = src1->nb[3] / 4; // nb is byte offset, src is type float32 + const uint32_t batch_offset = src1->nb[is_2D ? 3 : 2] / 4; // nb is byte offset, src is type float32 const uint32_t pelements = OW * KW * KH; @@ -5025,10 +5386,10 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t p = ctx->device->pipeline_matmul_f32_f16->a_s; shname = "F32_F16_ALIGNED_S"; } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16_f32->a_s; + p = ctx->device->pipeline_matmul_f16_f32.f32acc->a_s; shname = "F16_F32_ALIGNED_S"; } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16->a_s; + p = ctx->device->pipeline_matmul_f16.f32acc->a_s; shname = "F16_ALIGNED_S"; } else { GGML_ABORT("fatal error"); @@ -5041,10 +5402,10 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t p = ctx->device->pipeline_matmul_f32_f16->a_m; shname = "F32_F16_ALIGNED_M"; } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16_f32->a_m; + p = ctx->device->pipeline_matmul_f16_f32.f32acc->a_m; shname = "F16_F32_ALIGNED_M"; } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16->a_m; + p = ctx->device->pipeline_matmul_f16.f32acc->a_m; shname = "F16_ALIGNED_M"; } else { GGML_ABORT("fatal error"); @@ -5057,10 +5418,10 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t p = ctx->device->pipeline_matmul_f32_f16->a_l; shname = "F32_F16_ALIGNED_L"; } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16_f32->a_l; + p = ctx->device->pipeline_matmul_f16_f32.f32acc->a_l; shname = "F16_F32_ALIGNED_L"; } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16->a_l; + p = ctx->device->pipeline_matmul_f16.f32acc->a_l; shname = "F16_ALIGNED_L"; } else { GGML_ABORT("fatal error"); @@ -5080,10 +5441,10 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t p = ctx->device->pipeline_matmul_f32_f16->s; shname = "F32_F16_S"; } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16_f32->s; + p = ctx->device->pipeline_matmul_f16_f32.f32acc->s; shname = "F16_F32_S"; } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16->s; + p = ctx->device->pipeline_matmul_f16.f32acc->s; shname = "F16_S"; } } else if (shader_size == 1) { @@ -5094,10 +5455,10 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t p = ctx->device->pipeline_matmul_f32_f16->m; shname = "F32_F16_M"; } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16_f32->m; + p = ctx->device->pipeline_matmul_f16_f32.f32acc->m; shname = "F16_F32_M"; } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16->m; + p = ctx->device->pipeline_matmul_f16.f32acc->m; shname = "F16_M"; } } else if (shader_size == 2) { @@ -5108,10 +5469,10 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t p = ctx->device->pipeline_matmul_f32_f16->l; shname = "F32_F16_L"; } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16_f32->l; + p = ctx->device->pipeline_matmul_f16_f32.f32acc->l; shname = "F16_F32_L"; } else if (std::is_same() && std::is_same()) { - p = ctx->device->pipeline_matmul_f16->l; + p = ctx->device->pipeline_matmul_f16.f32acc->l; shname = "F16_L"; } } @@ -5165,16 +5526,16 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t ggml_vk_buffer_write(d_Y, 0, y, sizeof(Y_TYPE) * k * n * batch); vk_context subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue); + ggml_vk_ctx_begin(ctx->device, subctx); for (size_t i = 0; i < num_it; i++) { - ggml_vk_ctx_begin(ctx->device, subctx); ggml_vk_matmul( ctx, subctx, p, ggml_vk_subbuffer(d_X), ggml_vk_subbuffer(d_Y), ggml_vk_subbuffer(d_D), ggml_vk_subbuffer(ctx->prealloc_split_k), m, n, k, k, k, m, k*m, k*n, m*n, split_k, batch, batch, batch, 1, 1 ); - ggml_vk_ctx_end(subctx); } + ggml_vk_ctx_end(subctx); auto begin = std::chrono::high_resolution_clock::now(); ggml_vk_submit(subctx, ctx->fence); @@ -5440,13 +5801,13 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, vk_pipeline p; std::string shname; if (shader_size == 0) { - p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_s; + p = ctx->device->pipeline_dequant_mul_mat_mat[quant].f32acc->a_s; shname = std::string(ggml_type_name(quant)) + "_ALIGNED_S"; } else if (shader_size == 1) { - p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_m; + p = ctx->device->pipeline_dequant_mul_mat_mat[quant].f32acc->a_m; shname = std::string(ggml_type_name(quant)) + "_ALIGNED_M"; } else if (shader_size == 2) { - p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->a_l; + p = ctx->device->pipeline_dequant_mul_mat_mat[quant].f32acc->a_l; shname = std::string(ggml_type_name(quant)) + "_ALIGNED_L"; } else { GGML_ASSERT(0); @@ -5456,13 +5817,13 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, if (k != kpad) { if (shader_size == 0) { - p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->s; + p = ctx->device->pipeline_dequant_mul_mat_mat[quant].f32acc->s; shname = std::string(ggml_type_name(quant)) + "_S"; } else if (shader_size == 1) { - p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->m; + p = ctx->device->pipeline_dequant_mul_mat_mat[quant].f32acc->m; shname = std::string(ggml_type_name(quant)) + "_M"; } else if (shader_size == 2) { - p = ctx->device->pipeline_dequant_mul_mat_mat[quant]->l; + p = ctx->device->pipeline_dequant_mul_mat_mat[quant].f32acc->l; shname = std::string(ggml_type_name(quant)) + "_L"; } else { GGML_ASSERT(0); @@ -5512,16 +5873,16 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, ggml_vk_buffer_write(y_buf, 0, y, y_sz); vk_context subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue); + ggml_vk_ctx_begin(ctx->device, subctx); for (size_t i = 0; i < num_it; i++) { - ggml_vk_ctx_begin(ctx->device, subctx); ggml_vk_matmul( ctx, subctx, p, ggml_vk_subbuffer(qx_buf), ggml_vk_subbuffer(y_buf), ggml_vk_subbuffer(d_buf), ggml_vk_subbuffer(ctx->prealloc_split_k), m, n, k, k, k, m, k*m, k*n, m*n, split_k, batch, batch, batch, 1, 1 ); - ggml_vk_ctx_end(subctx); } + ggml_vk_ctx_end(subctx); auto begin = std::chrono::high_resolution_clock::now(); @@ -5742,7 +6103,8 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { 4096, 512, 11008, 32000, 512, 4096, }; - const size_t num_it = 1; + const size_t num_it = 100; + for (size_t i = 0; i < vals.size(); i += 3) { ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 0); ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 1); @@ -5797,6 +6159,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod const ggml_tensor * src0 = node->src[0]; const ggml_tensor * src1 = node->src[1]; const ggml_tensor * src2 = node->src[2]; + const ggml_tensor * src3 = node->src[3]; switch (node->op) { // Return on empty ops to avoid generating a compute_ctx and setting exit_tensor @@ -5849,6 +6212,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_POOL_2D: case GGML_OP_LEAKY_RELU: + case GGML_OP_FLASH_ATTN_EXT: break; default: std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(node->op) << std::endl; @@ -5866,6 +6230,48 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod } else { compute_ctx = ctx->compute_ctx.lock(); } + } else { + switch (node->op) { + case GGML_OP_REPEAT: + case GGML_OP_ACC: + case GGML_OP_GET_ROWS: + case GGML_OP_ADD: + case GGML_OP_MUL: + case GGML_OP_DIV: + case GGML_OP_CONCAT: + case GGML_OP_UPSCALE: + case GGML_OP_SCALE: + case GGML_OP_SQR: + case GGML_OP_SIN: + case GGML_OP_COS: + case GGML_OP_CLAMP: + case GGML_OP_PAD: + case GGML_OP_CPY: + case GGML_OP_CONT: + case GGML_OP_DUP: + case GGML_OP_NORM: + case GGML_OP_GROUP_NORM: + case GGML_OP_RMS_NORM: + case GGML_OP_UNARY: + case GGML_OP_DIAG_MASK_INF: + case GGML_OP_SOFT_MAX: + case GGML_OP_ROPE: + case GGML_OP_ARGSORT: + case GGML_OP_SUM_ROWS: + case GGML_OP_IM2COL: + case GGML_OP_TIMESTEP_EMBEDDING: + case GGML_OP_POOL_2D: + case GGML_OP_LEAKY_RELU: + { + // These operations all go through ggml_vk_op_f32, so short-circuit and + // do the only thing needed for the dryrun. + vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, src2, node, node->op); + ggml_pipeline_request_descriptor_sets(ctx->device, pipeline, 1); + return false; + } + default: + break; + } } switch (node->op) { @@ -5999,6 +6405,11 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod case GGML_OP_MUL_MAT_ID: ggml_vk_mul_mat_id(ctx, compute_ctx, src0, src1, src2, node, dryrun); + break; + + case GGML_OP_FLASH_ATTN_EXT: + ggml_vk_flash_attn(ctx, compute_ctx, src0, src1, src2, src3, node, dryrun); + break; default: return false; @@ -6099,6 +6510,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor * break; case GGML_OP_MUL_MAT: case GGML_OP_MUL_MAT_ID: + case GGML_OP_FLASH_ATTN_EXT: buf = tensor->buffer; break; @@ -6595,16 +7007,17 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg bool first_node_in_batch = true; // true if next node will be first node in a batch int submit_node_idx = 0; // index to first node in a batch - // submit work every submit_count node to overlap CPU cmdbuffer generation with GPU execution - constexpr int submit_count = 100; + // Submit work every nodes_per_submit nodes to overlap CPU cmdbuffer generation with GPU execution. + // Start with a smaller count to get work submitted right away, and increase it after each submit. + int nodes_per_submit = 20; int submitted_nodes = 0; + int submit_count = 0; for (int i = 0; i < cgraph->n_nodes; i++) { if (first_node_in_batch) { submit_node_idx = i; } - bool submit = (submitted_nodes >= submit_count) || (i == last_node); - + bool submit = (submitted_nodes >= nodes_per_submit) || (i == last_node); bool enqueued = ggml_vk_build_graph(ctx, cgraph->nodes[i], i, cgraph->nodes[submit_node_idx], submit_node_idx, false, i == last_node, submit); @@ -6621,6 +7034,15 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg if (submit) { first_node_in_batch = true; submitted_nodes = 0; + switch (submit_count) { + case 0: + nodes_per_submit = 50; + break; + default: + nodes_per_submit = 100; + break; + } + submit_count++; } } @@ -6777,6 +7199,12 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_OP_MUL_MAT: case GGML_OP_MUL_MAT_ID: { + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; + if (op->op == GGML_OP_MUL_MAT_ID && + ggml_vk_get_device(ctx->device)->properties.limits.maxComputeSharedMemorySize < 32768) { + // If there's not enough shared memory for row_ids and the result tile, fallback to CPU + return false; + } switch (op->src[0]->type) { case GGML_TYPE_F32: case GGML_TYPE_F16: @@ -6807,8 +7235,64 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm if (a->ne[3] != b->ne[3]) { return false; } + if (!(ggml_vk_dim01_contiguous(op->src[0]) || op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) || + !(ggml_vk_dim01_contiguous(op->src[1]) || op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == GGML_TYPE_F16)) { + return false; + } + return true; } break; + case GGML_OP_FLASH_ATTN_EXT: + { + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; + if (!ggml_vk_get_device(ctx->device)->coopmat2) { + return false; + } + switch (op->src[0]->ne[0]) { + case 64: + case 80: + case 96: + case 112: + case 128: + case 256: + break; + default: + return false; + } + if (op->src[0]->type != GGML_TYPE_F32) { + return false; + } + if (op->type != GGML_TYPE_F32) { + return false; + } + if (op->src[3] && op->src[3]->type != GGML_TYPE_F16) { + return false; + } + // It's straightforward to support different K/V dequant, but would + // significantly increase the number of pipelines + if (op->src[1]->type != op->src[2]->type) { + return false; + } + switch (op->src[1]->type) { + case GGML_TYPE_F16: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + // K dequants currently disabled because D dimension is rounded up to 256 and runs inefficiently + //case GGML_TYPE_Q2_K: + //case GGML_TYPE_Q3_K: + //case GGML_TYPE_Q4_K: + //case GGML_TYPE_Q5_K: + //case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ4_NL: + break; + default: + return false; + } + return true; + } case GGML_OP_GET_ROWS: { switch (op->src[0]->type) { @@ -6969,8 +7453,9 @@ static const struct ggml_backend_reg_i ggml_backend_vk_reg_i = { ggml_backend_reg_t ggml_backend_vk_reg() { static ggml_backend_reg reg = { - /* .iface = */ ggml_backend_vk_reg_i, - /* .context = */ nullptr, + /* .api_version = */ GGML_BACKEND_API_VERSION, + /* .iface = */ ggml_backend_vk_reg_i, + /* .context = */ nullptr, }; return ® @@ -7122,6 +7607,7 @@ static void ggml_vk_check_results_0(ggml_tensor * tensor) { ggml_tensor * src0 = tensor->src[0]; ggml_tensor * src1 = tensor->src[1]; ggml_tensor * src2 = tensor->src[2]; + ggml_tensor * src3 = tensor->src[3]; struct ggml_init_params iparams = { /*.mem_size =*/ 2ul*1024ul*1024ul*1024ul, @@ -7134,15 +7620,18 @@ static void ggml_vk_check_results_0(ggml_tensor * tensor) { struct ggml_tensor * src0_clone = nullptr; struct ggml_tensor * src1_clone = nullptr; struct ggml_tensor * src2_clone = nullptr; + struct ggml_tensor * src3_clone = nullptr; struct ggml_tensor * tensor_clone = nullptr; size_t src0_size; size_t src1_size; size_t src2_size; + size_t src3_size; void * src0_buffer = nullptr; void * src1_buffer = nullptr; void * src2_buffer = nullptr; + void * src3_buffer = nullptr; if (src0 != nullptr) { src0_clone = ggml_dup_tensor(ggml_ctx, src0); @@ -7270,8 +7759,53 @@ static void ggml_vk_check_results_0(ggml_tensor * tensor) { ggml_vk_print_tensor(src2, "src2"); } } + if (src3 != nullptr) { + src3_clone = ggml_dup_tensor(ggml_ctx, src3); - if (tensor->op == GGML_OP_MUL_MAT) { + src3_size = ggml_nbytes(src3); + + src3_buffer = malloc(src3_size); + src3_clone->data = src3_buffer; + if (ggml_backend_buffer_is_host(src3->buffer)) { + memcpy(src3_clone->data, src3->data, src3_size); + memcpy(src3_clone->nb, src3->nb, sizeof(size_t) * GGML_MAX_DIMS); + } else if (ggml_backend_buffer_is_vk(src3->buffer)) { + ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)src3->buffer->context; + vk_buffer& buffer_gpu = buf_ctx->dev_buffer; + uint64_t offset = vk_tensor_offset(src3) + src3->view_offs; + if (!ggml_is_contiguous(src3) && ggml_vk_dim01_contiguous(src3)) { + for (int i3 = 0; i3 < src3->ne[3]; i3++) { + for (int i2 = 0; i2 < src3->ne[2]; i2++) { + const int idx = i3*src3->ne[2] + i2; + ggml_vk_buffer_read(buffer_gpu, offset + idx * src3->nb[2], ((char *)src3_clone->data + idx * src3_clone->nb[2]), src3->ne[1] * src3->nb[1]); + } + } + + src3_clone->nb[0] = src3->nb[0]; + src3_clone->nb[1] = src3->nb[1]; + for (int i = 2; i < GGML_MAX_DIMS; i++) { + src3_clone->nb[i] = src3_clone->nb[i - 1]*src3_clone->ne[i - 1]; + } + } else { + if (offset + src3_size >= buffer_gpu->size) { + src3_size = buffer_gpu->size - offset; + } + ggml_vk_buffer_read(buffer_gpu, offset, src3_clone->data, src3_size); + memcpy(src3_clone->nb, src3->nb, sizeof(size_t) * GGML_MAX_DIMS); + } + } else { + GGML_ABORT("fatal error"); + } + + if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { + ggml_vk_print_tensor(src3, "src3"); + } + } + + if (tensor->op == GGML_OP_FLASH_ATTN_EXT) { + const float *params = (const float *)tensor->op_params; + tensor_clone = ggml_flash_attn_ext(ggml_ctx, src0_clone, src1_clone, src2_clone, src3_clone, params[0], params[1], params[2]); + } else if (tensor->op == GGML_OP_MUL_MAT) { tensor_clone = ggml_mul_mat(ggml_ctx, src0_clone, src1_clone); } else if (tensor->op == GGML_OP_MUL_MAT_ID) { tensor_clone = ggml_mul_mat_id(ggml_ctx, src0_clone, src1_clone, src2_clone); @@ -7387,7 +7921,7 @@ static void ggml_vk_check_results_0(ggml_tensor * tensor) { const int32_t max_period = tensor->op_params[1]; tensor_clone = ggml_timestep_embedding(ggml_ctx, src0_clone, dim, max_period); } else if (tensor->op == GGML_OP_POOL_2D) { - enum ggml_op_pool op = static_cast(dst->op_params[0]); + enum ggml_op_pool op = static_cast(tensor->op_params[0]); const int32_t k0 = tensor->op_params[1]; const int32_t k1 = tensor->op_params[2]; const int32_t s0 = tensor->op_params[3]; @@ -7596,3 +8130,5 @@ static void ggml_vk_check_results_1(ggml_tensor * tensor) { VK_LOG_DEBUG("END ggml_vk_check_results_1(" << tensor->name << ")"); } #endif + +GGML_BACKEND_DL_IMPL(ggml_backend_vk_reg) diff --git a/ggml/src/vulkan-shaders/CMakeLists.txt b/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt similarity index 56% rename from ggml/src/vulkan-shaders/CMakeLists.txt rename to ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt index 10075db33..bd0c74cb1 100644 --- a/ggml/src/vulkan-shaders/CMakeLists.txt +++ b/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt @@ -1,7 +1,9 @@ find_package (Threads REQUIRED) +find_package(Vulkan COMPONENTS glslc REQUIRED) set(TARGET vulkan-shaders-gen) add_executable(${TARGET} vulkan-shaders-gen.cpp) install(TARGETS ${TARGET} RUNTIME) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) target_link_libraries(vulkan-shaders-gen PUBLIC Threads::Threads) +target_link_libraries(vulkan-shaders-gen PRIVATE Vulkan::Vulkan) diff --git a/ggml/src/vulkan-shaders/acc.comp b/ggml/src/ggml-vulkan/vulkan-shaders/acc.comp similarity index 51% rename from ggml/src/vulkan-shaders/acc.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/acc.comp index 4c8739efe..4f5a04e71 100644 --- a/ggml/src/vulkan-shaders/acc.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/acc.comp @@ -3,6 +3,8 @@ #include "types.comp" #include "generic_binary_head.comp" +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + void main() { const uint idx = gl_GlobalInvocationID.x; if (idx >= p.ne) { @@ -15,10 +17,13 @@ void main() { const uint oy = (src1_i - (oz * p.nb02)) / p.nb01; const uint ox = src1_i % p.nb01; + uint i00, i01, i02, i03; + get_indices(idx, i00, i01, i02, i03); + if (ox < p.ne10 && oy < p.ne11 && oz < p.ne12) { - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(idx)]) + FLOAT_TYPE(data_b[ox + oy * p.ne10 + oz * p.ne10 * p.ne11])); + data_d[p.d_offset + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(i00, i01, i02, i03)]) + FLOAT_TYPE(data_b[ox + oy * p.ne10 + oz * p.ne10 * p.ne11])); } else { - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(idx)])); + data_d[p.d_offset + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(i00, i01, i02, i03)])); } } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/add.comp b/ggml/src/ggml-vulkan/vulkan-shaders/add.comp new file mode 100644 index 000000000..da61b76df --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/add.comp @@ -0,0 +1,29 @@ +#version 450 + +#extension GL_EXT_shader_16bit_storage : require + +#include "types.comp" +#include "generic_binary_head.comp" + +const uint num_threads = 256; + +layout(local_size_x = num_threads, local_size_y = 1, local_size_z = 1) in; + +void main() { + uint idx = get_idx(); + + // num_threads * num_iter must equal 512, to match the wg_denoms and get_idx calculation + const uint num_iter = 2; + + [[unroll]] for (uint i = 0; i < num_iter; ++i) { + if (idx >= p.ne) { + continue; + } + uint i00, i01, i02, i03; + get_indices(idx, i00, i01, i02, i03); + + data_d[p.d_offset + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(i00, i01, i02, i03)]) + FLOAT_TYPE(data_b[src1_idx(i00, i01, i02, i03)])); + + idx += num_threads; + } +} diff --git a/ggml/src/vulkan-shaders/argsort.comp b/ggml/src/ggml-vulkan/vulkan-shaders/argsort.comp similarity index 100% rename from ggml/src/vulkan-shaders/argsort.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/argsort.comp diff --git a/ggml/src/vulkan-shaders/clamp.comp b/ggml/src/ggml-vulkan/vulkan-shaders/clamp.comp similarity index 83% rename from ggml/src/vulkan-shaders/clamp.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/clamp.comp index 7071302a4..ae8fa8753 100644 --- a/ggml/src/vulkan-shaders/clamp.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/clamp.comp @@ -3,6 +3,8 @@ #include "types.comp" #include "generic_unary_head.comp" +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + void main() { const uint idx = get_idx(); diff --git a/ggml/src/vulkan-shaders/concat.comp b/ggml/src/ggml-vulkan/vulkan-shaders/concat.comp similarity index 95% rename from ggml/src/vulkan-shaders/concat.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/concat.comp index c23b6eb1b..683f9ac3c 100644 --- a/ggml/src/vulkan-shaders/concat.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/concat.comp @@ -3,6 +3,8 @@ #include "types.comp" #include "generic_binary_head.comp" +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + void main() { const uint idx = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; const int dim = p.param3; diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/contig_copy.comp b/ggml/src/ggml-vulkan/vulkan-shaders/contig_copy.comp new file mode 100644 index 000000000..9acbdd3d2 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/contig_copy.comp @@ -0,0 +1,42 @@ +#version 450 + +#include "types.comp" +#include "generic_unary_head.comp" + +#extension GL_EXT_control_flow_attributes : require + +const uint num_threads = 128; + +layout(local_size_x = num_threads, local_size_y = 1, local_size_z = 1) in; + +void main() { + uint idx = get_idx(); + + // num_threads * num_iter must equal 512, to match the wg_denoms and get_idx calculation + const uint num_iter = 4; + + // fast path for when all four iterations are in-bounds + if (idx + (num_iter-1)*num_threads < p.ne) { + [[unroll]] for (uint i = 0; i < num_iter; ++i) { +#ifndef OPTIMIZATION_ERROR_WORKAROUND + data_d[p.d_offset + idx] = D_TYPE(data_a[idx]); +#else + data_d[p.d_offset + idx] = data_a[idx]; +#endif + idx += num_threads; + } + } else { + [[unroll]] for (uint i = 0; i < num_iter; ++i) { + if (idx >= p.ne) { + continue; + } + +#ifndef OPTIMIZATION_ERROR_WORKAROUND + data_d[p.d_offset + idx] = D_TYPE(data_a[idx]); +#else + data_d[p.d_offset + idx] = data_a[idx]; +#endif + idx += num_threads; + } + } +} diff --git a/ggml/src/vulkan-shaders/copy.comp b/ggml/src/ggml-vulkan/vulkan-shaders/copy.comp similarity index 83% rename from ggml/src/vulkan-shaders/copy.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/copy.comp index c26917c0f..2775068f9 100644 --- a/ggml/src/vulkan-shaders/copy.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/copy.comp @@ -3,6 +3,8 @@ #include "types.comp" #include "generic_unary_head.comp" +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + void main() { const uint idx = get_idx(); diff --git a/ggml/src/vulkan-shaders/cos.comp b/ggml/src/ggml-vulkan/vulkan-shaders/cos.comp similarity index 80% rename from ggml/src/vulkan-shaders/cos.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/cos.comp index f9a858cbf..fbd9d272c 100644 --- a/ggml/src/vulkan-shaders/cos.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/cos.comp @@ -3,6 +3,8 @@ #include "types.comp" #include "generic_unary_head.comp" +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + void main() { const uint idx = get_idx(); diff --git a/ggml/src/vulkan-shaders/dequant_f32.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_f32.comp similarity index 100% rename from ggml/src/vulkan-shaders/dequant_f32.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_f32.comp diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.comp new file mode 100644 index 000000000..5fc1ba4ad --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.comp @@ -0,0 +1,116 @@ +#if !defined(DATA_A_F32) && !defined(DATA_A_F16) +#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require +#endif + +#include "types.comp" + +#if defined(A_TYPE_PACKED16) +layout (binding = 0) readonly buffer A_PACKED16 {A_TYPE_PACKED16 data_a_packed16[];}; +#endif +#if defined(A_TYPE_PACKED32) +layout (binding = 0) readonly buffer A_PACKED32 {A_TYPE_PACKED32 data_a_packed32[];}; +#endif + +#if defined(DATA_A_F32) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + return vec2(data_a[a_offset + ib], data_a[a_offset + ib + 1]); +} +#endif + +#if defined(DATA_A_F16) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + return vec2(data_a[a_offset + ib], data_a[a_offset + ib + 1]); +} +#endif + +#if defined(DATA_A_Q4_0) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a[a_offset + ib].d); + const uint vui = uint(data_a[a_offset + ib].qs[iqs]); + return (vec2(vui & 0xF, vui >> 4) - 8.0f) * d; +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a_packed16[a_offset + ib].d); + const uint vui = uint(data_a_packed16[a_offset + ib].qs[iqs/2]); + return (vec4(vui & 0xF, (vui >> 4) & 0xF, (vui >> 8) & 0xF, (vui >> 12) & 0xF) - 8.0f) * d; +} +#endif + +#if defined(DATA_A_Q4_1) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a[a_offset + ib].d); + const float m = float(data_a[a_offset + ib].m); + const uint vui = uint(data_a[a_offset + ib].qs[iqs]); + return vec2(vui & 0xF, vui >> 4) * d + m; +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a_packed16[a_offset + ib].d); + const float m = float(data_a_packed16[a_offset + ib].m); + const uint vui = uint(data_a_packed16[a_offset + ib].qs[iqs/2]); + return vec4(vui & 0xF, (vui >> 4) & 0xF, (vui >> 8) & 0xF, (vui >> 12) & 0xF) * d + m; +} +#endif + +#if defined(DATA_A_Q5_0) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a[a_offset + ib].d); + const uint uint_qh = uint(data_a[a_offset + ib].qh[1]) << 16 | data_a[a_offset + ib].qh[0]; + const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); + const uint vui = uint(data_a[a_offset + ib].qs[iqs]); + return (vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) - 16.0f) * d; +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a_packed16[a_offset + ib].d); + const uint uint_qh = uint(data_a_packed16[a_offset + ib].qh[1]) << 16 | data_a_packed16[a_offset + ib].qh[0]; + const ivec2 qh0 = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); + const ivec2 qh1 = ivec2(((uint_qh >> (iqs + 1)) << 4) & 0x10, (uint_qh >> (iqs + 13)) & 0x10); + const uint vui = uint(data_a_packed16[a_offset + ib].qs[iqs/2]); + return (vec4(((vui >> 0) & 0xF) | qh0.x, ((vui >> 4) & 0xF) | qh0.y, ((vui >> 8) & 0xF) | qh1.x, ((vui >> 12) & 0xF) | qh1.y) - 16.0f) * d; +} +#endif + +#if defined(DATA_A_Q5_1) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a[a_offset + ib].d); + const float m = float(data_a[a_offset + ib].m); + const uint uint_qh = data_a[a_offset + ib].qh; + const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); + const uint vui = uint(data_a[a_offset + ib].qs[iqs]); + return vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) * d + m; +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a_packed16[a_offset + ib].d); + const float m = float(data_a_packed16[a_offset + ib].m); + const uint uint_qh = data_a_packed16[a_offset + ib].qh; + const ivec2 qh0 = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); + const ivec2 qh1 = ivec2(((uint_qh >> (iqs + 1)) << 4) & 0x10, (uint_qh >> (iqs + 13)) & 0x10); + const uint vui = uint(data_a_packed16[a_offset + ib].qs[iqs/2]); + return vec4(((vui >> 0) & 0xF) | qh0.x, ((vui >> 4) & 0xF) | qh0.y, ((vui >> 8) & 0xF) | qh1.x, ((vui >> 12) & 0xF) | qh1.y) * d + m; +} +#endif + +#if defined(DATA_A_Q8_0) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a[a_offset + ib].d); + return vec2(int(data_a[a_offset + ib].qs[iqs]), int(data_a[a_offset + ib].qs[iqs + 1])) * d; +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a_packed16[a_offset + ib].d); + uint32_t v0 = data_a_packed16[a_offset + ib].qs[iqs/2]; + uint32_t v1 = data_a_packed16[a_offset + ib].qs[iqs/2 + 1]; + return vec4(int8_t(v0 & 0xFF), int8_t((v0 >> 8) & 0xFF), int8_t(v1 & 0xFF), int8_t((v1 >> 8) & 0xFF)) * d; +} +#endif + +#if defined(DATA_A_IQ4_NL) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a[a_offset + ib].d); + const uint vui = uint(data_a[a_offset + ib].qs[iqs]); + return vec2(kvalues_iq4nl[vui & 0xF], kvalues_iq4nl[vui >> 4]) * d; +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a_packed16[a_offset + ib].d); + const uint vui = uint(data_a_packed16[a_offset + ib].qs[iqs/2]); + return vec4(kvalues_iq4nl[vui & 0xF], kvalues_iq4nl[(vui >> 4) & 0xF], kvalues_iq4nl[(vui >> 8) & 0xF], kvalues_iq4nl[(vui >> 12) & 0xF]) * d; +} +#endif diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp new file mode 100644 index 000000000..a8707b621 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.comp @@ -0,0 +1,305 @@ + +#include "types.comp" + +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufQ4_0 { + block_q4_0_packed16 block; +}; + +float16_t dequantFuncQ4_0(const in decodeBufQ4_0 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float16_t d = bl.block.d; + const uint idx = coordInBlock[1]; + const uint shift = (idx & 0x10) >> 2; + uint32_t qs = unpack8(uint32_t(bl.block.qs[(idx & 0xE) >> 1]))[idx & 1]; + qs >>= shift; + qs &= 0xF; + float16_t ret = (float16_t(qs) - float16_t(8)) * d; + return ret; +} + +layout(buffer_reference, std430, buffer_reference_align = 4) buffer decodeBufQ4_1 { + block_q4_1 block; +}; + +float16_t dequantFuncQ4_1(const in decodeBufQ4_1 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float16_t d = bl.block.d; + const float16_t m = bl.block.m; + const uint idx = coordInBlock[1]; + const uint iqs = idx & 0xF; + const uint shift = (idx & 0x10) >> 2; + uint32_t qs = bl.block.qs[iqs]; + qs >>= shift; + qs &= 0xF; + float16_t ret = float16_t(qs) * d + m; + return ret; +} + +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufQ5_0 { + block_q5_0 block; +}; + +float16_t dequantFuncQ5_0(const in decodeBufQ5_0 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float16_t d = bl.block.d; + const uint idx = coordInBlock[1]; + const uint iqs = idx & 0xF; + + const uint uint_qh = uint(bl.block.qh[1]) << 16 | bl.block.qh[0]; + const uint qh = ((uint_qh >> idx) << 4) & 0x10; + + const uint shift = (idx & 0x10) >> 2; + uint32_t qs = bl.block.qs[iqs]; + qs >>= shift; + qs &= 0xF; + + float16_t ret = (float16_t(qs | qh) - float16_t(16)) * d; + return ret; +} + +layout(buffer_reference, std430, buffer_reference_align = 8) buffer decodeBufQ5_1 { + block_q5_1 block; +}; + +float16_t dequantFuncQ5_1(const in decodeBufQ5_1 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float16_t d = bl.block.d; + const float16_t m = bl.block.m; + const uint idx = coordInBlock[1]; + const uint iqs = idx & 0xF; + + const uint uint_qh = bl.block.qh; + const uint qh = ((uint_qh >> idx) << 4) & 0x10; + + const uint shift = (idx & 0x10) >> 2; + uint32_t qs = bl.block.qs[iqs]; + qs >>= shift; + qs &= 0xF; + + float16_t ret = float16_t(qs | qh) * d + m; + return ret; +} + +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufQ8_0 { + block_q8_0_packed16 block; +}; + +float16_t dequantFuncQ8_0(const in decodeBufQ8_0 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float16_t d = bl.block.d; + const uint idx = coordInBlock[1]; + const uint iqs = idx; + + // Load 16b and select the byte for this element + int32_t qs = unpack8(int32_t(bl.block.qs[(iqs & 0x1E) >> 1]))[iqs & 1]; + float16_t ret = float16_t(qs) * d; + return ret; +} + +layout(buffer_reference, std430, buffer_reference_align = 4) buffer decodeBufQ2_K { + block_q2_K block; +}; + +float16_t dequantFuncQ2_K(const in decodeBufQ2_K bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const f16vec2 d = bl.block.d; + const uint idx = coordInBlock[1]; + const uint iqs = idx; + + const uint qsi = (iqs / 128) * 32 + (iqs % 32); // 0..31 + const uint scalesi = iqs / 16; // 0..15 + const uint qsshift = ((iqs % 128) / 32) * 2; // 0,2,4,6 + + uint32_t qs = bl.block.qs[qsi]; + const uint scales = bl.block.scales[scalesi]; + float16_t ret = d.x * float16_t(scales & 0xF) * float16_t((qs >> qsshift) & 3) - d.y * float16_t(scales >> 4); + return ret; +} + +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufQ3_K { + block_q3_K block; +}; + +float16_t dequantFuncQ3_K(const in decodeBufQ3_K bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const uint idx = coordInBlock[1]; + const uint iqs = idx; + + const uint n = iqs / 128; // 0,1 + const uint qsi = n * 32 + (iqs % 32); // 0..63 + const uint hmi = (iqs % 32); // 0..31 + const uint j = (iqs % 128) / 8; // 0..15 + const uint is = iqs / 16; // 0..15 + const uint halfsplit = ((iqs % 128) / 32); // 0,1,2,3 + const uint qsshift = halfsplit * 2; // 0,2,4,6 + const uint m = 1 << (4 * n + halfsplit); // 1,2,4,8,16,32,64,128 + + uint32_t scaleidx0 = (is < 8) ? is : (is-8); + uint32_t scaleidx0shift = (is < 8) ? 0 : 4; + uint32_t scaleidx1 = is + 8 - (is/4)*4; + uint32_t scaleidx1shift = (is/4)*2; + + const int8_t us = int8_t(((bl.block.scales[scaleidx0] >> scaleidx0shift) & 0xF) | (((bl.block.scales[scaleidx1] >> scaleidx1shift) & 3) << 4)); + + const float16_t dl = bl.block.d * float16_t(us - 32); + + float16_t ret = dl * float16_t(int8_t((bl.block.qs[qsi ] >> qsshift) & 3) - (((bl.block.hmask[hmi ] & m) != 0) ? 0 : 4)); + + return ret; +} + +layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ4_K { + block_q4_K block; +}; + +float16_t dequantFuncQ4_K(const in decodeBufQ4_K bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const uint idx = coordInBlock[1]; + const uint iqs = idx; + + const uint n = iqs / 64; // 0,1,2,3 + const uint b = (iqs % 64) / 32; // 0,1 + const uint is = (idx & 0xE0) >> 5; // 0..7 + const uint qsi = n * 32 + (iqs % 32); // 0..127 + + const f16vec2 loadd = bl.block.d; + + uint32_t sc; + uint32_t mbyte; + + uint32_t scidx0 = (is < 4) ? is : (is + 4); + uint32_t scidx1 = (is < 4) ? is : (is - 4); + uint32_t scidxmask1 = (is < 4) ? 0x30 : 0xC0; + uint32_t scidxshift1 = (is < 4) ? 0 : 2; + uint32_t mbidx0 = is + 4; + uint32_t mbidx1 = (is < 4) ? is + 4 : is; + uint32_t mbidxmask0 = (is < 4) ? 0xF : 0xF0; + uint32_t mbidxshift0 = (is < 4) ? 0 : 4; + uint32_t mbidxmask1 = (is < 4) ? 0x30 : 0xC0; + uint32_t mbidxshift1 = (is < 4) ? 0 : 2; + + sc = uint8_t((bl.block.scales[scidx0] & 0xF) | ((bl.block.scales[scidx1] & scidxmask1) >> scidxshift1)); + mbyte = uint8_t(((bl.block.scales[mbidx0] & mbidxmask0) >> mbidxshift0) | ((bl.block.scales[mbidx1] & mbidxmask1) >> mbidxshift1)); + + const float16_t d = loadd.x * float16_t(sc); + const float16_t m = loadd.y * float16_t(mbyte); + + uint32_t dmask = 0xF << (b * 4); + + float16_t ret = d * float16_t((bl.block.qs[qsi ] & dmask) >> (b * 4)) - m; + + return ret; +} + +layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ5_K { + block_q5_K block; +}; + +float16_t dequantFuncQ5_K(const in decodeBufQ5_K bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const uint idx = coordInBlock[1]; + const uint iqs = idx; + + const uint n = iqs / 64; // 0,1,2,3 + const uint b = (iqs % 64) / 32; // 0,1 + const uint is = (idx & 0xE0) >> 5; // 0..7 + const uint qsi = n * 32 + (iqs % 32); // 0..127 + const uint qhi = (iqs % 32); // 0..31 + + const uint8_t hm = uint8_t(1 << (iqs / 32)); + + const f16vec2 loadd = bl.block.d; + + uint32_t sc; + uint32_t mbyte; + + uint32_t scidx0 = (is < 4) ? is : (is + 4); + uint32_t scidx1 = (is < 4) ? is : (is - 4); + uint32_t scidxmask1 = (is < 4) ? 0x30 : 0xC0; + uint32_t scidxshift1 = (is < 4) ? 0 : 2; + uint32_t mbidx0 = is + 4; + uint32_t mbidx1 = (is < 4) ? is + 4 : is; + uint32_t mbidxmask0 = (is < 4) ? 0xF : 0xF0; + uint32_t mbidxshift0 = (is < 4) ? 0 : 4; + uint32_t mbidxmask1 = (is < 4) ? 0x30 : 0xC0; + uint32_t mbidxshift1 = (is < 4) ? 0 : 2; + + sc = uint8_t((bl.block.scales[scidx0] & 0xF) | ((bl.block.scales[scidx1] & scidxmask1) >> scidxshift1)); + mbyte = uint8_t(((bl.block.scales[mbidx0] & mbidxmask0) >> mbidxshift0) | ((bl.block.scales[mbidx1] & mbidxmask1) >> mbidxshift1)); + + const float16_t d = loadd.x * float16_t(sc); + const float16_t m = loadd.y * float16_t(mbyte); + + uint32_t dmask = 0xF << (b * 4); + + float16_t ret = d * (float16_t((bl.block.qs[qsi ] & dmask) >> (b * 4)) + float16_t((bl.block.qh[qhi ] & hm) != 0 ? 16 : 0)) - m; + + return ret; +} + +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufQ6_K { + block_q6_K block; +}; + +float16_t dequantFuncQ6_K(const in decodeBufQ6_K bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const uint idx = coordInBlock[1]; + const uint iqs = idx; + + const uint n = iqs / 128; // 0,1 + const uint b = (iqs % 128) / 64; // 0,1 + const uint is_b = (iqs % 32) / 16; // 0,1 + const uint qhshift = ((iqs % 128) / 32) * 2;// 0,2,4,6 + const uint is = 8 * n + qhshift + is_b; // 0..15 + const uint qsi = n * 64 + (iqs % 64); // 0..127 + const uint qhi = n * 32 + (iqs % 32); // 0..63 + + const float16_t dscale = bl.block.d * float16_t(bl.block.scales[is]); + + float16_t ret = dscale * float16_t(int8_t(((bl.block.ql[qsi ] >> (b * 4)) & 0xF) | (((bl.block.qh[qhi ] >> qhshift) & 3) << 4)) - 32); + + return ret; +} + +#if defined(DATA_A_IQ4_NL) +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ4_NL { + block_iq4_nl block; +}; + +float16_t dequantFuncIQ4_NL(const in decodeBufIQ4_NL bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float16_t d = bl.block.d; + const uint idx = coordInBlock[1]; + const uint iqs = idx & 0xF; + const uint shift = (idx & 0x10) >> 2; + uint32_t qs = bl.block.qs[iqs]; + qs >>= shift; + qs &= 0xF; + float16_t ret = float16_t(kvalues_iq4nl[qs]) * d; + return ret; +} +#endif + +#if defined(DATA_A_Q4_0) +#define dequantFuncA dequantFuncQ4_0 +#elif defined(DATA_A_Q4_1) +#define dequantFuncA dequantFuncQ4_1 +#elif defined(DATA_A_Q5_0) +#define dequantFuncA dequantFuncQ5_0 +#elif defined(DATA_A_Q5_1) +#define dequantFuncA dequantFuncQ5_1 +#elif defined(DATA_A_Q8_0) +#define dequantFuncA dequantFuncQ8_0 +#elif defined(DATA_A_Q2_K) +#define dequantFuncA dequantFuncQ2_K +#elif defined(DATA_A_Q3_K) +#define dequantFuncA dequantFuncQ3_K +#elif defined(DATA_A_Q4_K) +#define dequantFuncA dequantFuncQ4_K +#elif defined(DATA_A_Q5_K) +#define dequantFuncA dequantFuncQ5_K +#elif defined(DATA_A_Q6_K) +#define dequantFuncA dequantFuncQ6_K +#elif defined(DATA_A_IQ4_NL) +#define dequantFuncA dequantFuncIQ4_NL +#endif diff --git a/ggml/src/vulkan-shaders/dequant_head.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_head.comp similarity index 100% rename from ggml/src/vulkan-shaders/dequant_head.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_head.comp diff --git a/ggml/src/vulkan-shaders/dequant_iq4_nl.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq4_nl.comp similarity index 97% rename from ggml/src/vulkan-shaders/dequant_iq4_nl.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq4_nl.comp index 34ef3da30..8de14fc03 100644 --- a/ggml/src/vulkan-shaders/dequant_iq4_nl.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq4_nl.comp @@ -10,6 +10,8 @@ layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; void main() { const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; + init_iq4nl_shmem(); + const uint tid = gl_LocalInvocationID.x % 64; const uint il = tid/32; const uint ir = tid%32; diff --git a/ggml/src/vulkan-shaders/dequant_q2_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q2_k.comp similarity index 100% rename from ggml/src/vulkan-shaders/dequant_q2_k.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_q2_k.comp diff --git a/ggml/src/vulkan-shaders/dequant_q3_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q3_k.comp similarity index 100% rename from ggml/src/vulkan-shaders/dequant_q3_k.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_q3_k.comp diff --git a/ggml/src/vulkan-shaders/dequant_q4_0.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q4_0.comp similarity index 100% rename from ggml/src/vulkan-shaders/dequant_q4_0.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_q4_0.comp diff --git a/ggml/src/vulkan-shaders/dequant_q4_1.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q4_1.comp similarity index 100% rename from ggml/src/vulkan-shaders/dequant_q4_1.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_q4_1.comp diff --git a/ggml/src/vulkan-shaders/dequant_q4_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q4_k.comp similarity index 100% rename from ggml/src/vulkan-shaders/dequant_q4_k.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_q4_k.comp diff --git a/ggml/src/vulkan-shaders/dequant_q5_0.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q5_0.comp similarity index 100% rename from ggml/src/vulkan-shaders/dequant_q5_0.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_q5_0.comp diff --git a/ggml/src/vulkan-shaders/dequant_q5_1.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q5_1.comp similarity index 100% rename from ggml/src/vulkan-shaders/dequant_q5_1.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_q5_1.comp diff --git a/ggml/src/vulkan-shaders/dequant_q5_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q5_k.comp similarity index 100% rename from ggml/src/vulkan-shaders/dequant_q5_k.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_q5_k.comp diff --git a/ggml/src/vulkan-shaders/dequant_q6_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q6_k.comp similarity index 100% rename from ggml/src/vulkan-shaders/dequant_q6_k.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_q6_k.comp diff --git a/ggml/src/vulkan-shaders/dequant_q8_0.comp b/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q8_0.comp similarity index 100% rename from ggml/src/vulkan-shaders/dequant_q8_0.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/dequant_q8_0.comp diff --git a/ggml/src/vulkan-shaders/diag_mask_inf.comp b/ggml/src/ggml-vulkan/vulkan-shaders/diag_mask_inf.comp similarity index 100% rename from ggml/src/vulkan-shaders/diag_mask_inf.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/diag_mask_inf.comp diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/div.comp b/ggml/src/ggml-vulkan/vulkan-shaders/div.comp new file mode 100644 index 000000000..e581905b3 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/div.comp @@ -0,0 +1,27 @@ +#version 450 + +#include "types.comp" +#include "generic_binary_head.comp" + +const uint num_threads = 256; + +layout(local_size_x = num_threads, local_size_y = 1, local_size_z = 1) in; + +void main() { + uint idx = get_idx(); + + // num_threads * num_iter must equal 512, to match the wg_denoms and get_idx calculation + const uint num_iter = 2; + + [[unroll]] for (uint i = 0; i < num_iter; ++i) { + if (idx >= p.ne) { + continue; + } + uint i00, i01, i02, i03; + get_indices(idx, i00, i01, i02, i03); + + data_d[p.d_offset + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(i00, i01, i02, i03)]) / FLOAT_TYPE(data_b[src1_idx(i00, i01, i02, i03)])); + + idx += num_threads; + } +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp new file mode 100644 index 000000000..c5be8131b --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp @@ -0,0 +1,289 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : enable +#extension GL_EXT_shader_16bit_storage : require + +#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require +#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require +#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require + +#extension GL_KHR_memory_scope_semantics : enable +#extension GL_KHR_cooperative_matrix : enable +#extension GL_NV_cooperative_matrix2 : enable +#extension GL_EXT_buffer_reference : enable +#extension GL_KHR_shader_subgroup_ballot : enable +#extension GL_KHR_shader_subgroup_vote : enable +#extension GL_EXT_null_initializer : enable + +#include "types.comp" +#include "dequant_funcs_cm2.comp" + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (constant_id = 1) const uint32_t Br = 32; +layout (constant_id = 2) const uint32_t Bc = 32; +layout (constant_id = 3) const uint32_t D = 32; +layout (constant_id = 4) const uint32_t Clamp = gl_CooperativeMatrixClampModeConstantNV; + +layout (push_constant) uniform parameter { + uint32_t N; + uint32_t KV; + + uint32_t ne1; + uint32_t ne2; + uint32_t ne3; + + uint32_t neq2; + uint32_t neq3; + uint32_t nek2; + uint32_t nek3; + uint32_t nev2; + uint32_t nev3; + uint32_t nem1; + + uint32_t nb02; + uint32_t nb03; + uint32_t nb12; + uint32_t nb13; + uint32_t nb22; + uint32_t nb23; + uint32_t nb31; + + float scale; + float max_bias; + float logit_softcap; + + uint32_t mask; + uint32_t n_head_log2; + float m0; + float m1; +} p; + +layout (binding = 0) readonly buffer Q {uint8_t data_q[];}; +layout (binding = 1) readonly buffer K {uint8_t data_k[];}; +layout (binding = 2) readonly buffer V {uint8_t data_v[];}; +layout (binding = 3) readonly buffer M {uint8_t data_m[];}; +layout (binding = 4) writeonly buffer O {D_TYPE data_o[];}; + +#define CEIL_DIV(a, b) (((a) + (b) - 1) / (b)) + +ACC_TYPE maxReduce(const in ACC_TYPE x, const in ACC_TYPE y) { + return max(x, y); +} + +ACC_TYPE smearReduce(const in ACC_TYPE x, const in ACC_TYPE y) { + return x; +} + +// Replace matrix elements >= numRows or numCols with 'replace' +ACC_TYPE replacePadding(const in uint32_t row, const in uint32_t col, const in ACC_TYPE elem, const in ACC_TYPE replace, const in uint32_t numRows, const in uint32_t numCols) { + if (row >= numRows || col >= numCols) { + return replace; + } + return elem; +} + +ACC_TYPE Exp(const in uint32_t row, const in uint32_t col, const in ACC_TYPE elem) +{ + return exp(elem); +} + +ACC_TYPE Max(const in uint32_t row, const in uint32_t col, const in ACC_TYPE elem0, const in ACC_TYPE elem1) +{ + return max(elem0, elem1); +} + +#if defined(BLOCK_SIZE) +#define DECODEFUNC , DEQUANTFUNC +#else +#define DECODEFUNC +#endif + +void main() { +#if defined(DATA_A_IQ4_NL) + init_iq4nl_shmem(); +#endif + + const uint32_t N = p.N; + const uint32_t KV = p.KV; + + const uint32_t Tr = CEIL_DIV(N, Br); + const uint32_t Tc = CEIL_DIV(KV, Bc); + + const uint32_t i = gl_WorkGroupID.x; + + const uint32_t iq2 = gl_WorkGroupID.y; + const uint32_t iq3 = gl_WorkGroupID.z; + + // broadcast factors + const uint32_t rk2 = p.neq2/p.nek2; + const uint32_t rk3 = p.neq3/p.nek3; + + const uint32_t rv2 = p.neq2/p.nev2; + const uint32_t rv3 = p.neq3/p.nev3; + + // k indices + const uint32_t ik3 = iq3 / rk3; + const uint32_t ik2 = iq2 / rk2; + + // v indices + const uint32_t iv3 = iq3 / rv3; + const uint32_t iv2 = iq2 / rv2; + + tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutQ = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV); + tensorLayoutNV<2, Clamp> tensorLayoutK = createTensorLayoutNV(2, Clamp); + tensorLayoutNV<2, Clamp> tensorLayoutV = createTensorLayoutNV(2, Clamp); + + tensorViewNV<2, false, 1, 0> tensorViewTranspose = createTensorViewNV(2, false, 1, 0); + +#if defined(BLOCK_SIZE) + tensorLayoutK = setTensorLayoutBlockSizeNV(tensorLayoutK, 1, BLOCK_SIZE); + tensorLayoutV = setTensorLayoutBlockSizeNV(tensorLayoutV, 1, BLOCK_SIZE); +#endif + + tensorLayoutQ = setTensorLayoutDimensionNV(tensorLayoutQ, N, D); + tensorLayoutK = setTensorLayoutDimensionNV(tensorLayoutK, KV, D); + tensorLayoutV = setTensorLayoutDimensionNV(tensorLayoutV, KV, D); + + coopmat Q; + coopmat Qf16; + + uint32_t q_offset = iq2*p.nb02+iq3*p.nb03; + coopMatLoadTensorNV(Q, data_q, q_offset, sliceTensorLayoutNV(tensorLayoutQ, i * Br, Br, 0, D)); + + Qf16 = coopmat(Q); + Qf16 *= float16_t(p.scale); + + coopmat O = coopmat(0); + + coopmat L, M; + + L = coopmat(0); + M = coopmat(-1.0/0.0); + + ACC_TYPE slope = ACC_TYPE(1.0); + + // ALiBi + if (p.max_bias > 0.0f) { + const uint32_t h = iq2; + + const ACC_TYPE base = ACC_TYPE(h < p.n_head_log2 ? p.m0 : p.m1); + const int exph = int(h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1); + + slope = pow(base, ACC_TYPE(exph)); + } + + [[dont_unroll]] + for (uint32_t j = 0; j < Tc; ++j) { + + coopmat S = coopmat(0); + + coopmat K_T; + + uint32_t k_offset = ik2*p.nb12 + ik3*p.nb13; + coopMatLoadTensorNV(K_T, data_k, k_offset, sliceTensorLayoutNV(tensorLayoutK, j * Bc, Bc, 0, D), tensorViewTranspose DECODEFUNC); + S = coopMatMulAdd(Qf16, K_T, S); + + if (p.logit_softcap != 0.0f) { + [[unroll]] + for (int k = 0; k < S.length(); ++k) { + S[k] = ACC_TYPE(p.logit_softcap)*tanh(S[k]); + } + } + + if (p.mask != 0) { + tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutM = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV); + tensorLayoutM = setTensorLayoutDimensionNV(tensorLayoutM, p.nem1, KV); + + coopmat mv; + + coopMatLoadTensorNV(mv, data_m, 0, sliceTensorLayoutNV(tensorLayoutM, i * Br, Br, j * Bc, Bc)); + + S += slope*coopmat(mv); + } + + // Clear padding elements to -inf, so they don't contribute to rowmax + if (Clamp != 0 && + ((j + 1) * Bc > KV || + (i + 1) * Br > N)) { + + uint R = ((i + 1) * Br > N) ? (N % Br) : Br; + uint C = ((j + 1) * Bc > KV) ? (KV % Bc) : Bc; + + coopMatPerElementNV(S, S, replacePadding, ACC_TYPE(-1.0/0.0), R, C); + } + + coopmat rowmax, P, rowsum, eM; + + coopMatReduceNV(rowmax, S, gl_CooperativeMatrixReduceRowNV, maxReduce); + + coopmat Mold = M; + + // M = max(rowmax, Mold) + // P = e^(S - M) + // eM = e^(Mold - M) + coopMatPerElementNV(M, rowmax, Max, Mold); + coopMatPerElementNV(P, S - M, Exp); + coopMatPerElementNV(eM, Mold - M, Exp); + + // Clear padding elements to 0, so they don't contribute to rowsum + if (Clamp != 0 && + ((j + 1) * Bc > KV || + (i + 1) * Br > N)) { + + uint R = ((i + 1) * Br > N) ? (N % Br) : Br; + uint C = ((j + 1) * Bc > KV) ? (KV % Bc) : Bc; + + coopMatPerElementNV(P, P, replacePadding, ACC_TYPE(0.0), R, C); + } + + coopmat P_A = coopmat(P); + + // compute rowsum by multiplying by matrix of all ones. + coopmat One = coopmat(1.0); + + rowsum = coopmat(0.0); + rowsum = coopMatMulAdd(P_A, One, rowsum); + + coopmat V; + uint32_t v_offset = iv2*p.nb22 + iv3*p.nb23; + coopMatLoadTensorNV(V, data_v, v_offset, sliceTensorLayoutNV(tensorLayoutV, j * Bc, Bc, 0, D) DECODEFUNC); + + L = eM*L + rowsum; + + // This is the "diagonal" matrix in the paper, but since we do componentwise + // multiply rather than matrix multiply it has the diagonal element smeared + // across the row + coopmat eMdiag; + + // resize eM by using smear/reduce + coopMatReduceNV(eMdiag, eM, gl_CooperativeMatrixReduceRowNV, smearReduce); + + O = eMdiag * O; + + O = coopMatMulAdd(P_A, V, O); + } + + coopmat Ldiag; + + // resize L by using smear/reduce + coopMatReduceNV(Ldiag, L, gl_CooperativeMatrixReduceRowNV, smearReduce); + + [[unroll]] + for (int k = 0; k < Ldiag.length(); ++k) { + Ldiag[k] = ACC_TYPE(1.0) / Ldiag[k]; + } + + O = Ldiag*O; + + tensorLayoutNV<3, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutD = createTensorLayoutNV(3, gl_CooperativeMatrixClampModeConstantNV); + tensorLayoutD = setTensorLayoutDimensionNV(tensorLayoutD, p.ne2, p.ne1, D); + + // permute dimensions + tensorViewNV<3, false, 1, 0, 2> tensorViewPermute = createTensorViewNV(3, false, 1, 0, 2); + uint32_t o_offset = iq3*p.ne2*p.ne1; + + coopmat O_D = coopmat(O); + coopMatStoreTensorNV(O_D, data_o, o_offset, sliceTensorLayoutNV(tensorLayoutD, i * Br, Br, iq2, 1, 0, D), tensorViewPermute); +} diff --git a/ggml/src/vulkan-shaders/gelu.comp b/ggml/src/ggml-vulkan/vulkan-shaders/gelu.comp similarity index 100% rename from ggml/src/vulkan-shaders/gelu.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/gelu.comp diff --git a/ggml/src/vulkan-shaders/gelu_quick.comp b/ggml/src/ggml-vulkan/vulkan-shaders/gelu_quick.comp similarity index 100% rename from ggml/src/vulkan-shaders/gelu_quick.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/gelu_quick.comp diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/generic_binary_head.comp b/ggml/src/ggml-vulkan/vulkan-shaders/generic_binary_head.comp new file mode 100644 index 000000000..a6555fa27 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/generic_binary_head.comp @@ -0,0 +1,60 @@ +#extension GL_EXT_shader_16bit_storage : require +#extension GL_EXT_control_flow_attributes : require + +layout (push_constant) uniform parameter +{ + uint ne; + uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03; + uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13; + uint ne20; uint ne21; uint ne22; uint ne23; uint nb20; uint nb21; uint nb22; uint nb23; + uint d_offset; + float param1; float param2; int param3; +} p; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; +layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; + +// true if src0/src1 are the same shape and the indices can be reused without additional modulus +layout(constant_id = 0) const bool norepeat = false; + +uint get_idx() { + return gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; +} + +// mod and div are expensive and coordinates/dimensions are often power of 2 or equal to 1 +uint fastmod(uint a, uint b) { + if ((b & (b-1)) == 0) { + return a & (b-1); + } + return a % b; +} + +uint fastdiv(uint a, uint b) { + return (a < b) ? 0 : (a / b); +} + +void get_indices(uint idx, out uint i00, out uint i01, out uint i02, out uint i03) { + i03 = fastdiv(idx, (p.ne02*p.ne01*p.ne00)); + const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00; + i02 = fastdiv((idx - i03_offset), (p.ne01*p.ne00)); + const uint i02_offset = i02*p.ne01*p.ne00; + i01 = (idx - i03_offset - i02_offset) / p.ne00; + i00 = idx - i03_offset - i02_offset - i01*p.ne00; +} + +uint src0_idx(uint i00, uint i01, uint i02, uint i03) { + return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00; +} + +uint src1_idx(uint i00, uint i01, uint i02, uint i03) { + if (norepeat) { + return i03*p.nb13 + i02*p.nb12 + i01*p.nb11 + i00*p.nb10; + } else { + return fastmod(i03, p.ne13)*p.nb13 + fastmod(i02, p.ne12)*p.nb12 + fastmod(i01, p.ne11)*p.nb11 + fastmod(i00, p.ne10)*p.nb10; + } +} + +uint dst_idx(uint i00, uint i01, uint i02, uint i03) { + return i03*p.nb23 + i02*p.nb22 + i01*p.nb21 + i00*p.nb20; +} diff --git a/ggml/src/vulkan-shaders/generic_head.comp b/ggml/src/ggml-vulkan/vulkan-shaders/generic_head.comp similarity index 100% rename from ggml/src/vulkan-shaders/generic_head.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/generic_head.comp diff --git a/ggml/src/vulkan-shaders/generic_unary_head.comp b/ggml/src/ggml-vulkan/vulkan-shaders/generic_unary_head.comp similarity index 55% rename from ggml/src/vulkan-shaders/generic_unary_head.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/generic_unary_head.comp index eacdefc7d..ab7c9d7eb 100644 --- a/ggml/src/vulkan-shaders/generic_unary_head.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/generic_unary_head.comp @@ -1,4 +1,5 @@ #extension GL_EXT_shader_16bit_storage : require +#extension GL_EXT_control_flow_attributes : require layout (push_constant) uniform parameter { @@ -7,9 +8,14 @@ layout (push_constant) uniform parameter uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13; uint d_offset; float param1; float param2; -} p; -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + uint ne0_012mp; uint ne0_012L; + uint ne0_01mp; uint ne0_01L; + uint ne0_0mp; uint ne0_0L; + uint ne1_012mp; uint ne1_012L; + uint ne1_01mp; uint ne1_01L; + uint ne1_0mp; uint ne1_0L; +} p; layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; @@ -18,22 +24,30 @@ uint get_idx() { return gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; } +// see init_fastdiv_values in ggml-vulkan.cpp +uint fastdiv(uint n, uint mp, uint L) { + uint msbs, lsbs; + // msbs = mulhi(n, mp) + umulExtended(n, mp, msbs, lsbs); + return (msbs + n) >> L; +} + uint src0_idx(uint idx) { - const uint i03 = idx / (p.ne02*p.ne01*p.ne00); + const uint i03 = fastdiv(idx, p.ne0_012mp, p.ne0_012L); const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00; - const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00); + const uint i02 = fastdiv(idx - i03_offset, p.ne0_01mp, p.ne0_01L); const uint i02_offset = i02*p.ne01*p.ne00; - const uint i01 = (idx - i03_offset - i02_offset) / p.ne00; + const uint i01 = fastdiv(idx - i03_offset - i02_offset, p.ne0_0mp, p.ne0_0L); const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00; return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00; } uint dst_idx(uint idx) { - const uint i13 = idx / (p.ne12*p.ne11*p.ne10); + const uint i13 = fastdiv(idx, p.ne1_012mp, p.ne1_012L); const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10; - const uint i12 = (idx - i13_offset) / (p.ne11*p.ne10); + const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, p.ne1_01L); const uint i12_offset = i12*p.ne11*p.ne10; - const uint i11 = (idx - i13_offset - i12_offset) / p.ne10; + const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, p.ne1_0L); const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10; return i13*p.nb13 + i12*p.nb12 + i11*p.nb11 + i10*p.nb10; } diff --git a/ggml/src/vulkan-shaders/get_rows.comp b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp similarity index 91% rename from ggml/src/vulkan-shaders/get_rows.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp index e9ff22efa..a7b81e52c 100644 --- a/ggml/src/vulkan-shaders/get_rows.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp @@ -3,6 +3,8 @@ #include "types.comp" #include "generic_binary_head.comp" +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + void main() { const uint i00 = gl_GlobalInvocationID.x; const uint i10 = gl_GlobalInvocationID.y; diff --git a/ggml/src/vulkan-shaders/get_rows_quant.comp b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp similarity index 88% rename from ggml/src/vulkan-shaders/get_rows_quant.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp index 53a9a96f2..7f608315b 100644 --- a/ggml/src/vulkan-shaders/get_rows_quant.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp @@ -4,12 +4,18 @@ #include "generic_binary_head.comp" #include "dequant_funcs.comp" +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + void main() { const uint i00 = (gl_GlobalInvocationID.x)*2; const uint i10 = gl_GlobalInvocationID.y; const uint i11 = (gl_GlobalInvocationID.z)/p.ne12; const uint i12 = (gl_GlobalInvocationID.z)%p.ne12; +#if defined(DATA_A_IQ4_NL) + init_iq4nl_shmem(); +#endif + if (i00 >= p.ne00) { return; } diff --git a/ggml/src/vulkan-shaders/group_norm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/group_norm.comp similarity index 96% rename from ggml/src/vulkan-shaders/group_norm.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/group_norm.comp index 5ad9b28da..b6a0d5645 100644 --- a/ggml/src/vulkan-shaders/group_norm.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/group_norm.comp @@ -19,7 +19,7 @@ void main() { const uint tid = gl_LocalInvocationID.x; const uint start = gl_WorkGroupID.x * group_size + tid; - const uint end = start + group_size; + const uint end = (gl_WorkGroupID.x + 1) * group_size; tmp[tid] = 0.0f; diff --git a/ggml/src/vulkan-shaders/im2col.comp b/ggml/src/ggml-vulkan/vulkan-shaders/im2col.comp similarity index 100% rename from ggml/src/vulkan-shaders/im2col.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/im2col.comp diff --git a/ggml/src/vulkan-shaders/leaky_relu.comp b/ggml/src/ggml-vulkan/vulkan-shaders/leaky_relu.comp similarity index 100% rename from ggml/src/vulkan-shaders/leaky_relu.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/leaky_relu.comp diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul.comp new file mode 100644 index 000000000..5ce57cbcf --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul.comp @@ -0,0 +1,27 @@ +#version 450 + +#include "types.comp" +#include "generic_binary_head.comp" + +const uint num_threads = 256; + +layout(local_size_x = num_threads, local_size_y = 1, local_size_z = 1) in; + +void main() { + uint idx = get_idx(); + + // num_threads * num_iter must equal 512, to match the wg_denoms and get_idx calculation + const uint num_iter = 2; + + [[unroll]] for (uint i = 0; i < num_iter; ++i) { + if (idx >= p.ne) { + continue; + } + uint i00, i01, i02, i03; + get_indices(idx, i00, i01, i02, i03); + + data_d[p.d_offset + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(i00, i01, i02, i03)]) * FLOAT_TYPE(data_b[src1_idx(i00, i01, i02, i03)])); + + idx += num_threads; + } +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_split_k_reduce.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_split_k_reduce.comp new file mode 100644 index 000000000..4c64fd47a --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_split_k_reduce.comp @@ -0,0 +1,48 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : enable + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {float data_a[];}; +layout (binding = 0) readonly buffer A4 {vec4 data_a4[];}; +layout (binding = 1) writeonly buffer D {float data_d[];}; +layout (binding = 1) writeonly buffer D4 {vec4 data_d4[];}; + +layout (push_constant) uniform parameter { + uint ne; + uint k_num; +} p; + +void main() { + // Each invocation handles four consecutive components + const uint idx = gl_GlobalInvocationID.x * 4; + + if (idx >= p.ne) { + return; + } + + // Check if all four components are in bounds and aligned, + // then use vector loads + if (idx + 3 < p.ne && (p.ne % 4) == 0) { + vec4 result = vec4(0.0f); + + [[unroll]] for (uint i = 0; i < p.k_num; i++) { + result += data_a4[(i * p.ne + idx) / 4]; + } + + data_d4[idx / 4] = result; + } else { + [[unroll]] for (uint j = 0; j < 4; ++j) { + if (idx + j < p.ne) { + float result = 0.0f; + + [[unroll]] for (uint i = 0; i < p.k_num; i++) { + result += data_a[i * p.ne + idx + j]; + } + + data_d[idx + j] = result; + } + } + } +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp new file mode 100644 index 000000000..2d5b8e466 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp @@ -0,0 +1,177 @@ +#version 450 + +#ifdef FLOAT16 +#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require +#endif +#extension GL_EXT_shader_explicit_arithmetic_types : require + +#include "mul_mat_vec_base.comp" + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (constant_id = 0) const uint BLOCK_SIZE = 32; +layout (constant_id = 1) const uint NUM_ROWS = 1; + +#if !defined(DATA_A_F32) && !defined(DATA_A_F16) +#define K_PER_ITER 8 +#else +#define K_PER_ITER 2 +#endif + + +uint a_offset, b_offset, d_offset, y_offset; + +shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE]; + +void iter(inout FLOAT_TYPE temp[NUM_ROWS], const uint first_row, const uint num_rows, const uint tid, const uint i, bool lastiter) +{ + const uint col = i*BLOCK_SIZE + K_PER_ITER*tid; + const uint iqs = (col%QUANT_K)/QUANT_R; // quant index + const uint iybs = col - col%QUANT_K; // y block start index + +#if K_PER_ITER == 8 +#if QUANT_R == 2 + B_TYPE_VEC4 bv02 = data_b_v4[(b_offset + iybs + iqs) / 4]; + B_TYPE_VEC4 bv13 = data_b_v4[(b_offset + iybs + iqs + y_offset) / 4]; + FLOAT_TYPE b0 = FLOAT_TYPE(bv02.x); + FLOAT_TYPE b1 = FLOAT_TYPE(bv13.x); + FLOAT_TYPE b2 = FLOAT_TYPE(bv02.y); + FLOAT_TYPE b3 = FLOAT_TYPE(bv13.y); + FLOAT_TYPE b4 = FLOAT_TYPE(bv02.z); + FLOAT_TYPE b5 = FLOAT_TYPE(bv13.z); + FLOAT_TYPE b6 = FLOAT_TYPE(bv02.w); + FLOAT_TYPE b7 = FLOAT_TYPE(bv13.w); +#else + B_TYPE_VEC4 bv0 = data_b_v4[(b_offset + iybs + iqs) / 4]; + B_TYPE_VEC4 bv1 = data_b_v4[(b_offset + iybs + iqs) / 4 + 1]; + FLOAT_TYPE b0 = FLOAT_TYPE(bv0.x); + FLOAT_TYPE b1 = FLOAT_TYPE(bv0.y); + FLOAT_TYPE b2 = FLOAT_TYPE(bv0.z); + FLOAT_TYPE b3 = FLOAT_TYPE(bv0.w); + FLOAT_TYPE b4 = FLOAT_TYPE(bv1.x); + FLOAT_TYPE b5 = FLOAT_TYPE(bv1.y); + FLOAT_TYPE b6 = FLOAT_TYPE(bv1.z); + FLOAT_TYPE b7 = FLOAT_TYPE(bv1.w); +#endif +#else + // Check if the second of the pair of elements is OOB, and don't fetch B or + // accumulate it. We still fetch a pair of elements for A, which is fine for + // quantized formats since they'll be within the same block. We should + // probably skip fetching the second element for F16/F32, but as of now we + // still do. + const bool OOB = lastiter && (iybs + iqs + y_offset >= p.ncols); + + FLOAT_TYPE b0 = 0, b1 = 0; + b0 = FLOAT_TYPE(data_b[b_offset + iybs + iqs]); + if (!OOB) { + b1 = FLOAT_TYPE(data_b[b_offset + iybs + iqs + y_offset]); + } +#endif + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + const uint ib = ((first_row + n)*p.ncols + col)/QUANT_K; // block index + +#if K_PER_ITER == 8 + const vec4 v = dequantize4(ib, iqs, a_offset); + const vec4 v2 = dequantize4(ib, iqs+(4/QUANT_R), a_offset); + + // matrix multiplication + temp[n] = fma(FLOAT_TYPE(v.x), b0, temp[n]); + temp[n] = fma(FLOAT_TYPE(v.y), b1, temp[n]); + temp[n] = fma(FLOAT_TYPE(v.z), b2, temp[n]); + temp[n] = fma(FLOAT_TYPE(v.w), b3, temp[n]); + temp[n] = fma(FLOAT_TYPE(v2.x), b4, temp[n]); + temp[n] = fma(FLOAT_TYPE(v2.y), b5, temp[n]); + temp[n] = fma(FLOAT_TYPE(v2.z), b6, temp[n]); + temp[n] = fma(FLOAT_TYPE(v2.w), b7, temp[n]); +#else + const vec2 v = dequantize(ib, iqs, a_offset); + + // matrix multiplication + temp[n] = fma(FLOAT_TYPE(v.x), b0, temp[n]); + if (!OOB) { + temp[n] = fma(FLOAT_TYPE(v.y), b1, temp[n]); + } +#endif + } +} + +void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { + const uint tid = gl_LocalInvocationID.x; + + get_offsets(a_offset, b_offset, d_offset); + a_offset /= QUANT_K; + + y_offset = QUANT_R == 1 ? 1 : QUANT_K/2; + + FLOAT_TYPE temp[NUM_ROWS]; + + for (uint i = 0; i < NUM_ROWS; ++i) { + temp[i] = FLOAT_TYPE(0); + } + + uint num_iters = p.ncols / (K_PER_ITER * BLOCK_SIZE); + if (num_iters * K_PER_ITER * BLOCK_SIZE + K_PER_ITER*tid < p.ncols) { + num_iters++; + } + int unroll_count = 4; + uint unrolled_iters = num_iters & ~(unroll_count - 1); + + uint i = 0; + while (i < unrolled_iters) { + // Manually partially unroll the loop + [[unroll]] for (uint k = 0; k < unroll_count; ++k) { + iter(temp, first_row, num_rows, tid, i*K_PER_ITER, false); + i++; + } + } + unroll_count = 2; + unrolled_iters = num_iters & ~(unroll_count - 1); + while (i < unrolled_iters) { + // Manually partially unroll the loop + [[unroll]] for (uint k = 0; k < unroll_count; ++k) { + iter(temp, first_row, num_rows, tid, i*K_PER_ITER, false); + i++; + } + } + while (i < num_iters) { + iter(temp, first_row, num_rows, tid, i*K_PER_ITER, true); + i++; + } + + // sum up partial sums and write back result + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + tmpsh[n][tid] = temp[n]; + } + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) { + if (tid < s) { + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + tmpsh[n][tid] += tmpsh[n][tid + s]; + } + } + barrier(); + } + if (tid == 0) { + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + data_d[d_offset + first_row + n] = D_TYPE(tmpsh[n][0]); + } + } +} + +void main() { + const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z); + +#if defined(DATA_A_IQ4_NL) + init_iq4nl_shmem(); +#endif + + // do NUM_ROWS at a time, unless there aren't enough remaining rows + if (first_row + NUM_ROWS <= p.stride_d) { + compute_outputs(first_row, NUM_ROWS); + } else { + if (first_row >= p.stride_d) { + return; + } + compute_outputs(first_row, p.stride_d - first_row); + } +} diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_base.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.comp similarity index 78% rename from ggml/src/vulkan-shaders/mul_mat_vec_base.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.comp index 5920bc936..2ec1af5c7 100644 --- a/ggml/src/vulkan-shaders/mul_mat_vec_base.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.comp @@ -12,6 +12,9 @@ layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; +layout (binding = 1) readonly buffer BV2 {B_TYPE_VEC2 data_b_v2[];}; +layout (binding = 1) readonly buffer BV4 {B_TYPE_VEC4 data_b_v4[];}; + layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; #ifdef MUL_MAT_ID layout (binding = 3) readonly buffer IDS {int data_ids[];}; @@ -49,13 +52,16 @@ void get_offsets(out uint a_offset, out uint b_offset, out uint d_offset) { #endif #ifndef MUL_MAT_ID - const uint i13 = batch_idx / p.ne12; - const uint i12 = batch_idx % p.ne12; + uint batch_idx_a = 0; + if (batch_idx != 0) { + const uint i13 = batch_idx / p.ne12; + const uint i12 = batch_idx % p.ne12; - const uint i03 = i13 / p.broadcast3; - const uint i02 = i12 / p.broadcast2; + const uint i03 = i13 / p.broadcast3; + const uint i02 = i12 / p.broadcast2; - const uint batch_idx_a = i03 * p.ne02 + i02; + batch_idx_a = i03 * p.ne02 + i02; + } #else const uint expert_id = data_ids[expert_idx]; #endif diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_nc.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_nc.comp similarity index 100% rename from ggml/src/vulkan-shaders/mul_mat_vec_nc.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_nc.comp diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_p021.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_p021.comp similarity index 100% rename from ggml/src/vulkan-shaders/mul_mat_vec_p021.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_p021.comp diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp new file mode 100644 index 000000000..fcf02210e --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp @@ -0,0 +1,108 @@ +#version 450 +#extension GL_EXT_shader_explicit_arithmetic_types : require + +#include "mul_mat_vec_base.comp" + +layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; + +shared FLOAT_TYPE tmp[32]; + +void main() { + const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; + + if (row >= p.stride_d) { + return; + } + + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); + + const uint num_blocks_per_row = p.ncols / QUANT_K; + const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; + + const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 + + const uint step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 + + const uint v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... + const uint v_in = tid - step*v_im; // 0...15 or 0...7 + + const uint l0 = K_QUANTS_PER_ITERATION*v_in; // 0...15 + const uint q_offset = 32*v_im + l0; + const uint s_offset = 8*v_im; + const uint y_offset = 128*v_im + l0; + + FLOAT_TYPE temp = FLOAT_TYPE(0.0); // partial sum for thread in warp + + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { + const uint y_idx = i * QUANT_K + y_offset; + + f16vec2 d = data_a[ib0 + i].d; + const FLOAT_TYPE dall = d.x; + const FLOAT_TYPE dmin = d.y; + + B_TYPE_VEC2 b0 = data_b_v2[(b_offset + y_idx) / 2 + 0]; + B_TYPE_VEC2 b16 = data_b_v2[(b_offset + y_idx) / 2 + 8]; + B_TYPE_VEC2 b32 = data_b_v2[(b_offset + y_idx) / 2 + 16]; + B_TYPE_VEC2 b48 = data_b_v2[(b_offset + y_idx) / 2 + 24]; + B_TYPE_VEC2 b64 = data_b_v2[(b_offset + y_idx) / 2 + 32]; + B_TYPE_VEC2 b80 = data_b_v2[(b_offset + y_idx) / 2 + 40]; + B_TYPE_VEC2 b96 = data_b_v2[(b_offset + y_idx) / 2 + 48]; + B_TYPE_VEC2 b112 = data_b_v2[(b_offset + y_idx) / 2 + 56]; + + uint32_t s0_u32 = data_a_packed32[ib0 + i].scales[s_offset / 4 + 0]; + uint32_t s4_u32 = data_a_packed32[ib0 + i].scales[s_offset / 4 + 1]; + + uint32_t s0_lo4_u32 = s0_u32 & 0x0F0F0F0F; + uint32_t s0_hi4_u32 = (s0_u32 >> 4) & 0x0F0F0F0F; + uint32_t s4_lo4_u32 = s4_u32 & 0x0F0F0F0F; + uint32_t s4_hi4_u32 = (s4_u32 >> 4) & 0x0F0F0F0F; + + uvec4 s0_lo4 = uvec4(unpack8(s0_lo4_u32)); + uvec4 s4_lo4 = uvec4(unpack8(s4_lo4_u32)); + uvec4 s0_hi4 = uvec4(unpack8(s0_hi4_u32)); + uvec4 s4_hi4 = uvec4(unpack8(s4_hi4_u32)); + + uint16_t qs0_u16 = data_a_packed16[ib0 + i].qs[q_offset / 2 + 0]; + uint16_t qs16_u16 = data_a_packed16[ib0 + i].qs[q_offset / 2 + 8]; + uvec2 qs0 = uvec2(unpack8(qs0_u16)); + uvec2 qs16 = uvec2(unpack8(qs16_u16)); + + FLOAT_TYPE sum1 = FLOAT_TYPE(0.0); + FLOAT_TYPE sum2 = FLOAT_TYPE(0.0); + [[unroll]] for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { + sum1 = fma(FLOAT_TYPE(b0[l]), FLOAT_TYPE(s0_lo4[0]) * FLOAT_TYPE((qs0[l] >> 0) & 3), + fma(FLOAT_TYPE(b16[l]), FLOAT_TYPE(s0_lo4[1]) * FLOAT_TYPE((qs16[l] >> 0) & 3), + fma(FLOAT_TYPE(b32[l]), FLOAT_TYPE(s0_lo4[2]) * FLOAT_TYPE((qs0[l] >> 2) & 3), + fma(FLOAT_TYPE(b48[l]), FLOAT_TYPE(s0_lo4[3]) * FLOAT_TYPE((qs16[l] >> 2) & 3), + fma(FLOAT_TYPE(b64[l]), FLOAT_TYPE(s4_lo4[0]) * FLOAT_TYPE((qs0[l] >> 4) & 3), + fma(FLOAT_TYPE(b80[l]), FLOAT_TYPE(s4_lo4[1]) * FLOAT_TYPE((qs16[l] >> 4) & 3), + fma(FLOAT_TYPE(b96[l]), FLOAT_TYPE(s4_lo4[2]) * FLOAT_TYPE((qs0[l] >> 6) & 3), + fma(FLOAT_TYPE(b112[l]), FLOAT_TYPE(s4_lo4[3]) * FLOAT_TYPE((qs16[l] >> 6) & 3), sum1)))))))); + sum2 = fma(FLOAT_TYPE(b0[l]), FLOAT_TYPE(s0_hi4[0]), + fma(FLOAT_TYPE(b16[l]), FLOAT_TYPE(s0_hi4[1]), + fma(FLOAT_TYPE(b32[l]), FLOAT_TYPE(s0_hi4[2]), + fma(FLOAT_TYPE(b48[l]), FLOAT_TYPE(s0_hi4[3]), + fma(FLOAT_TYPE(b64[l]), FLOAT_TYPE(s4_hi4[0]), + fma(FLOAT_TYPE(b80[l]), FLOAT_TYPE(s4_hi4[1]), + fma(FLOAT_TYPE(b96[l]), FLOAT_TYPE(s4_hi4[2]), + fma(FLOAT_TYPE(b112[l]), FLOAT_TYPE(s4_hi4[3]), sum2)))))))); + } + temp = fma(dall, sum1, fma(-dmin, sum2, temp)); + } + + tmp[gl_LocalInvocationID.x] = temp; + + // sum up partial sums and write back result + barrier(); + [[unroll]] for (uint s = 16; s > 0; s >>= 1) { + if (tid < s) { + tmp[tid] += tmp[tid + s]; + } + barrier(); + } + if (tid == 0) { + data_d[d_offset + row] = D_TYPE(tmp[0]); + } +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp new file mode 100644 index 000000000..723fadde0 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp @@ -0,0 +1,95 @@ +#version 450 +#extension GL_EXT_shader_explicit_arithmetic_types : require + +#include "mul_mat_vec_base.comp" + +layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; + +shared FLOAT_TYPE tmp[32]; + +void main() { + const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; + + if (row >= p.stride_d) { + return; + } + + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); + + const uint num_blocks_per_row = p.ncols / QUANT_K; + const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; + + const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 + + const uint step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 + + const uint v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... + const uint v_in = tid - step*v_im; // 0...15 or 0...7 + + const uint8_t m = uint8_t(1 << (4 * v_im)); + + const uint l0 = K_QUANTS_PER_ITERATION*v_in; // 0...15 + const uint q_offset = 32*v_im + l0; + const uint y_offset = 128*v_im + l0; + + FLOAT_TYPE temp = FLOAT_TYPE(0.0); // partial sum for thread in warp + + const uint s_shift = 4 * v_im; + + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { + const uint y_idx = i * QUANT_K + y_offset; + + const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); + + B_TYPE_VEC2 b0 = data_b_v2[(b_offset + y_idx) / 2 + 0]; + B_TYPE_VEC2 b16 = data_b_v2[(b_offset + y_idx) / 2 + 8]; + B_TYPE_VEC2 b32 = data_b_v2[(b_offset + y_idx) / 2 + 16]; + B_TYPE_VEC2 b48 = data_b_v2[(b_offset + y_idx) / 2 + 24]; + B_TYPE_VEC2 b64 = data_b_v2[(b_offset + y_idx) / 2 + 32]; + B_TYPE_VEC2 b80 = data_b_v2[(b_offset + y_idx) / 2 + 40]; + B_TYPE_VEC2 b96 = data_b_v2[(b_offset + y_idx) / 2 + 48]; + B_TYPE_VEC2 b112 = data_b_v2[(b_offset + y_idx) / 2 + 56]; + + uint16_t s0_16 = data_a_packed16[ib0 + i].scales[0]; + uint16_t s2_16 = data_a_packed16[ib0 + i].scales[1]; + uint16_t s4_16 = data_a_packed16[ib0 + i].scales[2]; + uint16_t s6_16 = data_a_packed16[ib0 + i].scales[3]; + uint16_t s8_16 = data_a_packed16[ib0 + i].scales[4]; + uint16_t s10_16 = data_a_packed16[ib0 + i].scales[5]; + u8vec2 s0 = unpack8(s0_16); + u8vec2 s2 = unpack8(s2_16); + u8vec2 s4 = unpack8(s4_16); + u8vec2 s6 = unpack8(s6_16); + u8vec2 s8 = unpack8(s8_16); + u8vec2 s10 = unpack8(s10_16); + + FLOAT_TYPE sum = FLOAT_TYPE(0.0); + for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { + sum = fma(FLOAT_TYPE(b0[l]) * FLOAT_TYPE(int8_t(((s0[0] >> s_shift) & 0xF) | ((s8[0] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 0)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(b32[l]) * FLOAT_TYPE(int8_t(((s2[0] >> s_shift) & 0xF) | ((s10[0] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 1)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(b64[l]) * FLOAT_TYPE(int8_t(((s4[0] >> s_shift) & 0xF) | ((s8[0] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 2)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(b96[l]) * FLOAT_TYPE(int8_t(((s6[0] >> s_shift) & 0xF) | ((s10[0] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 3)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(b16[l]) * FLOAT_TYPE(int8_t(((s0[1] >> s_shift) & 0xF) | ((s8[1] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 0)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(b48[l]) * FLOAT_TYPE(int8_t(((s2[1] >> s_shift) & 0xF) | ((s10[1] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 1)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(b80[l]) * FLOAT_TYPE(int8_t(((s4[1] >> s_shift) & 0xF) | ((s8[1] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 2)) != 0) ? 0 : 4)), + fma(FLOAT_TYPE(b112[l]) * FLOAT_TYPE(int8_t(((s6[1] >> s_shift) & 0xF) | ((s10[1] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 3)) != 0) ? 0 : 4)), sum)))))))); + } + temp = fma(d, sum, temp); + } + + tmp[gl_LocalInvocationID.x] = temp; + + // sum up partial sums and write back result + barrier(); + [[unroll]] for (uint s = 16; s > 0; s >>= 1) { + if (tid < s) { + tmp[tid] += tmp[tid + s]; + } + barrier(); + } + if (tid == 0) { + data_d[d_offset + row] = D_TYPE(tmp[0]); + } +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp new file mode 100644 index 000000000..5846f2e86 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp @@ -0,0 +1,127 @@ +#version 450 + +#extension GL_EXT_shader_explicit_arithmetic_types : require + +#include "mul_mat_vec_base.comp" + +layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; + +shared FLOAT_TYPE tmp[32]; + +// This shader assumes K_QUANTS_PER_ITERATION == 2 for alignment of loads +void main() { + const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; + + if (row >= p.stride_d) { + return; + } + + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); + + const uint num_blocks_per_row = p.ncols / QUANT_K; + const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; + + const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 + + const uint step = 8/K_QUANTS_PER_ITERATION; // 8 or 4 + + const uint il = tid/step; // 0...3 + const uint ir = tid - step*il; // 0...7 or 0...3 + const uint n = 2 * K_QUANTS_PER_ITERATION; // 2 or 4 + + const uint v_im = il / 2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 + const uint v_in = il % 2; + + const uint l0 = n * (2 * ir + v_in); // 0...15 + const uint q_offset = 32*v_im + l0; + const uint y_offset = 64*v_im + l0; + + FLOAT_TYPE temp = FLOAT_TYPE(0.0); // partial sum for thread in warp + + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { + const uint y1_idx = i * QUANT_K + y_offset; + const uint y2_idx = y1_idx + 128; + + f16vec2 d = data_a[ib0 + i].d; + const FLOAT_TYPE dall = FLOAT_TYPE(d.x); + const FLOAT_TYPE dmin = FLOAT_TYPE(d.y); + + uint32_t scale0_u32 = data_a_packed16[ib0 + i].scales[v_im ]; + uint32_t scale4_u32 = data_a_packed16[ib0 + i].scales[v_im + 2]; + uint32_t scale8_u32 = data_a_packed16[ib0 + i].scales[v_im + 4]; + uvec4 scale0 = uvec4(unpack8(scale0_u32)); + uvec4 scale4 = uvec4(unpack8(scale4_u32)); + uvec4 scale8 = uvec4(unpack8(scale8_u32)); + + const uint32_t sc0 = ( scale0.x & 0x3f); + const uint32_t sc1 = ( scale0.y & 0x3f); + const uint32_t sc2 = ( scale4.x & 0x3f); + const uint32_t sc3 = ( scale4.y & 0x3f); + const uint32_t sc4 = (( scale8.x & 0x0f) | ((scale0.x & 0xc0) >> 2)); + const uint32_t sc5 = (( scale8.y & 0x0f) | ((scale0.y & 0xc0) >> 2)); + const uint32_t sc6 = (((scale8.x >> 4) & 0x0f) | ((scale4.x & 0xc0) >> 2)); + const uint32_t sc7 = (((scale8.y >> 4) & 0x0f) | ((scale4.y & 0xc0) >> 2)); + + uint32_t qs0_u32 = data_a_packed32[ib0 + i].qs[q_offset / 4]; + uint32_t qs64_u32 = data_a_packed32[ib0 + i].qs[q_offset / 4 + 16]; + + uint32_t qs0_u32_lo4 = qs0_u32 & 0x0F0F0F0F; + uint32_t qs0_u32_hi4 = (qs0_u32 >> 4) & 0x0F0F0F0F; + uint32_t qs64_u32_lo4 = qs64_u32 & 0x0F0F0F0F; + uint32_t qs64_u32_hi4 = (qs64_u32 >> 4) & 0x0F0F0F0F; + + uvec4 qs0_lo4 = uvec4(unpack8(qs0_u32_lo4)); + uvec4 qs64_lo4 = uvec4(unpack8(qs64_u32_lo4)); + uvec4 qs0_hi4 = uvec4(unpack8(qs0_u32_hi4)); + uvec4 qs64_hi4 = uvec4(unpack8(qs64_u32_hi4)); + + const uint32_t q4_0 = qs0_lo4.x; + const uint32_t q4_1 = qs0_lo4.y; + const uint32_t q4_2 = qs0_lo4.z; + const uint32_t q4_3 = qs0_lo4.w; + const uint32_t q4_4 = qs0_hi4.x; + const uint32_t q4_5 = qs0_hi4.y; + const uint32_t q4_6 = qs0_hi4.z; + const uint32_t q4_7 = qs0_hi4.w; + const uint32_t q4_8 = qs64_lo4.x; + const uint32_t q4_9 = qs64_lo4.y; + const uint32_t q4_10 = qs64_lo4.z; + const uint32_t q4_11 = qs64_lo4.w; + const uint32_t q4_12 = qs64_hi4.x; + const uint32_t q4_13 = qs64_hi4.y; + const uint32_t q4_14 = qs64_hi4.z; + const uint32_t q4_15 = qs64_hi4.w; + + B_TYPE_VEC4 by10 = data_b_v4[(b_offset + y1_idx) / 4]; + B_TYPE_VEC4 by132 = data_b_v4[(b_offset + y1_idx) / 4 + 8]; + B_TYPE_VEC4 by20 = data_b_v4[(b_offset + y2_idx) / 4]; + B_TYPE_VEC4 by232 = data_b_v4[(b_offset + y2_idx) / 4 + 8]; + + const FLOAT_TYPE sx = fma(FLOAT_TYPE(by10.x), q4_0, fma(FLOAT_TYPE(by10.y), q4_1, fma(FLOAT_TYPE(by10.z), q4_2, FLOAT_TYPE(by10.w) * q4_3))); + const FLOAT_TYPE sy = fma(FLOAT_TYPE(by132.x), q4_4, fma(FLOAT_TYPE(by132.y), q4_5, fma(FLOAT_TYPE(by132.z), q4_6, FLOAT_TYPE(by132.w) * q4_7))); + const FLOAT_TYPE sz = fma(FLOAT_TYPE(by20.x), q4_8, fma(FLOAT_TYPE(by20.y), q4_9, fma(FLOAT_TYPE(by20.z), q4_10, FLOAT_TYPE(by20.w) * q4_11))); + const FLOAT_TYPE sw = fma(FLOAT_TYPE(by232.x), q4_12, fma(FLOAT_TYPE(by232.y), q4_13, fma(FLOAT_TYPE(by232.z), q4_14, FLOAT_TYPE(by232.w) * q4_15))); + const FLOAT_TYPE smin = + fma(FLOAT_TYPE(by10.x), sc2, fma(FLOAT_TYPE(by132.x), sc3, fma(FLOAT_TYPE(by20.x), sc6, fma(FLOAT_TYPE(by232.x), sc7, + fma(FLOAT_TYPE(by10.y), sc2, fma(FLOAT_TYPE(by132.y), sc3, fma(FLOAT_TYPE(by20.y), sc6, fma(FLOAT_TYPE(by232.y), sc7, + fma(FLOAT_TYPE(by10.z), sc2, fma(FLOAT_TYPE(by132.z), sc3, fma(FLOAT_TYPE(by20.z), sc6, fma(FLOAT_TYPE(by232.z), sc7, + fma(FLOAT_TYPE(by10.w), sc2, fma(FLOAT_TYPE(by132.w), sc3, fma(FLOAT_TYPE(by20.w), sc6, FLOAT_TYPE(by232.w) * sc7))))))))))))))); + temp = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, temp)); + } + + tmp[gl_LocalInvocationID.x] = temp; + + // sum up partial sums and write back result + barrier(); + [[unroll]] for (uint s = 16; s > 0; s >>= 1) { + if (tid < s) { + tmp[tid] += tmp[tid + s]; + } + barrier(); + } + if (tid == 0) { + data_d[d_offset + row] = D_TYPE(tmp[0]); + } +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp new file mode 100644 index 000000000..b455cbd31 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp @@ -0,0 +1,155 @@ +#version 450 + +#extension GL_EXT_shader_explicit_arithmetic_types : require + +#include "mul_mat_vec_base.comp" + +layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; + +shared FLOAT_TYPE tmp[32]; + +void main() { + const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; + + if (row >= p.stride_d) { + return; + } + + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); + + const uint num_blocks_per_row = p.ncols / QUANT_K; + const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; + + const uint tid = gl_LocalInvocationID.x/2; // 0...31 or 0...16 + const uint ix = gl_LocalInvocationID.x%2; // 0 or 0, 1 + + const uint il = tid/4; // 0...3 + const uint ir = tid - 4*il; // 0...7 or 0...3 + + const uint v_im = il / 2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 + const uint v_in = il % 2; + + const uint l0 = 4*ir + 2*v_in; // 0...15 + const uint q_offset = 32*v_im + l0; + const uint y_offset = 64*v_im + l0; + + FLOAT_TYPE temp = FLOAT_TYPE(0.0); // partial sum for thread in warp + + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += 2) { + const uint y1_idx = i * QUANT_K + y_offset; + const uint y2_idx = y1_idx + 128; + + f16vec2 d = data_a[ib0 + i].d; + const FLOAT_TYPE dall = FLOAT_TYPE(d.x); + const FLOAT_TYPE dmin = FLOAT_TYPE(d.y); + + uint32_t scale0_u32 = data_a_packed16[ib0 + i].scales[v_im ]; + uint32_t scale4_u32 = data_a_packed16[ib0 + i].scales[v_im + 2]; + uint32_t scale8_u32 = data_a_packed16[ib0 + i].scales[v_im + 4]; + uvec4 scale0 = uvec4(unpack8(scale0_u32)); + uvec4 scale4 = uvec4(unpack8(scale4_u32)); + uvec4 scale8 = uvec4(unpack8(scale8_u32)); + + const uint32_t sc0 = ( scale0.x & 0x3f); + const uint32_t sc1 = ( scale0.y & 0x3f); + const uint32_t sc2 = ( scale4.x & 0x3f); + const uint32_t sc3 = ( scale4.y & 0x3f); + const uint32_t sc4 = (( scale8.x & 0x0f) | ((scale0.x & 0xc0) >> 2)); + const uint32_t sc5 = (( scale8.y & 0x0f) | ((scale0.y & 0xc0) >> 2)); + const uint32_t sc6 = (((scale8.x >> 4) & 0x0f) | ((scale4.x & 0xc0) >> 2)); + const uint32_t sc7 = (((scale8.y >> 4) & 0x0f) | ((scale4.y & 0xc0) >> 2)); + + uint32_t qs0_16_u32 = uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 8]) << 16); + uint32_t qs64_80_u32 = uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 32]) | (uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 40]) << 16); + + uint32_t qs0_16_u32_lo4 = qs0_16_u32 & 0x0F0F0F0F; + uint32_t qs0_16_u32_hi4 = (qs0_16_u32 >> 4) & 0x0F0F0F0F; + uint32_t qs64_80_u32_lo4 = qs64_80_u32 & 0x0F0F0F0F; + uint32_t qs64_80_u32_hi4 = (qs64_80_u32 >> 4) & 0x0F0F0F0F; + + uint32_t qh = pack32(u16vec2(data_a_packed16[ib0 + i].qh[l0 / 2], data_a_packed16[ib0 + i].qh[l0 / 2 + 8])); + + uint32_t qs0_16_lo4_offset16 = ((qh >> (2*v_im)) & 0x01010101) << 4; + uint32_t qs0_16_hi4_offset16 = ((qh >> (2*v_im)) & 0x02020202) << 3; + uint32_t qs64_80_lo4_offset16 = ((qh >> (2*v_im)) & 0x10101010) << 0; + uint32_t qs64_80_hi4_offset16 = ((qh >> (2*v_im)) & 0x20202020) >> 1; + + qs0_16_u32_lo4 += qs0_16_lo4_offset16; + qs0_16_u32_hi4 += qs0_16_hi4_offset16; + qs64_80_u32_lo4 += qs64_80_lo4_offset16; + qs64_80_u32_hi4 += qs64_80_hi4_offset16; + + uvec4 qs0_16_lo4 = uvec4(unpack8(qs0_16_u32_lo4)); + uvec4 qs64_80_lo4 = uvec4(unpack8(qs64_80_u32_lo4)); + uvec4 qs0_16_hi4 = uvec4(unpack8(qs0_16_u32_hi4)); + uvec4 qs64_80_hi4 = uvec4(unpack8(qs64_80_u32_hi4)); + + const uint32_t q4_0 = qs0_16_lo4.x; + const uint32_t q4_1 = qs0_16_lo4.y; + const uint32_t q4_2 = qs0_16_lo4.z; + const uint32_t q4_3 = qs0_16_lo4.w; + const uint32_t q4_4 = qs0_16_hi4.x; + const uint32_t q4_5 = qs0_16_hi4.y; + const uint32_t q4_6 = qs0_16_hi4.z; + const uint32_t q4_7 = qs0_16_hi4.w; + const uint32_t q4_8 = qs64_80_lo4.x; + const uint32_t q4_9 = qs64_80_lo4.y; + const uint32_t q4_10 = qs64_80_lo4.z; + const uint32_t q4_11 = qs64_80_lo4.w; + const uint32_t q4_12 = qs64_80_hi4.x; + const uint32_t q4_13 = qs64_80_hi4.y; + const uint32_t q4_14 = qs64_80_hi4.z; + const uint32_t q4_15 = qs64_80_hi4.w; + + B_TYPE_VEC2 by10 = data_b_v2[(b_offset + y1_idx) / 2]; + B_TYPE_VEC2 by116 = data_b_v2[(b_offset + y1_idx) / 2 + 8]; + B_TYPE_VEC2 by132 = data_b_v2[(b_offset + y1_idx) / 2 + 16]; + B_TYPE_VEC2 by148 = data_b_v2[(b_offset + y1_idx) / 2 + 24]; + B_TYPE_VEC2 by20 = data_b_v2[(b_offset + y2_idx) / 2]; + B_TYPE_VEC2 by216 = data_b_v2[(b_offset + y2_idx) / 2 + 8]; + B_TYPE_VEC2 by232 = data_b_v2[(b_offset + y2_idx) / 2 + 16]; + B_TYPE_VEC2 by248 = data_b_v2[(b_offset + y2_idx) / 2 + 24]; + + const FLOAT_TYPE sx = + fma(FLOAT_TYPE(by10.x), q4_0, + fma(FLOAT_TYPE(by10.y), q4_1, + fma(FLOAT_TYPE(by116.x), q4_2, + FLOAT_TYPE(by116.y) * q4_3))); + const FLOAT_TYPE sy = + fma(FLOAT_TYPE(by132.x), q4_4, + fma(FLOAT_TYPE(by132.y), q4_5, + fma(FLOAT_TYPE(by148.x), q4_6, + FLOAT_TYPE(by148.y) * q4_7))); + const FLOAT_TYPE sz = + fma(FLOAT_TYPE(by20.x), q4_8, + fma(FLOAT_TYPE(by20.y), q4_9, + fma(FLOAT_TYPE(by216.x), q4_10, + FLOAT_TYPE(by216.y) * q4_11))); + const FLOAT_TYPE sw = + fma(FLOAT_TYPE(by232.x), q4_12, + fma(FLOAT_TYPE(by232.y), q4_13, + fma(FLOAT_TYPE(by248.x), q4_14, + FLOAT_TYPE(by248.y) * q4_15))); + const FLOAT_TYPE smin = + fma(FLOAT_TYPE(by10.x) + FLOAT_TYPE(by10.y) + FLOAT_TYPE(by116.x) + FLOAT_TYPE(by116.y), sc2, + fma(FLOAT_TYPE(by132.x) + FLOAT_TYPE(by132.y) + FLOAT_TYPE(by148.x) + FLOAT_TYPE(by148.y), sc3, + fma(FLOAT_TYPE(by20.x) + FLOAT_TYPE(by20.y) + FLOAT_TYPE(by216.x) + FLOAT_TYPE(by216.y), sc6, + (FLOAT_TYPE(by232.x) + FLOAT_TYPE(by232.y) + FLOAT_TYPE(by248.x) + FLOAT_TYPE(by248.y)) * sc7))); + temp = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, temp)); + } + + tmp[gl_LocalInvocationID.x] = temp; + + // sum up partial sums and write back result + barrier(); + [[unroll]] for (uint s = 16; s > 0; s >>= 1) { + if (tid < s) { + tmp[tid] += tmp[tid + s]; + } + barrier(); + } + if (tid == 0) { + data_d[d_offset + row] = D_TYPE(tmp[0]); + } +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp new file mode 100644 index 000000000..760aff854 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp @@ -0,0 +1,110 @@ +#version 450 + +#extension GL_EXT_shader_explicit_arithmetic_types : require + +#include "mul_mat_vec_base.comp" + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (constant_id = 0) const uint BLOCK_SIZE = 32; + +shared FLOAT_TYPE tmp[BLOCK_SIZE]; + +void main() { + const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; + + if (row >= p.stride_d) { + return; + } + + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); + + const uint num_blocks_per_row = p.ncols / QUANT_K; + const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; + + // 16 threads are used to process each block + const uint it_size = gl_WorkGroupSize.x/16; + const uint tid = gl_LocalInvocationID.x; + const uint itid = tid%16; // 0...16 + const uint ix = tid/16; + + const uint step = 8; + + const uint v_im = itid/step; // 0 or 1. 0 computes 0..., 1 computes 128... + const uint v_in = itid - step*v_im; // 0...15 or 0...7 + + const uint l0 = 4 * v_in; // 0, 4, 8, ..., 28 + const uint is = v_in / 4; + + const uint ql_offset = 64*v_im + l0; + const uint qh_offset = 32*v_im + l0; + const uint s_offset = 8*v_im + is; + const uint y_offset = 128*v_im + l0; + + FLOAT_TYPE temp = FLOAT_TYPE(0.0); // partial sum for thread in warp + + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) { + const uint y_idx = i * QUANT_K + y_offset; + + const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); + + FLOAT_TYPE scales[4]; + scales[0] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]); + scales[1] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]); + scales[2] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]); + scales[3] = FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]); + + uint32_t ql0_u32 = uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 1]) << 16); + uint32_t ql32_u32 = uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 16]) | (uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 17]) << 16); + + uint32_t ql0_u32_lo4 = ql0_u32 & 0x0F0F0F0F; + uint32_t ql0_u32_hi4 = (ql0_u32 >> 4) & 0x0F0F0F0F; + uint32_t ql32_u32_lo4 = ql32_u32 & 0x0F0F0F0F; + uint32_t ql32_u32_hi4 = (ql32_u32 >> 4) & 0x0F0F0F0F; + + uint32_t qh_u32 = uint32_t(data_a_packed16[ib0 + i].qh[qh_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].qh[qh_offset / 2 + 1]) << 16); + uint32_t qh0_u32 = (qh_u32 & 0x03030303) << 4; + uint32_t qh2_u32 = (qh_u32 & 0x0C0C0C0C) << 2; + uint32_t qh4_u32 = (qh_u32 & 0x30303030) << 0; + uint32_t qh6_u32 = (qh_u32 & 0xC0C0C0C0) >> 2; + + uint32_t q0_u32 = ql0_u32_lo4 | qh0_u32; + uint32_t q1_u32 = ql32_u32_lo4 | qh2_u32; + uint32_t q2_u32 = ql0_u32_hi4 | qh4_u32; + uint32_t q3_u32 = ql32_u32_hi4 | qh6_u32; + + uvec4 q0 = uvec4(unpack8(q0_u32)); + uvec4 q1 = uvec4(unpack8(q1_u32)); + uvec4 q2 = uvec4(unpack8(q2_u32)); + uvec4 q3 = uvec4(unpack8(q3_u32)); + + B_TYPE_VEC4 by0 = data_b_v4[(b_offset + y_idx) / 4]; + B_TYPE_VEC4 by32 = data_b_v4[(b_offset + y_idx) / 4 + 8]; + B_TYPE_VEC4 by64 = data_b_v4[(b_offset + y_idx) / 4 + 16]; + B_TYPE_VEC4 by96 = data_b_v4[(b_offset + y_idx) / 4 + 24]; + + FLOAT_TYPE sum = FLOAT_TYPE(0.0); + [[unroll]] for (int l = 0; l < 4; ++l) { + sum = fma(FLOAT_TYPE(by0[l]) * scales[0], FLOAT_TYPE(int8_t(q0[l]) - 32), + fma(FLOAT_TYPE(by32[l]) * scales[1], FLOAT_TYPE(int8_t(q1[l]) - 32), + fma(FLOAT_TYPE(by64[l]) * scales[2], FLOAT_TYPE(int8_t(q2[l]) - 32), + fma(FLOAT_TYPE(by96[l]) * scales[3], FLOAT_TYPE(int8_t(q3[l]) - 32), sum)))); + } + temp += sum * d; + } + + tmp[gl_LocalInvocationID.x] = temp; + // sum up partial sums and write back result + + barrier(); + [[unroll]] for (uint s = gl_WorkGroupSize.x/2; s > 0; s >>= 1) { + if (tid < s) { + tmp[tid] += tmp[tid + s]; + } + barrier(); + } + if (tid == 0) { + data_d[d_offset + row] = D_TYPE(tmp[0]); + } +} diff --git a/ggml/src/vulkan-shaders/mul_mm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp similarity index 99% rename from ggml/src/vulkan-shaders/mul_mm.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp index fffdd1818..2ff5c4305 100644 --- a/ggml/src/vulkan-shaders/mul_mm.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp @@ -75,6 +75,10 @@ shared u16vec2 row_ids[3072]; #endif void main() { +#if defined(DATA_A_IQ4_NL) + init_iq4nl_shmem(); +#endif + #ifdef MUL_MAT_ID const uint expert_idx = gl_GlobalInvocationID.z; #else diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp new file mode 100644 index 000000000..cbfa5dce1 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp @@ -0,0 +1,328 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : enable +#extension GL_EXT_shader_16bit_storage : require + +#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require +#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require +#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require + +#extension GL_KHR_memory_scope_semantics : enable +#extension GL_KHR_cooperative_matrix : enable +#extension GL_NV_cooperative_matrix2 : enable +#extension GL_EXT_buffer_reference : enable +#extension GL_KHR_shader_subgroup_ballot : enable +#extension GL_KHR_shader_subgroup_vote : enable + +#include "types.comp" + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (constant_id = 1) const uint BM = 64; +layout (constant_id = 2) const uint BN = 64; +layout (constant_id = 3) const uint BK = 16; // Assumed to be 32 if working with a quant + +layout (push_constant) uniform parameter +{ + uint M; + uint N; + uint K; + uint stride_a; + uint stride_b; + uint stride_d; + + uint batch_stride_a; + uint batch_stride_b; + uint batch_stride_d; + +#ifdef MUL_MAT_ID + uint nei0; + uint nei1; + uint nbi1; + uint ne11; +#else + uint k_split; + uint ne02; + uint ne12; + uint broadcast2; + uint broadcast3; +#endif +} p; + + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; +layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; + +#if QUANT_K > 1 +#define DECODEFUNCA , dequantFuncA +#define MAT_A_TYPE float16_t + +#include "dequant_funcs_cm2.comp" + +#else +#define DECODEFUNCA +#define MAT_A_TYPE A_TYPE +#endif + +#define MAT_B_TYPE B_TYPE + +#ifdef MUL_MAT_ID +layout (binding = 3) readonly buffer IDS {int data_ids[];}; + +shared u16vec4 row_ids[3072]; + +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufB { + B_TYPE b[]; +}; + +uint _ne1; +shared uint _ne1_sh; + +B_TYPE decodeFuncB(const in decodeBufB bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const uint row_i = blockCoords[0]; + + if (row_i >= _ne1) { + return B_TYPE(0.0); + } + + const u16vec4 row_idx = row_ids[row_i]; + B_TYPE ret = data_b[row_idx.y * p.batch_stride_b + row_idx.x * p.stride_b + blockCoords[1]]; + + return ret; +} + +D_TYPE perElemOpD(const in uint32_t r, const in uint32_t c, const in D_TYPE elem, const in uint32_t ir, const in uint32_t ic) +{ + uint dr = ir * BM + r; + uint dc = ic * BN + c; + + if (dr < p.M && dc < _ne1) { + uint row_i = dc; + const u16vec4 row_idx = row_ids[row_i]; + data_d[row_idx.y * p.batch_stride_d + row_idx.z * p.stride_d + dr] = elem; + } + return elem; +} + +#endif + +void main() { +#if defined(DATA_A_IQ4_NL) + init_iq4nl_shmem(); +#endif + +#ifdef MUL_MAT_ID + const uint expert_idx = gl_GlobalInvocationID.z; +#else + const uint batch_idx = gl_GlobalInvocationID.z; + + const uint i13 = batch_idx / p.ne12; + const uint i12 = batch_idx % p.ne12; + + const uint i03 = i13 / p.broadcast3; + const uint i02 = i12 / p.broadcast2; + + const uint batch_idx_a = i03 * p.ne02 + i02; +#endif + + const uint blocks_m = (p.M + BM - 1) / BM; + const uint ir = gl_WorkGroupID.x % blocks_m; + const uint ik = gl_WorkGroupID.x / blocks_m; + const uint ic = gl_WorkGroupID.y; + +#ifdef MUL_MAT_ID + // Spread the search across all elements in the first subgroup + if (gl_SubgroupID == 0) { + _ne1 = 0; + uint num_elements = p.nei1 * p.nei0; + + for (uint i = gl_SubgroupInvocationID; subgroupAny(i < num_elements); i += gl_SubgroupSize) { + bool in_range = i < num_elements; + uint ii0 = i % p.nei0; + uint ii1 = i / p.nei0; + uint id = in_range ? data_ids[ii1*p.nbi1 + ii0] : 0; + uvec4 ballot = subgroupBallot(in_range && id == expert_idx); + uint idx = subgroupBallotExclusiveBitCount(ballot); + if (in_range && id == expert_idx) { + row_ids[_ne1 + idx] = u16vec4(ii0 % p.ne11, ii1, ii0, 0); + } + _ne1 += subgroupBallotBitCount(ballot); + } + _ne1_sh = _ne1; + } + + barrier(); + + _ne1 = _ne1_sh; + + // Workgroup has no work + if (ic * BN >= _ne1) return; +#endif + +#ifdef MUL_MAT_ID + uint start_k = 0; + const uint end_k = p.K; +#else + uint start_k = ik * p.k_split; + const uint end_k = min(p.K, (ik + 1) * p.k_split); +#endif + + coopmat sum; + sum = coopmat(0.0); + +#ifdef MUL_MAT_ID + uint pos_a = (expert_idx * p.batch_stride_a) / QUANT_K; + uint pos_b = 0; +#else + uint pos_a = (batch_idx_a * p.batch_stride_a) / QUANT_K; + uint pos_b = batch_idx * p.batch_stride_b; +#endif + + uint stride_a = p.stride_a / QUANT_K; + uint stride_b = p.stride_b; + + // Hint to the compiler that values are aligned (want 16B alignment). + // Quants are always block-aligned, no alignment needed. +#if ALIGNED +#if QUANT_K == 1 + stride_a &= ~7; +#endif + stride_b &= ~7; +#endif + + // Create layouts for both clamped and unclamped accesses + tensorLayoutNV<2> tensorLayoutA = createTensorLayoutNV(2); + tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutAClamp = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV); + tensorLayoutNV<2> tensorLayoutB = createTensorLayoutNV(2); + tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutBClamp = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV); + tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutD = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV); + +#if QUANT_K > 1 + tensorLayoutA = setTensorLayoutBlockSizeNV(tensorLayoutA, 1, QUANT_K); + tensorLayoutAClamp = setTensorLayoutBlockSizeNV(tensorLayoutAClamp, 1, QUANT_K); +#endif + + // Use end_k rather than p.K as the dimension because that's what + // we need to bound check against when using split_k + tensorLayoutA = setTensorLayoutDimensionNV(tensorLayoutA, p.M, end_k); + tensorLayoutB = setTensorLayoutDimensionNV(tensorLayoutB, p.N, end_k); + tensorLayoutD = setTensorLayoutDimensionNV(tensorLayoutD, p.N, p.M); + tensorLayoutAClamp = setTensorLayoutDimensionNV(tensorLayoutAClamp, p.M, end_k); + tensorLayoutBClamp = setTensorLayoutDimensionNV(tensorLayoutBClamp, p.N, end_k); + + tensorViewNV<2, false, 1, 0> tensorViewTranspose = createTensorViewNV(2, false, 1, 0); + +#if !defined(MUL_MAT_ID) + // Detect a fast path where all loads are entirely in bounds and no clamping is required + if ((ir + 1) * BM <= p.M && (ic + 1) * BN <= p.N && (start_k % BK) == 0 && (end_k % BK) == 0 && +#if QUANT_K == 1 + (stride_a % 8) == 0 && +#endif + (stride_b % 8) == 0 && (start_k % 8) == 0) { + // Hint to the compiler that values are aligned (want 16B alignment) + start_k &= ~7; + stride_b &= ~7; +#if QUANT_K == 1 + stride_a &= ~7; +#endif + + tensorLayoutA = setTensorLayoutStrideNV(tensorLayoutA, stride_a, 1); + tensorLayoutB = setTensorLayoutStrideNV(tensorLayoutB, stride_b, 1); + + uint k_iters = (end_k - start_k + BK - 1) / BK; + + for (uint block_k = start_k, i = 0; i < k_iters; block_k += BK, ++i) { + + coopmat mat_a; + coopmat mat_b; + + coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA); + coopmat mat_a_ft = coopmat(mat_a); + + coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BN, block_k, BK), tensorViewTranspose); + coopmat mat_b_ft = coopmat(mat_b); + + sum = coopMatMulAdd(mat_a_ft, mat_b_ft, sum); + } + } else +#endif // !defined(MUL_MAT_ID) + { + tensorLayoutA = setTensorLayoutStrideNV(tensorLayoutA, stride_a, 1); + + tensorLayoutAClamp = setTensorLayoutStrideNV(tensorLayoutAClamp, stride_a, 1); + + tensorLayoutB = setTensorLayoutStrideNV(tensorLayoutB, stride_b, 1); + + tensorLayoutBClamp = setTensorLayoutStrideNV(tensorLayoutBClamp, stride_b, 1); + + [[dont_unroll]] + for (uint block_k = start_k; block_k < end_k; block_k += BK) { + + coopmat mat_a; + coopmat mat_b; + coopmat mat_a_ft; + coopmat mat_b_ft; + + // Clamping is expensive, so detect different code paths for each combination + // of A and B needing clamping. + bool unclampedA = (ir + 1) * BM <= p.M && block_k + BK <= end_k && (block_k % 8) == 0; +#ifdef MUL_MAT_ID + bool unclampedB = true; +#else + bool unclampedB = (ic + 1) * BN <= p.N && block_k + BK <= end_k && (block_k % 8) == 0; +#endif + if (unclampedA && unclampedB) { + coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, (block_k & ~7), BK) DECODEFUNCA); +#ifdef MUL_MAT_ID + coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BN, block_k, BK), tensorViewTranspose, decodeFuncB); +#else + coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BN, (block_k & ~7), BK), tensorViewTranspose); +#endif + mat_a_ft = coopmat(mat_a); + mat_b_ft = coopmat(mat_b); + sum = coopMatMulAdd(mat_a_ft, mat_b_ft, sum); + } else if (unclampedA && !unclampedB) { + coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, (block_k & ~7), BK) DECODEFUNCA); + coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutBClamp, ic * BN, BN, block_k, BK), tensorViewTranspose); + + mat_a_ft = coopmat(mat_a); + mat_b_ft = coopmat(mat_b); + sum = coopMatMulAdd(mat_a_ft, mat_b_ft, sum); + } else if (!unclampedA && unclampedB) { + coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutAClamp, ir * BM, BM, block_k, BK) DECODEFUNCA); +#ifdef MUL_MAT_ID + coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BN, block_k, BK), tensorViewTranspose, decodeFuncB); +#else + coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BN, (block_k & ~7), BK), tensorViewTranspose); +#endif + mat_a_ft = coopmat(mat_a); + mat_b_ft = coopmat(mat_b); + sum = coopMatMulAdd(mat_a_ft, mat_b_ft, sum); + } else if (!unclampedA && !unclampedB) { + coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutAClamp, ir * BM, BM, block_k, BK) DECODEFUNCA); + coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutBClamp, ic * BN, BN, block_k, BK), tensorViewTranspose); + + mat_a_ft = coopmat(mat_a); + mat_b_ft = coopmat(mat_b); + sum = coopMatMulAdd(mat_a_ft, mat_b_ft, sum); + } + } + } + + // Convert from ACC_TYPE to D_TYPE + coopmat mat_d; + mat_d = coopmat(sum); + +#ifdef MUL_MAT_ID + // Call callback to store each element, remapping row through shared memory + coopMatPerElementNV(mat_d, mat_d, perElemOpD, ir, ic); +#else + tensorLayoutD = setTensorLayoutStrideNV(tensorLayoutD, p.stride_d, 1); + + uint pos_d = batch_idx * p.batch_stride_d + ik * p.batch_stride_d * gl_NumWorkGroups.z; + coopMatStoreTensorNV(mat_d, data_d, pos_d, sliceTensorLayoutNV(tensorLayoutD, ic * BN, BN, ir * BM, BM), tensorViewTranspose); +#endif +} diff --git a/ggml/src/vulkan-shaders/norm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/norm.comp similarity index 100% rename from ggml/src/vulkan-shaders/norm.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/norm.comp diff --git a/ggml/src/vulkan-shaders/pad.comp b/ggml/src/ggml-vulkan/vulkan-shaders/pad.comp similarity index 92% rename from ggml/src/vulkan-shaders/pad.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/pad.comp index a465cd52b..e87d8b18b 100644 --- a/ggml/src/vulkan-shaders/pad.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/pad.comp @@ -3,6 +3,8 @@ #include "types.comp" #include "generic_unary_head.comp" +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + void main() { const uint idx = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; diff --git a/ggml/src/vulkan-shaders/pool2d.comp b/ggml/src/ggml-vulkan/vulkan-shaders/pool2d.comp similarity index 100% rename from ggml/src/vulkan-shaders/pool2d.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/pool2d.comp diff --git a/ggml/src/vulkan-shaders/relu.comp b/ggml/src/ggml-vulkan/vulkan-shaders/relu.comp similarity index 100% rename from ggml/src/vulkan-shaders/relu.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/relu.comp diff --git a/ggml/src/vulkan-shaders/repeat.comp b/ggml/src/ggml-vulkan/vulkan-shaders/repeat.comp similarity index 91% rename from ggml/src/vulkan-shaders/repeat.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/repeat.comp index a86af87e7..c03f737cc 100644 --- a/ggml/src/vulkan-shaders/repeat.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/repeat.comp @@ -3,6 +3,8 @@ #include "types.comp" #include "generic_unary_head.comp" +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + uint src0_idx_mod(uint idx) { const uint i13 = idx / (p.ne12*p.ne11*p.ne10); const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10; diff --git a/ggml/src/vulkan-shaders/rms_norm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp similarity index 100% rename from ggml/src/vulkan-shaders/rms_norm.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp diff --git a/ggml/src/vulkan-shaders/rope_head.comp b/ggml/src/ggml-vulkan/vulkan-shaders/rope_head.comp similarity index 100% rename from ggml/src/vulkan-shaders/rope_head.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/rope_head.comp diff --git a/ggml/src/vulkan-shaders/rope_neox.comp b/ggml/src/ggml-vulkan/vulkan-shaders/rope_neox.comp similarity index 100% rename from ggml/src/vulkan-shaders/rope_neox.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/rope_neox.comp diff --git a/ggml/src/vulkan-shaders/rope_norm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/rope_norm.comp similarity index 100% rename from ggml/src/vulkan-shaders/rope_norm.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/rope_norm.comp diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/scale.comp b/ggml/src/ggml-vulkan/vulkan-shaders/scale.comp new file mode 100644 index 000000000..5cfee8c3b --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/scale.comp @@ -0,0 +1,24 @@ +#version 450 + +#include "types.comp" +#include "generic_unary_head.comp" + +const uint num_threads = 128; + +layout(local_size_x = num_threads, local_size_y = 1, local_size_z = 1) in; + +void main() { + uint idx = get_idx(); + + // num_threads * num_iter must equal 512, to match the wg_denoms and get_idx calculation + const uint num_iter = 4; + + [[unroll]] for (uint i = 0; i < num_iter; ++i) { + if (idx >= p.ne) { + continue; + } + + data_d[p.d_offset + idx] = D_TYPE(FLOAT_TYPE(data_a[idx]) * FLOAT_TYPE(p.param1)); + idx += num_threads; + } +} diff --git a/ggml/src/vulkan-shaders/silu.comp b/ggml/src/ggml-vulkan/vulkan-shaders/silu.comp similarity index 100% rename from ggml/src/vulkan-shaders/silu.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/silu.comp diff --git a/ggml/src/vulkan-shaders/sin.comp b/ggml/src/ggml-vulkan/vulkan-shaders/sin.comp similarity index 80% rename from ggml/src/vulkan-shaders/sin.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/sin.comp index 7faf9be93..67c48fb9a 100644 --- a/ggml/src/vulkan-shaders/sin.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/sin.comp @@ -3,6 +3,8 @@ #include "types.comp" #include "generic_unary_head.comp" +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + void main() { const uint idx = get_idx(); diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp new file mode 100644 index 000000000..6e20b6411 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp @@ -0,0 +1,174 @@ +#version 450 + +#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require +#extension GL_EXT_control_flow_attributes : enable + +layout (push_constant) uniform parameter +{ + uint KX; + uint KY; + float scale; + float max_bias; + float m0; + float m1; + uint n_head_log2; + uint nrows_x; +} p; + +#include "types.comp" + +layout(constant_id = 0) const uint BLOCK_SIZE = 32; +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer Y {B_TYPE data_b[];}; +layout (binding = 2) buffer D {D_TYPE data_d[];}; + +shared FLOAT_TYPE vals[BLOCK_SIZE]; + +// num_iters is the number of BLOCK_SIZE loop iterations we need to iterate +// over all the columns. The main function tries to pass a constant here, +// as if it were a template function, to allow unrolling. +void soft_max(uint num_iters) { + const uint tid = gl_LocalInvocationID.x; + const uint rowx = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x; + const uint rowy = rowx % p.KY; + + if (rowx >= p.nrows_x) { + return; + } + + float slope = 1.0f; + + // ALiBi + if (p.max_bias > 0.0f) { + const uint h = rowx/p.KY; // head index + + const float base = h < p.n_head_log2 ? p.m0 : p.m1; + const uint exp = h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1; + + slope = pow(base, exp); + } + + // Find max + FLOAT_TYPE max_val = uintBitsToFloat(0xFF800000); + + // Cache values while we compute the max, so we don't need to read them + // again when we're ready to compute exp(x-max). + const uint DATA_CACHE_SIZE = 16; + FLOAT_TYPE data_cache[DATA_CACHE_SIZE]; + + [[unroll]] for (uint col0 = 0, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) { + const uint col = col0 + tid; + + FLOAT_TYPE a = FLOAT_TYPE(0); + if (col < p.KX) { + a = data_a[rowx * p.KX + col]; + } + + FLOAT_TYPE b = FLOAT_TYPE(0); + if (p.KY > 0 && col < p.KX) { + b = data_b[rowy * p.KX + col]; + } + + FLOAT_TYPE v = a * p.scale + slope * b; + + if (col < p.KX) { + max_val = max(max_val, v); + } + + if (idx < DATA_CACHE_SIZE) { + data_cache[idx] = v; + } + } + + // reduce across the workgroup + vals[tid] = max_val; + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + vals[tid] = max(vals[tid], vals[tid + s]); + } + barrier(); + } + + max_val = vals[0]; + barrier(); + + FLOAT_TYPE sum = FLOAT_TYPE(0.0f); + + // Compute sum{exp(x - max)} + [[unroll]] for (uint col0 = 0, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) { + const uint col = col0 + tid; + + if (col >= p.KX) { + break; + } + + // compute exp(a*scale+b*slope), add it to sum, and cache the new value + // in data_cache if possible. + const uint i = rowx * p.KX + col; + FLOAT_TYPE val; + if (idx < DATA_CACHE_SIZE) { + val = exp(data_cache[idx] - max_val); + } else { + val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val); + } + sum += val; + if (idx < DATA_CACHE_SIZE) { + data_cache[idx] = val; + } else { + data_d[i] = D_TYPE(val); + } + } + + // reduce across the workgroup + vals[tid] = sum; + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + vals[tid] += vals[tid + s]; + } + barrier(); + } + sum = vals[0]; + + FLOAT_TYPE rcpdivisor = 1.0/sum; + + [[unroll]] for (uint col0 = 0, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) { + const uint col = col0 + tid; + + if (col >= p.KX) { + continue; + } + + if (idx < DATA_CACHE_SIZE) { + data_d[rowx*p.KX + col] = D_TYPE(data_cache[idx] * rcpdivisor); + } else { + data_d[rowx*p.KX + col] *= D_TYPE(rcpdivisor); + } + } +} + +void main() { + // instantiate the soft_max function for several different + // dimensions, to allow loop unrolling + uint num_blocks = (p.KX + BLOCK_SIZE - 1) / BLOCK_SIZE; + if (num_blocks > 32) { + soft_max(num_blocks); + } else if (num_blocks > 16) { + soft_max(32); + } else if (num_blocks > 8) { + soft_max(16); + } else if (num_blocks > 4) { + soft_max(8); + } else if (num_blocks == 4) { + soft_max(4); + } else if (num_blocks == 3) { + soft_max(3); + } else if (num_blocks == 2) { + soft_max(2); + } else if (num_blocks == 1) { + soft_max(1); + } +} diff --git a/ggml/src/vulkan-shaders/square.comp b/ggml/src/ggml-vulkan/vulkan-shaders/square.comp similarity index 80% rename from ggml/src/vulkan-shaders/square.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/square.comp index 1fa118c99..2ff48ddc5 100644 --- a/ggml/src/vulkan-shaders/square.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/square.comp @@ -3,6 +3,8 @@ #include "types.comp" #include "generic_unary_head.comp" +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + void main() { const uint idx = get_idx(); diff --git a/ggml/src/vulkan-shaders/sum_rows.comp b/ggml/src/ggml-vulkan/vulkan-shaders/sum_rows.comp similarity index 100% rename from ggml/src/vulkan-shaders/sum_rows.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/sum_rows.comp diff --git a/ggml/src/vulkan-shaders/tanh.comp b/ggml/src/ggml-vulkan/vulkan-shaders/tanh.comp similarity index 100% rename from ggml/src/vulkan-shaders/tanh.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/tanh.comp diff --git a/ggml/src/vulkan-shaders/timestep_embedding.comp b/ggml/src/ggml-vulkan/vulkan-shaders/timestep_embedding.comp similarity index 100% rename from ggml/src/vulkan-shaders/timestep_embedding.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/timestep_embedding.comp diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/types.comp b/ggml/src/ggml-vulkan/vulkan-shaders/types.comp new file mode 100644 index 000000000..eecc47f3a --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/types.comp @@ -0,0 +1,323 @@ + +#if !defined(GGML_TYPES_COMP) +#define GGML_TYPES_COMP + +#extension GL_EXT_shader_explicit_arithmetic_types : require + +#if defined(DATA_A_F32) +#define QUANT_K 1 +#define QUANT_R 1 + +#if !defined(LOAD_VEC_A) || LOAD_VEC_A == 1 +#define A_TYPE float +#elif LOAD_VEC_A == 4 +#define A_TYPE vec4 +#elif LOAD_VEC_A == 8 +#define A_TYPE mat2x4 +#endif +#endif + +#if defined(DATA_A_F16) +#define QUANT_K 1 +#define QUANT_R 1 + +#if !defined(LOAD_VEC_A) || LOAD_VEC_A == 1 +#define A_TYPE float16_t +#elif LOAD_VEC_A == 4 +#define A_TYPE f16vec4 +#elif LOAD_VEC_A == 8 +#define A_TYPE f16mat2x4 +#endif +#endif + +#define QUANT_K_Q4_0 32 +#define QUANT_R_Q4_0 2 + +struct block_q4_0 +{ + float16_t d; + uint8_t qs[16]; +}; +struct block_q4_0_packed16 +{ + float16_t d; + uint16_t qs[16/2]; +}; + +#if defined(DATA_A_Q4_0) +#define QUANT_K QUANT_K_Q4_0 +#define QUANT_R QUANT_R_Q4_0 +#define A_TYPE block_q4_0 +#define A_TYPE_PACKED16 block_q4_0_packed16 +#endif + +#define QUANT_K_Q4_1 32 +#define QUANT_R_Q4_1 2 + +struct block_q4_1 +{ + float16_t d; + float16_t m; + uint8_t qs[16]; +}; + +struct block_q4_1_packed16 +{ + float16_t d; + float16_t m; + uint16_t qs[16/2]; +}; + +#if defined(DATA_A_Q4_1) +#define QUANT_K QUANT_K_Q4_1 +#define QUANT_R QUANT_R_Q4_1 +#define A_TYPE block_q4_1 +#define A_TYPE_PACKED16 block_q4_1_packed16 +#endif + +#define QUANT_K_Q5_0 32 +#define QUANT_R_Q5_0 2 + +struct block_q5_0 +{ + float16_t d; + uint16_t qh[2]; + uint8_t qs[16]; +}; + +struct block_q5_0_packed16 +{ + float16_t d; + uint16_t qh[2]; + uint16_t qs[16/2]; +}; + +#if defined(DATA_A_Q5_0) +#define QUANT_K QUANT_K_Q5_0 +#define QUANT_R QUANT_R_Q5_0 +#define A_TYPE block_q5_0 +#define A_TYPE_PACKED16 block_q5_0_packed16 +#endif + +#define QUANT_K_Q5_1 32 +#define QUANT_R_Q5_1 2 + +struct block_q5_1 +{ + float16_t d; + float16_t m; + uint qh; + uint8_t qs[16]; +}; + +struct block_q5_1_packed16 +{ + float16_t d; + float16_t m; + uint qh; + uint16_t qs[16/2]; +}; + +#if defined(DATA_A_Q5_1) +#define QUANT_K QUANT_K_Q5_1 +#define QUANT_R QUANT_R_Q5_1 +#define A_TYPE block_q5_1 +#define A_TYPE_PACKED16 block_q5_1_packed16 +#endif + +#define QUANT_K_Q8_0 32 +#define QUANT_R_Q8_0 1 + +struct block_q8_0 +{ + float16_t d; + int8_t qs[32]; +}; +struct block_q8_0_packed16 +{ + float16_t d; + uint16_t qs[32/2]; +}; + +#if defined(DATA_A_Q8_0) +#define QUANT_K QUANT_K_Q8_0 +#define QUANT_R QUANT_R_Q8_0 +#define A_TYPE block_q8_0 +#define A_TYPE_PACKED16 block_q8_0_packed16 +#endif + +// K-quants +#define QUANT_K_Q2_K 256 + +struct block_q2_K +{ + uint8_t scales[QUANT_K_Q2_K/16]; + uint8_t qs[QUANT_K_Q2_K/4]; + f16vec2 d; +}; + +struct block_q2_K_packed16 +{ + uint16_t scales[QUANT_K_Q2_K/16/2]; + uint16_t qs[QUANT_K_Q2_K/4/2]; + f16vec2 d; +}; + +struct block_q2_K_packed32 +{ + uint32_t scales[QUANT_K_Q2_K/16/4]; + uint32_t qs[QUANT_K_Q2_K/4/4]; + f16vec2 d; +}; + +#if defined(DATA_A_Q2_K) +#define QUANT_K QUANT_K_Q2_K +#define A_TYPE block_q2_K +#define A_TYPE_PACKED16 block_q2_K_packed16 +#define A_TYPE_PACKED32 block_q2_K_packed32 +#endif + +#define QUANT_K_Q3_K 256 + +struct block_q3_K +{ + uint8_t hmask[QUANT_K_Q3_K/8]; + uint8_t qs[QUANT_K_Q3_K/4]; + uint8_t scales[12]; + float16_t d; +}; + +struct block_q3_K_packed16 +{ + uint16_t hmask[QUANT_K_Q3_K/8/2]; + uint16_t qs[QUANT_K_Q3_K/4/2]; + uint16_t scales[12/2]; + float16_t d; +}; + +#if defined(DATA_A_Q3_K) +#define QUANT_K QUANT_K_Q3_K +#define A_TYPE block_q3_K +#define A_TYPE_PACKED16 block_q3_K_packed16 +#endif + +#define QUANT_K_Q4_K 256 + +struct block_q4_K +{ + f16vec2 d; + uint8_t scales[3*QUANT_K_Q4_K/64]; + uint8_t qs[QUANT_K_Q4_K/2]; +}; + +struct block_q4_K_packed16 +{ + f16vec2 d; + uint16_t scales[3*QUANT_K_Q4_K/64/2]; + uint16_t qs[QUANT_K_Q4_K/2/2]; +}; + +struct block_q4_K_packed32 +{ + f16vec2 d; + uint32_t scales[3*QUANT_K_Q4_K/64/4]; + uint32_t qs[QUANT_K_Q4_K/2/4]; +}; + +#if defined(DATA_A_Q4_K) +#define QUANT_K QUANT_K_Q4_K +#define A_TYPE block_q4_K +#define A_TYPE_PACKED16 block_q4_K_packed16 +#define A_TYPE_PACKED32 block_q4_K_packed32 +#endif + +#define QUANT_K_Q5_K 256 + +struct block_q5_K +{ + f16vec2 d; + uint8_t scales[12]; + uint8_t qh[QUANT_K_Q5_K/8]; + uint8_t qs[QUANT_K_Q5_K/2]; +}; + +struct block_q5_K_packed16 +{ + f16vec2 d; + uint16_t scales[12/2]; + uint16_t qh[QUANT_K_Q5_K/8/2]; + uint16_t qs[QUANT_K_Q5_K/2/2]; +}; + +#if defined(DATA_A_Q5_K) +#define QUANT_K QUANT_K_Q5_K +#define A_TYPE block_q5_K +#define A_TYPE_PACKED16 block_q5_K_packed16 +#endif + +#define QUANT_K_Q6_K 256 + +struct block_q6_K +{ + uint8_t ql[QUANT_K_Q6_K/2]; + uint8_t qh[QUANT_K_Q6_K/4]; + int8_t scales[QUANT_K_Q6_K/16]; + float16_t d; +}; + +struct block_q6_K_packed16 +{ + uint16_t ql[QUANT_K_Q6_K/2/2]; + uint16_t qh[QUANT_K_Q6_K/4/2]; + int8_t scales[QUANT_K_Q6_K/16]; + float16_t d; +}; + +#if defined(DATA_A_Q6_K) +#define QUANT_K QUANT_K_Q6_K +#define A_TYPE block_q6_K +#define A_TYPE_PACKED16 block_q6_K_packed16 +#endif + +// IQuants + +#define QUANT_K_IQ4_NL 32 +#define QUANT_R_IQ4_NL 2 + +struct block_iq4_nl +{ + float16_t d; + uint8_t qs[QUANT_K_IQ4_NL/2]; +}; + +struct block_iq4_nl_packed16 +{ + float16_t d; + uint16_t qs[QUANT_K_IQ4_NL/2/2]; +}; + +#if defined(DATA_A_IQ4_NL) + +const int8_t kvalues_iq4nl_const[16] = { + int8_t(-127), int8_t(-104), int8_t(-83), int8_t(-65), int8_t(-49), int8_t(-35), int8_t(-22), int8_t(-10), + int8_t(1), int8_t(13), int8_t(25), int8_t(38), int8_t(53), int8_t(69), int8_t(89), int8_t(113) +}; + +shared FLOAT_TYPE kvalues_iq4nl[16]; + +void init_iq4nl_shmem() +{ + // copy the table into shared memory and sync + if (gl_LocalInvocationIndex.x < 16) { + kvalues_iq4nl[gl_LocalInvocationIndex.x] = FLOAT_TYPE(kvalues_iq4nl_const[gl_LocalInvocationIndex.x]); + } + barrier(); +} + +#define QUANT_K QUANT_K_IQ4_NL +#define QUANT_R QUANT_R_IQ4_NL +#define A_TYPE block_iq4_nl +#define A_TYPE_PACKED16 block_iq4_nl_packed16 +#endif + +#endif // !defined(GGML_TYPES_COMP) diff --git a/ggml/src/vulkan-shaders/upscale.comp b/ggml/src/ggml-vulkan/vulkan-shaders/upscale.comp similarity index 100% rename from ggml/src/vulkan-shaders/upscale.comp rename to ggml/src/ggml-vulkan/vulkan-shaders/upscale.comp diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp new file mode 100644 index 000000000..4716e2c80 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp @@ -0,0 +1,578 @@ + + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#ifdef _WIN32 + #include + #include // For _mkdir on Windows + #include // For std::replace on w64devkit +#else + #include + #include + #include +#endif + +#include + +#define ASYNCIO_CONCURRENCY 64 + +std::mutex lock; +std::vector> shader_fnames; + +std::string GLSLC = "glslc"; +std::string input_dir = "vulkan-shaders"; +std::string output_dir = "/tmp"; +std::string target_hpp = "ggml-vulkan-shaders.hpp"; +std::string target_cpp = "ggml-vulkan-shaders.cpp"; +bool no_clean = false; + +const std::vector type_names = { + "f32", + "f16", + "q4_0", + "q4_1", + "q5_0", + "q5_1", + "q8_0", + "q2_k", + "q3_k", + "q4_k", + "q5_k", + "q6_k", + "iq4_nl" +}; + +void execute_command(const std::string& command, std::string& stdout_str, std::string& stderr_str) { +#ifdef _WIN32 + HANDLE stdout_read, stdout_write; + HANDLE stderr_read, stderr_write; + SECURITY_ATTRIBUTES sa = { sizeof(SECURITY_ATTRIBUTES), NULL, TRUE }; + + if (!CreatePipe(&stdout_read, &stdout_write, &sa, 0) || + !SetHandleInformation(stdout_read, HANDLE_FLAG_INHERIT, 0)) { + throw std::runtime_error("Failed to create stdout pipe"); + } + + if (!CreatePipe(&stderr_read, &stderr_write, &sa, 0) || + !SetHandleInformation(stderr_read, HANDLE_FLAG_INHERIT, 0)) { + throw std::runtime_error("Failed to create stderr pipe"); + } + + PROCESS_INFORMATION pi; + STARTUPINFOA si = { sizeof(STARTUPINFOA) }; + si.dwFlags = STARTF_USESTDHANDLES; + si.hStdOutput = stdout_write; + si.hStdError = stderr_write; + + std::vector cmd(command.begin(), command.end()); + cmd.push_back('\0'); + + if (!CreateProcessA(NULL, cmd.data(), NULL, NULL, TRUE, 0, NULL, NULL, &si, &pi)) { + throw std::runtime_error("Failed to create process"); + } + + CloseHandle(stdout_write); + CloseHandle(stderr_write); + + std::array buffer; + DWORD bytes_read; + + while (ReadFile(stdout_read, buffer.data(), (DWORD)buffer.size(), &bytes_read, NULL) && bytes_read > 0) { + stdout_str.append(buffer.data(), bytes_read); + } + + while (ReadFile(stderr_read, buffer.data(), (DWORD)buffer.size(), &bytes_read, NULL) && bytes_read > 0) { + stderr_str.append(buffer.data(), bytes_read); + } + + CloseHandle(stdout_read); + CloseHandle(stderr_read); + WaitForSingleObject(pi.hProcess, INFINITE); + CloseHandle(pi.hProcess); + CloseHandle(pi.hThread); +#else +int stdout_pipe[2]; + int stderr_pipe[2]; + + if (pipe(stdout_pipe) != 0 || pipe(stderr_pipe) != 0) { + throw std::runtime_error("Failed to create pipes"); + } + + pid_t pid = fork(); + if (pid < 0) { + throw std::runtime_error("Failed to fork process"); + } + + if (pid == 0) { + close(stdout_pipe[0]); + close(stderr_pipe[0]); + dup2(stdout_pipe[1], STDOUT_FILENO); + dup2(stderr_pipe[1], STDERR_FILENO); + close(stdout_pipe[1]); + close(stderr_pipe[1]); + execl("/bin/sh", "sh", "-c", command.c_str(), (char*) nullptr); + _exit(EXIT_FAILURE); + } else { + close(stdout_pipe[1]); + close(stderr_pipe[1]); + + std::array buffer; + ssize_t bytes_read; + + while ((bytes_read = read(stdout_pipe[0], buffer.data(), buffer.size())) > 0) { + stdout_str.append(buffer.data(), bytes_read); + } + + while ((bytes_read = read(stderr_pipe[0], buffer.data(), buffer.size())) > 0) { + stderr_str.append(buffer.data(), bytes_read); + } + + close(stdout_pipe[0]); + close(stderr_pipe[0]); + waitpid(pid, nullptr, 0); + } +#endif +} + +bool directory_exists(const std::string& path) { + struct stat info; + if (stat(path.c_str(), &info) != 0) { + return false; // Path doesn't exist or can't be accessed + } + return (info.st_mode & S_IFDIR) != 0; // Check if it is a directory +} + +bool create_directory(const std::string& path) { +#ifdef _WIN32 + return _mkdir(path.c_str()) == 0 || errno == EEXIST; // EEXIST means the directory already exists +#else + return mkdir(path.c_str(), 0755) == 0 || errno == EEXIST; // 0755 is the directory permissions +#endif +} + +std::string to_uppercase(const std::string& input) { + std::string result = input; + for (char& c : result) { + c = std::toupper(c); + } + return result; +} + +bool string_ends_with(const std::string& str, const std::string& suffix) { + if (suffix.size() > str.size()) { + return false; + } + return std::equal(suffix.rbegin(), suffix.rend(), str.rbegin()); +} + +static const char path_separator = '/'; + +std::string join_paths(const std::string& path1, const std::string& path2) { + return path1 + path_separator + path2; +} + +std::string basename(const std::string &path) { + return path.substr(path.find_last_of("/\\") + 1); +} + +// variables to track number of compiles in progress +static uint32_t compile_count = 0; +static std::mutex compile_count_mutex; +static std::condition_variable compile_count_cond; + +void string_to_spv_func(const std::string& _name, const std::string& in_fname, const std::map& defines, bool fp16 = true, bool coopmat2 = false, bool f16acc = false) { + std::string name = _name + (f16acc ? "_f16acc" : "") + (coopmat2 ? "_cm2" : (fp16 ? "" : "_fp32")); + std::string out_fname = join_paths(output_dir, name + ".spv"); + std::string in_path = join_paths(input_dir, in_fname); + + std::string target_env = (name.find("_cm2") != std::string::npos) ? "--target-env=vulkan1.3" : "--target-env=vulkan1.2"; + + #ifdef _WIN32 + std::vector cmd = {GLSLC, "-fshader-stage=compute", target_env, "-O", "\"" + in_path + "\"", "-o", "\"" + out_fname + "\""}; + #else + std::vector cmd = {GLSLC, "-fshader-stage=compute", target_env, "-O", in_path, "-o", out_fname}; + #endif + + #ifdef GGML_VULKAN_SHADER_DEBUG_INFO + cmd.push_back("-g"); + #endif + + for (const auto& define : defines) { + cmd.push_back("-D" + define.first + "=" + define.second); + } + + std::string command; + for (const auto& part : cmd) { + command += part + " "; + } + + std::string stdout_str, stderr_str; + try { + // std::cout << "Executing command: "; + // for (const auto& part : cmd) { + // std::cout << part << " "; + // } + // std::cout << std::endl; + + execute_command(command, stdout_str, stderr_str); + if (!stderr_str.empty()) { + std::cerr << "cannot compile " << name << "\n\n" << command << "\n\n" << stderr_str << std::endl; + return; + } + + std::lock_guard guard(lock); + shader_fnames.push_back(std::make_pair(name, out_fname)); + } catch (const std::exception& e) { + std::cerr << "Error executing command for " << name << ": " << e.what() << std::endl; + } + { + std::lock_guard guard(compile_count_mutex); + assert(compile_count > 0); + compile_count--; + } + compile_count_cond.notify_all(); +} + +std::map merge_maps(const std::map& a, const std::map& b) { + std::map result = a; + result.insert(b.begin(), b.end()); + return result; +} + +static std::vector> compiles; +void string_to_spv(const std::string& _name, const std::string& in_fname, const std::map& defines, bool fp16 = true, bool coopmat2 = false, bool f16acc = false) { + { + // wait until fewer than N compiles are in progress. + // 16 is an arbitrary limit, the goal is to avoid "failed to create pipe" errors. + uint32_t N = 16; + std::unique_lock guard(compile_count_mutex); + while (compile_count >= N) { + compile_count_cond.wait(guard); + } + compile_count++; + } + compiles.push_back(std::async(string_to_spv_func, _name, in_fname, defines, fp16, coopmat2, f16acc)); +} + +void matmul_shaders(bool fp16, bool matmul_id, bool coopmat2, bool f16acc) { + std::string load_vec = coopmat2 ? "1" : fp16 ? "8" : "4"; + std::string aligned_b_type_f32 = coopmat2 ? "float" : fp16 ? "mat2x4" : "vec4"; + std::string aligned_b_type_f16 = coopmat2 ? "float16_t" : fp16 ? "f16mat2x4" : "f16vec4"; + + std::map base_dict = {{"FLOAT_TYPE", (coopmat2 || fp16) ? "float16_t" : "float"}}; + std::string shader_name = "matmul"; + + if (matmul_id) { + base_dict["MUL_MAT_ID"] = "1"; + shader_name = "matmul_id"; + } + + if (fp16) { + base_dict["FLOAT16"] = "1"; + } + + base_dict["ACC_TYPE"] = f16acc ? "float16_t" : "float"; + + std::string source_name = coopmat2 ? "mul_mm_cm2.comp" : "mul_mm.comp"; + + // Shaders with f16 B_TYPE + string_to_spv(shader_name + "_f32_f16", source_name, merge_maps(base_dict, {{"DATA_A_F32", "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}, }), fp16, coopmat2, f16acc); + string_to_spv(shader_name + "_f32_f16_aligned", source_name, merge_maps(base_dict, {{"DATA_A_F32", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}, {"ALIGNED", "1"}}), fp16, coopmat2, f16acc); + + string_to_spv(shader_name + "_f16_aligned", source_name, merge_maps(base_dict, {{"DATA_A_F16", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}, {"ALIGNED", "1"}}), fp16, coopmat2, f16acc); + string_to_spv(shader_name + "_f16", source_name, merge_maps(base_dict, {{"DATA_A_F16", "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}), fp16, coopmat2, f16acc); + + for (const auto& tname : type_names) { + std::string data_a_key = "DATA_A_" + to_uppercase(tname); + // For unaligned, load one at a time for f32/f16, or two at a time for quants + std::string load_vec_a_unaligned = (coopmat2 || tname == "f32" || tname == "f16") ? "1" : "2"; + // For aligned matmul loads + std::string load_vec_a = (coopmat2 || tname == "f32" || tname == "f16") ? load_vec : "2"; + + string_to_spv(shader_name + "_" + tname + "_f32", source_name, merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a_unaligned}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"B_IS_FLOAT", "1"}}), fp16, coopmat2, f16acc); + string_to_spv(shader_name + "_" + tname + "_f32_aligned", source_name, merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f32}, {"D_TYPE", "float"}, {"B_IS_FLOAT", "1"}, {"ALIGNED", "1"}}), fp16, coopmat2, f16acc); + + if (tname != "f16" && tname != "f32") { + string_to_spv(shader_name + "_" + tname + "_f16", source_name, merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a_unaligned}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}, {"B_IS_FLOAT", "1"}}), fp16, coopmat2, f16acc); + string_to_spv(shader_name + "_" + tname + "_f16_aligned", source_name, merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}, {"B_IS_FLOAT", "1"}, {"ALIGNED", "1"}}), fp16, coopmat2, f16acc); + } + } +} + +void process_shaders() { + std::cout << "ggml_vulkan: Generating and compiling shaders to SPIR-V" << std::endl; + std::map base_dict = {{"FLOAT_TYPE", "float"}}; + + // matmul + for (const auto& fp16 : {false, true}) { + for (const auto& matmul_id : {false, true}) { + for (const auto& coopmat2 : {false, true}) { + for (const auto& f16acc : {false, true}) { +#if !defined(VK_NV_cooperative_matrix2) + if (coopmat2) { + continue; + } +#endif + if (coopmat2 && !fp16) { + continue; + } + if (!coopmat2 && f16acc) { + continue; + } + matmul_shaders(fp16, matmul_id, coopmat2, f16acc); + } + } + } + } + +#if defined(VK_NV_cooperative_matrix2) + // flash attention + for (const auto& f16acc : {false, true}) { + std::string acctype = f16acc ? "float16_t" : "float"; + + for (const auto& tname : type_names) { + if (tname == "f32") { + continue; + } + + if (tname == "f16") { + string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm2.comp", + merge_maps(base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"ACC_TYPE", acctype}}), true, true, f16acc); + } else { + std::string data_a_key = "DATA_A_" + to_uppercase(tname); + string_to_spv("flash_attn_f32_f16_" + tname, "flash_attn_cm2.comp", + merge_maps(base_dict, {{data_a_key, "1"}, {"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"ACC_TYPE", acctype}, {"DEQUANTFUNC", "dequantFunc"+to_uppercase(tname) }, {"BLOCK_SIZE", "QUANT_K_"+to_uppercase(tname) }}), true, true, f16acc); + } + } + } +#endif + + for (const auto& tname : type_names) { + // mul mat vec + std::string data_a_key = "DATA_A_" + to_uppercase(tname); + std::string shader = (string_ends_with(tname, "_k")) ? "mul_mat_vec_" + tname + ".comp" : "mul_mat_vec.comp"; + + string_to_spv("mul_mat_vec_" + tname + "_f32_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPE_VEC2", "vec2"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}})); + string_to_spv("mul_mat_vec_" + tname + "_f16_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float16_t"}, {"B_TYPE_VEC2", "f16vec2"}, {"B_TYPE_VEC4", "f16vec4"}, {"D_TYPE", "float"}})); + + string_to_spv("mul_mat_vec_id_" + tname + "_f32", shader, merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPE_VEC2", "vec2"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}})); + + // Dequant shaders + if (tname != "f16") { + string_to_spv("dequant_" + tname, "dequant_" + tname + ".comp", merge_maps(base_dict, {{data_a_key, "1"}, {"D_TYPE", "float16_t"}})); + } + + if (!string_ends_with(tname, "_k")) { + shader = (tname == "f32" || tname == "f16") ? "get_rows.comp" : "get_rows_quant.comp"; + + if (tname == "f16") { + string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}})); + } else { + string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}})); + } + string_to_spv("get_rows_" + tname + "_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float"}})); + } + } + + string_to_spv("mul_mat_vec_p021_f16_f32", "mul_mat_vec_p021.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("mul_mat_vec_nc_f16_f32", "mul_mat_vec_nc.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); + + // Norms + string_to_spv("norm_f32", "norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("group_norm_f32", "group_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("rms_norm_f32", "rms_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); + + string_to_spv("cpy_f32_f32", "copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("cpy_f32_f16", "copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}}); + string_to_spv("cpy_f16_f16", "copy.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); + string_to_spv("contig_cpy_f32_f32", "contig_copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("contig_cpy_f32_f16", "contig_copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}}); + string_to_spv("contig_cpy_f16_f16", "contig_copy.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); + + string_to_spv("add_f32", "add.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); + string_to_spv("add_f16_f32_f16", "add.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float16_t"}, {"FLOAT_TYPE", "float"}}); + + string_to_spv("acc_f32", "acc.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); + + string_to_spv("split_k_reduce", "mul_mat_split_k_reduce.comp", {}); + + string_to_spv("mul_f32", "mul.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); + + string_to_spv("div_f32", "div.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); + + string_to_spv("repeat_f32", "repeat.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + + string_to_spv("scale_f32", "scale.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); + + string_to_spv("sqr_f32", "square.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); + + string_to_spv("sin_f32", "sin.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); + + string_to_spv("cos_f32", "cos.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); + + string_to_spv("clamp_f32", "clamp.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); + + string_to_spv("pad_f32", "pad.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + + string_to_spv("concat_f32", "concat.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("concat_f16", "concat.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); + string_to_spv("concat_i32", "concat.comp", {{"A_TYPE", "int"}, {"B_TYPE", "int"}, {"D_TYPE", "int"}}); + + string_to_spv("upscale_f32", "upscale.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); + + string_to_spv("gelu_f32", "gelu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("gelu_quick_f32", "gelu_quick.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("silu_f32", "silu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("relu_f32", "relu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("leaky_relu_f32", "leaky_relu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("tanh_f32", "tanh.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + + string_to_spv("diag_mask_inf_f32", "diag_mask_inf.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + + string_to_spv("soft_max_f32", "soft_max.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("soft_max_f32_f16", "soft_max.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}})); + + string_to_spv("rope_norm_f32", "rope_norm.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("rope_norm_f16", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); + + string_to_spv("rope_neox_f32", "rope_neox.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("rope_neox_f16", "rope_neox.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); + + string_to_spv("argsort_f32", "argsort.comp", {{"A_TYPE", "float"}}); + + string_to_spv("sum_rows_f32", "sum_rows.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); + + string_to_spv("im2col_f32", "im2col.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("im2col_f32_f16", "im2col.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}})); + + string_to_spv("timestep_embedding_f32", "timestep_embedding.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); + + string_to_spv("pool2d_f32", "pool2d.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); + + for (auto &c : compiles) { + c.wait(); + } +} + +void write_output_files() { + FILE* hdr = fopen(target_hpp.c_str(), "w"); + FILE* src = fopen(target_cpp.c_str(), "w"); + + fprintf(hdr, "#include \n\n"); + fprintf(src, "#include \"%s\"\n\n", basename(target_hpp).c_str()); + + for (const auto& pair : shader_fnames) { + const std::string& name = pair.first; + #ifdef _WIN32 + std::string path = pair.second; + std::replace(path.begin(), path.end(), '/', '\\' ); + #else + const std::string& path = pair.second; + #endif + + FILE* spv = fopen(path.c_str(), "rb"); + if (!spv) { + std::cerr << "Error opening SPIR-V file: " << path << " (" << strerror(errno) << ")\n"; + continue; + } + + fseek(spv, 0, SEEK_END); + size_t size = ftell(spv); + fseek(spv, 0, SEEK_SET); + + std::vector data(size); + size_t read_size = fread(data.data(), 1, size, spv); + fclose(spv); + if (read_size != size) { + std::cerr << "Error reading SPIR-V file: " << path << " (" << strerror(errno) << ")\n"; + continue; + } + + fprintf(hdr, "extern unsigned char %s_data[%zu];\n", name.c_str(), size); + fprintf(hdr, "const uint64_t %s_len = %zu;\n\n", name.c_str(), size); + + fprintf(src, "unsigned char %s_data[%zu] = {\n", name.c_str(), size); + for (size_t i = 0; i < size; ++i) { + fprintf(src, "0x%02x,", data[i]); + if ((i + 1) % 12 == 0) fprintf(src, "\n"); + } + fprintf(src, "\n};\n\n"); + + if (!no_clean) { + std::remove(path.c_str()); + } + } + + fclose(hdr); + fclose(src); +} + +int main(int argc, char** argv) { + std::map args; + for (int i = 1; i < argc; ++i) { + std::string arg = argv[i]; + if (arg.rfind("--", 0) == 0) { + if (i + 1 < argc && argv[i + 1][0] != '-') { + args[arg] = argv[i + 1]; + ++i; + } else { + args[arg] = ""; + } + } + } + + if (args.find("--glslc") != args.end()) { + GLSLC = args["--glslc"]; // Path to glslc + } + if (args.find("--input-dir") != args.end()) { + input_dir = args["--input-dir"]; // Directory containing shader sources + } + if (args.find("--output-dir") != args.end()) { + output_dir = args["--output-dir"]; // Directory for containing SPIR-V output + } + if (args.find("--target-hpp") != args.end()) { + target_hpp = args["--target-hpp"]; // Path to generated header file + } + if (args.find("--target-cpp") != args.end()) { + target_cpp = args["--target-cpp"]; // Path to generated cpp file + } + if (args.find("--no-clean") != args.end()) { + no_clean = true; // Keep temporary SPIR-V files in output-dir after build + } + + if (!directory_exists(input_dir)) { + std::cerr << "\"" << input_dir << "\" must be a valid directory containing shader sources" << std::endl; + return EXIT_FAILURE; + } + + if (!directory_exists(output_dir)) { + if (!create_directory(output_dir)) { + std::cerr << "Error creating output directory: " << output_dir << "\n"; + return EXIT_FAILURE; + } + } + + process_shaders(); + + write_output_files(); + + return EXIT_SUCCESS; +} diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 0d99b0791..2c338dee5 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -1,11 +1,13 @@ -#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnings on Windows +#define _CRT_SECURE_NO_DEPRECATE // Disables "unsafe" warnings on Windows #define _USE_MATH_DEFINES // For M_PI on MSVC #include "ggml-backend.h" #include "ggml-impl.h" -#include "ggml-cpu-impl.h" -#include "ggml-quants.h" +#include "ggml-threading.h" #include "ggml.h" + +// FIXME: required here for quantization functions +#include "ggml-quants.h" #include "ggml-aarch64.h" #if defined(_MSC_VER) || defined(__MINGW32__) @@ -31,168 +33,38 @@ #include #endif -#ifdef GGML_USE_OPENMP -#include -#endif - -#if defined(__ARM_FEATURE_SVE) || defined(__ARM_FEATURE_MATMUL_INT8) -#undef GGML_USE_LLAMAFILE -#endif - -#ifdef GGML_USE_LLAMAFILE -#include -#endif - -#if defined(_MSC_VER) -// disable "possible loss of data" to avoid hundreds of casts -// we should just be careful :) -#pragma warning(disable: 4244 4267) - -// disable POSIX deprecation warnings -// these functions are never going away, anyway -#pragma warning(disable: 4996) - -// unreachable code because of multiple instances of code after GGML_ABORT -#pragma warning(disable: 4702) -#endif - -// Note: once we move threading into a separate C++ file -// will use std::hardware_destructive_interference_size instead of hardcoding it here -// and we'll use C++ attribute syntax. -#define GGML_CACHE_LINE 64 - -#if defined(__clang__) || defined(__GNUC__) -#define GGML_CACHE_ALIGN __attribute__((aligned(GGML_CACHE_LINE))) -#endif - -#if defined(__has_feature) -#if __has_feature(thread_sanitizer) -#define GGML_TSAN_ENABLED 1 -#endif -#else // __has_feature -#if defined(__SANITIZE_THREAD__) -#define GGML_TSAN_ENABLED 1 -#endif -#endif // __has_feature - -#if defined(_WIN32) - -#define WIN32_LEAN_AND_MEAN -#ifndef NOMINMAX - #define NOMINMAX -#endif -#include - -#if !defined(__clang__) -#define GGML_CACHE_ALIGN __declspec(align(GGML_CACHE_LINE)) - -typedef volatile LONG atomic_int; -typedef atomic_int atomic_bool; -typedef atomic_int atomic_flag; - -#define ATOMIC_FLAG_INIT 0 - -typedef enum { - memory_order_relaxed, - memory_order_consume, - memory_order_acquire, - memory_order_release, - memory_order_acq_rel, - memory_order_seq_cst -} memory_order; - -static void atomic_store(atomic_int * ptr, LONG val) { - InterlockedExchange(ptr, val); -} -static void atomic_store_explicit(atomic_int * ptr, LONG val, memory_order mo) { - // TODO: add support for explicit memory order - InterlockedExchange(ptr, val); -} -static LONG atomic_load(atomic_int * ptr) { - return InterlockedCompareExchange(ptr, 0, 0); -} -static LONG atomic_load_explicit(atomic_int * ptr, memory_order mo) { - // TODO: add support for explicit memory order - return InterlockedCompareExchange(ptr, 0, 0); -} -static LONG atomic_fetch_add(atomic_int * ptr, LONG inc) { - return InterlockedExchangeAdd(ptr, inc); -} -static LONG atomic_fetch_add_explicit(atomic_int * ptr, LONG inc, memory_order mo) { - // TODO: add support for explicit memory order - return InterlockedExchangeAdd(ptr, inc); -} -static atomic_bool atomic_flag_test_and_set(atomic_flag * ptr) { - return InterlockedExchange(ptr, 1); -} -static void atomic_flag_clear(atomic_flag * ptr) { - InterlockedExchange(ptr, 0); -} -static void atomic_thread_fence(memory_order mo) { - MemoryBarrier(); -} -#else // clang -#include -#endif - -typedef HANDLE pthread_t; - -typedef DWORD thread_ret_t; -static int pthread_create(pthread_t * out, void * unused, thread_ret_t(*func)(void *), void * arg) { - (void) unused; - HANDLE handle = CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE) func, arg, 0, NULL); - if (handle == NULL) - { - return EAGAIN; - } - - *out = handle; - return 0; -} - -static int pthread_join(pthread_t thread, void * unused) { - (void) unused; - int ret = (int) WaitForSingleObject(thread, INFINITE); - CloseHandle(thread); - return ret; -} - -static int sched_yield (void) { - Sleep (0); - return 0; -} -#else - -#include -#include -#include -#if defined(__FreeBSD__) -#include -#endif - -typedef void * thread_ret_t; - -#include -#include -#include - -#endif - -typedef pthread_t ggml_thread_t; - -#ifdef GGML_USE_CPU_HBM -#include -#endif - #if defined(__APPLE__) #include #include #include #endif +#if defined(_WIN32) +#define WIN32_LEAN_AND_MEAN +#ifndef NOMINMAX + #define NOMINMAX +#endif +#include +#endif + +#define UNUSED GGML_UNUSED + +#if defined(_MSC_VER) +#define m512bh(p) p +#define m512i(p) p +#else +#define m512bh(p) (__m512bh)(p) +#define m512i(p) (__m512i)(p) +#endif + +// precomputed f32 table for f16 (256 KB) (ggml-impl.h) +float ggml_table_f32_f16[1 << 16]; + #if (defined(__linux__) || defined(__APPLE__) || defined(__FreeBSD__) || defined(__NetBSD__) || defined(__OpenBSD__)) && \ (!defined(TARGET_OS_TV) && !defined(TARGET_OS_WATCH)) - +#include +#include +#include #include #if defined(__ANDROID__) @@ -305,14 +177,6 @@ void ggml_abort(const char * file, int line, const char * fmt, ...) { abort(); } -#define GGML_DEBUG 0 -#define GGML_GELU_FP16 -#define GGML_GELU_QUICK_FP16 - -#define GGML_SOFT_MAX_UNROLL 4 -#define GGML_VEC_DOT_UNROLL 2 -#define GGML_VEC_MAD_UNROLL 32 - // // logging // @@ -357,24 +221,6 @@ void ggml_log_callback_default(enum ggml_log_level level, const char * text, voi fflush(stderr); } -#if (GGML_DEBUG >= 1) -#define GGML_PRINT_DEBUG(...) GGML_LOG_DEBUG(__VA_ARGS__) -#else -#define GGML_PRINT_DEBUG(...) -#endif - -#if (GGML_DEBUG >= 5) -#define GGML_PRINT_DEBUG_5(...) GGML_LOG_DEBUG(__VA_ARGS__) -#else -#define GGML_PRINT_DEBUG_5(...) -#endif - -#if (GGML_DEBUG >= 10) -#define GGML_PRINT_DEBUG_10(...) GGML_LOG_DEBUG(__VA_ARGS__) -#else -#define GGML_PRINT_DEBUG_10(...) -#endif - // // end of logging block // @@ -387,17 +233,20 @@ void ggml_log_callback_default(enum ggml_log_level level, const char * text, voi void * ggml_aligned_malloc(size_t size) { + const int alignment = 64; + #if defined(_MSC_VER) || defined(__MINGW32__) - return _aligned_malloc(size, TENSOR_ALIGNMENT); + return _aligned_malloc(size, alignment); #else if (size == 0) { GGML_LOG_WARN("Behavior may be unexpected when allocating 0 bytes for ggml_aligned_malloc!\n"); return NULL; } void * aligned_memory = NULL; -#ifdef GGML_USE_CPU_HBM - int result = hbw_posix_memalign(&aligned_memory, TENSOR_ALIGNMENT, size); -#elif TARGET_OS_OSX + #ifdef GGML_USE_CPU_HBM + int result = hbw_posix_memalign(&aligned_memory, alignment, size); + #elif TARGET_OS_OSX + GGML_UNUSED(alignment); kern_return_t alloc_status = vm_allocate((vm_map_t) mach_task_self(), (vm_address_t *) &aligned_memory, size, VM_FLAGS_ANYWHERE); int result = EFAULT; switch (alloc_status) { @@ -414,12 +263,9 @@ void * ggml_aligned_malloc(size_t size) { result = EFAULT; break; } -#elif GGML_USE_METAL - const long page_size = sysconf(_SC_PAGESIZE); - int result = posix_memalign(&aligned_memory, MAX(TENSOR_ALIGNMENT, page_size), size); -#else - int result = posix_memalign(&aligned_memory, TENSOR_ALIGNMENT, size); -#endif + #else + int result = posix_memalign(&aligned_memory, alignment, size); + #endif if (result != 0) { // Handle allocation failure const char *error_desc = "unknown allocation error"; @@ -432,7 +278,6 @@ void * ggml_aligned_malloc(size_t size) { break; } GGML_LOG_ERROR("%s: %s (attempted to allocate %6.2f MB)\n", __func__, error_desc, size/(1024.0*1024.0)); - GGML_ABORT("fatal error"); return NULL; } return aligned_memory; @@ -489,44 +334,6 @@ inline static void * ggml_calloc(size_t num, size_t size) { #define GGML_FREE(ptr) free(ptr) -#define UNUSED GGML_UNUSED -#define SWAP(x, y, T) do { T SWAP = x; (x) = y; (y) = SWAP; } while (0) - -#if defined(GGML_USE_ACCELERATE) -#include -#endif - -// floating point type used to accumulate sums -typedef double ggml_float; - -#undef MIN -#undef MAX - -#define MIN(a, b) ((a) < (b) ? (a) : (b)) -#define MAX(a, b) ((a) > (b) ? (a) : (b)) - -// -// global data -// - -// precomputed gelu table for f16 (128 KB) -static ggml_fp16_t ggml_table_gelu_f16[1 << 16]; - -// precomputed quick gelu table for f16 (128 KB) -static ggml_fp16_t ggml_table_gelu_quick_f16[1 << 16]; - -// precomputed f32 table for f16 (256 KB) (ggml-impl.h) -float ggml_table_f32_f16[1 << 16]; - -#if defined(__ARM_ARCH) -struct ggml_arm_arch_features_type { - int has_neon; - int has_i8mm; - int has_sve; - int sve_cnt; -} ggml_arm_arch_features = {-1, -1, -1, 0}; -#endif - const char * ggml_status_to_string(enum ggml_status status) { switch (status) { case GGML_STATUS_ALLOC_FAILED: return "GGML status: error (failed to allocate memory)"; @@ -564,19 +371,23 @@ void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int64_t n) { } } +// FIXME: these functions must detect the instruction set at runtime, since they are part of the core ggml library +// currently, the ggml_cpu_has_* functions are entirely compile-time void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int64_t n) { int64_t i = 0; #if defined(__F16C__) - for (; i + 7 < n; i += 8) { - __m256 x_vec = _mm256_loadu_ps(x + i); - __m128i y_vec = _mm256_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT); - _mm_storeu_si128((__m128i *)(y + i), y_vec); - } - for(; i + 3 < n; i += 4) { - __m128 x_vec = _mm_loadu_ps(x + i); - __m128i y_vec = _mm_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT); - _mm_storel_epi64((__m128i *)(y + i), y_vec); - } + //if (ggml_cpu_has_f16c()) { + for (; i + 7 < n; i += 8) { + __m256 x_vec = _mm256_loadu_ps(x + i); + __m128i y_vec = _mm256_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT); + _mm_storeu_si128((__m128i *)(y + i), y_vec); + } + for(; i + 3 < n; i += 4) { + __m128 x_vec = _mm_loadu_ps(x + i); + __m128i y_vec = _mm_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT); + _mm_storel_epi64((__m128i *)(y + i), y_vec); + } + //} #endif for (; i < n; i++) { y[i] = GGML_FP32_TO_FP16(x[i]); @@ -586,25 +397,30 @@ void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int64_t n) { void ggml_bf16_to_fp32_row(const ggml_bf16_t * x, float * y, int64_t n) { int64_t i = 0; #if defined(__AVX512F__) - for (; i + 16 <= n; i += 16) { - _mm512_storeu_ps(y + i, - _mm512_castsi512_ps( - _mm512_slli_epi32( - _mm512_cvtepu16_epi32( - _mm256_loadu_si256( - (const __m256i *)(x + i))), - 16))); - } -#elif defined(__AVX2__) - for (; i + 8 <= n; i += 8) { - _mm256_storeu_ps(y + i, - _mm256_castsi256_ps( - _mm256_slli_epi32( - _mm256_cvtepu16_epi32( - _mm_loadu_si128( - (const __m128i *)(x + i))), - 16))); - } + //if (ggml_cpu_has_avx512()) { + for (; i + 16 <= n; i += 16) { + _mm512_storeu_ps(y + i, + _mm512_castsi512_ps( + _mm512_slli_epi32( + _mm512_cvtepu16_epi32( + _mm256_loadu_si256( + (const __m256i *)(x + i))), + 16))); + } + //} +#endif +#if defined(__AVX2__) + //if (ggml_cpu_has_avx2()) { + for (; i + 8 <= n; i += 8) { + _mm256_storeu_ps(y + i, + _mm256_castsi256_ps( + _mm256_slli_epi32( + _mm256_cvtepu16_epi32( + _mm_loadu_si128( + (const __m128i *)(x + i))), + 16))); + } + //} #endif for (; i < n; i++) { y[i] = GGML_BF16_TO_FP32(x[i]); @@ -736,24 +552,8 @@ FILE * ggml_fopen(const char * fname, const char * mode) { #else return fopen(fname, mode); #endif + } - -// -// cache line -// - -#if defined(__cpp_lib_hardware_interference_size) -#define CACHE_LINE_SIZE hardware_destructive_interference_size -#else -#if defined(__POWER9_VECTOR__) -#define CACHE_LINE_SIZE 128 -#else -#define CACHE_LINE_SIZE 64 -#endif -#endif - -static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float); - static void ggml_vec_dot_f32(int n, float * restrict s, size_t bs, const float * restrict x, size_t bx, const float * restrict y, size_t by, int nrc); static void ggml_vec_dot_f16(int n, float * restrict s, size_t bs, ggml_fp16_t * restrict x, size_t bx, ggml_fp16_t * restrict y, size_t by, int nrc); static void ggml_vec_dot_bf16(int n, float * restrict s, size_t bs, ggml_bf16_t * restrict x, size_t bx, ggml_bf16_t * restrict y, size_t by, int nrc); @@ -788,16 +588,12 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .blck_size = 1, .type_size = sizeof(double), .is_quantized = false, - .nrows = 1, }, [GGML_TYPE_F32] = { .type_name = "f32", .blck_size = 1, .type_size = sizeof(float), .is_quantized = false, - .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f32, - .vec_dot_type = GGML_TYPE_F32, - .nrows = 1, }, [GGML_TYPE_F16] = { .type_name = "f16", @@ -805,11 +601,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(ggml_fp16_t), .is_quantized = false, .to_float = (ggml_to_float_t) ggml_fp16_to_fp32_row, - .from_float = (ggml_from_float_t) ggml_fp32_to_fp16_row, .from_float_ref = (ggml_from_float_t) ggml_fp32_to_fp16_row, - .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f16, - .vec_dot_type = GGML_TYPE_F16, - .nrows = 1, }, [GGML_TYPE_Q4_0] = { .type_name = "q4_0", @@ -817,15 +609,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q4_0), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q4_0, - .from_float = quantize_row_q4_0, .from_float_ref = (ggml_from_float_t) quantize_row_q4_0_ref, - .vec_dot = ggml_vec_dot_q4_0_q8_0, - .vec_dot_type = GGML_TYPE_Q8_0, -#if defined (__ARM_FEATURE_MATMUL_INT8) - .nrows = 2, -#else - .nrows = 1, -#endif }, [GGML_TYPE_Q4_1] = { .type_name = "q4_1", @@ -833,39 +617,19 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q4_1), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q4_1, - .from_float = quantize_row_q4_1, .from_float_ref = (ggml_from_float_t) quantize_row_q4_1_ref, - .vec_dot = ggml_vec_dot_q4_1_q8_1, - .vec_dot_type = GGML_TYPE_Q8_1, -#if defined (__ARM_FEATURE_MATMUL_INT8) - .nrows = 2, -#else - .nrows = 1, -#endif }, [4] = { // GGML_TYPE_Q4_2 .type_name = "DEPRECATED", .blck_size = 0, .type_size = 0, .is_quantized = false, - .to_float = NULL, - .from_float = NULL, - .from_float_ref = NULL, - .vec_dot = NULL, - .vec_dot_type = GGML_TYPE_COUNT, - .nrows = 1, }, [5] = { // GGML_TYPE_Q4_3 .type_name = "DEPRECATED", .blck_size = 0, .type_size = 0, .is_quantized = false, - .to_float = NULL, - .from_float = NULL, - .from_float_ref = NULL, - .vec_dot = NULL, - .vec_dot_type = GGML_TYPE_COUNT, - .nrows = 1, }, [GGML_TYPE_Q5_0] = { .type_name = "q5_0", @@ -873,11 +637,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q5_0), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q5_0, - .from_float = quantize_row_q5_0, .from_float_ref = (ggml_from_float_t) quantize_row_q5_0_ref, - .vec_dot = ggml_vec_dot_q5_0_q8_0, - .vec_dot_type = GGML_TYPE_Q8_0, - .nrows = 1, }, [GGML_TYPE_Q5_1] = { .type_name = "q5_1", @@ -885,11 +645,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q5_1), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q5_1, - .from_float = quantize_row_q5_1, .from_float_ref = (ggml_from_float_t) quantize_row_q5_1_ref, - .vec_dot = ggml_vec_dot_q5_1_q8_1, - .vec_dot_type = GGML_TYPE_Q8_1, - .nrows = 1, }, [GGML_TYPE_Q8_0] = { .type_name = "q8_0", @@ -897,26 +653,14 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q8_0), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q8_0, - .from_float = quantize_row_q8_0, .from_float_ref = (ggml_from_float_t) quantize_row_q8_0_ref, - .from_float_to_mat = quantize_mat_q8_0, - .vec_dot = ggml_vec_dot_q8_0_q8_0, - .vec_dot_type = GGML_TYPE_Q8_0, -#if defined (__ARM_FEATURE_MATMUL_INT8) - .nrows = 2, -#else - .nrows = 1, -#endif }, [GGML_TYPE_Q8_1] = { .type_name = "q8_1", .blck_size = QK8_1, .type_size = sizeof(block_q8_1), .is_quantized = true, - .from_float = quantize_row_q8_1, .from_float_ref = (ggml_from_float_t) quantize_row_q8_1_ref, - .vec_dot_type = GGML_TYPE_Q8_1, - .nrows = 1, }, [GGML_TYPE_Q2_K] = { .type_name = "q2_K", @@ -924,11 +668,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q2_K), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q2_K, - .from_float = quantize_row_q2_K, .from_float_ref = (ggml_from_float_t) quantize_row_q2_K_ref, - .vec_dot = ggml_vec_dot_q2_K_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_Q3_K] = { .type_name = "q3_K", @@ -936,11 +676,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q3_K), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q3_K, - .from_float = quantize_row_q3_K, .from_float_ref = (ggml_from_float_t) quantize_row_q3_K_ref, - .vec_dot = ggml_vec_dot_q3_K_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_Q4_K] = { .type_name = "q4_K", @@ -948,11 +684,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q4_K), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q4_K, - .from_float = quantize_row_q4_K, .from_float_ref = (ggml_from_float_t) quantize_row_q4_K_ref, - .vec_dot = ggml_vec_dot_q4_K_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_Q5_K] = { .type_name = "q5_K", @@ -960,11 +692,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q5_K), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q5_K, - .from_float = quantize_row_q5_K, .from_float_ref = (ggml_from_float_t) quantize_row_q5_K_ref, - .vec_dot = ggml_vec_dot_q5_K_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_Q6_K] = { .type_name = "q6_K", @@ -972,11 +700,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q6_K), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_q6_K, - .from_float = quantize_row_q6_K, .from_float_ref = (ggml_from_float_t) quantize_row_q6_K_ref, - .vec_dot = ggml_vec_dot_q6_K_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_IQ2_XXS] = { .type_name = "iq2_xxs", @@ -984,11 +708,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_iq2_xxs), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq2_xxs, - .from_float = NULL, .from_float_ref = NULL, - .vec_dot = ggml_vec_dot_iq2_xxs_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_IQ2_XS] = { .type_name = "iq2_xs", @@ -996,11 +716,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_iq2_xs), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq2_xs, - .from_float = NULL, .from_float_ref = NULL, - .vec_dot = ggml_vec_dot_iq2_xs_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_IQ3_XXS] = { .type_name = "iq3_xxs", @@ -1008,11 +724,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_iq3_xxs), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq3_xxs, - .from_float = quantize_row_iq3_xxs, .from_float_ref = (ggml_from_float_t)quantize_row_iq3_xxs_ref, - .vec_dot = ggml_vec_dot_iq3_xxs_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_IQ3_S] = { .type_name = "iq3_s", @@ -1020,11 +732,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_iq3_s), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq3_s, - .from_float = quantize_row_iq3_s, .from_float_ref = (ggml_from_float_t)quantize_row_iq3_s_ref, - .vec_dot = ggml_vec_dot_iq3_s_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_IQ2_S] = { .type_name = "iq2_s", @@ -1032,11 +740,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_iq2_s), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq2_s, - .from_float = quantize_row_iq2_s, .from_float_ref = (ggml_from_float_t)quantize_row_iq2_s_ref, - .vec_dot = ggml_vec_dot_iq2_s_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_IQ1_S] = { .type_name = "iq1_s", @@ -1044,11 +748,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_iq1_s), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq1_s, - .from_float = NULL, .from_float_ref = NULL, - .vec_dot = ggml_vec_dot_iq1_s_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_IQ1_M] = { .type_name = "iq1_m", @@ -1056,11 +756,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_iq1_m), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq1_m, - .from_float = NULL, .from_float_ref = NULL, - .vec_dot = ggml_vec_dot_iq1_m_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_IQ4_NL] = { .type_name = "iq4_nl", @@ -1068,11 +764,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_iq4_nl), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq4_nl, - .from_float = quantize_row_iq4_nl, .from_float_ref = (ggml_from_float_t)quantize_row_iq4_nl_ref, - .vec_dot = ggml_vec_dot_iq4_nl_q8_0, - .vec_dot_type = GGML_TYPE_Q8_0, - .nrows = 1, }, [GGML_TYPE_IQ4_XS] = { .type_name = "iq4_xs", @@ -1080,18 +772,13 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_iq4_xs), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq4_xs, - .from_float = quantize_row_iq4_xs, .from_float_ref = (ggml_from_float_t)quantize_row_iq4_xs_ref, - .vec_dot = ggml_vec_dot_iq4_xs_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_Q8_K] = { .type_name = "q8_K", .blck_size = QK_K, .type_size = sizeof(block_q8_K), .is_quantized = true, - .from_float = quantize_row_q8_K, }, [GGML_TYPE_BF16] = { .type_name = "bf16", @@ -1099,11 +786,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(ggml_bf16_t), .is_quantized = false, .to_float = (ggml_to_float_t) ggml_bf16_to_fp32_row, - .from_float = (ggml_from_float_t) ggml_fp32_to_bf16_row, .from_float_ref = (ggml_from_float_t) ggml_fp32_to_bf16_row_ref, - .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_bf16, - .vec_dot_type = GGML_TYPE_BF16, - .nrows = 1, }, [GGML_TYPE_Q4_0_4_4] = { .type_name = "q4_0_4x4", @@ -1112,14 +795,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q4_0), .is_quantized = true, .to_float = NULL, - .from_float = NULL, .from_float_ref = NULL, - .vec_dot = NULL, - .vec_dot_type = GGML_TYPE_Q8_0, - .nrows = 1, - .ncols = 4, - .gemv = ggml_gemv_q4_0_4x4_q8_0, - .gemm = ggml_gemm_q4_0_4x4_q8_0, }, [GGML_TYPE_Q4_0_4_8] = { .type_name = "q4_0_4x8", @@ -1128,14 +804,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q4_0), .is_quantized = true, .to_float = NULL, - .from_float = NULL, .from_float_ref = NULL, - .vec_dot = NULL, - .vec_dot_type = GGML_TYPE_Q8_0, - .nrows = 1, - .ncols = 4, - .gemv = ggml_gemv_q4_0_4x8_q8_0, - .gemm = ggml_gemm_q4_0_4x8_q8_0, }, [GGML_TYPE_Q4_0_8_8] = { .type_name = "q4_0_8x8", @@ -1144,14 +813,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q4_0), .is_quantized = true, .to_float = NULL, - .from_float = NULL, .from_float_ref = NULL, - .vec_dot = NULL, - .vec_dot_type = GGML_TYPE_Q8_0, - .nrows = 1, - .ncols = 8, - .gemv = ggml_gemv_q4_0_8x8_q8_0, - .gemm = ggml_gemm_q4_0_8x8_q8_0, }, [GGML_TYPE_TQ1_0] = { .type_name = "tq1_0", @@ -1159,11 +821,7 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_tq1_0), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_tq1_0, - .from_float = quantize_row_tq1_0, .from_float_ref = (ggml_from_float_t) quantize_row_tq1_0_ref, - .vec_dot = ggml_vec_dot_tq1_0_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, }, [GGML_TYPE_TQ2_0] = { .type_name = "tq2_0", @@ -1171,826 +829,24 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_tq2_0), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_tq2_0, - .from_float = quantize_row_tq2_0, .from_float_ref = (ggml_from_float_t) quantize_row_tq2_0_ref, - .vec_dot = ggml_vec_dot_tq2_0_q8_K, - .vec_dot_type = GGML_TYPE_Q8_K, - .nrows = 1, + }, + [GGML_TYPE_IQ4_NL_4_4] = { + .type_name = "iq4_nl_4x4", + .blck_size = QK4_NL, + .blck_size_interleave = 4, + .type_size = sizeof(block_iq4_nl), + .is_quantized = true, + .to_float = NULL, + .from_float_ref = NULL, }, }; -// For internal test use const struct ggml_type_traits * ggml_get_type_traits(enum ggml_type type) { GGML_ASSERT(type < GGML_TYPE_COUNT); return &type_traits[type]; } -// -// simd mappings -// - -// we define a common set of C macros which map to specific intrinsics based on the current architecture -// we then implement the fundamental computation operations below using only these macros -// adding support for new architectures requires to define the corresponding SIMD macros -// -// GGML_F32_STEP / GGML_F16_STEP -// number of elements to process in a single step -// -// GGML_F32_EPR / GGML_F16_EPR -// number of elements to fit in a single register -// - -#if defined(__ARM_NEON) && defined(__ARM_FEATURE_FMA) - -#define GGML_SIMD - -// F32 NEON - -#define GGML_F32_STEP 16 -#define GGML_F32_EPR 4 - -#define GGML_F32x4 float32x4_t -#define GGML_F32x4_ZERO vdupq_n_f32(0.0f) -#define GGML_F32x4_SET1(x) vdupq_n_f32(x) -#define GGML_F32x4_LOAD vld1q_f32 -#define GGML_F32x4_STORE vst1q_f32 -#define GGML_F32x4_FMA(a, b, c) vfmaq_f32(a, b, c) -#define GGML_F32x4_ADD vaddq_f32 -#define GGML_F32x4_MUL vmulq_f32 -#define GGML_F32x4_REDUCE_ONE(x) vaddvq_f32(x) -#define GGML_F32x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - (x)[i] = vaddq_f32((x)[i], (x)[offset+i]); \ - } \ - (res) = GGML_F32x4_REDUCE_ONE((x)[0]); \ -} - -#define GGML_F32_VEC GGML_F32x4 -#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD -#define GGML_F32_VEC_STORE GGML_F32x4_STORE -#define GGML_F32_VEC_FMA GGML_F32x4_FMA -#define GGML_F32_VEC_ADD GGML_F32x4_ADD -#define GGML_F32_VEC_MUL GGML_F32x4_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE - -// F16 NEON - -#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) - #define GGML_F16_STEP 32 - #define GGML_F16_EPR 8 - - #define GGML_F16x8 float16x8_t - #define GGML_F16x8_ZERO vdupq_n_f16(0.0f) - #define GGML_F16x8_SET1(x) vdupq_n_f16(x) - #define GGML_F16x8_LOAD(x) vld1q_f16((const ggml_fp16_internal_t *)(x)) - #define GGML_F16x8_STORE vst1q_f16 - #define GGML_F16x8_FMA(a, b, c) vfmaq_f16(a, b, c) - #define GGML_F16x8_ADD vaddq_f16 - #define GGML_F16x8_MUL vmulq_f16 - #define GGML_F16x8_REDUCE(res, x) \ - do { \ - int offset = GGML_F16_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - (x)[i] = vaddq_f16((x)[i], (x)[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - (x)[i] = vaddq_f16((x)[i], (x)[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - (x)[i] = vaddq_f16((x)[i], (x)[offset+i]); \ - } \ - const float32x4_t t0 = vcvt_f32_f16(vget_low_f16 ((x)[0])); \ - const float32x4_t t1 = vcvt_f32_f16(vget_high_f16((x)[0])); \ - (res) = (ggml_float) vaddvq_f32(vaddq_f32(t0, t1)); \ - } while (0) - - #define GGML_F16_VEC GGML_F16x8 - #define GGML_F16_VEC_ZERO GGML_F16x8_ZERO - #define GGML_F16_VEC_SET1 GGML_F16x8_SET1 - #define GGML_F16_VEC_LOAD(p, i) GGML_F16x8_LOAD(p) - #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE((ggml_fp16_internal_t *)(p), (r)[i]) - #define GGML_F16_VEC_FMA GGML_F16x8_FMA - #define GGML_F16_VEC_ADD GGML_F16x8_ADD - #define GGML_F16_VEC_MUL GGML_F16x8_MUL - #define GGML_F16_VEC_REDUCE GGML_F16x8_REDUCE -#else - // if FP16 vector arithmetic is not supported, we use FP32 instead - // and take advantage of the vcvt_ functions to convert to/from FP16 - - #define GGML_F16_STEP 16 - #define GGML_F16_EPR 4 - - #define GGML_F32Cx4 float32x4_t - #define GGML_F32Cx4_ZERO vdupq_n_f32(0.0f) - #define GGML_F32Cx4_SET1(x) vdupq_n_f32(x) - #define GGML_F32Cx4_LOAD(x) vcvt_f32_f16(vld1_f16((const ggml_fp16_internal_t *)(x))) - #define GGML_F32Cx4_STORE(x, y) vst1_f16(x, vcvt_f16_f32(y)) - #define GGML_F32Cx4_FMA(a, b, c) vfmaq_f32(a, b, c) - #define GGML_F32Cx4_ADD vaddq_f32 - #define GGML_F32Cx4_MUL vmulq_f32 - #define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE - - #define GGML_F16_VEC GGML_F32Cx4 - #define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO - #define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 - #define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) - #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE((ggml_fp16_internal_t *)(p), r[i]) - #define GGML_F16_VEC_FMA GGML_F32Cx4_FMA - #define GGML_F16_VEC_ADD GGML_F32Cx4_ADD - #define GGML_F16_VEC_MUL GGML_F32Cx4_MUL - #define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE -#endif - -#elif defined(__AVX512F__) - -#define GGML_SIMD - -// F32 AVX512 - -#define GGML_F32_STEP 64 -#define GGML_F32_EPR 16 - -#define GGML_F32x16 __m512 -#define GGML_F32x16_ZERO _mm512_setzero_ps() -#define GGML_F32x16_SET1(x) _mm512_set1_ps(x) -#define GGML_F32x16_LOAD _mm512_loadu_ps -#define GGML_F32x16_STORE _mm512_storeu_ps -// _mm512_fmadd_ps is defined in AVX512F so no guard is required -#define GGML_F32x16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a) -#define GGML_F32x16_ADD _mm512_add_ps -#define GGML_F32x16_MUL _mm512_mul_ps -#define GGML_F32x16_REDUCE(res, x) \ -do { \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm512_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm512_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm512_add_ps(x[i], x[offset+i]); \ - } \ - res = _mm512_reduce_add_ps(x[0]); \ -} while (0) - -// TODO: is this optimal ? - -#define GGML_F32_VEC GGML_F32x16 -#define GGML_F32_VEC_ZERO GGML_F32x16_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x16_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x16_LOAD -#define GGML_F32_VEC_STORE GGML_F32x16_STORE -#define GGML_F32_VEC_FMA GGML_F32x16_FMA -#define GGML_F32_VEC_ADD GGML_F32x16_ADD -#define GGML_F32_VEC_MUL GGML_F32x16_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x16_REDUCE - -// F16 AVX512 - -// F16 AVX - -#define GGML_F16_STEP 64 -#define GGML_F16_EPR 16 - -// AVX512 has FP16 extension (AVX512_FP16) but I don't have it on my machine so I use FP32 instead - -#define GGML_F32Cx16 __m512 -#define GGML_F32Cx16_ZERO _mm512_setzero_ps() -#define GGML_F32Cx16_SET1(x) _mm512_set1_ps(x) - -// unlike _mm256_cvt intrinsics that require F16C, _mm512_cvt is defined in AVX512F -// so F16C guard isn't required -#define GGML_F32Cx16_LOAD(x) _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(x))) -#define GGML_F32Cx16_STORE(x, y) _mm256_storeu_si256((__m256i *)(x), _mm512_cvtps_ph(y, 0)) - -#define GGML_F32Cx16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a) -#define GGML_F32Cx16_ADD _mm512_add_ps -#define GGML_F32Cx16_MUL _mm512_mul_ps -#define GGML_F32Cx16_REDUCE(res, x) \ -do { \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm512_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm512_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm512_add_ps(x[i], x[offset+i]); \ - } \ - res = _mm512_reduce_add_ps(x[0]); \ -} while (0) - -#define GGML_F16_VEC GGML_F32Cx16 -#define GGML_F16_VEC_ZERO GGML_F32Cx16_ZERO -#define GGML_F16_VEC_SET1 GGML_F32Cx16_SET1 -#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx16_LOAD(p) -#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx16_STORE(p, r[i]) -#define GGML_F16_VEC_FMA GGML_F32Cx16_FMA -#define GGML_F16_VEC_ADD GGML_F32Cx16_ADD -#define GGML_F16_VEC_MUL GGML_F32Cx16_MUL -#define GGML_F16_VEC_REDUCE GGML_F32Cx16_REDUCE - -#elif defined(__AVX__) - -#define GGML_SIMD - -// F32 AVX - -#define GGML_F32_STEP 32 -#define GGML_F32_EPR 8 - -#define GGML_F32x8 __m256 -#define GGML_F32x8_ZERO _mm256_setzero_ps() -#define GGML_F32x8_SET1(x) _mm256_set1_ps(x) -#define GGML_F32x8_LOAD _mm256_loadu_ps -#define GGML_F32x8_STORE _mm256_storeu_ps -#if defined(__FMA__) - #define GGML_F32x8_FMA(a, b, c) _mm256_fmadd_ps(b, c, a) -#else - #define GGML_F32x8_FMA(a, b, c) _mm256_add_ps(_mm256_mul_ps(b, c), a) -#endif -#define GGML_F32x8_ADD _mm256_add_ps -#define GGML_F32x8_MUL _mm256_mul_ps -#define GGML_F32x8_REDUCE(res, x) \ -do { \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm256_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm256_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm256_add_ps(x[i], x[offset+i]); \ - } \ - const __m128 t0 = _mm_add_ps(_mm256_castps256_ps128(x[0]), \ - _mm256_extractf128_ps(x[0], 1)); \ - const __m128 t1 = _mm_hadd_ps(t0, t0); \ - res = (ggml_float) _mm_cvtss_f32(_mm_hadd_ps(t1, t1)); \ -} while (0) -// TODO: is this optimal ? - -#define GGML_F32_VEC GGML_F32x8 -#define GGML_F32_VEC_ZERO GGML_F32x8_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x8_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x8_LOAD -#define GGML_F32_VEC_STORE GGML_F32x8_STORE -#define GGML_F32_VEC_FMA GGML_F32x8_FMA -#define GGML_F32_VEC_ADD GGML_F32x8_ADD -#define GGML_F32_VEC_MUL GGML_F32x8_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE - -// F16 AVX - -#define GGML_F16_STEP 32 -#define GGML_F16_EPR 8 - -// F16 arithmetic is not supported by AVX, so we use F32 instead - -#define GGML_F32Cx8 __m256 -#define GGML_F32Cx8_ZERO _mm256_setzero_ps() -#define GGML_F32Cx8_SET1(x) _mm256_set1_ps(x) - -#if defined(__F16C__) -// the _mm256_cvt intrinsics require F16C -#define GGML_F32Cx8_LOAD(x) _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)(x))) -#define GGML_F32Cx8_STORE(x, y) _mm_storeu_si128((__m128i *)(x), _mm256_cvtps_ph(y, 0)) -#else -static inline __m256 __avx_f32cx8_load(ggml_fp16_t *x) { - float tmp[8]; - - for (int i = 0; i < 8; i++) { - tmp[i] = GGML_FP16_TO_FP32(x[i]); - } - - return _mm256_loadu_ps(tmp); -} -static inline void __avx_f32cx8_store(ggml_fp16_t *x, __m256 y) { - float arr[8]; - - _mm256_storeu_ps(arr, y); - - for (int i = 0; i < 8; i++) - x[i] = GGML_FP32_TO_FP16(arr[i]); -} -#define GGML_F32Cx8_LOAD(x) __avx_f32cx8_load(x) -#define GGML_F32Cx8_STORE(x, y) __avx_f32cx8_store(x, y) -#endif - -#define GGML_F32Cx8_FMA GGML_F32x8_FMA -#define GGML_F32Cx8_ADD _mm256_add_ps -#define GGML_F32Cx8_MUL _mm256_mul_ps -#define GGML_F32Cx8_REDUCE GGML_F32x8_REDUCE - -#define GGML_F16_VEC GGML_F32Cx8 -#define GGML_F16_VEC_ZERO GGML_F32Cx8_ZERO -#define GGML_F16_VEC_SET1 GGML_F32Cx8_SET1 -#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx8_LOAD(p) -#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i]) -#define GGML_F16_VEC_FMA GGML_F32Cx8_FMA -#define GGML_F16_VEC_ADD GGML_F32Cx8_ADD -#define GGML_F16_VEC_MUL GGML_F32Cx8_MUL -#define GGML_F16_VEC_REDUCE GGML_F32Cx8_REDUCE - -#elif defined(__POWER9_VECTOR__) - -#define GGML_SIMD - -// F32 POWER9 - -#define GGML_F32_STEP 32 -#define GGML_F32_EPR 4 - -#define GGML_F32x4 vector float -#define GGML_F32x4_ZERO 0.0f -#define GGML_F32x4_SET1 vec_splats -#define GGML_F32x4_LOAD(p) vec_xl(0, p) -#define GGML_F32x4_STORE(p, r) vec_xst(r, 0, p) -#define GGML_F32x4_FMA(a, b, c) vec_madd(b, c, a) -#define GGML_F32x4_ADD vec_add -#define GGML_F32x4_MUL vec_mul -#define GGML_F32x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = vec_add(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = vec_add(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = vec_add(x[i], x[offset+i]); \ - } \ - res = vec_extract(x[0], 0) + \ - vec_extract(x[0], 1) + \ - vec_extract(x[0], 2) + \ - vec_extract(x[0], 3); \ -} - -#define GGML_F32_VEC GGML_F32x4 -#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD -#define GGML_F32_VEC_STORE GGML_F32x4_STORE -#define GGML_F32_VEC_FMA GGML_F32x4_FMA -#define GGML_F32_VEC_ADD GGML_F32x4_ADD -#define GGML_F32_VEC_MUL GGML_F32x4_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE - -// F16 POWER9 -#define GGML_F16_STEP GGML_F32_STEP -#define GGML_F16_EPR GGML_F32_EPR -#define GGML_F16_VEC GGML_F32x4 -#define GGML_F16_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F16_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F16_VEC_FMA GGML_F32x4_FMA -#define GGML_F16_VEC_ADD GGML_F32x4_ADD -#define GGML_F16_VEC_MUL GGML_F32x4_MUL -#define GGML_F16_VEC_REDUCE GGML_F32x4_REDUCE -// Use vec_xl, not vec_ld, in case the load address is not aligned. -#define GGML_F16_VEC_LOAD(p, i) (i & 0x1) ? \ - vec_extract_fp32_from_shorth(vec_xl(0, p - GGML_F16_EPR)) : \ - vec_extract_fp32_from_shortl(vec_xl(0, p)) -#define GGML_ENDIAN_BYTE(i) ((unsigned char *)&(uint16_t){1})[i] -#define GGML_F16_VEC_STORE(p, r, i) \ - if (i & 0x1) \ - vec_xst(vec_pack_to_short_fp32(r[i - GGML_ENDIAN_BYTE(1)], \ - r[i - GGML_ENDIAN_BYTE(0)]), \ - 0, p - GGML_F16_EPR) - -#elif defined(__wasm_simd128__) - -#define GGML_SIMD - -// F32 WASM - -#define GGML_F32_STEP 16 -#define GGML_F32_EPR 4 - -#define GGML_F32x4 v128_t -#define GGML_F32x4_ZERO wasm_f32x4_splat(0.0f) -#define GGML_F32x4_SET1(x) wasm_f32x4_splat(x) -#define GGML_F32x4_LOAD wasm_v128_load -#define GGML_F32x4_STORE wasm_v128_store -#define GGML_F32x4_FMA(a, b, c) wasm_f32x4_add(wasm_f32x4_mul(b, c), a) -#define GGML_F32x4_ADD wasm_f32x4_add -#define GGML_F32x4_MUL wasm_f32x4_mul -#define GGML_F32x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ - } \ - res = wasm_f32x4_extract_lane(x[0], 0) + \ - wasm_f32x4_extract_lane(x[0], 1) + \ - wasm_f32x4_extract_lane(x[0], 2) + \ - wasm_f32x4_extract_lane(x[0], 3); \ -} - -#define GGML_F32_VEC GGML_F32x4 -#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD -#define GGML_F32_VEC_STORE GGML_F32x4_STORE -#define GGML_F32_VEC_FMA GGML_F32x4_FMA -#define GGML_F32_VEC_ADD GGML_F32x4_ADD -#define GGML_F32_VEC_MUL GGML_F32x4_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE - -// F16 WASM - -#define GGML_F16_STEP 16 -#define GGML_F16_EPR 4 - -inline static v128_t __wasm_f16x4_load(const ggml_fp16_t * p) { - float tmp[4]; - - tmp[0] = GGML_FP16_TO_FP32(p[0]); - tmp[1] = GGML_FP16_TO_FP32(p[1]); - tmp[2] = GGML_FP16_TO_FP32(p[2]); - tmp[3] = GGML_FP16_TO_FP32(p[3]); - - return wasm_v128_load(tmp); -} - -inline static void __wasm_f16x4_store(ggml_fp16_t * p, v128_t x) { - float tmp[4]; - - wasm_v128_store(tmp, x); - - p[0] = GGML_FP32_TO_FP16(tmp[0]); - p[1] = GGML_FP32_TO_FP16(tmp[1]); - p[2] = GGML_FP32_TO_FP16(tmp[2]); - p[3] = GGML_FP32_TO_FP16(tmp[3]); -} - -#define GGML_F16x4 v128_t -#define GGML_F16x4_ZERO wasm_f32x4_splat(0.0f) -#define GGML_F16x4_SET1(x) wasm_f32x4_splat(x) -#define GGML_F16x4_LOAD(x) __wasm_f16x4_load(x) -#define GGML_F16x4_STORE(x, y) __wasm_f16x4_store(x, y) -#define GGML_F16x4_FMA GGML_F32x4_FMA -#define GGML_F16x4_ADD wasm_f32x4_add -#define GGML_F16x4_MUL wasm_f32x4_mul -#define GGML_F16x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F16_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ - } \ - res = wasm_f32x4_extract_lane(x[0], 0) + \ - wasm_f32x4_extract_lane(x[0], 1) + \ - wasm_f32x4_extract_lane(x[0], 2) + \ - wasm_f32x4_extract_lane(x[0], 3); \ -} - -#define GGML_F16_VEC GGML_F16x4 -#define GGML_F16_VEC_ZERO GGML_F16x4_ZERO -#define GGML_F16_VEC_SET1 GGML_F16x4_SET1 -#define GGML_F16_VEC_LOAD(p, i) GGML_F16x4_LOAD(p) -#define GGML_F16_VEC_STORE(p, r, i) GGML_F16x4_STORE(p, r[i]) -#define GGML_F16_VEC_FMA GGML_F16x4_FMA -#define GGML_F16_VEC_ADD GGML_F16x4_ADD -#define GGML_F16_VEC_MUL GGML_F16x4_MUL -#define GGML_F16_VEC_REDUCE GGML_F16x4_REDUCE - -#elif defined(__SSE3__) - -#define GGML_SIMD - -// F32 SSE - -#define GGML_F32_STEP 32 -#define GGML_F32_EPR 4 - -#define GGML_F32x4 __m128 -#define GGML_F32x4_ZERO _mm_setzero_ps() -#define GGML_F32x4_SET1(x) _mm_set1_ps(x) -#define GGML_F32x4_LOAD _mm_loadu_ps -#define GGML_F32x4_STORE _mm_storeu_ps -#if defined(__FMA__) - // TODO: Does this work? - #define GGML_F32x4_FMA(a, b, c) _mm_fmadd_ps(b, c, a) -#else - #define GGML_F32x4_FMA(a, b, c) _mm_add_ps(_mm_mul_ps(b, c), a) -#endif -#define GGML_F32x4_ADD _mm_add_ps -#define GGML_F32x4_MUL _mm_mul_ps -#define GGML_F32x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm_add_ps(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = _mm_add_ps(x[i], x[offset+i]); \ - } \ - const __m128 t0 = _mm_hadd_ps(x[0], x[0]); \ - res = (ggml_float) _mm_cvtss_f32(_mm_hadd_ps(t0, t0)); \ -} -// TODO: is this optimal ? - -#define GGML_F32_VEC GGML_F32x4 -#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD -#define GGML_F32_VEC_STORE GGML_F32x4_STORE -#define GGML_F32_VEC_FMA GGML_F32x4_FMA -#define GGML_F32_VEC_ADD GGML_F32x4_ADD -#define GGML_F32_VEC_MUL GGML_F32x4_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE - -// F16 SSE - -#define GGML_F16_STEP 32 -#define GGML_F16_EPR 4 - -static inline __m128 __sse_f16x4_load(ggml_fp16_t *x) { - float tmp[4]; - - tmp[0] = GGML_FP16_TO_FP32(x[0]); - tmp[1] = GGML_FP16_TO_FP32(x[1]); - tmp[2] = GGML_FP16_TO_FP32(x[2]); - tmp[3] = GGML_FP16_TO_FP32(x[3]); - - return _mm_loadu_ps(tmp); -} - -static inline void __sse_f16x4_store(ggml_fp16_t *x, __m128 y) { - float arr[4]; - - _mm_storeu_ps(arr, y); - - x[0] = GGML_FP32_TO_FP16(arr[0]); - x[1] = GGML_FP32_TO_FP16(arr[1]); - x[2] = GGML_FP32_TO_FP16(arr[2]); - x[3] = GGML_FP32_TO_FP16(arr[3]); -} - -#define GGML_F32Cx4 __m128 -#define GGML_F32Cx4_ZERO _mm_setzero_ps() -#define GGML_F32Cx4_SET1(x) _mm_set1_ps(x) -#define GGML_F32Cx4_LOAD(x) __sse_f16x4_load(x) -#define GGML_F32Cx4_STORE(x, y) __sse_f16x4_store(x, y) -#define GGML_F32Cx4_FMA GGML_F32x4_FMA -#define GGML_F32Cx4_ADD _mm_add_ps -#define GGML_F32Cx4_MUL _mm_mul_ps -#define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE - -#define GGML_F16_VEC GGML_F32Cx4 -#define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO -#define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 -#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) -#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i]) -#define GGML_F16_VEC_FMA GGML_F32Cx4_FMA -#define GGML_F16_VEC_ADD GGML_F32Cx4_ADD -#define GGML_F16_VEC_MUL GGML_F32Cx4_MUL -#define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE - -#elif defined(__loongarch_asx) - -#define GGML_SIMD - -// F32 LASX -#define GGML_F32_STEP 32 -#define GGML_F32_EPR 8 - -#define GGML_F32x8 __m256 -#define GGML_F32x8_ZERO (__m256)__lasx_xvldi(0) -#define GGML_F32x8_SET1(x) (__m256)__lasx_xvreplfr2vr_s((x)) -#define GGML_F32x8_LOAD(x) (__m256)__lasx_xvld((x), 0) -#define GGML_F32x8_STORE(x,y) __lasx_xvst((y), (x), 0) -#define GGML_F32x8_FMA(a, b, c) __lasx_xvfmadd_s(b, c, a) -#define GGML_F32x8_ADD __lasx_xvfadd_s -#define GGML_F32x8_MUL __lasx_xvfmul_s -#define GGML_F32x8_REDUCE(res, x) \ -do { \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ - } \ - float *tmp_p = (float *)&x[0]; \ - res = tmp_p[0] + tmp_p[1] + tmp_p[2] + tmp_p[3] + tmp_p[4] + tmp_p[5] + tmp_p[6] + tmp_p[7]; \ -} while (0) -// TODO: is this optimal ? - -#define GGML_F32_VEC GGML_F32x8 -#define GGML_F32_VEC_ZERO GGML_F32x8_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x8_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x8_LOAD -#define GGML_F32_VEC_STORE GGML_F32x8_STORE -#define GGML_F32_VEC_FMA GGML_F32x8_FMA -#define GGML_F32_VEC_ADD GGML_F32x8_ADD -#define GGML_F32_VEC_MUL GGML_F32x8_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE - -// F16 LASX - -#define GGML_F16_STEP 32 -#define GGML_F16_EPR 8 - -// F16 arithmetic is not supported by AVX, so we use F32 instead - -#define GGML_F32Cx8 __m256 -#define GGML_F32Cx8_ZERO (__m256)__lasx_xvldi(0) -#define GGML_F32Cx8_SET1(x) (__m256)__lasx_xvreplgr2vr_w((x)) - -static inline __m256 __lasx_f32cx8_load(const ggml_fp16_t * x) { - float tmp[8]; - - for (int i = 0; i < 8; i++) { - tmp[i] = GGML_FP16_TO_FP32(x[i]); - } - - return (__m256)__lasx_xvld(tmp, 0); -} -static inline void __lasx_f32cx8_store(ggml_fp16_t * x, __m256 y) { - float arr[8]; - - __lasx_xvst(y, arr, 0); - - for (int i = 0; i < 8; i++) { - x[i] = GGML_FP32_TO_FP16(arr[i]); - } -} -#define GGML_F32Cx8_LOAD(x) __lasx_f32cx8_load(x) -#define GGML_F32Cx8_STORE(x, y) __lasx_f32cx8_store(x, y) - -#define GGML_F32Cx8_FMA GGML_F32x8_FMA -#define GGML_F32Cx8_ADD __lasx_xvfadd_s -#define GGML_F32Cx8_MUL __lasx_xvfmul_s -#define GGML_F32Cx8_REDUCE GGML_F32x8_REDUCE - -#define GGML_F16_VEC GGML_F32Cx8 -#define GGML_F16_VEC_ZERO GGML_F32Cx8_ZERO -#define GGML_F16_VEC_SET1 GGML_F32Cx8_SET1 -#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx8_LOAD(p) -#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i]) -#define GGML_F16_VEC_FMA GGML_F32Cx8_FMA -#define GGML_F16_VEC_ADD GGML_F32Cx8_ADD -#define GGML_F16_VEC_MUL GGML_F32Cx8_MUL -#define GGML_F16_VEC_REDUCE GGML_F32Cx8_REDUCE - -#elif defined(__loongarch_sx) - -#define GGML_SIMD - -// F32 LSX - -#define GGML_F32_STEP 32 -#define GGML_F32_EPR 4 - -#define GGML_F32x4 __m128 -#define GGML_F32x4_ZERO __lsx_vldi(0) -#define GGML_F32x4_SET1(x) __lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0) -#define GGML_F32x4_LOAD(x) __lsx_vld((x), 0) -#define GGML_F32x4_STORE((x),(y)) __lsx_vst((y), (x), 0) -#define GGML_F32x4_FMA(a, b, c) __lsx_vfmadd_s(b, c, a) -#define GGML_F32x4_ADD __lsx_vfadd_s -#define GGML_F32x4_MUL __lsx_vfmul_s -#define GGML_F32x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lsx_vfadd_s(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lsx_vfadd_s(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lsx_vfadd_s(x[i], x[offset+i]); \ - } \ - __m128i tmp = __lsx_vsrli_d((__m128i)x[0], 32); \ - tmp = (__m128i)__lsx_vfadd_s((__m128)tmp, x[0]); \ - tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \ - const __m128 t0 = __lsx_vshuf4i_w(tmp, 0x88); \ - tmp = __lsx_vsrli_d((__m128i)t0, 32); \ - tmp = (__m128i)__lsx_vfadd_s((__m128)tmp, t0); \ - tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \ - res = (ggml_float) __lsx_vpickve2gr_w(__lsx_vshuf4i_w(tmp, 0x88), 0); \ -} - -#define GGML_F32_VEC GGML_F32x4 -#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO -#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 -#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD -#define GGML_F32_VEC_STORE GGML_F32x4_STORE -#define GGML_F32_VEC_FMA GGML_F32x4_FMA -#define GGML_F32_VEC_ADD GGML_F32x4_ADD -#define GGML_F32_VEC_MUL GGML_F32x4_MUL -#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE - -// F16 LSX - -#define GGML_F16_STEP 32 -#define GGML_F16_EPR 4 - -static inline __m128 __lsx_f16x4_load(const ggml_fp16_t * x) { - float tmp[4]; - - tmp[0] = GGML_FP16_TO_FP32(x[0]); - tmp[1] = GGML_FP16_TO_FP32(x[1]); - tmp[2] = GGML_FP16_TO_FP32(x[2]); - tmp[3] = GGML_FP16_TO_FP32(x[3]); - - return __lsx_vld(tmp, 0); -} - -static inline void __lsx_f16x4_store(ggml_fp16_t * x, __m128 y) { - float arr[4]; - - __lsx_vst(y, arr, 0); - - x[0] = GGML_FP32_TO_FP16(arr[0]); - x[1] = GGML_FP32_TO_FP16(arr[1]); - x[2] = GGML_FP32_TO_FP16(arr[2]); - x[3] = GGML_FP32_TO_FP16(arr[3]); -} - -#define GGML_F32Cx4 __m128 -#define GGML_F32Cx4_ZERO __lsx_vldi(0) -#define GGML_F32Cx4_SET1(x) __lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0) -#define GGML_F32Cx4_LOAD(x) __lsx_f16x4_load(x) -#define GGML_F32Cx4_STORE(x, y) __lsx_f16x4_store(x, y) -#define GGML_F32Cx4_FMA GGML_F32x4_FMA -#define GGML_F32Cx4_ADD __lsx_vfadd_s -#define GGML_F32Cx4_MUL __lsx_vfmul_s -#define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE - -#define GGML_F16_VEC GGML_F32Cx4 -#define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO -#define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 -#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) -#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i]) -#define GGML_F16_VEC_FMA GGML_F32Cx4_FMA -#define GGML_F16_VEC_ADD GGML_F32Cx4_ADD -#define GGML_F16_VEC_MUL GGML_F32Cx4_MUL -#define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE - -#endif - -// GGML_F32_ARR / GGML_F16_ARR -// number of registers to use per step -#ifdef GGML_SIMD -#define GGML_F32_ARR (GGML_F32_STEP/GGML_F32_EPR) -#define GGML_F16_ARR (GGML_F16_STEP/GGML_F16_EPR) -#endif - // // ggml object // @@ -2014,18 +870,14 @@ static const size_t GGML_OBJECT_SIZE = sizeof(struct ggml_object); struct ggml_context { size_t mem_size; - void* mem_buffer; + void * mem_buffer; bool mem_buffer_owned; bool no_alloc; - bool no_alloc_save; // this is used to save the no_alloc state when using scratch buffers int n_objects; struct ggml_object * objects_begin; struct ggml_object * objects_end; - - struct ggml_scratch scratch; - struct ggml_scratch scratch_save; }; struct ggml_context_container { @@ -2034,972 +886,6 @@ struct ggml_context_container { struct ggml_context context; }; -// -// Threading defs -// - -typedef pthread_t ggml_thread_t; - -#if defined(_WIN32) - -typedef CONDITION_VARIABLE ggml_cond_t; -typedef SRWLOCK ggml_mutex_t; - -#define ggml_mutex_init(m) InitializeSRWLock(m) -#define ggml_mutex_destroy(m) -#define ggml_mutex_lock(m) AcquireSRWLockExclusive(m) -#define ggml_mutex_unlock(m) ReleaseSRWLockExclusive(m) -#define ggml_mutex_lock_shared(m) AcquireSRWLockShared(m) -#define ggml_mutex_unlock_shared(m) ReleaseSRWLockShared(m) - -#define ggml_cond_init(c) InitializeConditionVariable(c) -#define ggml_cond_destroy(c) -#define ggml_cond_wait(c, m) SleepConditionVariableSRW(c, m, INFINITE, CONDITION_VARIABLE_LOCKMODE_SHARED) -#define ggml_cond_broadcast(c) WakeAllConditionVariable(c) - -#define ggml_thread_create pthread_create -#define ggml_thread_join pthread_join - -#else - -typedef pthread_cond_t ggml_cond_t; -typedef pthread_mutex_t ggml_mutex_t; - -#define ggml_mutex_init(m) pthread_mutex_init(m, NULL) -#define ggml_mutex_destroy(m) pthread_mutex_destroy(m) -#define ggml_mutex_lock(m) pthread_mutex_lock(m) -#define ggml_mutex_unlock(m) pthread_mutex_unlock(m) -#define ggml_mutex_lock_shared(m) pthread_mutex_lock(m) -#define ggml_mutex_unlock_shared(m) pthread_mutex_unlock(m) - -#define ggml_lock_init(x) UNUSED(x) -#define ggml_lock_destroy(x) UNUSED(x) -#if defined(__x86_64__) || (defined(_MSC_VER) && defined(_M_AMD64)) -#define ggml_lock_lock(x) _mm_pause() -#else -#define ggml_lock_lock(x) UNUSED(x) -#endif -#define ggml_lock_unlock(x) UNUSED(x) - -#define GGML_LOCK_INITIALIZER 0 -#define ggml_cond_init(c) pthread_cond_init(c, NULL) -#define ggml_cond_destroy(c) pthread_cond_destroy(c) -#define ggml_cond_wait(c, m) pthread_cond_wait(c, m) -#define ggml_cond_broadcast(c) pthread_cond_broadcast(c) - -#define ggml_thread_create pthread_create -#define ggml_thread_join pthread_join - -#endif - -// Threadpool def -struct ggml_threadpool { - ggml_mutex_t mutex; // mutex for cond.var - ggml_cond_t cond; // cond.var for waiting for new work - - struct ggml_cgraph * cgraph; - struct ggml_cplan * cplan; - - // synchronization primitives - atomic_int n_graph; // incremented when there is work to be done (i.e each graph) - atomic_int GGML_CACHE_ALIGN n_barrier; - atomic_int GGML_CACHE_ALIGN n_barrier_passed; - atomic_int current_chunk; // currently processing chunk during Mat_Mul, shared between all the threads. - - // these are atomic as an annotation for thread-sanitizer - atomic_bool stop; // Used for stopping the threadpool altogether - atomic_bool pause; // Used for pausing the threadpool or individual threads - atomic_bool abort; // Used for aborting processing of a graph - - struct ggml_compute_state * workers; // per thread state - int n_threads_max; // number of threads in the pool - atomic_int n_threads_cur; // number of threads used in the current graph - - int32_t prio; // Scheduling priority - uint32_t poll; // Polling level (0 - no polling) - - enum ggml_status ec; -}; - -// Per-thread state -struct ggml_compute_state { -#ifndef GGML_USE_OPENMP - ggml_thread_t thrd; - bool cpumask[GGML_MAX_N_THREADS]; - int last_graph; - bool pending; -#endif - struct ggml_threadpool * threadpool; - int ith; -}; - -struct ggml_compute_params { - // ith = thread index, nth = number of threads - int ith, nth; - - // work buffer for all threads - size_t wsize; - void * wdata; - - struct ggml_threadpool * threadpool; -}; - -// -// fundamental operations -// - -inline static void ggml_vec_set_i8(const int n, int8_t * x, const int8_t v) { for (int i = 0; i < n; ++i) x[i] = v; } - -inline static void ggml_vec_set_i16(const int n, int16_t * x, const int16_t v) { for (int i = 0; i < n; ++i) x[i] = v; } - -inline static void ggml_vec_set_i32(const int n, int32_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; } - -inline static void ggml_vec_set_f16(const int n, ggml_fp16_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; } - -inline static void ggml_vec_set_bf16(const int n, ggml_bf16_t * x, const ggml_bf16_t v) { for (int i = 0; i < n; ++i) x[i] = v; } - -inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] + y[i]; } -inline static void ggml_vec_add1_f32(const int n, float * z, const float * x, const float v) { for (int i = 0; i < n; ++i) z[i] = x[i] + v; } -inline static void ggml_vec_acc_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] += x[i]; } -inline static void ggml_vec_acc1_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] += v; } -inline static void ggml_vec_sub_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] - y[i]; } -inline static void ggml_vec_set_f32 (const int n, float * x, const float v) { for (int i = 0; i < n; ++i) x[i] = v; } -inline static void ggml_vec_cpy_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]; } -inline static void ggml_vec_neg_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = -x[i]; } -inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]*y[i]; } -inline static void ggml_vec_div_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]/y[i]; } - -static void ggml_vec_dot_f32(int n, float * restrict s, size_t bs, const float * restrict x, size_t bx, const float * restrict y, size_t by, int nrc) { - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - -#if defined(GGML_SIMD) - float sumf = 0.0f; - const int np = (n & ~(GGML_F32_STEP - 1)); - - GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO }; - - GGML_F32_VEC ax[GGML_F32_ARR]; - GGML_F32_VEC ay[GGML_F32_ARR]; - - for (int i = 0; i < np; i += GGML_F32_STEP) { - for (int j = 0; j < GGML_F32_ARR; j++) { - ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR); - ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); - - sum[j] = GGML_F32_VEC_FMA(sum[j], ax[j], ay[j]); - } - } - - // reduce sum0..sum3 to sum0 - GGML_F32_VEC_REDUCE(sumf, sum); - - // leftovers - for (int i = np; i < n; ++i) { - sumf += x[i]*y[i]; - } -#else - // scalar - ggml_float sumf = 0.0; - for (int i = 0; i < n; ++i) { - sumf += (ggml_float)(x[i]*y[i]); - } -#endif - - *s = sumf; -} - -static void ggml_vec_dot_bf16(int n, float * restrict s, size_t bs, ggml_bf16_t * restrict x, size_t bx, ggml_bf16_t * restrict y, size_t by, int nrc) { - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - int i = 0; - ggml_float sumf = 0; - -#if defined(__AVX512BF16__) - __m512 c1 = _mm512_setzero_ps(); - __m512 c2 = _mm512_setzero_ps(); - for (; i + 64 <= n; i += 64) { - c1 = _mm512_dpbf16_ps(c1, m512bh(_mm512_loadu_si512((x + i))), - m512bh(_mm512_loadu_si512((y + i)))); - c2 = _mm512_dpbf16_ps(c2, m512bh(_mm512_loadu_si512((x + i + 32))), - m512bh(_mm512_loadu_si512((y + i + 32)))); - } - sumf += (ggml_float)_mm512_reduce_add_ps(c1); - sumf += (ggml_float)_mm512_reduce_add_ps(c2); - -#elif defined(__AVX512F__) -#define LOAD(p) _mm512_castsi512_ps(_mm512_slli_epi32(_mm512_cvtepu16_epi32(_mm256_loadu_si256((const __m256i *)(p))), 16)) - __m512 c1 = _mm512_setzero_ps(); - __m512 c2 = _mm512_setzero_ps(); - for (; i + 32 <= n; i += 32) { - c1 = _mm512_add_ps(_mm512_mul_ps(LOAD(x + i), LOAD(y + i)), c1); - c2 = _mm512_add_ps(_mm512_mul_ps(LOAD(x + i + 16), LOAD(y + i + 16)), c2); - } - sumf += (ggml_float)_mm512_reduce_add_ps(c1); - sumf += (ggml_float)_mm512_reduce_add_ps(c2); - -#undef LOAD -#elif defined(__AVX2__) -#define LOAD(p) _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_cvtepu16_epi32(_mm_loadu_si128((const __m128i *)(p))), 16)) - __m256 c1 = _mm256_setzero_ps(); - __m256 c2 = _mm256_setzero_ps(); - __m256 c3 = _mm256_setzero_ps(); - __m256 c4 = _mm256_setzero_ps(); - for (; i + 32 <= n; i += 32) { - c1 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i), LOAD(y + i)), c1); - c2 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 8), LOAD(y + i + 8)), c2); - c3 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 16), LOAD(y + i + 16)), c3); - c4 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 24), LOAD(y + i + 24)), c4); - } - __m128 g; - c1 = _mm256_add_ps(_mm256_add_ps(c1, c3), - _mm256_add_ps(c2, c4)); - g = _mm_add_ps(_mm256_extractf128_ps(c1, 1), - _mm256_castps256_ps128(c1)); - g = _mm_add_ps(g, _mm_movehl_ps(g, g)); - g = _mm_add_ss(g, _mm_movehdup_ps(g)); - sumf += (ggml_float)_mm_cvtss_f32(g); - -#undef LOAD -#endif - - for (; i < n; ++i) { - sumf += (ggml_float)(GGML_BF16_TO_FP32(x[i]) * - GGML_BF16_TO_FP32(y[i])); - } - *s = sumf; -} - -static void ggml_vec_dot_f16(int n, float * restrict s, size_t bs, ggml_fp16_t * restrict x, size_t bx, ggml_fp16_t * restrict y, size_t by, int nrc) { - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - ggml_float sumf = 0.0; - -#if defined(GGML_SIMD) - const int np = (n & ~(GGML_F16_STEP - 1)); - - GGML_F16_VEC sum[GGML_F16_ARR] = { GGML_F16_VEC_ZERO }; - - GGML_F16_VEC ax[GGML_F16_ARR]; - GGML_F16_VEC ay[GGML_F16_ARR]; - - for (int i = 0; i < np; i += GGML_F16_STEP) { - for (int j = 0; j < GGML_F16_ARR; j++) { - ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j); - ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); - - sum[j] = GGML_F16_VEC_FMA(sum[j], ax[j], ay[j]); - } - } - - // reduce sum0..sum3 to sum0 - GGML_F16_VEC_REDUCE(sumf, sum); - - // leftovers - for (int i = np; i < n; ++i) { - sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i])); - } -#else - for (int i = 0; i < n; ++i) { - sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i])); - } -#endif - - *s = sumf; -} - -// compute GGML_VEC_DOT_UNROLL dot products at once -// xs - x row stride in bytes -inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * restrict s, void * restrict xv, ggml_fp16_t * restrict y) { - ggml_float sumf[GGML_VEC_DOT_UNROLL] = { 0.0 }; - - ggml_fp16_t * restrict x[GGML_VEC_DOT_UNROLL]; - - for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) { - x[i] = (ggml_fp16_t *) ((char *) xv + i*xs); - } - -#if defined(GGML_SIMD) - const int np = (n & ~(GGML_F16_STEP - 1)); - - GGML_F16_VEC sum[GGML_VEC_DOT_UNROLL][GGML_F16_ARR] = { { GGML_F16_VEC_ZERO } }; - - GGML_F16_VEC ax[GGML_F16_ARR]; - GGML_F16_VEC ay[GGML_F16_ARR]; - - for (int i = 0; i < np; i += GGML_F16_STEP) { - for (int j = 0; j < GGML_F16_ARR; j++) { - ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); - - for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) { - ax[j] = GGML_F16_VEC_LOAD(x[k] + i + j*GGML_F16_EPR, j); - - sum[k][j] = GGML_F16_VEC_FMA(sum[k][j], ax[j], ay[j]); - } - } - } - - // reduce sum0..sum3 to sum0 - for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) { - GGML_F16_VEC_REDUCE(sumf[k], sum[k]); - } - - // leftovers - for (int i = np; i < n; ++i) { - for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) { - sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i])); - } - } -#else - for (int i = 0; i < n; ++i) { - for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) { - sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i])); - } - } -#endif - - for (int i = 0; i < GGML_VEC_DOT_UNROLL; ++i) { - s[i] = sumf[i]; - } -} - -inline static void ggml_vec_mad_f32(const int n, float * restrict y, const float * restrict x, const float v) { -#if defined(GGML_SIMD) - const int np = (n & ~(GGML_F32_STEP - 1)); - - GGML_F32_VEC vx = GGML_F32_VEC_SET1(v); - - GGML_F32_VEC ax[GGML_F32_ARR]; - GGML_F32_VEC ay[GGML_F32_ARR]; - - for (int i = 0; i < np; i += GGML_F32_STEP) { - for (int j = 0; j < GGML_F32_ARR; j++) { - ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR); - ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); - ay[j] = GGML_F32_VEC_FMA(ay[j], ax[j], vx); - - GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); - } - } - - // leftovers - for (int i = np; i < n; ++i) { - y[i] += x[i]*v; - } -#else - // scalar - for (int i = 0; i < n; ++i) { - y[i] += x[i]*v; - } -#endif -} - -inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * restrict y, const ggml_fp16_t * restrict x, const float v) { -#if defined(GGML_SIMD) - const int np = (n & ~(GGML_F16_STEP - 1)); - - GGML_F16_VEC vx = GGML_F16_VEC_SET1(v); - - GGML_F16_VEC ax[GGML_F16_ARR]; - GGML_F16_VEC ay[GGML_F16_ARR]; - - for (int i = 0; i < np; i += GGML_F16_STEP) { - for (int j = 0; j < GGML_F16_ARR; j++) { - ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j); - ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); - ay[j] = GGML_F16_VEC_FMA(ay[j], ax[j], vx); - - GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j); - } - } - - // leftovers - for (int i = np; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v); - } -#else - // scalar - for (int i = 0; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v); - } -#endif -} - -// xs and vs are byte strides of x and v -inline static void ggml_vec_mad_f32_unroll(const int n, const int xs, const int vs, float * restrict y, const float * restrict xv, const float * restrict vv) { - - const float * restrict x[GGML_VEC_MAD_UNROLL]; - const float * restrict v[GGML_VEC_MAD_UNROLL]; - - for (int i = 0; i < GGML_VEC_MAD_UNROLL; ++i) { - x[i] = (const float *) ((const char *) xv + i*xs); - v[i] = (const float *) ((const char *) vv + i*vs); - } - -#if defined(GGML_SIMD) - const int np = (n & ~(GGML_F32_STEP - 1)); - - GGML_F32_VEC vx[GGML_VEC_MAD_UNROLL]; - - for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { - vx[k] = GGML_F32_VEC_SET1(v[k][0]); - } - - GGML_F32_VEC ax[GGML_VEC_MAD_UNROLL][GGML_F32_ARR]; - GGML_F32_VEC ay[GGML_F32_ARR]; - - for (int i = 0; i < np; i += GGML_F32_STEP) { - for (int j = 0; j < GGML_F32_ARR; j++) { - ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); - - for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { - ax[k][j] = GGML_F32_VEC_LOAD(x[k] + i + j*GGML_F32_EPR); - ay[j] = GGML_F32_VEC_FMA(ay[j], ax[k][j], vx[k]); - } - - GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); - } - } - - // leftovers - for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { - for (int i = np; i < n; ++i) { - y[i] += x[k][i]*v[k][0]; - } - } -#else - // scalar - for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) { - for (int i = 0; i < n; ++i) { - y[i] += x[k][i]*v[k][0]; - } - } -#endif -} - -//inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] *= v; } -inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { -#if defined(GGML_USE_ACCELERATE) - vDSP_vsmul(y, 1, &v, y, 1, n); -#elif defined(GGML_SIMD) - const int np = (n & ~(GGML_F32_STEP - 1)); - - GGML_F32_VEC vx = GGML_F32_VEC_SET1(v); - - GGML_F32_VEC ay[GGML_F32_ARR]; - - for (int i = 0; i < np; i += GGML_F32_STEP) { - for (int j = 0; j < GGML_F32_ARR; j++) { - ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR); - ay[j] = GGML_F32_VEC_MUL(ay[j], vx); - - GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]); - } - } - - // leftovers - for (int i = np; i < n; ++i) { - y[i] *= v; - } -#else - // scalar - for (int i = 0; i < n; ++i) { - y[i] *= v; - } -#endif -} - -inline static void ggml_vec_scale_f16(const int n, ggml_fp16_t * y, const float v) { -#if defined(GGML_SIMD) - const int np = (n & ~(GGML_F16_STEP - 1)); - - GGML_F16_VEC vx = GGML_F16_VEC_SET1(v); - - GGML_F16_VEC ay[GGML_F16_ARR]; - - for (int i = 0; i < np; i += GGML_F16_STEP) { - for (int j = 0; j < GGML_F16_ARR; j++) { - ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); - ay[j] = GGML_F16_VEC_MUL(ay[j], vx); - - GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j); - } - } - - // leftovers - for (int i = np; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i])*v); - } -#else - // scalar - for (int i = 0; i < n; ++i) { - y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i])*v); - } -#endif -} - -inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, 0, x, 0, x, 0, 1); *s = sqrtf(*s); } -inline static void ggml_vec_sqr_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]*x[i]; } -inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrtf(x[i]); } -inline static void ggml_vec_log_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = logf(x[i]); } -inline static void ggml_vec_sin_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sinf(x[i]); } -inline static void ggml_vec_cos_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = cosf(x[i]); } -inline static void ggml_vec_abs_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fabsf(x[i]); } -inline static void ggml_vec_sgn_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : ((x[i] < 0.f) ? -1.f : 0.f); } -inline static void ggml_vec_step_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : 0.f; } -inline static void ggml_vec_tanh_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = tanhf(x[i]); } -inline static void ggml_vec_elu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expm1f(x[i]); } -inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; } -inline static void ggml_vec_leaky_relu_f32 (const int n, float * y, const float * x, const float ns) { for (int i = 0; i < n; ++i) y[i] = ((x[i] > 0.f) ? x[i] : 0.f) + ns * ((x[i] < 0.0f) ? x[i] : 0.f); } -inline static void ggml_vec_sigmoid_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = 1.f / (1.f + expf(-x[i])); } -// TODO: optimize performance -inline static void ggml_vec_hardswish_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i] * fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); } -inline static void ggml_vec_hardsigmoid_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); } -inline static void ggml_vec_exp_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = expf(x[i]); } - -static const float GELU_COEF_A = 0.044715f; -static const float GELU_QUICK_COEF = -1.702f; -static const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; - -inline static float ggml_gelu_f32(float x) { - return 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x))); -} - -inline static void ggml_vec_gelu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { - const uint16_t * i16 = (const uint16_t *) x; - for (int i = 0; i < n; ++i) { - y[i] = ggml_table_gelu_f16[i16[i]]; - } -} - -#ifdef GGML_GELU_FP16 -inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { - uint16_t t; - for (int i = 0; i < n; ++i) { - if (x[i] <= -10.0f) { - y[i] = 0.0f; - } else if (x[i] >= 10.0f) { - y[i] = x[i]; - } else { - ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); - memcpy(&t, &fp16, sizeof(uint16_t)); - y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_f16[t]); - } - } -} -#else -inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { - for (int i = 0; i < n; ++i) { - y[i] = ggml_gelu_f32(x[i]); - } -} -#endif - -inline static float ggml_gelu_quick_f32(float x) { - return x*(1.0f/(1.0f+expf(GELU_QUICK_COEF*x))); -} - -//inline static void ggml_vec_gelu_quick_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { -// const uint16_t * i16 = (const uint16_t *) x; -// for (int i = 0; i < n; ++i) { -// y[i] = ggml_table_gelu_quick_f16[i16[i]]; -// } -//} - -#ifdef GGML_GELU_QUICK_FP16 -inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) { - uint16_t t; - for (int i = 0; i < n; ++i) { - ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); - memcpy(&t, &fp16, sizeof(uint16_t)); - y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_quick_f16[t]); - } -} -#else -inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) { - for (int i = 0; i < n; ++i) { - y[i] = ggml_gelu_quick_f32(x[i]); - } -} -#endif - -// Sigmoid Linear Unit (SiLU) function -inline static float ggml_silu_f32(float x) { - return x/(1.0f + expf(-x)); -} - -#if __FINITE_MATH_ONLY__ -#error "some routines in ggml.c require non-finite math arithmetics -- pass -fno-finite-math-only to the compiler to fix" -#error "ref: https://github.com/ggerganov/llama.cpp/pull/7154#issuecomment-2143844461" -#endif - -#if defined(__ARM_NEON) && defined(__aarch64__) - -// adapted from arm limited optimized routine -// the maximum error is 1.45358 plus 0.5 ulps -// numbers above 88.38 will flush to infinity -// numbers beneath -103.97 will flush to zero -inline static float32x4_t ggml_v_expf(float32x4_t x) { - const float32x4_t r = vdupq_n_f32(0x1.8p23f); - const float32x4_t z = vfmaq_f32(r, x, vdupq_n_f32(0x1.715476p+0f)); - const float32x4_t n = vsubq_f32(z, r); - const float32x4_t b = vfmsq_f32(vfmsq_f32(x, n, vdupq_n_f32(0x1.62e4p-1f)), n, - vdupq_n_f32(0x1.7f7d1cp-20f)); - const uint32x4_t e = vshlq_n_u32(vreinterpretq_u32_f32(z), 23); - const float32x4_t k = vreinterpretq_f32_u32(vaddq_u32(e, vreinterpretq_u32_f32(vdupq_n_f32(1)))); - const uint32x4_t c = vcagtq_f32(n, vdupq_n_f32(126)); - const float32x4_t u = vmulq_f32(b, b); - const float32x4_t j = vfmaq_f32( - vmulq_f32(vdupq_n_f32(0x1.ffffecp-1f), b), - vfmaq_f32(vfmaq_f32(vdupq_n_f32(0x1.fffdb6p-2f), vdupq_n_f32(0x1.555e66p-3f), b), - vfmaq_f32(vdupq_n_f32(0x1.573e2ep-5f), vdupq_n_f32(0x1.0e4020p-7f), b), u), u); - if (!vpaddd_u64(vreinterpretq_u64_u32(c))) - return vfmaq_f32(k, j, k); - const uint32x4_t d = vandq_u32(vclezq_f32(n), vdupq_n_u32(0x82000000)); - const float32x4_t s1 = vreinterpretq_f32_u32(vaddq_u32(d, vdupq_n_u32(0x7f000000))); - const float32x4_t s2 = vreinterpretq_f32_u32(vsubq_u32(e, d)); - return vbslq_f32(vcagtq_f32(n, vdupq_n_f32(192)), vmulq_f32(s1, s1), - vbslq_f32(c, vmulq_f32(vfmaq_f32(s2, s2, j), s1), vfmaq_f32(k, k, j))); -} - -// computes silu x/(1+exp(-x)) in single precision vector -inline static float32x4_t ggml_v_silu(float32x4_t x) { - const float32x4_t one = vdupq_n_f32(1.0f); - const float32x4_t zero = vdupq_n_f32(0.0f); - const float32x4_t neg_x = vsubq_f32(zero, x); - const float32x4_t exp_neg_x = ggml_v_expf(neg_x); - const float32x4_t one_plus_exp_neg_x = vaddq_f32(one, exp_neg_x); - return vdivq_f32(x, one_plus_exp_neg_x); -} - -#elif defined(__AVX512F__) && defined(__AVX512DQ__) - -// adapted from arm limited optimized routine -// the maximum error is 1.45358 plus 0.5 ulps -// numbers above 88.38 will flush to infinity -// numbers beneath -103.97 will flush to zero -inline static __m512 ggml_v_expf(__m512 x) { - const __m512 r = _mm512_set1_ps(0x1.8p23f); - const __m512 z = _mm512_fmadd_ps(x, _mm512_set1_ps(0x1.715476p+0f), r); - const __m512 n = _mm512_sub_ps(z, r); - const __m512 b = - _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.7f7d1cp-20f), - _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.62e4p-1f), x)); - const __mmask16 d = - _mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(192), _CMP_GT_OQ); - const __m512 u = _mm512_mul_ps(b, b); - const __m512 j = _mm512_fmadd_ps( - _mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_set1_ps(0x1.0e4020p-7f), b, - _mm512_set1_ps(0x1.573e2ep-5f)), - u, - _mm512_fmadd_ps(_mm512_set1_ps(0x1.555e66p-3f), b, - _mm512_set1_ps(0x1.fffdb6p-2f))), - u, - _mm512_fmadd_ps(_mm512_set1_ps(0x1.ffffecp-1f), b, _mm512_set1_ps(1.0F))); - const __m512 res = _mm512_scalef_ps(j, n); - if (_mm512_kortestz(d, d)) - return res; - const __m512 zero = _mm512_setzero_ps(); - const __m512 alt = _mm512_mask_blend_ps( - _mm512_cmp_ps_mask(n, zero, _CMP_LE_OQ), _mm512_set1_ps(INFINITY), zero); - return _mm512_mask_blend_ps(d, res, alt); -} - -// computes silu x/(1+exp(-x)) in single precision vector -inline static __m512 ggml_v_silu(__m512 x) { - const __m512 one = _mm512_set1_ps(1); - const __m512 zero = _mm512_setzero_ps(); - const __m512 neg_x = _mm512_sub_ps(zero, x); - const __m512 exp_neg_x = ggml_v_expf(neg_x); - const __m512 one_plus_exp_neg_x = _mm512_add_ps(one, exp_neg_x); - return _mm512_div_ps(x, one_plus_exp_neg_x); -} - -#elif defined(__AVX2__) && defined(__FMA__) - -// adapted from arm limited optimized routine -// the maximum error is 1.45358 plus 0.5 ulps -// numbers above 88.38 will flush to infinity -// numbers beneath -103.97 will flush to zero -inline static __m256 ggml_v_expf(__m256 x) { - const __m256 r = _mm256_set1_ps(0x1.8p23f); - const __m256 z = _mm256_fmadd_ps(x, _mm256_set1_ps(0x1.715476p+0f), r); - const __m256 n = _mm256_sub_ps(z, r); - const __m256 b = _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.7f7d1cp-20f), - _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.62e4p-1f), x)); - const __m256i e = _mm256_slli_epi32(_mm256_castps_si256(z), 23); - const __m256 k = _mm256_castsi256_ps( - _mm256_add_epi32(e, _mm256_castps_si256(_mm256_set1_ps(1)))); - const __m256i c = _mm256_castps_si256( - _mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), - _mm256_set1_ps(126), _CMP_GT_OQ)); - const __m256 u = _mm256_mul_ps(b, b); - const __m256 j = _mm256_fmadd_ps(_mm256_fmadd_ps(_mm256_fmadd_ps(_mm256_set1_ps(0x1.0e4020p-7f), b, - _mm256_set1_ps(0x1.573e2ep-5f)), u, - _mm256_fmadd_ps(_mm256_set1_ps(0x1.555e66p-3f), b, - _mm256_set1_ps(0x1.fffdb6p-2f))), - u, _mm256_mul_ps(_mm256_set1_ps(0x1.ffffecp-1f), b)); - if (!_mm256_movemask_ps(_mm256_castsi256_ps(c))) - return _mm256_fmadd_ps(j, k, k); - const __m256i g = _mm256_and_si256( - _mm256_castps_si256(_mm256_cmp_ps(n, _mm256_setzero_ps(), _CMP_LE_OQ)), - _mm256_set1_epi32(0x82000000u)); - const __m256 s1 = - _mm256_castsi256_ps(_mm256_add_epi32(g, _mm256_set1_epi32(0x7f000000u))); - const __m256 s2 = _mm256_castsi256_ps(_mm256_sub_epi32(e, g)); - const __m256i d = _mm256_castps_si256( - _mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), - _mm256_set1_ps(192), _CMP_GT_OQ)); - return _mm256_or_ps( - _mm256_and_ps(_mm256_castsi256_ps(d), _mm256_mul_ps(s1, s1)), - _mm256_andnot_ps( - _mm256_castsi256_ps(d), - _mm256_or_ps( - _mm256_and_ps(_mm256_castsi256_ps(c), - _mm256_mul_ps(_mm256_fmadd_ps(s2, j, s2), s1)), - _mm256_andnot_ps(_mm256_castsi256_ps(c), _mm256_fmadd_ps(k, j, k))))); -} - -// computes silu x/(1+exp(-x)) in single precision vector -inline static __m256 ggml_v_silu(__m256 x) { - const __m256 one = _mm256_set1_ps(1); - const __m256 zero = _mm256_setzero_ps(); - const __m256 neg_x = _mm256_sub_ps(zero, x); - const __m256 exp_neg_x = ggml_v_expf(neg_x); - const __m256 one_plus_exp_neg_x = _mm256_add_ps(one, exp_neg_x); - return _mm256_div_ps(x, one_plus_exp_neg_x); -} - -#elif defined(__SSE2__) // __AVX2__ / __ARM_NEON - -#if defined(__FMA__) -#define MADD128(x, y, z) _mm_fmadd_ps(x, y, z) -#define NMADD128(x, y, z) _mm_fnmadd_ps(x, y, z) -#else -#define MADD128(x, y, z) _mm_add_ps(_mm_mul_ps(x, y), z) -#define NMADD128(x, y, z) _mm_sub_ps(z, _mm_mul_ps(x, y)) -#endif - -// adapted from arm limited optimized routine -// the maximum error is 1.45358 plus 0.5 ulps -// numbers above 88.38 will flush to infinity -// numbers beneath -103.97 will flush to zero -inline static __m128 ggml_v_expf(__m128 x) { - const __m128 r = _mm_set1_ps(0x1.8p23f); - const __m128 z = MADD128(x, _mm_set1_ps(0x1.715476p+0f), r); - const __m128 n = _mm_sub_ps(z, r); - const __m128 b = - NMADD128(n, _mm_set1_ps(0x1.7f7d1cp-20f), NMADD128(n, _mm_set1_ps(0x1.62e4p-1f), x)); - const __m128i e = _mm_slli_epi32(_mm_castps_si128(z), 23); - const __m128 k = _mm_castsi128_ps(_mm_add_epi32(e, _mm_castps_si128(_mm_set1_ps(1)))); - const __m128i c = - _mm_castps_si128(_mm_cmpgt_ps(_mm_andnot_ps(_mm_set1_ps(-0.f), n), _mm_set1_ps(126))); - const __m128 u = _mm_mul_ps(b, b); - const __m128 j = - MADD128(MADD128(MADD128(_mm_set1_ps(0x1.0e4020p-7f), b, _mm_set1_ps(0x1.573e2ep-5f)), u, - MADD128(_mm_set1_ps(0x1.555e66p-3f), b, _mm_set1_ps(0x1.fffdb6p-2f))), - u, _mm_mul_ps(_mm_set1_ps(0x1.ffffecp-1f), b)); - if (!_mm_movemask_epi8(c)) - return MADD128(j, k, k); - const __m128i g = _mm_and_si128(_mm_castps_si128(_mm_cmple_ps(n, _mm_setzero_ps())), - _mm_set1_epi32(0x82000000u)); - const __m128 s1 = _mm_castsi128_ps(_mm_add_epi32(g, _mm_set1_epi32(0x7f000000u))); - const __m128 s2 = _mm_castsi128_ps(_mm_sub_epi32(e, g)); - const __m128i d = - _mm_castps_si128(_mm_cmpgt_ps(_mm_andnot_ps(_mm_set1_ps(-0.f), n), _mm_set1_ps(192))); - return _mm_or_ps( - _mm_and_ps(_mm_castsi128_ps(d), _mm_mul_ps(s1, s1)), - _mm_andnot_ps(_mm_castsi128_ps(d), - _mm_or_ps(_mm_and_ps(_mm_castsi128_ps(c), _mm_mul_ps(MADD128(s2, j, s2), s1)), - _mm_andnot_ps(_mm_castsi128_ps(c), MADD128(k, j, k))))); -} - -// computes silu x/(1+exp(-x)) in single precision vector -inline static __m128 ggml_v_silu(__m128 x) { - const __m128 one = _mm_set1_ps(1); - const __m128 zero = _mm_setzero_ps(); - const __m128 neg_x = _mm_sub_ps(zero, x); - const __m128 exp_neg_x = ggml_v_expf(neg_x); - const __m128 one_plus_exp_neg_x = _mm_add_ps(one, exp_neg_x); - return _mm_div_ps(x, one_plus_exp_neg_x); -} - -#endif // __ARM_NEON / __AVX2__ / __SSE2__ - -static void ggml_vec_silu_f32(const int n, float * y, const float * x) { - int i = 0; -#if defined(__AVX512F__) && defined(__AVX512DQ__) - for (; i + 15 < n; i += 16) { - _mm512_storeu_ps(y + i, ggml_v_silu(_mm512_loadu_ps(x + i))); - } -#elif defined(__AVX2__) && defined(__FMA__) - for (; i + 7 < n; i += 8) { - _mm256_storeu_ps(y + i, ggml_v_silu(_mm256_loadu_ps(x + i))); - } -#elif defined(__SSE2__) - for (; i + 3 < n; i += 4) { - _mm_storeu_ps(y + i, ggml_v_silu(_mm_loadu_ps(x + i))); - } -#elif defined(__ARM_NEON) && defined(__aarch64__) - for (; i + 3 < n; i += 4) { - vst1q_f32(y + i, ggml_v_silu(vld1q_f32(x + i))); - } -#endif - for (; i < n; ++i) { - y[i] = ggml_silu_f32(x[i]); - } -} - -static ggml_float ggml_vec_soft_max_f32(const int n, float * y, const float * x, float max) { - int i = 0; - ggml_float sum = 0; -#if defined(__AVX512F__) && defined(__AVX512DQ__) - for (; i + 15 < n; i += 16) { - __m512 val = ggml_v_expf(_mm512_sub_ps(_mm512_loadu_ps(x + i), - _mm512_set1_ps(max))); - _mm512_storeu_ps(y + i, val); - sum += (ggml_float)_mm512_reduce_add_ps(val); - } -#elif defined(__AVX2__) && defined(__FMA__) - for (; i + 7 < n; i += 8) { - __m256 val = ggml_v_expf(_mm256_sub_ps(_mm256_loadu_ps(x + i), - _mm256_set1_ps(max))); - _mm256_storeu_ps(y + i, val); - __m128 val2 = _mm_add_ps(_mm256_extractf128_ps(val, 1), - _mm256_castps256_ps128(val)); - val2 = _mm_add_ps(val2, _mm_movehl_ps(val2, val2)); - val2 = _mm_add_ss(val2, _mm_movehdup_ps(val2)); - sum += (ggml_float)_mm_cvtss_f32(val2); - } -#elif defined(__SSE2__) - for (; i + 3 < n; i += 4) { - __m128 val = ggml_v_expf(_mm_sub_ps(_mm_loadu_ps(x + i), - _mm_set1_ps(max))); - _mm_storeu_ps(y + i, val); -#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) - val = _mm_add_ps(val, _mm_movehl_ps(val, val)); - val = _mm_add_ss(val, _mm_movehdup_ps(val)); -#else - __m128 tmp = _mm_shuffle_ps(val, val, _MM_SHUFFLE(2, 3, 0, 1)); - val = _mm_add_ps(val, tmp); - tmp = _mm_movehl_ps(tmp, val); - val = _mm_add_ss(val, tmp); -#endif - sum += (ggml_float)_mm_cvtss_f32(val); - } -#elif defined(__ARM_NEON) && defined(__aarch64__) - for (; i + 3 < n; i += 4) { - float32x4_t val = ggml_v_expf(vsubq_f32(vld1q_f32(x + i), - vdupq_n_f32(max))); - vst1q_f32(y + i, val); - sum += (ggml_float)vaddvq_f32(val); - } -#endif - for (; i < n; ++i) { - float val = expf(x[i] - max); - sum += (ggml_float)val; - y[i] = val; - } - return sum; -} - -static ggml_float ggml_vec_log_soft_max_f32(const int n, float * y, const float * x, float max) { - // log(soft_max) = log(soft_max_i / soft_max_sum) = log(soft_max_i) - log(soft_max_sum) = (logit_i - max) - log(soft_max_i) - - int i = 0; - ggml_float sum = 0; - for (; i < n; ++i) { - float val = x[i] - max; - y[i] = val; - sum += (ggml_float)expf(val); - } - return sum = (ggml_float)logf(sum); -} - -inline static float ggml_silu_backward_f32(float x, float dy) { - const float s = 1.0f/(1.0f + expf(-x)); - return dy*s*(1.0f + x*(1.0f - s)); -} - -inline static void ggml_vec_silu_backward_f32(const int n, float * dx, const float * x, const float * dy) { - for (int i = 0; i < n; ++i) { - dx[i] = ggml_silu_backward_f32(x[i], dy[i]); - } -} - -inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) { -#ifndef GGML_USE_ACCELERATE - ggml_float sum = 0.0; - for (int i = 0; i < n; ++i) { - sum += (ggml_float)x[i]; - } - *s = sum; -#else - vDSP_sve(x, 1, s, n); -#endif -} - -inline static void ggml_vec_sum_f32_ggf(const int n, ggml_float * s, const float * x) { - ggml_float sum = 0.0; - for (int i = 0; i < n; ++i) { - sum += (ggml_float)x[i]; - } - *s = sum; -} - -inline static void ggml_vec_sum_f16_ggf(const int n, float * s, const ggml_fp16_t * x) { - float sum = 0.0f; - for (int i = 0; i < n; ++i) { - sum += GGML_FP16_TO_FP32(x[i]); - } - *s = sum; -} - -inline static void ggml_vec_sum_bf16_ggf(const int n, float * s, const ggml_bf16_t * x) { - float sum = 0.0f; - for (int i = 0; i < n; ++i) { - sum += GGML_BF16_TO_FP32(x[i]); - } - *s = sum; -} - -inline static void ggml_vec_max_f32(const int n, float * s, const float * x) { -#ifndef GGML_USE_ACCELERATE - float max = -INFINITY; - for (int i = 0; i < n; ++i) { - max = MAX(max, x[i]); - } - *s = max; -#else - vDSP_maxv(x, 1, s, n); -#endif -} - -inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x) { - ggml_vec_norm_f32(n, s, x); - *s = 1.f/(*s); -} - -inline static void ggml_vec_argmax_f32(const int n, int * s, const float * x) { - float max = -INFINITY; - int idx = 0; - for (int i = 0; i < n; ++i) { - max = MAX(max, x[i]); - if (max == x[i]) { idx = i; } - } - *s = idx; -} - // // data types // @@ -3064,6 +950,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "POOL_2D_BACK", "UPSCALE", "PAD", + "PAD_REFLECT_1D", "ARANGE", "TIMESTEP_EMBEDDING", "ARGSORT", @@ -3077,7 +964,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "WIN_UNPART", "GET_REL_POS", "ADD_REL_POS", - "RWKV_WKV", + "RWKV_WKV6", "UNARY", @@ -3097,7 +984,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "OPT_STEP_ADAMW", }; -static_assert(GGML_OP_COUNT == 81, "GGML_OP_COUNT != 81"); +static_assert(GGML_OP_COUNT == 82, "GGML_OP_COUNT != 82"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -3159,6 +1046,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "pool_2d_back(x)", "upscale(x)", "pad(x)", + "pad_reflect_1d(x)", "arange(start, stop, step)", "timestep_embedding(timesteps, dim, max_period)", "argsort(x)", @@ -3172,7 +1060,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "win_unpart(x)", "get_rel_pos(x)", "add_rel_pos(x)", - "rwkv_wkv(k, v, r, tf, td, s)", + "rwkv_wkv6(k, v, r, tf, td, s)", "unary(x)", @@ -3192,7 +1080,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "adamw(x)", }; -static_assert(GGML_OP_COUNT == 81, "GGML_OP_COUNT != 81"); +static_assert(GGML_OP_COUNT == 82, "GGML_OP_COUNT != 82"); static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2"); @@ -3220,215 +1108,6 @@ static_assert(GGML_UNARY_OP_COUNT == 14, "GGML_UNARY_OP_COUNT != 14"); static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN"); static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN"); -// Helpers for polling loops -#if defined(__aarch64__) && ( defined(__clang__) || defined(__GNUC__) ) -static inline void ggml_thread_cpu_relax(void) { - __asm__ volatile("yield" ::: "memory"); -} -#elif defined(__x86_64__) -static inline void ggml_thread_cpu_relax(void) { - _mm_pause(); -} -#else -static inline void ggml_thread_cpu_relax(void) {;} -#endif - -// -// NUMA support -// - -#define GGML_NUMA_MAX_NODES 8 -#define GGML_NUMA_MAX_CPUS 512 - -struct ggml_numa_node { - uint32_t cpus[GGML_NUMA_MAX_CPUS]; // hardware threads on this node - uint32_t n_cpus; -}; - -struct ggml_numa_nodes { - enum ggml_numa_strategy numa_strategy; - struct ggml_numa_node nodes[GGML_NUMA_MAX_NODES]; - uint32_t n_nodes; - uint32_t total_cpus; // hardware threads on system - uint32_t current_node; // node on which main process is execting -#if defined(__gnu_linux__) - cpu_set_t cpuset; // cpuset from numactl -#else - uint32_t cpuset; // no NUMA support outside of Linux at this time. Use a portable datatype -#endif -}; - -// -// ggml state -// - -struct ggml_state { - struct ggml_context_container contexts[GGML_MAX_CONTEXTS]; - struct ggml_numa_nodes numa; -}; - -// global state -static struct ggml_state g_state; -static atomic_flag g_state_critical = ATOMIC_FLAG_INIT; - -// critical section via spin lock -inline static void ggml_critical_section_start(void) { - while (atomic_flag_test_and_set(&g_state_critical)) { - // spin - sched_yield(); - } -} - -static void ggml_barrier(struct ggml_threadpool * tp) { - int n_threads = atomic_load_explicit(&tp->n_threads_cur, memory_order_relaxed); - if (n_threads == 1) { - return; - } - -#ifdef GGML_USE_OPENMP - #pragma omp barrier -#else - int n_passed = atomic_load_explicit(&tp->n_barrier_passed, memory_order_relaxed); - - // enter barrier (full seq-cst fence) - int n_barrier = atomic_fetch_add_explicit(&tp->n_barrier, 1, memory_order_seq_cst); - - if (n_barrier == (n_threads - 1)) { - // last thread - atomic_store_explicit(&tp->n_barrier, 0, memory_order_relaxed); - - // exit barrier (fill seq-cst fence) - atomic_fetch_add_explicit(&tp->n_barrier_passed, 1, memory_order_seq_cst); - return; - } - - // wait for other threads - while (atomic_load_explicit(&tp->n_barrier_passed, memory_order_relaxed) == n_passed) { - ggml_thread_cpu_relax(); - } - - // exit barrier (full seq-cst fence) - // TSAN doesn't support standalone fence yet, we use a dummy read-modify-write instead - #ifdef GGML_TSAN_ENABLED - atomic_fetch_add_explicit(&tp->n_barrier_passed, 0, memory_order_seq_cst); - #else - atomic_thread_fence(memory_order_seq_cst); - #endif -#endif -} - -// TODO: make this somehow automatically executed -// some sort of "sentry" mechanism -inline static void ggml_critical_section_end(void) { - atomic_flag_clear(&g_state_critical); -} - -#if defined(__gnu_linux__) -static cpu_set_t ggml_get_numa_affinity(void) { - cpu_set_t cpuset; - pthread_t thread; - thread = pthread_self(); - CPU_ZERO(&cpuset); - pthread_getaffinity_np(thread, sizeof(cpu_set_t), &cpuset); - return cpuset; -} -#else -static uint32_t ggml_get_numa_affinity(void) { - return 0; // no NUMA support -} -#endif - -void ggml_numa_init(enum ggml_numa_strategy numa_flag) { - if (g_state.numa.n_nodes > 0) { - fprintf(stderr, "ggml_numa_init: NUMA already initialized\n"); - - return; - } - -#if defined(__gnu_linux__) - struct stat st; - char path[256]; - int rv; - - // set numa scheme - g_state.numa.numa_strategy = numa_flag; - - GGML_PRINT_DEBUG("numa strategy %u\n",g_state.numa.numa_strategy); - - g_state.numa.cpuset = ggml_get_numa_affinity(); - - // enumerate nodes - while (g_state.numa.n_nodes < GGML_NUMA_MAX_NODES) { - rv = snprintf(path, sizeof(path), "/sys/devices/system/node/node%u", g_state.numa.n_nodes); - GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path)); - if (stat(path, &st) != 0) { break; } - ++g_state.numa.n_nodes; - } - - // enumerate CPUs - while (g_state.numa.total_cpus < GGML_NUMA_MAX_CPUS) { - rv = snprintf(path, sizeof(path), "/sys/devices/system/cpu/cpu%u", g_state.numa.total_cpus); - GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path)); - if (stat(path, &st) != 0) { break; } - ++g_state.numa.total_cpus; - } - - GGML_PRINT_DEBUG("found %u numa nodes, %u CPUs\n", g_state.numa.n_nodes, g_state.numa.total_cpus); - - // figure out which node we're on - uint current_cpu; - int getcpu_ret = 0; -#if __GLIBC__ > 2 || (__GLIBC__ == 2 && __GLIBC_MINOR__ > 28) || defined(__COSMOPOLITAN__) - getcpu_ret = getcpu(¤t_cpu, &g_state.numa.current_node); -#else - // old glibc doesn't have a wrapper for this call. Fall back on direct syscall -# if !defined(SYS_getcpu) && defined(SYS_get_cpu) -# define SYS_getcpu SYS_get_cpu // some older glibc versions use this name -# endif - getcpu_ret = syscall(SYS_getcpu, ¤t_cpu, &g_state.numa.current_node); -#endif - - if (g_state.numa.n_nodes < 1 || g_state.numa.total_cpus < 1 || getcpu_ret != 0) { - g_state.numa.n_nodes = 0; - return; - } - - GGML_PRINT_DEBUG("found our process on numa node %u, CPU %u\n", g_state.numa.current_node, current_cpu); - - for (uint32_t n = 0; n < g_state.numa.n_nodes; ++n) { - struct ggml_numa_node * node = &g_state.numa.nodes[n]; - GGML_PRINT_DEBUG("CPUs on node %u:", n); - node->n_cpus = 0; - for (uint32_t c = 0; c < g_state.numa.total_cpus; ++c) { - rv = snprintf(path, sizeof(path), "/sys/devices/system/node/node%u/cpu%u", n, c); - GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path)); - if (stat(path, &st) == 0) { - node->cpus[node->n_cpus++] = c; - GGML_PRINT_DEBUG(" %u", c); - } - } - GGML_PRINT_DEBUG("\n"); - } - - if (ggml_is_numa()) { - FILE *fptr = fopen("/proc/sys/kernel/numa_balancing", "r"); - if (fptr != NULL) { - char buf[42]; - if (fgets(buf, sizeof(buf), fptr) && strncmp(buf, "0\n", sizeof(buf)) != 0) { - GGML_LOG_WARN("/proc/sys/kernel/numa_balancing is enabled, this has been observed to impair performance\n"); - } - fclose(fptr); - } - } -#else - UNUSED(numa_flag); - // TODO -#endif -} - -bool ggml_is_numa(void) { - return g_state.numa.n_nodes > 1; -} //////////////////////////////////////////////////////////////////////////////// @@ -3565,22 +1244,6 @@ int ggml_n_dims(const struct ggml_tensor * tensor) { return 1; } -static inline bool ggml_can_mul_mat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { - static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); - - return (t0->ne[0] == t1->ne[0]) && - (t1->ne[2]%t0->ne[2] == 0) && // verify t0 is broadcastable - (t1->ne[3]%t0->ne[3] == 0); -} - -static inline bool ggml_can_out_prod(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { - static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); - - return (t0->ne[1] == t1->ne[1]) && - (t1->ne[2]%t0->ne[2] == 0) && // verify t0 is broadcastable - (t1->ne[3]%t0->ne[3] == 0); -} - enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) { enum ggml_type wtype = GGML_TYPE_COUNT; @@ -3727,163 +1390,35 @@ static inline bool ggml_can_repeat_rows(const struct ggml_tensor * t0, const str return (t0->ne[0] == t1->ne[0]) && ggml_can_repeat(t0, t1); } -static inline int ggml_up32(int n) { - return (n + 31) & ~31; -} - -//static inline int ggml_up64(int n) { -// return (n + 63) & ~63; -//} - -static inline int ggml_up(int n, int m) { - // assert m is a power of 2 - GGML_ASSERT((m & (m - 1)) == 0); - return (n + m - 1) & ~(m - 1); -} - // assert that pointer is aligned to GGML_MEM_ALIGN #define GGML_ASSERT_ALIGNED(ptr) \ GGML_ASSERT(((uintptr_t) (ptr))%GGML_MEM_ALIGN == 0) //////////////////////////////////////////////////////////////////////////////// -#if defined(__ARM_ARCH) - -#if defined(__linux__) && defined(__aarch64__) -#include -#elif defined(__APPLE__) -#include -#endif - -#if !defined(HWCAP2_I8MM) -#define HWCAP2_I8MM 0 -#endif - -static void ggml_init_arm_arch_features(void) { -#if defined(__linux__) && defined(__aarch64__) - uint32_t hwcap = getauxval(AT_HWCAP); - uint32_t hwcap2 = getauxval(AT_HWCAP2); - - ggml_arm_arch_features.has_neon = !!(hwcap & HWCAP_ASIMD); - ggml_arm_arch_features.has_i8mm = !!(hwcap2 & HWCAP2_I8MM); - ggml_arm_arch_features.has_sve = !!(hwcap & HWCAP_SVE); - -#if defined(__ARM_FEATURE_SVE) - ggml_arm_arch_features.sve_cnt = PR_SVE_VL_LEN_MASK & prctl(PR_SVE_GET_VL); -#endif -#elif defined(__APPLE__) - int oldp = 0; - size_t size = sizeof(oldp); - if (sysctlbyname("hw.optional.AdvSIMD", &oldp, &size, NULL, 0) != 0) { - oldp = 0; - } - ggml_arm_arch_features.has_neon = oldp; - - if (sysctlbyname("hw.optional.arm.FEAT_I8MM", &oldp, &size, NULL, 0) != 0) { - oldp = 0; - } - ggml_arm_arch_features.has_i8mm = oldp; - - ggml_arm_arch_features.has_sve = 0; - ggml_arm_arch_features.sve_cnt = 0; -#else -// Run-time CPU feature detection not implemented for this platform, fallback to compile time -#if defined(__ARM_NEON) - ggml_arm_arch_features.has_neon = 1; -#else - ggml_arm_arch_features.has_neon = 0; -#endif - -#if defined(__ARM_FEATURE_MATMUL_INT8) - ggml_arm_arch_features.has_i8mm = 1; -#else - ggml_arm_arch_features.has_i8mm = 0; -#endif - -#if defined(__ARM_FEATURE_SVE) - ggml_arm_arch_features.has_sve = 1; - ggml_arm_arch_features.sve_cnt = 16; -#else - ggml_arm_arch_features.has_sve = 0; - ggml_arm_arch_features.sve_cnt = 0; -#endif -#endif -} -#endif - struct ggml_context * ggml_init(struct ggml_init_params params) { - // make this function thread safe - ggml_critical_section_start(); - static bool is_first_call = true; + ggml_critical_section_start(); + if (is_first_call) { // initialize time system (required on Windows) ggml_time_init(); - // initialize GELU, Quick GELU, SILU and EXP F32 tables - { - const uint64_t t_start = ggml_time_us(); UNUSED(t_start); - - for (int i = 0; i < (1 << 16); ++i) { - union { - uint16_t u16; - ggml_fp16_t fp16; - } u = {i}; - float f = ggml_table_f32_f16[i] = GGML_COMPUTE_FP16_TO_FP32(u.fp16); - ggml_table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f)); - ggml_table_gelu_quick_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_quick_f32(f)); - } - - const uint64_t t_end = ggml_time_us(); UNUSED(t_end); - - GGML_PRINT_DEBUG("%s: GELU, Quick GELU, SILU and EXP tables initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f); + for (int i = 0; i < (1 << 16); ++i) { + union { + uint16_t u16; + ggml_fp16_t fp16; + } u = {i}; + ggml_table_f32_f16[i] = GGML_COMPUTE_FP16_TO_FP32(u.fp16); } - // initialize g_state - { - const uint64_t t_start = ggml_time_us(); UNUSED(t_start); - - g_state = (struct ggml_state) { - /*.contexts =*/ { { 0 } }, - /*.numa =*/ { - .n_nodes = 0, - .total_cpus = 0, - }, - }; - - const uint64_t t_end = ggml_time_us(); UNUSED(t_end); - - GGML_PRINT_DEBUG("%s: g_state initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f); - } - -#if defined(__ARM_ARCH) - ggml_init_arm_arch_features(); -#endif - is_first_call = false; } - // find non-used context in g_state - struct ggml_context * ctx = NULL; + ggml_critical_section_end(); - for (int i = 0; i < GGML_MAX_CONTEXTS; i++) { - if (!g_state.contexts[i].used) { - g_state.contexts[i].used = true; - ctx = &g_state.contexts[i].context; - - GGML_PRINT_DEBUG("%s: found unused context %d\n", __func__, i); - break; - } - } - - if (ctx == NULL) { - GGML_PRINT_DEBUG("%s: no unused context found\n", __func__); - - ggml_critical_section_end(); - - return NULL; - } + struct ggml_context * ctx = GGML_MALLOC(sizeof(struct ggml_context)); // allow to call ggml_init with 0 size if (params.mem_size == 0) { @@ -3897,12 +1432,9 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { /*.mem_buffer =*/ params.mem_buffer ? params.mem_buffer : ggml_aligned_malloc(mem_size), /*.mem_buffer_owned =*/ params.mem_buffer ? false : true, /*.no_alloc =*/ params.no_alloc, - /*.no_alloc_save =*/ params.no_alloc, /*.n_objects =*/ 0, /*.objects_begin =*/ NULL, /*.objects_end =*/ NULL, - /*.scratch =*/ { 0, 0, NULL, }, - /*.scratch_save =*/ { 0, 0, NULL, }, }; GGML_ASSERT(ctx->mem_buffer != NULL); @@ -3911,56 +1443,35 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { GGML_PRINT_DEBUG("%s: context initialized\n", __func__); - ggml_critical_section_end(); - return ctx; } +void ggml_reset(struct ggml_context * ctx) { + if (ctx == NULL) { + return; + } + + ctx->n_objects = 0; + ctx->objects_begin = NULL; + ctx->objects_end = NULL; +} + void ggml_free(struct ggml_context * ctx) { if (ctx == NULL) { return; } - // make this function thread safe - ggml_critical_section_start(); - - bool found = false; - - for (int i = 0; i < GGML_MAX_CONTEXTS; i++) { - if (&g_state.contexts[i].context == ctx) { - g_state.contexts[i].used = false; - - GGML_PRINT_DEBUG("%s: context %d has been freed. memory used = %zu\n", - __func__, i, ggml_used_mem(ctx)); - - if (ctx->mem_buffer_owned) { - ggml_aligned_free(ctx->mem_buffer, ctx->mem_size); - } - - found = true; - break; - } + if (ctx->mem_buffer_owned) { + ggml_aligned_free(ctx->mem_buffer, ctx->mem_size); } - if (!found) { - GGML_PRINT_DEBUG("%s: context not found\n", __func__); - } - - ggml_critical_section_end(); + GGML_FREE(ctx); } size_t ggml_used_mem(const struct ggml_context * ctx) { return ctx->objects_end == NULL ? 0 : ctx->objects_end->offs + ctx->objects_end->size; } -size_t ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch) { - const size_t result = ctx->scratch.data ? ctx->scratch.offs : 0; - - ctx->scratch = scratch; - - return result; -} - bool ggml_get_no_alloc(struct ggml_context * ctx) { return ctx->no_alloc; } @@ -3988,27 +1499,6 @@ size_t ggml_get_max_tensor_size(const struct ggml_context * ctx) { return max_size; } -// IMPORTANT: -// when creating "opt" tensors, always save and load the scratch buffer -// this is an error prone process, but it is necessary to support inplace -// operators when using scratch buffers -// TODO: implement a better way -static void ggml_scratch_save(struct ggml_context * ctx) { - // this is needed to allow opt tensors to store their data - // TODO: again, need to find a better way - ctx->no_alloc_save = ctx->no_alloc; - ctx->no_alloc = false; - - ctx->scratch_save = ctx->scratch; - ctx->scratch.data = NULL; -} - -static void ggml_scratch_load(struct ggml_context * ctx) { - ctx->no_alloc = ctx->no_alloc_save; - - ctx->scratch = ctx->scratch_save; -} - //////////////////////////////////////////////////////////////////////////////// static struct ggml_object * ggml_new_object(struct ggml_context * ctx, enum ggml_object_type type, size_t size) { @@ -4089,29 +1579,13 @@ static struct ggml_tensor * ggml_new_tensor_impl( size_t obj_alloc_size = 0; if (view_src == NULL && !ctx->no_alloc) { - if (ctx->scratch.data != NULL) { - // allocate tensor data in the scratch buffer - if (ctx->scratch.offs + data_size > ctx->scratch.size) { - GGML_LOG_WARN("%s: not enough space in the scratch memory pool (needed %zu, available %zu)\n", - __func__, ctx->scratch.offs + data_size, ctx->scratch.size); - assert(false); - return NULL; - } - - data = (char * const) ctx->scratch.data + ctx->scratch.offs; - - ctx->scratch.offs += data_size; - } else { - // allocate tensor data in the context's memory pool - obj_alloc_size = data_size; - } + // allocate tensor data in the context's memory pool + obj_alloc_size = data_size; } struct ggml_object * const obj_new = ggml_new_object(ctx, GGML_OBJECT_TYPE_TENSOR, GGML_TENSOR_SIZE + obj_alloc_size); GGML_ASSERT(obj_new); - // TODO: for recoverable errors, we would need to free the data allocated from the scratch buffer here - struct ggml_tensor * const result = (struct ggml_tensor *)((char *)ctx->mem_buffer + obj_new->offs); #ifdef __clang__ @@ -4129,14 +1603,13 @@ static struct ggml_tensor * ggml_new_tensor_impl( /*.op =*/ GGML_OP_NONE, /*.op_params =*/ { 0 }, /*.flags =*/ 0, - /*.grad =*/ NULL, /*.src =*/ { NULL }, /*.view_src =*/ view_src, /*.view_offs =*/ view_offs, /*.data =*/ obj_alloc_size > 0 ? (void *)(result + 1) : data, /*.name =*/ { 0 }, /*.extra =*/ NULL, - ///*.padding =*/ { 0 }, + /*.padding =*/ { 0 }, }; #ifdef __clang__ @@ -4206,191 +1679,16 @@ struct ggml_tensor * ggml_new_tensor_4d( return ggml_new_tensor(ctx, type, 4, ne); } -struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value) { - ggml_scratch_save(ctx); +void * ggml_new_buffer(struct ggml_context * ctx, size_t nbytes) { + struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, nbytes); - struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 1); - - ggml_scratch_load(ctx); - - ggml_set_i32(result, value); - - return result; -} - -struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value) { - ggml_scratch_save(ctx); - - struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1); - - ggml_scratch_load(ctx); - - ggml_set_f32(result, value); - - return result; + return (uint8_t *)ctx->mem_buffer + obj->offs; } struct ggml_tensor * ggml_dup_tensor(struct ggml_context * ctx, const struct ggml_tensor * src) { return ggml_new_tensor(ctx, src->type, GGML_MAX_DIMS, src->ne); } -static void ggml_set_op_params(struct ggml_tensor * tensor, const void * params, size_t params_size) { - GGML_ASSERT(tensor != NULL); // silence -Warray-bounds warnings - assert(params_size <= GGML_MAX_OP_PARAMS); - memcpy(tensor->op_params, params, params_size); -} - -static int32_t ggml_get_op_params_i32(const struct ggml_tensor * tensor, uint32_t i) { - assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t)); - return ((const int32_t *)(tensor->op_params))[i]; -} - -static float ggml_get_op_params_f32(const struct ggml_tensor * tensor, uint32_t i) { - assert(i < GGML_MAX_OP_PARAMS / sizeof(float)); - return ((const float *)(tensor->op_params))[i]; -} - -static void ggml_set_op_params_i32(struct ggml_tensor * tensor, uint32_t i, int32_t value) { - assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t)); - ((int32_t *)(tensor->op_params))[i] = value; -} - -static void ggml_set_op_params_f32(struct ggml_tensor * tensor, uint32_t i, float value) { - assert(i < GGML_MAX_OP_PARAMS / sizeof(float)); - ((float *)(tensor->op_params))[i] = value; -} - -struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) { - if (ggml_is_empty(tensor)) { - return tensor; - } - if (tensor->buffer) { - ggml_backend_tensor_memset(tensor, 0, 0, ggml_nbytes(tensor)); - } else { - GGML_ASSERT(tensor->data); - memset(tensor->data, 0, ggml_nbytes(tensor)); - } - return tensor; -} - -struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value) { - const int n = ggml_nrows(tensor); - const int nc = tensor->ne[0]; - const size_t n1 = tensor->nb[1]; - - char * const data = tensor->data; - - switch (tensor->type) { - case GGML_TYPE_I8: - { - assert(tensor->nb[0] == sizeof(int8_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_i8(nc, (int8_t *)(data + i*n1), value); - } - } break; - case GGML_TYPE_I16: - { - assert(tensor->nb[0] == sizeof(int16_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_i16(nc, (int16_t *)(data + i*n1), value); - } - } break; - case GGML_TYPE_I32: - { - assert(tensor->nb[0] == sizeof(int32_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_i32(nc, (int32_t *)(data + i*n1), value); - } - } break; - case GGML_TYPE_F16: - { - assert(tensor->nb[0] == sizeof(ggml_fp16_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value)); - } - } break; - case GGML_TYPE_BF16: - { - assert(tensor->nb[0] == sizeof(ggml_fp16_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_bf16(nc, (ggml_bf16_t *)(data + i*n1), GGML_FP32_TO_BF16(value)); - } - } break; - case GGML_TYPE_F32: - { - assert(tensor->nb[0] == sizeof(float)); - for (int i = 0; i < n; i++) { - ggml_vec_set_f32(nc, (float *)(data + i*n1), value); - } - } break; - default: - { - GGML_ABORT("fatal error"); - } - } - - return tensor; -} - -struct ggml_tensor * ggml_set_f32(struct ggml_tensor * tensor, float value) { - const int n = ggml_nrows(tensor); - const int nc = tensor->ne[0]; - const size_t n1 = tensor->nb[1]; - - char * const data = tensor->data; - - switch (tensor->type) { - case GGML_TYPE_I8: - { - assert(tensor->nb[0] == sizeof(int8_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_i8(nc, (int8_t *)(data + i*n1), value); - } - } break; - case GGML_TYPE_I16: - { - assert(tensor->nb[0] == sizeof(int16_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_i16(nc, (int16_t *)(data + i*n1), value); - } - } break; - case GGML_TYPE_I32: - { - assert(tensor->nb[0] == sizeof(int32_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_i32(nc, (int32_t *)(data + i*n1), value); - } - } break; - case GGML_TYPE_F16: - { - assert(tensor->nb[0] == sizeof(ggml_fp16_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value)); - } - } break; - case GGML_TYPE_BF16: - { - assert(tensor->nb[0] == sizeof(ggml_bf16_t)); - for (int i = 0; i < n; i++) { - ggml_vec_set_bf16(nc, (ggml_bf16_t *)(data + i*n1), GGML_FP32_TO_BF16(value)); - } - } break; - case GGML_TYPE_F32: - { - assert(tensor->nb[0] == sizeof(float)); - for (int i = 0; i < n; i++) { - ggml_vec_set_f32(nc, (float *)(data + i*n1), value); - } - } break; - default: - { - GGML_ABORT("fatal error"); - } - } - - return tensor; -} - void ggml_unravel_index(const struct ggml_tensor * tensor, int64_t i, int64_t * i0, int64_t * i1, int64_t * i2, int64_t * i3) { const int64_t ne2 = tensor->ne[2]; const int64_t ne1 = tensor->ne[1]; @@ -4415,280 +1713,6 @@ void ggml_unravel_index(const struct ggml_tensor * tensor, int64_t i, int64_t * } } -int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i) { - if (!ggml_is_contiguous(tensor)) { - int64_t id[4] = { 0, 0, 0, 0 }; - ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]); - return ggml_get_i32_nd(tensor, id[0], id[1], id[2], id[3]); - } - switch (tensor->type) { - case GGML_TYPE_I8: - { - GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); - return ((int8_t *)(tensor->data))[i]; - } - case GGML_TYPE_I16: - { - GGML_ASSERT(tensor->nb[0] == sizeof(int16_t)); - return ((int16_t *)(tensor->data))[i]; - } - case GGML_TYPE_I32: - { - GGML_ASSERT(tensor->nb[0] == sizeof(int32_t)); - return ((int32_t *)(tensor->data))[i]; - } - case GGML_TYPE_F16: - { - GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); - return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]); - } - case GGML_TYPE_BF16: - { - GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t)); - return GGML_BF16_TO_FP32(((ggml_bf16_t *)(tensor->data))[i]); - } - case GGML_TYPE_F32: - { - GGML_ASSERT(tensor->nb[0] == sizeof(float)); - return ((float *)(tensor->data))[i]; - } - default: - { - GGML_ABORT("fatal error"); - } - } -} - -void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value) { - if (!ggml_is_contiguous(tensor)) { - int64_t id[4] = { 0, 0, 0, 0 }; - ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]); - ggml_set_i32_nd(tensor, id[0], id[1], id[2], id[3], value); - return; - } - switch (tensor->type) { - case GGML_TYPE_I8: - { - GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); - ((int8_t *)(tensor->data))[i] = value; - } break; - case GGML_TYPE_I16: - { - GGML_ASSERT(tensor->nb[0] == sizeof(int16_t)); - ((int16_t *)(tensor->data))[i] = value; - } break; - case GGML_TYPE_I32: - { - GGML_ASSERT(tensor->nb[0] == sizeof(int32_t)); - ((int32_t *)(tensor->data))[i] = value; - } break; - case GGML_TYPE_F16: - { - GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); - ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value); - } break; - case GGML_TYPE_BF16: - { - GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t)); - ((ggml_bf16_t *)(tensor->data))[i] = GGML_FP32_TO_BF16(value); - } break; - case GGML_TYPE_F32: - { - GGML_ASSERT(tensor->nb[0] == sizeof(float)); - ((float *)(tensor->data))[i] = value; - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -int32_t ggml_get_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3) { - void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; - switch (tensor->type) { - case GGML_TYPE_I8: - return ((int8_t *) data)[0]; - case GGML_TYPE_I16: - return ((int16_t *) data)[0]; - case GGML_TYPE_I32: - return ((int32_t *) data)[0]; - case GGML_TYPE_F16: - return GGML_FP16_TO_FP32(((ggml_fp16_t *) data)[0]); - case GGML_TYPE_BF16: - return GGML_BF16_TO_FP32(((ggml_bf16_t *) data)[0]); - case GGML_TYPE_F32: - return ((float *) data)[0]; - default: - GGML_ABORT("fatal error"); - } -} - -void ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, int32_t value) { - void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; - switch (tensor->type) { - case GGML_TYPE_I8: - { - ((int8_t *)(data))[0] = value; - } break; - case GGML_TYPE_I16: - { - ((int16_t *)(data))[0] = value; - } break; - case GGML_TYPE_I32: - { - ((int32_t *)(data))[0] = value; - } break; - case GGML_TYPE_F16: - { - ((ggml_fp16_t *)(data))[0] = GGML_FP32_TO_FP16(value); - } break; - case GGML_TYPE_BF16: - { - ((ggml_bf16_t *)(data))[0] = GGML_FP32_TO_BF16(value); - } break; - case GGML_TYPE_F32: - { - ((float *)(data))[0] = value; - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i) { - if (!ggml_is_contiguous(tensor)) { - int64_t id[4] = { 0, 0, 0, 0 }; - ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]); - return ggml_get_f32_nd(tensor, id[0], id[1], id[2], id[3]); - } - switch (tensor->type) { - case GGML_TYPE_I8: - { - return ((int8_t *)(tensor->data))[i]; - } - case GGML_TYPE_I16: - { - return ((int16_t *)(tensor->data))[i]; - } - case GGML_TYPE_I32: - { - return ((int32_t *)(tensor->data))[i]; - } - case GGML_TYPE_F16: - { - return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]); - } - case GGML_TYPE_BF16: - { - return GGML_BF16_TO_FP32(((ggml_bf16_t *)(tensor->data))[i]); - } - case GGML_TYPE_F32: - { - return ((float *)(tensor->data))[i]; - } - default: - { - GGML_ABORT("fatal error"); - } - } -} - -void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value) { - if (!ggml_is_contiguous(tensor)) { - int64_t id[4] = { 0, 0, 0, 0 }; - ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]); - ggml_set_f32_nd(tensor, id[0], id[1], id[2], id[3], value); - return; - } - switch (tensor->type) { - case GGML_TYPE_I8: - { - ((int8_t *)(tensor->data))[i] = value; - } break; - case GGML_TYPE_I16: - { - ((int16_t *)(tensor->data))[i] = value; - } break; - case GGML_TYPE_I32: - { - ((int32_t *)(tensor->data))[i] = value; - } break; - case GGML_TYPE_F16: - { - ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value); - } break; - case GGML_TYPE_BF16: - { - ((ggml_bf16_t *)(tensor->data))[i] = GGML_FP32_TO_BF16(value); - } break; - case GGML_TYPE_F32: - { - ((float *)(tensor->data))[i] = value; - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -float ggml_get_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3) { - void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; - switch (tensor->type) { - case GGML_TYPE_I8: - return ((int8_t *) data)[0]; - case GGML_TYPE_I16: - return ((int16_t *) data)[0]; - case GGML_TYPE_I32: - return ((int32_t *) data)[0]; - case GGML_TYPE_F16: - return GGML_FP16_TO_FP32(((ggml_fp16_t *) data)[0]); - case GGML_TYPE_BF16: - return GGML_BF16_TO_FP32(((ggml_bf16_t *) data)[0]); - case GGML_TYPE_F32: - return ((float *) data)[0]; - default: - GGML_ABORT("fatal error"); - } -} - -void ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, float value) { - void * data = (char *) tensor->data + i0*tensor->nb[0] + i1*tensor->nb[1] + i2*tensor->nb[2] + i3*tensor->nb[3]; - switch (tensor->type) { - case GGML_TYPE_I8: - { - ((int8_t *)(data))[0] = value; - } break; - case GGML_TYPE_I16: - { - ((int16_t *)(data))[0] = value; - } break; - case GGML_TYPE_I32: - { - ((int32_t *)(data))[0] = value; - } break; - case GGML_TYPE_F16: - { - ((ggml_fp16_t *)(data))[0] = GGML_FP32_TO_FP16(value); - } break; - case GGML_TYPE_BF16: - { - ((ggml_bf16_t *)(data))[0] = GGML_FP32_TO_BF16(value); - } break; - case GGML_TYPE_F32: - { - ((float *)(data))[0] = value; - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - void * ggml_get_data(const struct ggml_tensor * tensor) { return tensor->data; } @@ -5242,6 +2266,7 @@ struct ggml_tensor * ggml_argmax( struct ggml_context * ctx, struct ggml_tensor * a) { GGML_ASSERT(ggml_is_matrix(a)); + GGML_ASSERT(a->ne[0] <= INT32_MAX); struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, a->ne[1]); @@ -5663,6 +2688,14 @@ struct ggml_tensor * ggml_group_norm_inplace( // ggml_mul_mat +static inline bool ggml_can_mul_mat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + + return (t0->ne[0] == t1->ne[0]) && + (t1->ne[2]%t0->ne[2] == 0) && // verify t0 is broadcastable + (t1->ne[3]%t0->ne[3] == 0); +} + struct ggml_tensor * ggml_mul_mat( struct ggml_context * ctx, struct ggml_tensor * a, @@ -5732,6 +2765,14 @@ struct ggml_tensor * ggml_mul_mat_id( // ggml_out_prod +static inline bool ggml_can_out_prod(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + + return (t0->ne[1] == t1->ne[1]) && + (t1->ne[2]%t0->ne[2] == 0) && // verify t0 is broadcastable + (t1->ne[3]%t0->ne[3] == 0); +} + struct ggml_tensor * ggml_out_prod( struct ggml_context * ctx, struct ggml_tensor * a, @@ -6595,6 +3636,22 @@ struct ggml_tensor * ggml_rope_custom_inplace( ); } +// Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get +// `corr_dim(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))` +static float ggml_rope_yarn_corr_dim(int n_dims, int n_ctx_orig, float n_rot, float base) { + return n_dims * logf(n_ctx_orig / (n_rot * 2 * (float)M_PI)) / (2 * logf(base)); +} + +void ggml_rope_yarn_corr_dims( + int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2] +) { + // start and end correction dims + float start = floorf(ggml_rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_fast, freq_base)); + float end = ceilf(ggml_rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_slow, freq_base)); + dims[0] = MAX(0, start); + dims[1] = MIN(n_dims - 1, end); +} + // ggml_rope_back struct ggml_tensor * ggml_rope_back( @@ -7042,6 +4099,37 @@ struct ggml_tensor * ggml_pad( return result; } +// ggml_pad_reflect_1d + +struct ggml_tensor * ggml_pad_reflect_1d( + struct ggml_context * ctx, + struct ggml_tensor * a, + int p0, + int p1) { + GGML_ASSERT(p0 >= 0); + GGML_ASSERT(p1 >= 0); + + GGML_ASSERT(p0 < a->ne[0]); // padding length on each size must be less than the + GGML_ASSERT(p1 < a->ne[0]); // existing length of the dimension being padded + + GGML_ASSERT(ggml_is_contiguous(a)); + GGML_ASSERT(a->type == GGML_TYPE_F32); + + struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, + a->ne[0] + p0 + p1, + a->ne[1], + a->ne[2], + a->ne[3]); + + int32_t params[] = { p0, p1 }; + ggml_set_op_params(result, params, sizeof(params)); + + result->op = GGML_OP_PAD_REFLECT_1D; + result->src[0] = a; + + return result; +} + // ggml_arange struct ggml_tensor * ggml_arange( @@ -7093,6 +4181,7 @@ struct ggml_tensor * ggml_argsort( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_sort_order order) { + GGML_ASSERT(a->ne[0] <= INT32_MAX); struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_I32, GGML_MAX_DIMS, a->ne); ggml_set_op_params_i32(result, 0, (int32_t) order); @@ -7148,8 +4237,6 @@ struct ggml_tensor * ggml_flash_attn_ext( GGML_ASSERT(mask); } - bool is_node = false; - // permute(0, 2, 1, 3) int64_t ne[4] = { q->ne[0], q->ne[2], q->ne[1], q->ne[3] }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); @@ -7157,8 +4244,7 @@ struct ggml_tensor * ggml_flash_attn_ext( float params[] = { scale, max_bias, logit_softcap }; ggml_set_op_params(result, params, sizeof(params)); - result->op = GGML_OP_FLASH_ATTN_EXT; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_FLASH_ATTN_EXT; result->src[0] = q; result->src[1] = k; result->src[2] = v; @@ -7177,6 +4263,15 @@ void ggml_flash_attn_ext_set_prec( ggml_set_op_params_i32(a, 3, prec_i32); // scale is on first pos, max_bias on second } +enum ggml_prec ggml_flash_attn_ext_get_prec( + const struct ggml_tensor * a) { + GGML_ASSERT(a->op == GGML_OP_FLASH_ATTN_EXT); + + const int32_t prec_i32 = ggml_get_op_params_i32(a, 3); + + return (enum ggml_prec) prec_i32; +} + // ggml_flash_attn_back struct ggml_tensor * ggml_flash_attn_back( @@ -7217,14 +4312,6 @@ struct ggml_tensor * ggml_flash_attn_back( GGML_ASSERT(ne2 % kvne2 == 0); - bool is_node = false; - - if (q->grad || k->grad || v->grad) { - // when using this operation (in backwards pass) these grads are set. - // we don't want to create (big) grad of our result, so is_node is false. - is_node = false; - } - // store gradients of q, k and v as continuous tensors concatenated in result. // note: v and gradv are actually transposed, i.e. v->ne[0] != D. const int64_t elem_q = ggml_nelements(q); @@ -7247,8 +4334,7 @@ struct ggml_tensor * ggml_flash_attn_back( int32_t masked_i = masked ? 1 : 0; ggml_set_op_params(result, &masked_i, sizeof(masked_i)); - result->op = GGML_OP_FLASH_ATTN_BACK; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->op = GGML_OP_FLASH_ATTN_BACK; result->src[0] = q; result->src[1] = k; result->src[2] = v; @@ -7272,6 +4358,7 @@ struct ggml_tensor * ggml_ssm_conv( const int64_t n_s = sx->ne[2]; // TODO: maybe support other strides than 1? + // FIXME: this is always true? GGML_ASSERT(sx->ne[0] == d_conv - 1 + n_t); GGML_ASSERT(sx->ne[1] == d_inner); GGML_ASSERT(n_t >= 0); @@ -7451,9 +4538,9 @@ struct ggml_tensor * ggml_add_rel_pos_inplace( return ggml_add_rel_pos_impl(ctx, a, pw, ph, true); } -// ggml_rwkv_wkv +// ggml_rwkv_wkv6 -struct ggml_tensor * ggml_rwkv_wkv( +struct ggml_tensor * ggml_rwkv_wkv6( struct ggml_context * ctx, struct ggml_tensor * k, struct ggml_tensor * v, @@ -7485,7 +4572,7 @@ struct ggml_tensor * ggml_rwkv_wkv( const int64_t ne[4] = { S * H, n_tokens + S * n_seqs, 1, 1 }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); - result->op = GGML_OP_RWKV_WKV; + result->op = GGML_OP_RWKV_WKV6; result->src[0] = k; result->src[1] = v; result->src[2] = r; @@ -7703,11 +4790,6 @@ struct ggml_tensor * ggml_map_custom3_inplace_f32( } // ggml_map_custom1 -struct ggml_map_custom1_op_params { - ggml_custom1_op_t fun; - int n_tasks; - void * userdata; -}; static struct ggml_tensor * ggml_map_custom1_impl( struct ggml_context * ctx, @@ -7753,12 +4835,6 @@ struct ggml_tensor * ggml_map_custom1_inplace( // ggml_map_custom2 -struct ggml_map_custom2_op_params { - ggml_custom2_op_t fun; - int n_tasks; - void * userdata; -}; - static struct ggml_tensor * ggml_map_custom2_impl( struct ggml_context * ctx, struct ggml_tensor * a, @@ -7807,12 +4883,6 @@ struct ggml_tensor * ggml_map_custom2_inplace( // ggml_map_custom3 -struct ggml_map_custom3_op_params { - ggml_custom3_op_t fun; - int n_tasks; - void * userdata; -}; - static struct ggml_tensor * ggml_map_custom3_impl( struct ggml_context * ctx, struct ggml_tensor * a, @@ -7906,9709 +4976,30 @@ struct ggml_tensor * ggml_opt_step_adamw( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * grad, - float alpha, - float beta1, - float beta2, - float eps, - float wd) { + struct ggml_tensor * m, + struct ggml_tensor * v, + struct ggml_tensor * adamw_params) { GGML_ASSERT(a->flags & GGML_TENSOR_FLAG_PARAM); GGML_ASSERT(ggml_are_same_shape(a, grad)); - GGML_ASSERT(alpha > 0.0f); - GGML_ASSERT(beta1 >= 0.0f && beta1 <= 1.0f); - GGML_ASSERT(beta2 >= 0.0f && beta2 <= 1.0f); - GGML_ASSERT(eps >= 0.0f); - GGML_ASSERT(wd >= 0.0f && wd <= 1.0f); + GGML_ASSERT(ggml_are_same_shape(a, m)); + GGML_ASSERT(ggml_are_same_shape(a, v)); + GGML_ASSERT(adamw_params->type == GGML_TYPE_F32); + GGML_ASSERT(ggml_nelements(adamw_params) == 7); struct ggml_tensor * result = ggml_view_tensor(ctx, a); - const int64_t iter = 1; - memcpy(&result->op_params[0], &iter, sizeof(int64_t)); - ggml_set_op_params_f32(result, 2, alpha); - ggml_set_op_params_f32(result, 3, beta1); - ggml_set_op_params_f32(result, 4, beta2); - ggml_set_op_params_f32(result, 5, eps); - ggml_set_op_params_f32(result, 6, wd); - result->op = GGML_OP_OPT_STEP_ADAMW; result->src[0] = a; result->src[1] = grad; - result->src[2] = ggml_dup_tensor(ctx, grad); - result->src[3] = ggml_dup_tensor(ctx, grad); + result->src[2] = m; + result->src[3] = v; + result->src[4] = adamw_params; return result; } //////////////////////////////////////////////////////////////////////////////// -// ggml_compute_forward_dup - -static void ggml_compute_forward_dup_same_cont( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); - GGML_ASSERT(src0->type == dst->type); - - const size_t nb0 = ggml_type_size(src0->type); - - const int ith = params->ith; // thread index - const int nth = params->nth; // number of threads - - // parallelize by elements - const int ne = ggml_nelements(dst); - const int dr = (ne + nth - 1) / nth; - const int ie0 = dr * ith; - const int ie1 = MIN(ie0 + dr, ne); - - if (ie0 < ie1) { - memcpy( - ((char *) dst->data + ie0*nb0), - ((char *) src0->data + ie0*nb0), - (ie1 - ie0) * nb0); - } -} - -static void ggml_compute_forward_dup_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - - GGML_TENSOR_UNARY_OP_LOCALS - - const int ith = params->ith; // thread index - const int nth = params->nth; // number of threads - - // parallelize by rows - const int nr = ne01; - // number of rows per thread - const int dr = (nr + nth - 1) / nth; - // row range for this thread - const int ir0 = dr * ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (src0->type == dst->type && - ne00 == ne0 && - nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { - // copy by rows - const size_t rs = ne00*nb00; - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ir0; i01 < ir1; i01++) { - memcpy( - ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), - ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), - rs); - } - } - } - return; - } - - // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy - - if (ggml_is_contiguous(dst)) { - if (nb00 == sizeof(ggml_fp16_t)) { - if (dst->type == GGML_TYPE_F16) { - size_t id = 0; - const size_t rs = ne00 * nb00; - char * dst_ptr = (char *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; - memcpy(dst_ptr + id, src0_ptr, rs); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_F32) { - size_t id = 0; - float * dst_ptr = (float *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - for (int i00 = 0; i00 < ne00; i00++) { - dst_ptr[id] = GGML_FP16_TO_FP32(src0_ptr[i00]); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (type_traits[dst->type].from_float) { - ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float; - float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; - - size_t id = 0; - size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); - char * dst_ptr = (char *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - - for (int i00 = 0; i00 < ne00; i00++) { - src0_f32[i00] = GGML_FP16_TO_FP32(src0_ptr[i00]); - } - - quantize_row_q(src0_f32, dst_ptr + id, ne00); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } - } else { - //printf("%s: this is not optimal - fix me\n", __func__); - - if (dst->type == GGML_TYPE_F32) { - size_t id = 0; - float * dst_ptr = (float *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_F16) { - size_t id = 0; - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = *src0_ptr; - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } - } - return; - } - - // dst counters - int64_t i10 = 0; - int64_t i11 = 0; - int64_t i12 = 0; - int64_t i13 = 0; - - if (dst->type == GGML_TYPE_F16) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - memcpy(dst_ptr, src0_ptr, sizeof(ggml_fp16_t)); - - if (++i10 == ne00) { - i10 = 0; - if (++i11 == ne01) { - i11 = 0; - if (++i12 == ne02) { - i12 = 0; - if (++i13 == ne03) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else if (dst->type == GGML_TYPE_F32) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - *(float *) dst_ptr = GGML_FP16_TO_FP32(*(const ggml_fp16_t *) src0_ptr); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } -} - -static void ggml_compute_forward_dup_bf16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - - GGML_TENSOR_UNARY_OP_LOCALS - - const int ith = params->ith; // thread index - const int nth = params->nth; // number of threads - - // parallelize by rows - const int nr = ne01; - // number of rows per thread - const int dr = (nr + nth - 1) / nth; - // row range for this thread - const int ir0 = dr * ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (src0->type == dst->type && - ne00 == ne0 && - nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { - // copy by rows - const size_t rs = ne00*nb00; - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ir0; i01 < ir1; i01++) { - memcpy( - ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), - ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), - rs); - } - } - } - return; - } - - // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy - - if (ggml_is_contiguous(dst)) { - if (nb00 == sizeof(ggml_bf16_t)) { - if (dst->type == GGML_TYPE_BF16) { - size_t id = 0; - const size_t rs = ne00 * nb00; - char * dst_ptr = (char *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; - memcpy(dst_ptr + id, src0_ptr, rs); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_F16) { - size_t id = 0; - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - for (int i00 = 0; i00 < ne00; i00++) { - dst_ptr[id] = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(src0_ptr[i00])); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_F32) { - size_t id = 0; - float * dst_ptr = (float *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - for (int i00 = 0; i00 < ne00; i00++) { - dst_ptr[id] = GGML_BF16_TO_FP32(src0_ptr[i00]); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (type_traits[dst->type].from_float) { - ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float; - float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; - - size_t id = 0; - size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); - char * dst_ptr = (char *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - - for (int i00 = 0; i00 < ne00; i00++) { - src0_f32[i00] = GGML_BF16_TO_FP32(src0_ptr[i00]); - } - - quantize_row_q(src0_f32, dst_ptr + id, ne00); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } - } else { - //printf("%s: this is not optimal - fix me\n", __func__); - - if (dst->type == GGML_TYPE_F32) { - size_t id = 0; - float * dst_ptr = (float *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = GGML_BF16_TO_FP32(*src0_ptr); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_BF16) { - size_t id = 0; - ggml_bf16_t * dst_ptr = (ggml_bf16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = *src0_ptr; - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_F16) { - size_t id = 0; - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(*src0_ptr)); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } - } - return; - } - - // dst counters - int64_t i10 = 0; - int64_t i11 = 0; - int64_t i12 = 0; - int64_t i13 = 0; - - if (dst->type == GGML_TYPE_BF16) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - memcpy(dst_ptr, src0_ptr, sizeof(ggml_bf16_t)); - - if (++i10 == ne00) { - i10 = 0; - if (++i11 == ne01) { - i11 = 0; - if (++i12 == ne02) { - i12 = 0; - if (++i13 == ne03) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else if (dst->type == GGML_TYPE_F16) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - *(ggml_fp16_t *) dst_ptr = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(*(const ggml_bf16_t *) src0_ptr)); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else if (dst->type == GGML_TYPE_F32) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - *(float *) dst_ptr = GGML_BF16_TO_FP32(*(const ggml_bf16_t *) src0_ptr); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } -} - -static void ggml_compute_forward_dup_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - - GGML_TENSOR_UNARY_OP_LOCALS - - const int ith = params->ith; // thread index - const int nth = params->nth; // number of threads - - // parallelize by rows - const int nr = ne01; - // number of rows per thread - const int dr = (nr + nth - 1) / nth; - // row range for this thread - const int ir0 = dr * ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (src0->type == dst->type && - ne00 == ne0 && - nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { - // copy by rows - const size_t rs = ne00*nb00; - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ir0; i01 < ir1; i01++) { - memcpy( - ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), - ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), - rs); - } - } - } - return; - } - - if (ggml_is_contiguous(dst)) { - // TODO: simplify - if (nb00 == sizeof(float)) { - if (dst->type == GGML_TYPE_F32) { - size_t id = 0; - const size_t rs = ne00 * nb00; - char * dst_ptr = (char *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; - memcpy(dst_ptr + id, src0_ptr, rs); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else if (type_traits[dst->type].from_float) { - ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float; - - size_t id = 0; - size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); - char * dst_ptr = (char *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - const float * src0_ptr = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - quantize_row_q(src0_ptr, dst_ptr + id, ne00); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } - } else { - //printf("%s: this is not optimal - fix me\n", __func__); - - if (dst->type == GGML_TYPE_F32) { - size_t id = 0; - float * dst_ptr = (float *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = *src0_ptr; - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_F16) { - size_t id = 0; - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (dst->type == GGML_TYPE_BF16) { - size_t id = 0; - ggml_bf16_t * dst_ptr = (ggml_bf16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = GGML_FP32_TO_BF16(*src0_ptr); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } - } - - return; - } - - // dst counters - - int64_t i10 = 0; - int64_t i11 = 0; - int64_t i12 = 0; - int64_t i13 = 0; - - if (dst->type == GGML_TYPE_F32) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - memcpy(dst_ptr, src0_ptr, sizeof(float)); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else if (dst->type == GGML_TYPE_F16) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - *(ggml_fp16_t *) dst_ptr = GGML_FP32_TO_FP16(*(const float *) src0_ptr); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else if (dst->type == GGML_TYPE_BF16) { - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - *(ggml_bf16_t *) dst_ptr = GGML_FP32_TO_BF16(*(const float *) src0_ptr); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - } else { - GGML_ABORT("fatal error"); // TODO: implement - } -} - -// A simplified version of ggml_compute_forward_dup that doesn't do float upcasting, and just plain old memcpy. -static void ggml_compute_forward_dup_bytes( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - GGML_ASSERT(src0->type == dst->type); - - GGML_TENSOR_UNARY_OP_LOCALS; - - if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst)) { - ggml_compute_forward_dup_same_cont(params, dst); - return; - } - - const size_t type_size = ggml_type_size(src0->type); - const int ith = params->ith; // thread index - const int nth = params->nth; // number of threads - - - // parallelize by rows - const int nr = ne01; - // number of rows per thread - const int dr = (nr + nth - 1) / nth; - // row range for this thread - const int ir0 = dr * ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (src0->type == dst->type && - ne00 == ne0 && - nb00 == type_size && nb0 == type_size) { - // copy by rows - const size_t rs = ne00 * type_size; - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ir0; i01 < ir1; i01++) { - memcpy( - ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), - ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), - rs); - } - } - } - return; - } - - if (ggml_is_contiguous(dst)) { - size_t id = 0; - char * dst_ptr = (char *) dst->data; - const size_t rs = ne00 * type_size; - - if (nb00 == type_size) { - // src0 is contigous on first dimension, copy by rows - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int64_t i01 = ir0; i01 < ir1; i01++) { - const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; - memcpy(dst_ptr + id, src0_ptr, rs); - id += rs; - } - id += rs * (ne01 - ir1); - } - } - } else { - //printf("%s: this is not optimal - fix me\n", __func__); - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - id += rs * ir0; - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = (char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03; - memcpy(dst_ptr + id, src0_ptr, type_size); - - id += type_size; - } - } - id += rs * (ne01 - ir1); - } - } - } - - return; - } - - // dst counters - - int64_t i10 = 0; - int64_t i11 = 0; - int64_t i12 = 0; - int64_t i13 = 0; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - i10 += ne00 * ir0; - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - for (int64_t i01 = ir0; i01 < ir1; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); - - memcpy(dst_ptr, src0_ptr, type_size); - - if (++i10 == ne0) { - i10 = 0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } - i10 += ne00 * (ne01 - ir1); - while (i10 >= ne0) { - i10 -= ne0; - if (++i11 == ne1) { - i11 = 0; - if (++i12 == ne2) { - i12 = 0; - if (++i13 == ne3) { - i13 = 0; - } - } - } - } - } - } -} - -static void ggml_compute_forward_dup( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (src0->type == dst->type) { - ggml_compute_forward_dup_bytes(params, dst); - return; - } - - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_dup_f16(params, dst); - } break; - case GGML_TYPE_BF16: - { - ggml_compute_forward_dup_bf16(params, dst); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_dup_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_add - -static void ggml_compute_forward_add_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT( nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (nb10 == sizeof(float)) { - for (int ir = ir0; ir < ir1; ++ir) { - // src1 is broadcastable across src0 and dst in i1, i2, i3 - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - const int64_t nr0 = ne00 / ne10; - - float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); - float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); - float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); - - for (int64_t r = 0; r < nr0; ++r) { -#ifdef GGML_USE_ACCELERATE - vDSP_vadd(src0_ptr + r*ne10, 1, src1_ptr, 1, dst_ptr + r*ne10, 1, ne10); -#else - ggml_vec_add_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr); -#endif - } - } - } else { - // src1 is not contiguous - for (int ir = ir0; ir < ir1; ++ir) { - // src1 is broadcastable across src0 and dst in i1, i2, i3 - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - - float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); - float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); - - for (int64_t i0 = 0; i0 < ne0; ++i0) { - const int64_t i10 = i0 % ne10; - float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10); - - dst_ptr[i0] = src0_ptr[i0] + *src1_ptr; - } - } - } -} - -static void ggml_compute_forward_add_f16_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - if (dst->type == GGML_TYPE_F32) { - GGML_ASSERT( nb0 == sizeof(float)); - } - else { - GGML_ASSERT(dst->type == GGML_TYPE_F16); - GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); - } - - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (nb10 == sizeof(float)) { - if (dst->type == GGML_TYPE_F16) { - for (int ir = ir0; ir < ir1; ++ir) { - // src0, src1 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); - ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); - - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + src1_ptr[i]); - } - } - } else { - for (int ir = ir0; ir < ir1; ++ir) { - // src0, src1 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - float * dst_ptr = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); - ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); - - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP16_TO_FP32(src0_ptr[i]) + src1_ptr[i]; - } - } - } - } - else { - // src1 is not contiguous - GGML_ABORT("fatal error"); - } -} - -static void ggml_compute_forward_add_bf16_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_BF16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - if (dst->type == GGML_TYPE_F32) { - GGML_ASSERT( nb0 == sizeof(float)); - } - else { - GGML_ASSERT(dst->type == GGML_TYPE_BF16); - GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); - } - - GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (nb10 == sizeof(float)) { - if (dst->type == GGML_TYPE_BF16) { - for (int ir = ir0; ir < ir1; ++ir) { - // src0, src1 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); - ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); - - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + src1_ptr[i]); - } - } - } else { - for (int ir = ir0; ir < ir1; ++ir) { - // src0, src1 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - float * dst_ptr = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); - ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); - - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_BF16_TO_FP32(src0_ptr[i]) + src1_ptr[i]; - } - } - } - } - else { - // src1 is not contiguous - GGML_ABORT("fatal error"); - } -} - -static void ggml_compute_forward_add_f16_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F16); - GGML_ASSERT(dst->type == GGML_TYPE_F16); - - GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (nb10 == sizeof(ggml_fp16_t)) { - for (int ir = ir0; ir < ir1; ++ir) { - // src0, src1 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); - ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - ggml_fp16_t * src1_ptr = (ggml_fp16_t *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); - - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + GGML_FP16_TO_FP32(src1_ptr[i])); - } - } - } - else { - // src1 is not contiguous - GGML_ABORT("fatal error"); - } -} - -static void ggml_compute_forward_add_bf16_bf16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_BF16); - GGML_ASSERT(src1->type == GGML_TYPE_BF16); - GGML_ASSERT(dst->type == GGML_TYPE_BF16); - - GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (nb10 == sizeof(ggml_bf16_t)) { - for (int ir = ir0; ir < ir1; ++ir) { - // src0, src1 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); - ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - ggml_bf16_t * src1_ptr = (ggml_bf16_t *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); - - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + GGML_BF16_TO_FP32(src1_ptr[i])); - } - } - } - else { - // src1 is not contiguous - GGML_ABORT("fatal error"); - } -} - -static void ggml_compute_forward_add_q_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const enum ggml_type type = src0->type; - const enum ggml_type dtype = dst->type; - ggml_to_float_t const dequantize_row_q = type_traits[type].to_float; - ggml_from_float_t const quantize_row_q = type_traits[dtype].from_float; - - // we don't support permuted src0 or src1 - GGML_ASSERT(nb00 == ggml_type_size(type)); - GGML_ASSERT(nb10 == sizeof(float)); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - GGML_ASSERT(ggml_is_quantized(src0->type)); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - float * wdata = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 indices - const int i03 = ir/(ne02*ne01); - const int i02 = (ir - i03*ne02*ne01)/ne01; - const int i01 = (ir - i03*ne02*ne01 - i02*ne01); - - // src1 and dst are same shape as src0 => same indices - const int i13 = i03; - const int i12 = i02; - const int i11 = i01; - - const int i3 = i03; - const int i2 = i02; - const int i1 = i01; - - void * src0_row = (void *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)); - float * src1_row = (float *)((char *) src1->data + (i11*nb11 + i12*nb12 + i13*nb13)); - void * dst_row = (void *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); - - assert(ne00 % 32 == 0); - - // unquantize row from src0 to temp buffer - dequantize_row_q(src0_row, wdata, ne00); - // add src1 - ggml_vec_acc_f32(ne00, wdata, src1_row); - // quantize row to dst - if (quantize_row_q != NULL) { - quantize_row_q(wdata, dst_row, ne00); - } else { - memcpy(dst_row, wdata, ne0*nb0); - } - } -} - -static void ggml_compute_forward_add( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - if (src1->type == GGML_TYPE_F32) { - ggml_compute_forward_add_f32(params, dst); - } - else { - GGML_ABORT("fatal error"); - } - } break; - case GGML_TYPE_F16: - { - if (src1->type == GGML_TYPE_F16) { - ggml_compute_forward_add_f16_f16(params, dst); - } - else if (src1->type == GGML_TYPE_F32) { - ggml_compute_forward_add_f16_f32(params, dst); - } - else { - GGML_ABORT("fatal error"); - } - } break; - case GGML_TYPE_BF16: - { - if (src1->type == GGML_TYPE_BF16) { - ggml_compute_forward_add_bf16_bf16(params, dst); - } - else if (src1->type == GGML_TYPE_F32) { - ggml_compute_forward_add_bf16_f32(params, dst); - } - else { - GGML_ABORT("fatal error"); - } - } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_Q4_0_4_4: - case GGML_TYPE_Q4_0_4_8: - case GGML_TYPE_Q4_0_8_8: - { - ggml_compute_forward_add_q_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_add1 - -static void ggml_compute_forward_add1_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_scalar(src1)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT( nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - -#ifdef GGML_USE_ACCELERATE - UNUSED(ggml_vec_add1_f32); - - vDSP_vadd( - (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), 1, - (float *) ((char *) src1->data), 0, - (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), 1, - ne0); -#else - ggml_vec_add1_f32(ne0, - (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), - (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), - *(float *) src1->data); -#endif - } -} - -static void ggml_compute_forward_add1_f16_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_scalar(src1)); - - // scalar to add - const float v = *(float *) src1->data; - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F16); - - GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); - ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + v); - } - } -} - -static void ggml_compute_forward_add1_f16_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_scalar(src1)); - - // scalar to add - const float v = GGML_FP16_TO_FP32(*(ggml_fp16_t *) src1->data); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F16); - GGML_ASSERT(dst->type == GGML_TYPE_F16); - - GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); - ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + v); - } - } -} - -static void ggml_compute_forward_add1_q_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_scalar(src1)); - - // scalar to add - const float v = *(float *) src1->data; - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - - const enum ggml_type type = src0->type; - ggml_to_float_t const dequantize_row_q = type_traits[type].to_float; - ggml_from_float_t const quantize_row_q = type_traits[type].from_float; - - // we don't support permuted src0 - GGML_ASSERT(nb00 == ggml_type_size(type)); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - GGML_ASSERT(ggml_is_quantized(src0->type)); - GGML_ASSERT(dst->type == src0->type); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - float * wdata = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32) * ith; - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - void * src0_row = (void *) ((char *) src0->data + (i1*nb01 + i2*nb02 + i3*nb03)); - void * dst_row = (void *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb0 )); - - assert(ne0 % 32 == 0); - - // unquantize row from src0 to temp buffer - dequantize_row_q(src0_row, wdata, ne0); - // add src1 - ggml_vec_acc1_f32(ne0, wdata, v); - // quantize row to dst - quantize_row_q(wdata, dst_row, ne0); - } -} - -static void ggml_compute_forward_add1_bf16_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_scalar(src1)); - - // scalar to add - const float v = *(float *) src1->data; - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_BF16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_BF16); - - GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); - ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + v); - } - } -} - -static void ggml_compute_forward_add1_bf16_bf16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_scalar(src1)); - - // scalar to add - const float v = GGML_BF16_TO_FP32(*(ggml_bf16_t *) src1->data); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(src0->type == GGML_TYPE_BF16); - GGML_ASSERT(src1->type == GGML_TYPE_BF16); - GGML_ASSERT(dst->type == GGML_TYPE_BF16); - - GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are same shape => same indices - const int i3 = ir/(ne2*ne1); - const int i2 = (ir - i3*ne2*ne1)/ne1; - const int i1 = (ir - i3*ne2*ne1 - i2*ne1); - - ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); - ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); - for (int i = 0; i < ne0; i++) { - dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + v); - } - } -} - -static void ggml_compute_forward_add1( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_add1_f32(params, dst); - } break; - case GGML_TYPE_F16: - { - if (src1->type == GGML_TYPE_F16) { - ggml_compute_forward_add1_f16_f16(params, dst); - } - else if (src1->type == GGML_TYPE_F32) { - ggml_compute_forward_add1_f16_f32(params, dst); - } - else { - GGML_ABORT("fatal error"); - } - } break; - case GGML_TYPE_BF16: - { - if (src1->type == GGML_TYPE_BF16) { - ggml_compute_forward_add1_bf16_bf16(params, dst); - } - else if (src1->type == GGML_TYPE_F32) { - ggml_compute_forward_add1_bf16_f32(params, dst); - } - else { - GGML_ABORT("fatal error"); - } - } break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q8_1: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_Q4_0_4_4: - case GGML_TYPE_Q4_0_4_8: - case GGML_TYPE_Q4_0_8_8: - { - ggml_compute_forward_add1_q_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_acc - -static void ggml_compute_forward_acc_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); - - // view src0 and dst with these strides and data offset inbytes during acc - // nb0 is implicitly element_size because src0 and dst are contiguous - size_t nb1 = ((int32_t *) dst->op_params)[0]; - size_t nb2 = ((int32_t *) dst->op_params)[1]; - size_t nb3 = ((int32_t *) dst->op_params)[2]; - size_t offset = ((int32_t *) dst->op_params)[3]; - bool inplace = (bool) ((int32_t *) dst->op_params)[4]; - - if (!inplace) { - if (params->ith == 0) { - // memcpy needs to be synchronized across threads to avoid race conditions. - // => do it in INIT phase - memcpy( - ((char *) dst->data), - ((char *) src0->data), - ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - } - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src1); - const int nc = src1->ne[0]; - - GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) - GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) - - // src0 and dst as viewed during acc - const size_t nb0 = ggml_element_size(src0); - - const size_t nb00 = nb0; - const size_t nb01 = nb1; - const size_t nb02 = nb2; - const size_t nb03 = nb3; - - GGML_ASSERT(offset + (ne10 == 0 ? 0 : ne10-1)*nb0 + (ne11 == 0 ? 0 : ne11-1)*nb1 + (ne12 == 0 ? 0 : ne12-1)*nb2 + (ne13 == 0 ? 0 : ne13-1)*nb3 < ggml_nbytes(dst)); - GGML_ASSERT(offset + (ne10 == 0 ? 0 : ne10-1)*nb00 + (ne11 == 0 ? 0 : ne11-1)*nb01 + (ne12 == 0 ? 0 : ne12-1)*nb02 + (ne13 == 0 ? 0 : ne13-1)*nb03 < ggml_nbytes(src0)); - - GGML_ASSERT(nb10 == sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are viewed with shape of src1 and offset - // => same indices - const int i3 = ir/(ne12*ne11); - const int i2 = (ir - i3*ne12*ne11)/ne11; - const int i1 = (ir - i3*ne12*ne11 - i2*ne11); - -#ifdef GGML_USE_ACCELERATE - vDSP_vadd( - (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + offset), 1, - (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1, - (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), 1, nc); -#else - ggml_vec_add_f32(nc, - (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), - (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + offset), - (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); -#endif - } -} - -static void ggml_compute_forward_acc( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_acc_f32(params, dst); - } break; - case GGML_TYPE_F16: - case GGML_TYPE_BF16: - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q8_1: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_Q4_0_4_4: - case GGML_TYPE_Q4_0_4_8: - case GGML_TYPE_Q4_0_8_8: - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sub - -static void ggml_compute_forward_sub_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - assert(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT( nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - if (nb10 == sizeof(float)) { - for (int ir = ir0; ir < ir1; ++ir) { - // src1 is broadcastable across src0 and dst in i1, i2, i3 - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - const int64_t nr0 = ne00 / ne10; - - float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); - float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); - float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); - - for (int64_t r = 0; r < nr0; ++r) { -#ifdef GGML_USE_ACCELERATE - vDSP_vsub(src1_ptr, 1, src0_ptr + r*ne10, 1, dst_ptr + r*ne10, 1, ne10); -#else - ggml_vec_sub_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr); -#endif - } - } - } else { - // src1 is not contiguous - for (int ir = ir0; ir < ir1; ++ir) { - // src1 is broadcastable across src0 and dst in i1, i2, i3 - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - - float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); - float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); - - for (int64_t i0 = 0; i0 < ne0; ++i0) { - const int64_t i10 = i0 % ne10; - float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10); - - dst_ptr[i0] = src0_ptr[i0] - *src1_ptr; - } - } - } -} - -static void ggml_compute_forward_sub( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sub_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_mul - -static void ggml_compute_forward_mul_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT( nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - if (nb10 == sizeof(float)) { - for (int64_t ir = ith; ir < nr; ir += nth) { - // src0 and dst are same shape => same indices - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - const int64_t nr0 = ne00 / ne10; - - float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); - float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); - float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); - - for (int64_t r = 0 ; r < nr0; ++r) { -#ifdef GGML_USE_ACCELERATE - UNUSED(ggml_vec_mul_f32); - - vDSP_vmul(src0_ptr + r*ne10, 1, src1_ptr, 1, dst_ptr + r*ne10, 1, ne10); -#else - ggml_vec_mul_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr); -#endif - } - } - } else { - // src1 is not contiguous - for (int64_t ir = ith; ir < nr; ir += nth) { - // src0 and dst are same shape => same indices - // src1 is broadcastable across src0 and dst in i1, i2, i3 - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - - float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); - float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); - - for (int64_t i0 = 0; i0 < ne00; ++i0) { - const int64_t i10 = i0 % ne10; - float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10); - - dst_ptr[i0] = src0_ptr[i0] * (*src1_ptr); - } - } - } -} - -static void ggml_compute_forward_mul( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src1->type == GGML_TYPE_F32 && "only f32 src1 supported for now"); - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_mul_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_div - -static void ggml_compute_forward_div_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t nr = ggml_nrows(src0); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT( nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - if (nb10 == sizeof(float)) { - for (int64_t ir = ith; ir < nr; ir += nth) { - // src0 and dst are same shape => same indices - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - const int64_t nr0 = ne00 / ne10; - - float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); - float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); - float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11); - - for (int64_t r = 0; r < nr0; ++r) { -#ifdef GGML_USE_ACCELERATE - UNUSED(ggml_vec_div_f32); - - vDSP_vdiv(src1_ptr, 1, src0_ptr + r*ne10, 1, dst_ptr + r*ne10, 1, ne10); -#else - ggml_vec_div_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr); -#endif - } - } - } else { - // src1 is not contiguous - for (int64_t ir = ith; ir < nr; ir += nth) { - // src0 and dst are same shape => same indices - // src1 is broadcastable across src0 and dst in i1, i2, i3 - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; - - float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 ); - float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01); - - for (int64_t i0 = 0; i0 < ne00; ++i0) { - const int64_t i10 = i0 % ne10; - float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10); - - dst_ptr[i0] = src0_ptr[i0] / (*src1_ptr); - } - } - } -} - -static void ggml_compute_forward_div( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_div_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sqr - -static void ggml_compute_forward_sqr_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - assert( dst->nb[0] == sizeof(float)); - assert(src0->nb[0] == sizeof(float)); - - for (int i = 0; i < n; i++) { - ggml_vec_sqr_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_sqr( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sqr_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sqrt - -static void ggml_compute_forward_sqrt_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - assert( dst->nb[0] == sizeof(float)); - assert(src0->nb[0] == sizeof(float)); - - for (int i = 0; i < n; i++) { - ggml_vec_sqrt_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_sqrt( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sqrt_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_log - -static void ggml_compute_forward_log_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - GGML_ASSERT( dst->nb[0] == sizeof(float)); - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - for (int i = 0; i < n; i++) { - ggml_vec_log_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_log( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_log_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sin - -static void ggml_compute_forward_sin_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - GGML_ASSERT( dst->nb[0] == sizeof(float)); - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - for (int i = 0; i < n; i++) { - ggml_vec_sin_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_sin( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sin_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_cos - -static void ggml_compute_forward_cos_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - GGML_ASSERT( dst->nb[0] == sizeof(float)); - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - for (int i = 0; i < n; i++) { - ggml_vec_cos_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_cos( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_cos_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sum - -static void ggml_compute_forward_sum_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_scalar(dst)); - assert(src0->nb[0] == sizeof(float)); - - GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) - GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) - - ggml_float sum = 0; - ggml_float row_sum = 0; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - ggml_vec_sum_f32_ggf(ne00, - &row_sum, - (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03)); - sum += row_sum; - } - } - } - ((float *) dst->data)[0] = sum; -} - -static void ggml_compute_forward_sum_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_scalar(dst)); - - assert(src0->nb[0] == sizeof(ggml_fp16_t)); - - GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) - GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) - - float sum = 0; - float row_sum = 0; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - ggml_vec_sum_f16_ggf(ne00, - &row_sum, - (ggml_fp16_t *) ((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03)); - sum += row_sum; - } - } - } - ((ggml_fp16_t *) dst->data)[0] = GGML_FP32_TO_FP16(sum); -} - -static void ggml_compute_forward_sum_bf16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_scalar(dst)); - - assert(src0->nb[0] == sizeof(ggml_bf16_t)); - - GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) - GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) - - float sum = 0; - float row_sum = 0; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - ggml_vec_sum_bf16_ggf(ne00, - &row_sum, - (ggml_bf16_t *) ((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03)); - sum += row_sum; - } - } - } - ((ggml_bf16_t *) dst->data)[0] = GGML_FP32_TO_BF16(sum); -} - -static void ggml_compute_forward_sum( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sum_f32(params, dst); - } break; - case GGML_TYPE_F16: - { - ggml_compute_forward_sum_f16(params, dst); - } break; - case GGML_TYPE_BF16: - { - ggml_compute_forward_sum_bf16(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sum_rows - -static void ggml_compute_forward_sum_rows_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - GGML_ASSERT(dst->nb[0] == sizeof(float)); - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(ne0 == 1); - GGML_ASSERT(ne1 == ne01); - GGML_ASSERT(ne2 == ne02); - GGML_ASSERT(ne3 == ne03); - - for (int64_t i3 = 0; i3 < ne03; i3++) { - for (int64_t i2 = 0; i2 < ne02; i2++) { - for (int64_t i1 = 0; i1 < ne01; i1++) { - float * src_row = (float *) ((char *) src0->data + i1*nb01 + i2*nb02 + i3*nb03); - float * dst_row = (float *) ((char *) dst->data + i1*nb1 + i2*nb2 + i3*nb3); - float row_sum = 0; - ggml_vec_sum_f32(ne00, &row_sum, src_row); - dst_row[0] = row_sum; - } - } - } -} - -static void ggml_compute_forward_sum_rows( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sum_rows_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_mean - -static void ggml_compute_forward_mean_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(src0->nb[0] == sizeof(float)); - - GGML_TENSOR_UNARY_OP_LOCALS - - assert(ne0 == 1); - assert(ne1 == ne01); - assert(ne2 == ne02); - assert(ne3 == ne03); - - UNUSED(ne0); - UNUSED(ne1); - UNUSED(ne2); - UNUSED(ne3); - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - ggml_vec_sum_f32(ne00, - (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), - (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03)); - - *(float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3) /= (float) ne00; - } - } - } -} - -static void ggml_compute_forward_mean( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_mean_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_argmax - -static void ggml_compute_forward_argmax_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(src0->nb[0] == sizeof(float)); - assert(dst->nb[0] == sizeof(float)); - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - - const size_t nb01 = src0->nb[1]; - const size_t nb0 = dst->nb[0]; - - for (int64_t i1 = 0; i1 < ne01; i1++) { - float * src = (float *) ((char *) src0->data + i1*nb01); - int32_t * dst_ = (int32_t *) ((char *) dst->data + i1*nb0); - int v = 0; - ggml_vec_argmax_f32(ne00, &v, src); - dst_[0] = v; - } -} - -static void ggml_compute_forward_argmax( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_argmax_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_count_equal - -static void ggml_compute_forward_count_equal_i32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS; - - GGML_ASSERT(src0->type == GGML_TYPE_I32); - GGML_ASSERT(src1->type == GGML_TYPE_I32); - GGML_ASSERT(ggml_are_same_shape(src0, src1)); - GGML_ASSERT(ggml_is_scalar(dst)); - GGML_ASSERT(dst->type == GGML_TYPE_I64); - - const int64_t nr = ggml_nrows(src0); - - const int ith = params->ith; - const int nth = params->nth; - - int64_t * sums = (int64_t *) params->wdata; - int64_t sum_thread = 0; - - // rows per thread - const int64_t dr = (nr + nth - 1)/nth; - - // row range for this thread - const int64_t ir0 = dr*ith; - const int64_t ir1 = MIN(ir0 + dr, nr); - - for (int64_t ir = ir0; ir < ir1; ++ir) { - const int64_t i03 = ir / (ne02*ne01); - const int64_t i02 = (ir - i03*ne03) / ne01; - const int64_t i01 = ir - i03*ne03 - i02*ne02; - - const char * data0 = (const char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01; - const char * data1 = (const char *) src1->data + i03*nb13 + i02*nb12 + i01*nb11; - - for (int64_t i00 = 0; i00 < ne00; ++i00) { - const int32_t val0 = *((const int32_t *) (data0 + i00*nb00)); - const int32_t val1 = *((const int32_t *) (data1 + i00*nb10)); - - sum_thread += val0 == val1; - } - } - if (ith != 0) { - sums[ith] = sum_thread; - } - ggml_barrier(params->threadpool); - - if (ith != 0) { - return; - } - - for (int ith_other = 1; ith_other < nth; ++ith_other) { - sum_thread += sums[ith_other]; - } - *((int64_t *) dst->data) = sum_thread; -} - -static void ggml_compute_forward_count_equal( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_I32: - { - ggml_compute_forward_count_equal_i32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_repeat - -static void ggml_compute_forward_repeat_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_can_repeat(src0, dst)); - - GGML_TENSOR_UNARY_OP_LOCALS - - // guaranteed to be an integer due to the check in ggml_can_repeat - const int nr0 = (int)(ne0/ne00); - const int nr1 = (int)(ne1/ne01); - const int nr2 = (int)(ne2/ne02); - const int nr3 = (int)(ne3/ne03); - - // TODO: support for transposed / permuted tensors - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - // TODO: maybe this is not optimal? - for (int i3 = 0; i3 < nr3; i3++) { - for (int k3 = 0; k3 < ne03; k3++) { - for (int i2 = 0; i2 < nr2; i2++) { - for (int k2 = 0; k2 < ne02; k2++) { - for (int i1 = 0; i1 < nr1; i1++) { - for (int k1 = 0; k1 < ne01; k1++) { - for (int i0 = 0; i0 < nr0; i0++) { - ggml_vec_cpy_f32(ne00, - (float *) ((char *) dst->data + (i3*ne03 + k3)*nb3 + (i2*ne02 + k2)*nb2 + (i1*ne01 + k1)*nb1 + (i0*ne00)*nb0), - (float *) ((char *) src0->data + ( k3)*nb03 + ( k2)*nb02 + ( k1)*nb01)); - } - } - } - } - } - } - } -} - -static void ggml_compute_forward_repeat_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_can_repeat(src0, dst)); - - GGML_TENSOR_UNARY_OP_LOCALS - - // guaranteed to be an integer due to the check in ggml_can_repeat - const int nr0 = (int)(ne0/ne00); - const int nr1 = (int)(ne1/ne01); - const int nr2 = (int)(ne2/ne02); - const int nr3 = (int)(ne3/ne03); - - // TODO: support for transposed / permuted tensors - GGML_ASSERT(nb0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - - // TODO: maybe this is not optimal? - for (int i3 = 0; i3 < nr3; i3++) { - for (int k3 = 0; k3 < ne03; k3++) { - for (int i2 = 0; i2 < nr2; i2++) { - for (int k2 = 0; k2 < ne02; k2++) { - for (int i1 = 0; i1 < nr1; i1++) { - for (int k1 = 0; k1 < ne01; k1++) { - for (int i0 = 0; i0 < nr0; i0++) { - ggml_fp16_t * y = (ggml_fp16_t *) ((char *) dst->data + (i3*ne03 + k3)*nb3 + (i2*ne02 + k2)*nb2 + (i1*ne01 + k1)*nb1 + (i0*ne00)*nb0); - ggml_fp16_t * x = (ggml_fp16_t *) ((char *) src0->data + ( k3)*nb03 + ( k2)*nb02 + ( k1)*nb01); - // ggml_vec_cpy_f16(ne00, y, x) - for (int i = 0; i < ne00; ++i) { - y[i] = x[i]; - } - } - } - } - } - } - } - } -} - -static void ggml_compute_forward_repeat( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - case GGML_TYPE_BF16: - case GGML_TYPE_I16: - { - ggml_compute_forward_repeat_f16(params, dst); - } break; - case GGML_TYPE_F32: - case GGML_TYPE_I32: - { - ggml_compute_forward_repeat_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_repeat_back - -static void ggml_compute_forward_repeat_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_can_repeat(dst, src0)); - - GGML_TENSOR_UNARY_OP_LOCALS - - // guaranteed to be an integer due to the check in ggml_can_repeat - const int nr0 = (int)(ne00/ne0); - const int nr1 = (int)(ne01/ne1); - const int nr2 = (int)(ne02/ne2); - const int nr3 = (int)(ne03/ne3); - - // TODO: support for transposed / permuted tensors - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - if (ggml_is_contiguous(dst)) { - ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); - } else { - for (int k3 = 0; k3 < ne3; k3++) { - for (int k2 = 0; k2 < ne2; k2++) { - for (int k1 = 0; k1 < ne1; k1++) { - ggml_vec_set_f32(ne0, - (float *) ((char *) dst->data + k1*nb1 + k2*nb2 + k3*nb3), - 0); - } - } - } - } - - // TODO: maybe this is not optimal? - for (int i3 = 0; i3 < nr3; i3++) { - for (int k3 = 0; k3 < ne3; k3++) { - for (int i2 = 0; i2 < nr2; i2++) { - for (int k2 = 0; k2 < ne2; k2++) { - for (int i1 = 0; i1 < nr1; i1++) { - for (int k1 = 0; k1 < ne1; k1++) { - for (int i0 = 0; i0 < nr0; i0++) { - ggml_vec_acc_f32(ne0, - (float *) ((char *) dst->data + ( k3)*nb3 + ( k2)*nb2 + ( k1)*nb1), - (float *) ((char *) src0->data + (i3*ne3 + k3)*nb03 + (i2*ne2 + k2)*nb02 + (i1*ne1 + k1)*nb01 + (i0*ne0)*nb00)); - } - } - } - } - } - } - } -} - -static void ggml_compute_forward_repeat_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_repeat_back_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_concat - -static void ggml_compute_forward_concat_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int32_t dim = ggml_get_op_params_i32(dst, 0); - - GGML_ASSERT(dim >= 0 && dim < 4); - - int64_t o[4] = {0, 0, 0, 0}; - o[dim] = src0->ne[dim]; - - const float * x; - - // TODO: smarter multi-theading - for (int i3 = 0; i3 < ne3; i3++) { - for (int i2 = ith; i2 < ne2; i2 += nth) { - for (int i1 = 0; i1 < ne1; i1++) { - for (int i0 = 0; i0 < ne0; i0++) { - if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { - x = (const float *) ((const char *)src0->data + (i0 )*nb00 + (i1 )*nb01 + (i2 )*nb02 + (i3 )*nb03); - } else { - x = (const float *) ((const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13); - } - - float * y = (float *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); - - *y = *x; - } - } - } - } -} - -static void ggml_compute_forward_concat( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - case GGML_TYPE_I32: - { - ggml_compute_forward_concat_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_abs - -static void ggml_compute_forward_abs_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_abs_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_abs( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_abs_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sgn - -static void ggml_compute_forward_sgn_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_sgn_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_sgn( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sgn_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_neg - -static void ggml_compute_forward_neg_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_neg_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_neg( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_neg_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_step - -static void ggml_compute_forward_step_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_step_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_step( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_step_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_tanh - -static void ggml_compute_forward_tanh_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_tanh_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_tanh( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_tanh_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_elu - -static void ggml_compute_forward_elu_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_elu_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_elu( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_elu_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_relu - -static void ggml_compute_forward_relu_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_relu_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_relu( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_relu_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_sigmoid - -static void ggml_compute_forward_sigmoid_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_sigmoid_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_sigmoid( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_sigmoid_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_gelu - -static void ggml_compute_forward_gelu_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int i1 = ir0; i1 < ir1; i1++) { - ggml_vec_gelu_f32(nc, - (float *) ((char *) dst->data + i1*( dst->nb[1])), - (float *) ((char *) src0->data + i1*(src0->nb[1]))); - -#ifndef NDEBUG - for (int k = 0; k < nc; k++) { - const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; - UNUSED(x); - assert(!isnan(x)); - assert(!isinf(x)); - } -#endif - } -} - -static void ggml_compute_forward_gelu( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_gelu_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_gelu_quick - -static void ggml_compute_forward_gelu_quick_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int i1 = ir0; i1 < ir1; i1++) { - ggml_vec_gelu_quick_f32(nc, - (float *) ((char *) dst->data + i1*( dst->nb[1])), - (float *) ((char *) src0->data + i1*(src0->nb[1]))); - -#ifndef NDEBUG - for (int k = 0; k < nc; k++) { - const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; - UNUSED(x); - assert(!isnan(x)); - assert(!isinf(x)); - } -#endif - } -} - -static void ggml_compute_forward_gelu_quick( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_gelu_quick_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_silu - -static void ggml_compute_forward_silu_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int i1 = ir0; i1 < ir1; i1++) { - ggml_vec_silu_f32(nc, - (float *) ((char *) dst->data + i1*( dst->nb[1])), - (float *) ((char *) src0->data + i1*(src0->nb[1]))); - -#ifndef NDEBUG - for (int k = 0; k < nc; k++) { - const float x = ((float *) ((char *) dst->data + i1*(dst->nb[1])))[k]; - UNUSED(x); - assert(!isnan(x)); - assert(!isinf(x)); - } -#endif - } -} - -static void ggml_compute_forward_silu( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_silu_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} -// ggml_compute_forward_leaky_relu - -static void ggml_compute_forward_leaky_relu_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - float negative_slope; - memcpy(&negative_slope, dst->op_params, sizeof(float)); - - assert(dst->nb[0] == sizeof(float)); - assert(src0->nb[0] == sizeof(float)); - - for (int i = 0; i < n; i++) { - ggml_vec_leaky_relu_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1])), negative_slope); - } -} - -static void ggml_compute_forward_leaky_relu( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_leaky_relu_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_silu_back - -static void ggml_compute_forward_silu_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * grad = dst->src[1]; - - assert(ggml_is_contiguous_1(grad)); - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - assert(ggml_are_same_shape(src0, grad)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int i1 = ir0; i1 < ir1; i1++) { - ggml_vec_silu_backward_f32(nc, - (float *) ((char *) dst->data + i1*( dst->nb[1])), - (float *) ((char *) src0->data + i1*(src0->nb[1])), - (float *) ((char *) grad->data + i1*(grad->nb[1]))); - -#ifndef NDEBUG - for (int k = 0; k < nc; k++) { - const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; - UNUSED(x); - assert(!isnan(x)); - assert(!isinf(x)); - } -#endif - } -} - -static void ggml_compute_forward_silu_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_silu_back_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - - -static void ggml_compute_forward_hardswish_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_hardswish_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} -static void ggml_compute_forward_hardswish( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_hardswish_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -static void ggml_compute_forward_hardsigmoid_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_hardsigmoid_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_hardsigmoid( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_hardsigmoid_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -static void ggml_compute_forward_exp_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - ggml_vec_exp_f32(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_exp( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_exp_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - - -// ggml_compute_forward_norm - -static void ggml_compute_forward_norm_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); - - GGML_ASSERT(eps > 0.0f); - - // TODO: optimize - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ith; i01 < ne01; i01 += nth) { - const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - - ggml_float sum = 0.0; - for (int64_t i00 = 0; i00 < ne00; i00++) { - sum += (ggml_float)x[i00]; - } - - float mean = sum/ne00; - - float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); - - ggml_float sum2 = 0.0; - for (int64_t i00 = 0; i00 < ne00; i00++) { - float v = x[i00] - mean; - y[i00] = v; - sum2 += (ggml_float)(v*v); - } - - float variance = sum2/ne00; - const float scale = 1.0f/sqrtf(variance + eps); - - ggml_vec_scale_f32(ne00, y, scale); - } - } - } -} - -static void ggml_compute_forward_norm( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_norm_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_group_rms_norm - -static void ggml_compute_forward_rms_norm_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); - - GGML_ASSERT(eps > 0.0f); - - // TODO: optimize - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ith; i01 < ne01; i01 += nth) { - const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - - ggml_float sum = 0.0; - for (int64_t i00 = 0; i00 < ne00; i00++) { - sum += (ggml_float)(x[i00] * x[i00]); - } - - const float mean = sum/ne00; - - float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); - - memcpy(y, x, ne00 * sizeof(float)); - // for (int i00 = 0; i00 < ne00; i00++) { - // y[i00] = x[i00]; - // } - - const float scale = 1.0f/sqrtf(mean + eps); - - ggml_vec_scale_f32(ne00, y, scale); - } - } - } -} - -static void ggml_compute_forward_rms_norm( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_rms_norm_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -static void ggml_compute_forward_rms_norm_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst) && ggml_are_same_shape(src0, src1)); - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_BINARY_OP_LOCALS - - float eps; - memcpy(&eps, dst->op_params, sizeof(float)); - - // TODO: optimize - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = ith; i01 < ne01; i01 += nth) { - // src1 is same shape as src0 => same indices - const int64_t i11 = i01; - const int64_t i12 = i02; - const int64_t i13 = i03; - - const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - const float * dz = (float *) ((char *) src1->data + i11*nb11 + i12*nb12 + i13*nb13); - - ggml_float sum_xx = 0.0; - ggml_float sum_xdz = 0.0; - - for (int64_t i00 = 0; i00 < ne00; i00++) { - sum_xx += (ggml_float)(x[i00] * x[i00]); - sum_xdz += (ggml_float)(x[i00] * dz[i00]); - } - - //const float mean = (float)(sum_xx)/ne00; - const float mean_eps = (float)(sum_xx)/ne00 + eps; - const float sum_eps = (float)(sum_xx) + eps*ne00; - //const float mean_xdz = (float)(sum_xdz)/ne00; - // we could cache rms from forward pass to improve performance. - // to do this implement ggml_rms and compose ggml_rms_norm using ggml_rms. - //const float rms = sqrtf(mean_eps); - const float rrms = 1.0f / sqrtf(mean_eps); - //const float scale = -rrms/(ne00 * mean_eps); // -1/(n*rms**3) - - { - // z = rms_norm(x) - // - // rms_norm(src0) = - // scale( - // src0, - // div( - // 1, - // sqrt( - // add( - // scale( - // sum( - // sqr( - // src0)), - // (1.0/N)), - // eps)))); - - // postorder: - // ## op args grad - // 00 param src0 grad[#00] - // 01 const 1 - // 02 sqr (#00) grad[#02] - // 03 sum (#02) grad[#03] - // 04 const 1/N - // 05 scale (#03, #04) grad[#05] - // 06 const eps - // 07 add (#05, #06) grad[#07] - // 08 sqrt (#07) grad[#08] - // 09 div (#01,#08) grad[#09] - // 10 scale (#00,#09) grad[#10] - // - // backward pass, given grad[#10] - // #10: scale - // grad[#00] += scale(grad[#10],#09) - // grad[#09] += sum(mul(grad[#10],#00)) - // #09: div - // grad[#08] += neg(mul(grad[#09], div(#09,#08))) - // #08: sqrt - // grad[#07] += mul(grad[#08], div(0.5, #08)) - // #07: add - // grad[#05] += grad[#07] - // #05: scale - // grad[#03] += scale(grad[#05],#04) - // #03: sum - // grad[#02] += repeat(grad[#03], #02) - // #02: - // grad[#00] += scale(mul(#00, grad[#02]), 2.0) - // - // substitute and simplify: - // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, grad[#02]), 2.0) - // grad[#02] = repeat(grad[#03], #02) - // grad[#02] = repeat(scale(grad[#05],#04), #02) - // grad[#02] = repeat(scale(grad[#07],#04), #02) - // grad[#02] = repeat(scale(mul(grad[#08], div(0.5, #08)),#04), #02) - // grad[#02] = repeat(scale(mul(neg(mul(grad[#09], div(#09,#08))), div(0.5, #08)),#04), #02) - // grad[#02] = repeat(scale(mul(neg(mul(sum(mul(grad[#10],#00)), div(#09,#08))), div(0.5, #08)),#04), #02) - // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(#09,#08) * div(0.5, #08) * (1/N)), #02) - // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(div(#01,#08),#08) * div(0.5, #08) * (1/N)), #02) - // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(1,#08*#08) * div(0.5, #08) * (1/N)), #02) - // grad[#02] = repeat(-(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N)), #02) - // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, grad[#02]), 2.0) - // grad[#00] = scale(grad(#10), #09) + scale(mul(#00, repeat(-(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N)), #02)), 2.0) - // grad[#00] = scale(grad(#10), #09) + scale(scale(#00, -(sum(mul(grad[#10],#00)) * div(1,#07) * div(0.5, #08) * (1/N))), 2.0) - // grad[#00] = scale(grad(#10), #09) + scale(#00, -(sum(mul(grad[#10],#00)) * div(1,#07) * div(1,#08) * (1/N))) - // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,#07*#08) * (-1/N)) - // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,#07*#08) * (-1/N)) - // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(1,mean_eps*rms) * (-1/N)) - // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*mean_eps)) - // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*(sum_xx/N+eps))) - // grad[#00] = scale(grad(#10), #09) + scale(#00, sum(mul(grad[#10],#00)) * div(-1,rms*N*sum_xx+rms*N*eps)) - // grad[#00] = scale(dz, rrms) + scale(x, sum(mul(dz,x)) * div(-1,rms*N*mean_eps)) - // grad[#00] = scale(dz, rrms) + scale(x, sum_xdz * div(-1,rms*N*mean_eps)) - // a = b*c + d*e - // a = b*c*f/f + d*e*f/f - // a = (b*c*f + d*e*f)*(1/f) - // a = (b*c*(1/c) + d*e*(1/c))*(1/(1/c)) - // a = (b + d*e/c)*c - // b = dz, c = rrms, d = x, e = sum_xdz * div(-1,rms*N*mean_eps) - // a = (dz + x*sum_xdz * div(-1,rms*N*mean_eps)/rrms)*rrms - // a = (dz + x*sum_xdz * div(-1,rms*N*mean_eps)*rms)*rrms - // a = (dz + x*sum_xdz * div(-rms,rms*N*mean_eps))*rrms - // a = (dz + x*sum_xdz * div(-1,N*mean_eps))*rrms - // a = (dz + x*div(-sum_xdz,N*mean_eps))*rrms - // a = (dz + x*div(-mean_xdz,mean_eps))*rrms - // grad[#00] = scale(dz + scale(x, div(-mean_xdz,mean_eps)),rrms) - // grad[#00] = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) - // dx = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) - } - // dx = scale(dz + scale(x, -mean_xdz/mean_eps),rrms) - // post-order: - // dx := x - // dx := scale(dx,-mean_xdz/mean_eps) - // dx := add(dx, dz) - // dx := scale(dx, rrms) - float * dx = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); - - ggml_vec_cpy_f32 (ne00, dx, x); - // ggml_vec_scale_f32(ne00, dx, -mean_xdz/mean_eps); - ggml_vec_scale_f32(ne00, dx, (float)(-sum_xdz)/sum_eps); - ggml_vec_acc_f32 (ne00, dx, dz); - ggml_vec_scale_f32(ne00, dx, rrms); - } - } - } -} - -static void ggml_compute_forward_rms_norm_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_rms_norm_back_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_group_norm - -static void ggml_compute_forward_group_norm_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - // TODO: optimize - - float eps; - memcpy(&eps, dst->op_params + 1, sizeof(float)); - - int n_channels = src0->ne[2]; - int n_groups = dst->op_params[0]; - int n_channels_per_group = (n_channels + n_groups - 1) / n_groups; - for (int i = ith; i < n_groups; i += nth) { - int start = i * n_channels_per_group; - int end = start + n_channels_per_group; - if (end > n_channels) { - end = n_channels; - } - int step = end - start; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - ggml_float sum = 0.0; - for (int64_t i02 = start; i02 < end; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03); - - ggml_float sumr = 0.0; - for (int64_t i00 = 0; i00 < ne00; i00++) { - sumr += (ggml_float)x[i00]; - } - sum += sumr; - } - } - const float mean = sum / (ne00 * ne01 * step); - - ggml_float sum2 = 0.0; - for (int64_t i02 = start; i02 < end; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - const float * x = (float *)((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03); - - float * y = (float *)((char *) dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3); - - ggml_float sumr = 0.0; - for (int64_t i00 = 0; i00 < ne00; i00++) { - float v = x[i00] - mean; - y[i00] = v; - sumr += (ggml_float)(v * v); - } - sum2 += sumr; - } - } - const float variance = sum2 / (ne00 * ne01 * step); - const float scale = 1.0f / sqrtf(variance + eps); - - for (int64_t i02 = start; i02 < end; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - float * y = (float *)((char *) dst->data + i01 * nb1 + i02 * nb2 + i03 * nb3); - ggml_vec_scale_f32(ne00, y, scale); - } - } - } - } -} - -static void ggml_compute_forward_group_norm( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_group_norm_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_mul_mat - -static void ggml_compute_forward_mul_mat_one_chunk( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const int64_t num_rows_per_vec_dot, - const int64_t ir0_start, - const int64_t ir0_end, - const int64_t ir1_start, - const int64_t ir1_end) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const enum ggml_type type = src0->type; - - const bool src1_cont = ggml_is_contiguous(src1); - - ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot; - enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type; - - // broadcast factors - const int64_t r2 = ne12 / ne02; - const int64_t r3 = ne13 / ne03; - - //printf("ir0_start = %6lld, ir0_end = %6lld, ir1_start = %6lld, ir1_end = %6lld\n", ir0_start, ir0_end, ir1_start, ir1_end); - - // threads with no work simply yield (not sure if it helps) - if (ir0_start >= ir0_end || ir1_start >= ir1_end) { - return; - } - - const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; - const size_t row_size = ggml_row_size(vec_dot_type, ne10); - - assert(ne12 % ne02 == 0); - assert(ne13 % ne03 == 0); - - // block-tiling attempt - const int64_t blck_0 = 16; - const int64_t blck_1 = 16; - - const size_t src1_col_stride = src1_cont || src1->type != vec_dot_type ? row_size : nb11; - - // attempt to reduce false-sharing (does not seem to make a difference) - // 16 * 2, accounting for mmla kernels - float tmp[32]; - - for (int64_t iir1 = ir1_start; iir1 < ir1_end; iir1 += blck_1) { - for (int64_t iir0 = ir0_start; iir0 < ir0_end; iir0 += blck_0) { - for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir1_end; ir1 += num_rows_per_vec_dot) { - const int64_t i13 = (ir1 / (ne12 * ne1)); - const int64_t i12 = (ir1 - i13 * ne12 * ne1) / ne1; - const int64_t i11 = (ir1 - i13 * ne12 * ne1 - i12 * ne1); - - // broadcast src0 into src1 - const int64_t i03 = i13 / r3; - const int64_t i02 = i12 / r2; - - const int64_t i1 = i11; - const int64_t i2 = i12; - const int64_t i3 = i13; - - const char * src0_row = (const char*)src0->data + (0 + i02 * nb02 + i03 * nb03); - - // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides - // if it is, then we have either copied the data to params->wdata and made it contiguous or we are using - // the original src1 data pointer, so we should index using the indices directly - // TODO: this is a bit of a hack, we should probably have a better way to handle this - const char * src1_col = (const char*)wdata + - (src1_cont || src1->type != vec_dot_type - ? (i11 + i12 * ne11 + i13 * ne12 * ne11) * row_size - : (i11 * nb11 + i12 * nb12 + i13 * nb13)); - float * dst_col = (float*)((char*)dst->data + (i1 * nb1 + i2 * nb2 + i3 * nb3)); - - //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ++ir0) { - // vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col); - //} - - for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ir0 += num_rows_per_vec_dot) { - vec_dot(ne00, &tmp[ir0 - iir0], (num_rows_per_vec_dot > 1 ? 16 : 0), src0_row + ir0 * nb01, (num_rows_per_vec_dot > 1 ? nb01 : 0), src1_col, (num_rows_per_vec_dot > 1 ? src1_col_stride : 0), num_rows_per_vec_dot); - } - - for (int cn = 0; cn < num_rows_per_vec_dot; ++cn) { - memcpy(&dst_col[iir0 + cn * nb1 / nb0], tmp + (cn * 16), (MIN(iir0 + blck_0, ir0_end) - iir0) * sizeof(float)); - } - } - } - } -} - -static void ggml_compute_forward_mul_mat( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const enum ggml_type type = src0->type; - - enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type; - ggml_from_float_t const from_float = type_traits[vec_dot_type].from_float; - ggml_from_float_to_mat_t const from_float_to_mat = type_traits[vec_dot_type].from_float_to_mat; - int64_t const vec_dot_num_rows = type_traits[type].nrows; - int64_t const matmul_num_cols = type_traits[type].ncols; - int64_t const blck_size_interleave = type_traits[type].blck_size_interleave; - ggml_gemv_t const gemv = type_traits[type].gemv; - ggml_gemm_t const gemm = type_traits[type].gemm; - - GGML_ASSERT(ne0 == ne01); - GGML_ASSERT(ne1 == ne11); - GGML_ASSERT(ne2 == ne12); - GGML_ASSERT(ne3 == ne13); - - // we don't support permuted src0 or src1 - GGML_ASSERT(nb00 == ggml_type_size(type)); - GGML_ASSERT(nb10 == ggml_type_size(src1->type)); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - // nb01 >= nb00 - src0 is not transposed - // compute by src0 rows - -#if GGML_USE_LLAMAFILE - // broadcast factors - const int64_t r2 = ne12 / ne02; - const int64_t r3 = ne13 / ne03; - - const bool src1_cont = ggml_is_contiguous(src1); - - if (src1_cont) { - for (int64_t i13 = 0; i13 < ne13; i13++) - for (int64_t i12 = 0; i12 < ne12; i12++) - if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type), - (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03, - nb01/ggml_type_size(src0->type), - (const char *)src1->data + i12*nb12 + i13*nb13, - nb11/ggml_type_size(src1->type), - (char *)dst->data + i12*nb2 + i13*nb3, - nb1/ggml_type_size(dst->type), - ith, nth, - src0->type, - src1->type, - dst->type)) - goto UseGgmlGemm1; - return; - } -UseGgmlGemm1:; -#endif - - if (src1->type != vec_dot_type) { - char * wdata = params->wdata; - - const size_t nbw1 = ggml_row_size(vec_dot_type, ne10); - const size_t nbw2 = nbw1*ne11; - const size_t nbw3 = nbw2*ne12; - - assert(params->wsize >= ne13*nbw3); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - for (int64_t i13 = 0; i13 < ne13; ++i13) { - for (int64_t i12 = 0; i12 < ne12; ++i12) { - int64_t i11_processed = 0; - if ((ggml_n_dims(src1) == 2) && from_float_to_mat && gemm) { - for (int64_t i11 = ith * 4; i11 < ne11 - ne11 % 4; i11 += nth * 4) { - from_float_to_mat((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), - (void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1), - 4, ne10, blck_size_interleave); - } - i11_processed = ne11 - ne11 % 4; - } - for (int64_t i11 = i11_processed + ith; i11 < ne11; i11 += nth) { - from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), - (void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1), - ne10); - } - } - } - } - - if (ith == 0) { - // Every thread starts at ith, so the first unprocessed chunk is nth. This save a bit of coordination right at the start. - atomic_store_explicit(¶ms->threadpool->current_chunk, nth, memory_order_relaxed); - } - - ggml_barrier(params->threadpool); - -#if GGML_USE_LLAMAFILE - if (src1->type != vec_dot_type) { - const void* wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; - const size_t row_size = ggml_row_size(vec_dot_type, ne10); - - for (int64_t i13 = 0; i13 < ne13; i13++) - for (int64_t i12 = 0; i12 < ne12; i12++) - if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type), - (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03, - nb01/ggml_type_size(src0->type), - (const char *)wdata + (i12*ne11 + i13*ne12*ne11)*row_size, - row_size/ggml_type_size(vec_dot_type), - (char *)dst->data + i12*nb2 + i13*nb3, - nb1/ggml_type_size(dst->type), - ith, nth, - src0->type, - vec_dot_type, - dst->type)) - goto UseGgmlGemm2; - return; - } -UseGgmlGemm2:; -#endif - - // This is the size of the first dimension of the result, so we can iterate that way. (see the ASSERT above, these are the same numbers) - const int64_t nr0 = ne0; - - // This is the size of the rest of the dimensions of the result - const int64_t nr1 = ne1 * ne2 * ne3; - - // dot kernels can handle 1 row and col at a time, but mmla kernels can process 2 rows and cols - int64_t num_rows_per_vec_dot = vec_dot_num_rows; - // TODO: currently the mmla kernels support only even numbered rows/cols. - // this check can be removed once they are extended to support odd numbered rows/cols too - if ((nr0 % 2 != 0) || (ne11 % 2 != 0)) { - num_rows_per_vec_dot = 1; - } - - // Now select a reasonable chunk size. - int chunk_size = 16; - - // We need to step up the size if it's small - if (nr0 == 1 || nr1 == 1) { - chunk_size = 64; - } - - // distribute the work across the inner or outer loop based on which one is larger - // The number of chunks in the 0/1 dim. - // CEIL(nr0/chunk_size) - int64_t nchunk0 = (nr0 + chunk_size - 1) / chunk_size; - int64_t nchunk1 = (nr1 + chunk_size - 1) / chunk_size; - - // If the chunking is poor for the number of threads on this setup, scrap the whole plan. Re-chunk it by thread. - // Also, chunking by thread was measured to have perform better on NUMA systems. See https://github.com/ggerganov/llama.cpp/pull/6915 - // In theory, chunking should be just as useful on NUMA and non NUMA systems, but testing disagreed with that. - if (nchunk0 * nchunk1 < nth * 4 || ggml_is_numa()) { - // distribute the thread work across the inner or outer loop based on which one is larger - nchunk0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows - nchunk1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows - } - - // The number of elements in each chunk - const int64_t dr0 = (nr0 + nchunk0 - 1) / nchunk0; - const int64_t dr1 = (nr1 + nchunk1 - 1) / nchunk1; - - if ((ggml_n_dims(src0) == 2) && gemv) { - const void * src1_wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; - const size_t src1_col_stride = ggml_is_contiguous(src1) || src1->type != vec_dot_type ? ggml_row_size(vec_dot_type, ne10) : nb11; - int64_t src0_start = (ith * ne01) / nth; - int64_t src0_end = ((ith + 1) * ne01) / nth; - src0_start = (src0_start % matmul_num_cols) ? src0_start + matmul_num_cols - (src0_start % matmul_num_cols): src0_start; - src0_end = (src0_end % matmul_num_cols) ? src0_end + matmul_num_cols - (src0_end % matmul_num_cols): src0_end; - if (src0_start >= src0_end) return; - - // If there are more than three rows in src1, use gemm; otherwise, use gemv. - if (gemm && (ne11 > 3)) { - gemm(ne00, (float *)((char *) dst->data) + src0_start, ne01, (const char *) src0->data + src0_start * nb01, - (const char *) src1_wdata, ne11 - ne11 % 4, src0_end - src0_start); - } - for (int iter = gemm ? ne11 - ne11 % 4 : 0; iter < ne11; iter++) { - gemv(ne00, (float *)((char *) dst->data + (iter * nb1)) + src0_start, ne01, - (const char *) src0->data + src0_start * nb01, (const char *) src1_wdata + (src1_col_stride * iter), 1, - src0_end - src0_start); - } - return; - } - - // The first chunk comes from our thread_id, the rest will get auto-assigned. - int current_chunk = ith; - - while (current_chunk < nchunk0 * nchunk1) { - const int64_t ith0 = current_chunk % nchunk0; - const int64_t ith1 = current_chunk / nchunk0; - - const int64_t ir0_start = dr0 * ith0; - const int64_t ir0_end = MIN(ir0_start + dr0, nr0); - - const int64_t ir1_start = dr1 * ith1; - const int64_t ir1_end = MIN(ir1_start + dr1, nr1); - - ggml_compute_forward_mul_mat_one_chunk(params, dst, num_rows_per_vec_dot, ir0_start, ir0_end, ir1_start, ir1_end); - - if (nth >= nchunk0 * nchunk1) { - break; - } - - current_chunk = atomic_fetch_add_explicit(¶ms->threadpool->current_chunk, 1, memory_order_relaxed); - } -} - -// ggml_compute_forward_mul_mat_id - -static void ggml_compute_forward_mul_mat_id( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - const struct ggml_tensor * ids = dst->src[2]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const enum ggml_type type = src0->type; - - const bool src1_cont = ggml_is_contiguous(src1); - - ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot; - enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type; - ggml_from_float_t const from_float = type_traits[vec_dot_type].from_float; - int64_t const matmul_num_cols = type_traits[type].ncols; - ggml_gemv_t const gemv = type_traits[type].gemv; - - // we don't support permuted src0 or src1 - GGML_ASSERT(nb00 == ggml_type_size(type)); - GGML_ASSERT(nb10 == ggml_type_size(src1->type)); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - // row groups - const int n_ids = ids->ne[0]; // n_expert_used - const int n_as = ne02; // n_expert - - char * wdata_src1_end = (src1->type == vec_dot_type) ? - (char *) params->wdata : - (char *) params->wdata + GGML_PAD(ggml_row_size(vec_dot_type, ggml_nelements(src1)), sizeof(int64_t)); - - struct mmid_row_mapping { - int32_t i1; - int32_t i2; - }; - - int64_t * matrix_row_counts = (int64_t *) (wdata_src1_end); // [n_as] - struct mmid_row_mapping * matrix_rows = (struct mmid_row_mapping *)(matrix_row_counts + n_as); // [n_as][ne11] - - if (src1->type != vec_dot_type) { - char * wdata = params->wdata; - - const size_t nbw1 = ggml_row_size(vec_dot_type, ne10); - const size_t nbw2 = nbw1*ne11; - const size_t nbw3 = nbw2*ne12; - - assert(params->wsize >= ne13*nbw3); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - for (int64_t i13 = 0; i13 < ne13; ++i13) { - for (int64_t i12 = 0; i12 < ne12; ++i12) { - for (int64_t i11 = ith; i11 < ne11; i11 += nth) { - from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), - (void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1), - ne10); - } - } - } - } - -#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne12 + (i1)] - - if (ith == 0) { - // initialize matrix_row_counts - memset(matrix_row_counts, 0, n_as*sizeof(int64_t)); - - // group rows by src0 matrix - for (int64_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) { - for (int id = 0; id < n_ids; ++id) { - const int32_t i02 = *(const int32_t *) ((const char *) ids->data + iid1*ids->nb[1] + id*ids->nb[0]); - - assert(i02 >= 0 && i02 < n_as); - - MMID_MATRIX_ROW(i02, matrix_row_counts[i02]) = (struct mmid_row_mapping) {id, iid1}; - matrix_row_counts[i02] += 1; - } - } - } - - ggml_barrier(params->threadpool); - - // compute each matrix multiplication in sequence - for (int cur_a = 0; cur_a < n_as; ++cur_a) { - const int64_t cne1 = matrix_row_counts[cur_a]; - - if (cne1 == 0) { - continue; - } - - const char * src0_cur = (const char *) src0->data + cur_a*nb02; - - const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; - const size_t row_size = ggml_row_size(vec_dot_type, ne10); - - const int64_t nr0 = ne01; // src0 rows - const int64_t nr1 = cne1; // src1 rows - - if (((ggml_n_dims(src0) - 1) == 2) && gemv) { - int64_t src0_cur_start = (ith * ne01) / nth; - int64_t src0_cur_end = ((ith + 1) * ne01) / nth; - src0_cur_start = (src0_cur_start % matmul_num_cols) ? src0_cur_start + matmul_num_cols - (src0_cur_start % matmul_num_cols): src0_cur_start; - src0_cur_end = (src0_cur_end % matmul_num_cols) ? src0_cur_end + matmul_num_cols - (src0_cur_end % matmul_num_cols): src0_cur_end; - if (src0_cur_start >= src0_cur_end) return; - - for (int ir1 = 0; ir1 < nr1; ir1++) { - struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, ir1); - const int id = row_mapping.i1; // selected expert index - - const int64_t i11 = id % ne11; - const int64_t i12 = row_mapping.i2; // row index in src1 - - const int64_t i1 = id; // selected expert index - const int64_t i2 = i12; // row - - const char * src1_col = (const char *) wdata + - (src1_cont || src1->type != vec_dot_type - ? (i11 + i12 * ne11) * row_size - : (i11 * nb11 + i12 * nb12)); - - gemv(ne00, (float *)((char *) dst->data + (i1 * nb1 + i2 * nb2)) + src0_cur_start, ne01, - (const char *) src0_cur + src0_cur_start * nb01, src1_col, 1, src0_cur_end - src0_cur_start); - } - continue; - } - - // distribute the thread work across the inner or outer loop based on which one is larger - - const int64_t nth0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows - const int64_t nth1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows - - const int64_t ith0 = ith % nth0; - const int64_t ith1 = ith / nth0; - - const int64_t dr0 = (nr0 + nth0 - 1)/nth0; - const int64_t dr1 = (nr1 + nth1 - 1)/nth1; - - const int64_t ir010 = dr0*ith0; - const int64_t ir011 = MIN(ir010 + dr0, nr0); - - const int64_t ir110 = dr1*ith1; - const int64_t ir111 = MIN(ir110 + dr1, nr1); - - // threads with no work simply yield (not sure if it helps) - //if (ir010 >= ir011 || ir110 >= ir111) { - // sched_yield(); - // continue; - //} - - // block-tiling attempt - const int64_t blck_0 = 16; - const int64_t blck_1 = 16; - - // attempt to reduce false-sharing (does not seem to make a difference) - float tmp[16]; - - for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) { - for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) { - for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) { - const int64_t _i12 = ir1; // logical row index for this expert - - struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, _i12); - const int id = row_mapping.i1; // selected expert index - - const int64_t i11 = id % ne11; - const int64_t i12 = row_mapping.i2; // row index in src1 - - const int64_t i1 = id; // selected expert index - const int64_t i2 = i12; // row - - // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides - // if it is, then we have either copied the data to params->wdata and made it contiguous or we are using - // the original src1 data pointer, so we should index using the indices directly - // TODO: this is a bit of a hack, we should probably have a better way to handle this - const char * src1_col = (const char *) wdata + - (src1_cont || src1->type != vec_dot_type - ? (i11 + i12*ne11)*row_size - : (i11*nb11 + i12*nb12)); - - float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2)); - - //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) { - // vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col); - //} - - for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) { - vec_dot(ne00, &tmp[ir0 - iir0], 0, src0_cur + ir0*nb01, 0, src1_col, 0, 1); - } - - memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float)); - } - } - } - } - -#undef MMID_MATRIX_ROW -} - -// ggml_compute_forward_out_prod - -static void ggml_compute_forward_out_prod_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT(dst->type == GGML_TYPE_F32); - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_ASSERT(ne0 == ne00); - GGML_ASSERT(ne1 == ne10); - GGML_ASSERT(ne2 == ne02); - GGML_ASSERT(ne02 == ne12); - GGML_ASSERT(ne3 == ne13); - GGML_ASSERT(ne03 == ne13); - - // we don't support permuted src0 or src1 - GGML_ASSERT(nb00 == sizeof(float)); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - // GGML_ASSERT(nb0 <= nb1); - // GGML_ASSERT(nb1 <= nb2); - // GGML_ASSERT(nb2 <= nb3); - - // nb01 >= nb00 - src0 is not transposed - // compute by src0 rows - - if (ith == 0) { - ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); - } - ggml_barrier(params->threadpool); - - // dst[:,:,:,:] = 0 - // for i2,i3: - // for i1: - // for i01: - // for i0: - // dst[i0,i1,i2,i3] += src0[i0,i01,i2,i3] * src1[i1,i01,i2,i3] - - // parallelize by last three dimensions - - // total rows in dst - const int64_t nr = ne1*ne2*ne3; - - // rows per thread - const int64_t dr = (nr + nth - 1)/nth; - - // row range for this thread - const int64_t ir0 = dr*ith; - const int64_t ir1 = MIN(ir0 + dr, nr); - - // block-tiling attempt - const int64_t blck_0 = MAX(GGML_VEC_MAD_UNROLL, 32); - const int64_t blck_1 = 16; - - for (int64_t bir = ir0; bir < ir1; bir += blck_1) { - const int64_t bir1 = MIN(bir + blck_1, ir1); - for (int64_t bi01 = 0; bi01 < ne01; bi01 += blck_0) { - const int64_t bne01 = MIN(bi01 + blck_0, ne01); - for (int64_t ir = bir; ir < bir1; ++ir) { - // dst indices - const int64_t i3 = ir/(ne2*ne1); - const int64_t i2 = (ir - i3*ne2*ne1)/ne1; - const int64_t i1 = (ir - i3*ne2*ne1 - i2*ne1); - - const int64_t i02 = i2; - const int64_t i03 = i3; - - //const int64_t i10 = i1; - const int64_t i12 = i2; - const int64_t i13 = i3; - -#if GGML_VEC_MAD_UNROLL > 2 - const int64_t bne01_unroll = bne01 - (bne01 % GGML_VEC_MAD_UNROLL); - for (int64_t i01 = bi01; i01 < bne01_unroll; i01 += GGML_VEC_MAD_UNROLL) { - const int64_t i11 = i01; - - float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); - float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); - float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); - - ggml_vec_mad_f32_unroll(ne0, nb01, nb11, d, s0, s1); - } - for (int64_t i01 = bne01_unroll; i01 < bne01; ++i01) { - const int64_t i11 = i01; - - float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); - float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); - float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); - - ggml_vec_mad_f32(ne0, d, s0, *s1); - } -#else - for (int64_t i01 = bi01; i01 < bne01; ++i01) { - const int64_t i11 = i01; - - float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); - float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); - float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); - - ggml_vec_mad_f32(ne0, d, s0, *s1); - } -#endif - } - } - } -} - -static void ggml_compute_forward_out_prod_q_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS; - - const int ith = params->ith; - const int nth = params->nth; - - const enum ggml_type type = src0->type; - ggml_to_float_t const dequantize_row_q = type_traits[type].to_float; - - GGML_ASSERT(ne02 == ne12); - GGML_ASSERT(ne03 == ne13); - GGML_ASSERT(ne2 == ne12); - GGML_ASSERT(ne3 == ne13); - - // we don't support permuted src0 dim0 - GGML_ASSERT(nb00 == ggml_type_size(type)); - - // dst dim0 cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - // GGML_ASSERT(nb0 <= nb1); - // GGML_ASSERT(nb1 <= nb2); - // GGML_ASSERT(nb2 <= nb3); - - GGML_ASSERT(ne0 == ne00); - GGML_ASSERT(ne1 == ne10); - GGML_ASSERT(ne2 == ne02); - GGML_ASSERT(ne3 == ne03); - - // nb01 >= nb00 - src0 is not transposed - // compute by src0 rows - - if (ith == 0) { - ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0); - } - ggml_barrier(params->threadpool); - - // parallelize by last three dimensions - - // total rows in dst - const int64_t nr = ne1*ne2*ne3; - - // rows per thread - const int64_t dr = (nr + nth - 1)/nth; - - // row range for this thread - const int64_t ir0 = dr*ith; - const int64_t ir1 = MIN(ir0 + dr, nr); - - // dst[:,:,:,:] = 0 - // for i2,i3: - // for i1: - // for i01: - // for i0: - // dst[i0,i1,i2,i3] += src0[i0,i01,i2,i3] * src1[i1,i01,i2,i3] - - float * wdata = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32) * ith; - - for (int64_t ir = ir0; ir < ir1; ++ir) { - // dst indices - const int64_t i3 = ir/(ne2*ne1); - const int64_t i2 = (ir - i3*ne2*ne1)/ne1; - const int64_t i1 = (ir - i3*ne2*ne1 - i2*ne1); - - const int64_t i02 = i2; - const int64_t i03 = i3; - - //const int64_t i10 = i1; - const int64_t i12 = i2; - const int64_t i13 = i3; - - for (int64_t i01 = 0; i01 < ne01; ++i01) { - const int64_t i11 = i01; - - float * s0 = (float *) ((char *) src0->data + ( i01*nb01 + i02*nb02 + i03*nb03)); - float * s1 = (float *) ((char *) src1->data + (i1*nb10 + i11*nb11 + i12*nb12 + i13*nb13)); - float * d = (float *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); - - dequantize_row_q(s0, wdata, ne0); - ggml_vec_mad_f32(ne0, d, wdata, *s1); - } - } -} - -static void ggml_compute_forward_out_prod( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_Q4_0_4_4: - case GGML_TYPE_Q4_0_4_8: - case GGML_TYPE_Q4_0_8_8: - { - ggml_compute_forward_out_prod_q_f32(params, dst); - } break; - case GGML_TYPE_F16: - { - GGML_ABORT("fatal error"); // todo - // ggml_compute_forward_out_prod_f16_f32(params, dst); - } - case GGML_TYPE_F32: - { - ggml_compute_forward_out_prod_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_scale - -static void ggml_compute_forward_scale_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(ggml_is_contiguous(src0)); - GGML_ASSERT(ggml_is_contiguous(dst)); - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - - // scale factor - float v; - memcpy(&v, dst->op_params, sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - const size_t nb01 = src0->nb[1]; - - const size_t nb1 = dst->nb[1]; - - for (int i1 = ir0; i1 < ir1; i1++) { - if (dst->data != src0->data) { - // src0 is same shape as dst => same indices - memcpy((char *)dst->data + i1*nb1, (char *)src0->data + i1*nb01, nc * sizeof(float)); - } - ggml_vec_scale_f32(nc, (float *) ((char *) dst->data + i1*nb1), v); - } -} - -static void ggml_compute_forward_scale( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_scale_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_set - -static void ggml_compute_forward_set_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); - - // view src0 and dst with these strides and data offset inbytes during set - // nb0 is implicitly element_size because src0 and dst are contiguous - size_t nb1 = ((int32_t *) dst->op_params)[0]; - size_t nb2 = ((int32_t *) dst->op_params)[1]; - size_t nb3 = ((int32_t *) dst->op_params)[2]; - size_t offset = ((int32_t *) dst->op_params)[3]; - bool inplace = (bool) ((int32_t *) dst->op_params)[4]; - - if (!inplace) { - if (params->ith == 0) { - // memcpy needs to be synchronized across threads to avoid race conditions. - // => do it in INIT phase - memcpy( - ((char *) dst->data), - ((char *) src0->data), - ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - } - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src1); - const int nc = src1->ne[0]; - - GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) - GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) - - // src0 and dst as viewed during set - const size_t nb0 = ggml_element_size(src0); - - const int im0 = (ne10 == 0 ? 0 : ne10-1); - const int im1 = (ne11 == 0 ? 0 : ne11-1); - const int im2 = (ne12 == 0 ? 0 : ne12-1); - const int im3 = (ne13 == 0 ? 0 : ne13-1); - - GGML_ASSERT(offset + im0*nb0 + im1*nb1 + im2*nb2 + im3*nb3 <= ggml_nbytes(dst)); - - GGML_ASSERT(nb10 == sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 and dst are viewed with shape of src1 and offset - // => same indices - const int i3 = ir/(ne12*ne11); - const int i2 = (ir - i3*ne12*ne11)/ne11; - const int i1 = (ir - i3*ne12*ne11 - i2*ne11); - - ggml_vec_cpy_f32(nc, - (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + offset), - (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11)); - } -} - -static void ggml_compute_forward_set( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_set_f32(params, dst); - } break; - case GGML_TYPE_F16: - case GGML_TYPE_BF16: - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q8_1: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_Q4_0_4_4: - case GGML_TYPE_Q4_0_4_8: - case GGML_TYPE_Q4_0_8_8: - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_cpy - -static void ggml_compute_forward_cpy( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - ggml_compute_forward_dup(params, dst); -} - -// ggml_compute_forward_cont - -static void ggml_compute_forward_cont( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - ggml_compute_forward_dup(params, dst); -} - -// ggml_compute_forward_reshape - -static void ggml_compute_forward_reshape( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - // NOP - UNUSED(params); - UNUSED(dst); -} - -// ggml_compute_forward_view - -static void ggml_compute_forward_view( - const struct ggml_compute_params * params, - const struct ggml_tensor * dst) { - // NOP - UNUSED(params); - UNUSED(dst); -} - -// ggml_compute_forward_permute - -static void ggml_compute_forward_permute( - const struct ggml_compute_params * params, - const struct ggml_tensor * dst) { - // NOP - UNUSED(params); - UNUSED(dst); -} - -// ggml_compute_forward_transpose - -static void ggml_compute_forward_transpose( - const struct ggml_compute_params * params, - const struct ggml_tensor * dst) { - // NOP - UNUSED(params); - UNUSED(dst); -} - -// ggml_compute_forward_get_rows - -static void ggml_compute_forward_get_rows_q( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int64_t nc = ne00; - const int64_t nr = ggml_nelements(src1); - - const enum ggml_type type = src0->type; - ggml_to_float_t const dequantize_row_q = type_traits[type].to_float; - - assert(ne0 == nc); - assert(ne02 == ne11); - assert(nb00 == ggml_type_size(type)); - assert(ggml_nrows(dst) == nr); - - const int ith = params->ith; - const int nth = params->nth; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int64_t i = ir0; i < ir1; ++i) { - const int64_t i12 = i/(ne11*ne10); - const int64_t i11 = (i - i12*ne11*ne10)/ne10; - const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); - const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); - - GGML_ASSERT(i01 >= 0 && i01 < ne01); - - dequantize_row_q( - (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), - (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); - } -} - -static void ggml_compute_forward_get_rows_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int64_t nc = ne00; - const int64_t nr = ggml_nelements(src1); - - assert(ne0 == nc); - assert(ne02 == ne11); - assert(nb00 == sizeof(ggml_fp16_t)); - assert(ggml_nrows(dst) == nr); - - const int ith = params->ith; - const int nth = params->nth; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int64_t i = ir0; i < ir1; ++i) { - const int64_t i12 = i/(ne11*ne10); - const int64_t i11 = (i - i12*ne11*ne10)/ne10; - const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); - const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); - - GGML_ASSERT(i01 >= 0 && i01 < ne01); - - ggml_fp16_to_fp32_row( - (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), - (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); - } -} - -static void ggml_compute_forward_get_rows_bf16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int64_t nc = ne00; - const int64_t nr = ggml_nelements(src1); - - assert(ne0 == nc); - assert(ne02 == ne11); - assert(nb00 == sizeof(ggml_bf16_t)); - assert(ggml_nrows(dst) == nr); - - const int ith = params->ith; - const int nth = params->nth; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int64_t i = ir0; i < ir1; ++i) { - const int64_t i12 = i/(ne11*ne10); - const int64_t i11 = (i - i12*ne11*ne10)/ne10; - const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); - const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); - - GGML_ASSERT(i01 >= 0 && i01 < ne01); - - ggml_bf16_to_fp32_row( - (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), - (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); - } -} - -static void ggml_compute_forward_get_rows_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_TENSOR_BINARY_OP_LOCALS - - const int64_t nc = ne00; - const int64_t nr = ggml_nelements(src1); - - assert(ne0 == nc); - assert(ne02 == ne11); - assert(nb00 == sizeof(float)); - assert(ggml_nrows(dst) == nr); - - const int ith = params->ith; - const int nth = params->nth; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int64_t i = ir0; i < ir1; ++i) { - const int64_t i12 = i/(ne11*ne10); - const int64_t i11 = (i - i12*ne11*ne10)/ne10; - const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); - const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); - - GGML_ASSERT(i01 >= 0 && i01 < ne01); - - ggml_vec_cpy_f32(nc, - (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), - (float *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03)); - } -} - -static void ggml_compute_forward_get_rows( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q8_1: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_Q4_0_4_4: - case GGML_TYPE_Q4_0_4_8: - case GGML_TYPE_Q4_0_8_8: - { - ggml_compute_forward_get_rows_q(params, dst); - } break; - case GGML_TYPE_F16: - { - ggml_compute_forward_get_rows_f16(params, dst); - } break; - case GGML_TYPE_BF16: - { - ggml_compute_forward_get_rows_bf16(params, dst); - } break; - case GGML_TYPE_F32: - case GGML_TYPE_I32: - { - ggml_compute_forward_get_rows_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } - - //static bool first = true; - //printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]); - //if (first) { - // first = false; - //} else { - // for (int k = 0; k < dst->ne[1]; ++k) { - // for (int j = 0; j < dst->ne[0]/16; ++j) { - // for (int i = 0; i < 16; ++i) { - // printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]); - // } - // printf("\n"); - // } - // printf("\n"); - // } - // printf("\n"); - // exit(0); - //} -} - -// ggml_compute_forward_get_rows_back - -static void ggml_compute_forward_get_rows_back_f32_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_is_contiguous(dst)); - - // ggml_compute_forward_dup_same_cont(params, opt0, dst); - - memset(dst->data, 0, ggml_nbytes(dst)); - - const int nc = src0->ne[0]; - const int nr = ggml_nelements(src1); - - GGML_ASSERT( dst->ne[0] == nc); - GGML_ASSERT(src0->nb[0] == sizeof(ggml_fp16_t)); - - for (int i = 0; i < nr; ++i) { - const int r = ((int32_t *) src1->data)[i]; - - for (int j = 0; j < nc; ++j) { - ggml_fp16_t v = ((ggml_fp16_t *) ((char *) src0->data + i*src0->nb[1]))[j]; - ((float *) ((char *) dst->data + r*dst->nb[1]))[j] += GGML_FP16_TO_FP32(v); - } - } -} - -static void ggml_compute_forward_get_rows_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - if (params->ith != 0) { - return; - } - - GGML_ASSERT(ggml_is_contiguous(dst)); - - // ggml_compute_forward_dup_same_cont(params, opt0, dst); - - memset(dst->data, 0, ggml_nbytes(dst)); - - const int nc = src0->ne[0]; - const int nr = ggml_nelements(src1); - - GGML_ASSERT( dst->ne[0] == nc); - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - for (int i = 0; i < nr; ++i) { - const int r = ((int32_t *) src1->data)[i]; - - ggml_vec_add_f32(nc, - (float *) ((char *) dst->data + r*dst->nb[1]), - (float *) ((char *) dst->data + r*dst->nb[1]), - (float *) ((char *) src0->data + i*src0->nb[1])); - } -} - -static void ggml_compute_forward_get_rows_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_get_rows_back_f32_f16(params, dst); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_get_rows_back_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } - - //static bool first = true; - //printf("ne0 = %d, ne1 = %d, ne2 = %d\n", dst->ne[0], dst->ne[1], dst->ne[2]); - //if (first) { - // first = false; - //} else { - // for (int k = 0; k < dst->ne[1]; ++k) { - // for (int j = 0; j < dst->ne[0]/16; ++j) { - // for (int i = 0; i < 16; ++i) { - // printf("%8.4f ", ((float *) dst->data)[k*dst->ne[0] + j*16 + i]); - // } - // printf("\n"); - // } - // printf("\n"); - // } - // printf("\n"); - // exit(0); - //} -} - -// ggml_compute_forward_diag - -static void ggml_compute_forward_diag_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - // TODO: handle transposed/permuted matrices - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(ne00 == ne0); - GGML_ASSERT(ne00 == ne1); - GGML_ASSERT(ne01 == 1); - GGML_ASSERT(ne02 == ne2); - GGML_ASSERT(ne03 == ne3); - - GGML_ASSERT(nb00 == sizeof(float)); - GGML_ASSERT(nb0 == sizeof(float)); - - for (int i3 = 0; i3 < ne3; i3++) { - for (int i2 = 0; i2 < ne2; i2++) { - for (int i1 = 0; i1 < ne1; i1++) { - float * d = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); - float * s = (float *)((char *) src0->data + i3*nb03 + i2*nb02); - for (int i0 = 0; i0 < i1; i0++) { - d[i0] = 0; - } - d[i1] = s[i1]; - for (int i0 = i1+1; i0 < ne0; i0++) { - d[i0] = 0; - } - } - } - } -} - -static void ggml_compute_forward_diag( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_diag_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_diag_mask_inf - -static void ggml_compute_forward_diag_mask_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const float value) { - - const struct ggml_tensor * src0 = dst->src[0]; - - const int ith = params->ith; - const int nth = params->nth; - - const int n_past = ((int32_t *) dst->op_params)[0]; - const bool inplace = src0->data == dst->data; - - GGML_ASSERT(n_past >= 0); - - if (!inplace) { - if (ith == 0) { - // memcpy needs to be synchronized across threads to avoid race conditions. - // => do it in INIT phase - GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); - GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); - memcpy( - ((char *) dst->data), - ((char *) src0->data), - ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - } - - // TODO: handle transposed/permuted matrices - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - const int nr = src0->ne[1]; - const int nz = n/nr; - - GGML_ASSERT( dst->nb[0] == sizeof(float)); - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - for (int k = 0; k < nz; k++) { - for (int j = ith; j < nr; j += nth) { - for (int i = n_past; i < nc; i++) { - if (i > n_past + j) { - *(float *)((char *) dst->data + k*dst->nb[2] + j*dst->nb[1] + i*dst->nb[0]) = value; - } - } - } - } -} - -static void ggml_compute_forward_diag_mask_inf( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_diag_mask_f32(params, dst, -INFINITY); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -static void ggml_compute_forward_diag_mask_zero( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_diag_mask_f32(params, dst, 0); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_soft_max - -static void ggml_compute_forward_soft_max_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - assert(ggml_is_contiguous(dst)); - assert(ggml_are_same_shape(src0, dst)); - - float scale = 1.0f; - float max_bias = 0.0f; - - memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); - memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); - - // TODO: handle transposed/permuted matrices - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - //const int64_t ne11 = src1 ? src1->ne[1] : 1; - - // TODO: is this supposed to be ceil instead of floor? - // https://huggingface.co/mosaicml/mpt-7b/blob/main/attention.py#L370 - const uint32_t n_head = ne02; - const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head)); - - const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - float * wp = (float *) params->wdata + (nc + CACHE_LINE_SIZE_F32) * ith; - - const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16); - - for (int i1 = ir0; i1 < ir1; i1++) { - // ALiBi - const uint32_t h = (i1/ne01)%ne02; // head - const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f; - - float * sp = (float *)((char *) src0->data + i1*src0->nb[1]); - float * dp = (float *)((char *) dst->data + i1*dst->nb[1]); - - // broadcast the mask across rows - ggml_fp16_t * mp_f16 = src1 ? (ggml_fp16_t *)((char *) src1->data) + (i1%ne01)*ne00 : NULL; - float * mp_f32 = src1 ? (float *)((char *) src1->data) + (i1%ne01)*ne00 : NULL; - - ggml_vec_cpy_f32 (nc, wp, sp); - ggml_vec_scale_f32(nc, wp, scale); - if (mp_f32) { - if (use_f16) { - for (int i = 0; i < nc; ++i) { - wp[i] += slope*GGML_FP16_TO_FP32(mp_f16[i]); - } - } else { - for (int i = 0; i < nc; ++i) { - wp[i] += slope*mp_f32[i]; - } - } - } - -#ifndef NDEBUG - for (int i = 0; i < nc; ++i) { - //printf("p[%d] = %f\n", i, p[i]); - assert(!isnan(wp[i])); - } -#endif - - float max = -INFINITY; - ggml_vec_max_f32(nc, &max, wp); - - ggml_float sum = ggml_vec_soft_max_f32(nc, dp, wp, max); - assert(sum > 0.0); - - sum = 1.0/sum; - ggml_vec_scale_f32(nc, dp, sum); - -#ifndef NDEBUG - for (int i = 0; i < nc; ++i) { - assert(!isnan(dp[i])); - assert(!isinf(dp[i])); - } -#endif - } -} - -static void ggml_compute_forward_soft_max( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_soft_max_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - - -// ggml_compute_forward_soft_max_back - -static void ggml_compute_forward_soft_max_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(ggml_is_contiguous(src0)); - GGML_ASSERT(ggml_is_contiguous(src1)); - GGML_ASSERT(ggml_is_contiguous(dst)); - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_are_same_shape(src1, dst)); - - // TODO: handle transposed/permuted matrices - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src0->ne[0]; - const int nr = ggml_nrows(src0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int i1 = ir0; i1 < ir1; i1++) { - float *dy = (float *)((char *) src0->data + i1*src0->nb[1]); - float *y = (float *)((char *) src1->data + i1*src1->nb[1]); - float *dx = (float *)((char *) dst->data + i1*dst->nb[1]); - -#ifndef NDEBUG - for (int i = 0; i < nc; ++i) { - //printf("p[%d] = %f\n", i, p[i]); - assert(!isnan(dy[i])); - assert(!isnan(y[i])); - } -#endif - // Jii = yi - yi*yi - // Jij = -yi*yj - // J = diag(y)-y.T*y - // dx = J * dy - // dxk = sum_i(Jki * dyi) - // dxk = sum_i(-yk*yi * dyi) - (-yk*yk)*dyk + (yk - yk*yk)*dyk - // dxk = sum_i(-yk*yi * dyi) + yk*yk*dyk + yk*dyk - yk*yk*dyk - // dxk = sum_i(-yk*yi * dyi) + yk*dyk - // dxk = -yk * sum_i(yi * dyi) + yk*dyk - // dxk = -yk * dot(y, dy) + yk*dyk - // dxk = yk * (- dot(y, dy) + dyk) - // dxk = yk * (dyk - dot(y, dy)) - // - // post-order: - // dot_y_dy := dot(y, dy) - // dx := dy - // dx := dx - dot_y_dy - // dx := dx * y - - // linear runtime, no additional memory - float dot_y_dy = 0; - ggml_vec_dot_f32 (nc, &dot_y_dy, 0, y, 0, dy, 0, 1); - ggml_vec_cpy_f32 (nc, dx, dy); - ggml_vec_acc1_f32(nc, dx, -dot_y_dy); - ggml_vec_mul_f32 (nc, dx, dx, y); - -#ifndef NDEBUG - for (int i = 0; i < nc; ++i) { - assert(!isnan(dx[i])); - assert(!isinf(dx[i])); - } -#endif - } -} - -static void ggml_compute_forward_soft_max_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_soft_max_back_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_clamp - -static void ggml_compute_forward_clamp_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - float min; - float max; - memcpy(&min, (float *) dst->op_params + 0, sizeof(float)); - memcpy(&max, (float *) dst->op_params + 1, sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - - GGML_ASSERT( nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - - for (int j = ith; j < n; j += nth) { - float * dst_ptr = (float *) ((char *) dst->data + j*nb1); - float * src0_ptr = (float *) ((char *) src0->data + j*nb01); - - for (int i = 0; i < nc; i++) { - dst_ptr[i] = MAX(MIN(src0_ptr[i], max), min); - } - } -} - -static void ggml_compute_forward_clamp( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_clamp_f32(params, dst); - } break; - case GGML_TYPE_F16: - case GGML_TYPE_BF16: - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q8_1: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_TQ1_0: - case GGML_TYPE_TQ2_0: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_Q8_K: - case GGML_TYPE_Q4_0_4_4: - case GGML_TYPE_Q4_0_4_8: - case GGML_TYPE_Q4_0_8_8: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_I64: - case GGML_TYPE_F64: - case GGML_TYPE_COUNT: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_rope - -static float rope_yarn_ramp(const float low, const float high, const int i0) { - const float y = (i0 / 2 - low) / MAX(0.001f, high - low); - return 1 - MIN(1, MAX(0, y)); -} - -// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn -// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. -static void rope_yarn( - float theta_extrap, float freq_scale, float corr_dims[2], int64_t i0, float ext_factor, float mscale, - float * cos_theta, float * sin_theta) { - // Get n-d rotational scaling corrected for extrapolation - float theta_interp = freq_scale * theta_extrap; - float theta = theta_interp; - if (ext_factor != 0.0f) { - float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor; - theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; - - // Get n-d magnitude scaling corrected for interpolation - mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale); - } - *cos_theta = cosf(theta) * mscale; - *sin_theta = sinf(theta) * mscale; -} - -// Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get -// `corr_dim(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))` -static float ggml_rope_yarn_corr_dim(int n_dims, int n_ctx_orig, float n_rot, float base) { - return n_dims * logf(n_ctx_orig / (n_rot * 2 * (float)M_PI)) / (2 * logf(base)); -} - -static void ggml_rope_cache_init( - float theta_base, float freq_scale, const float * freq_factors, float corr_dims[2], int64_t ne0, float ext_factor, float mscale, - float * cache, float sin_sign, float theta_scale) { - // ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py - float theta = theta_base; - for (int64_t i0 = 0; i0 < ne0; i0 += 2) { - const float ff = freq_factors ? freq_factors[i0/2] : 1.0f; - rope_yarn( - theta/ff, freq_scale, corr_dims, i0, ext_factor, mscale, &cache[i0 + 0], &cache[i0 + 1] - ); - cache[i0 + 1] *= sin_sign; - - theta *= theta_scale; - } -} - -void ggml_rope_yarn_corr_dims( - int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2] -) { - // start and end correction dims - float start = floorf(ggml_rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_fast, freq_base)); - float end = ceilf(ggml_rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_slow, freq_base)); - dims[0] = MAX(0, start); - dims[1] = MIN(n_dims - 1, end); -} - -static void ggml_compute_forward_rope_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const bool forward) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - const struct ggml_tensor * src2 = dst->src[2]; - - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; - - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; - //const int n_ctx = ((int32_t *) dst->op_params)[3]; - const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; - - memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); - memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); - memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); - memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); - memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); - memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); - - GGML_TENSOR_UNARY_OP_LOCALS - - //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); - //printf("n_past = %d, ne2 = %d\n", n_past, ne2); - - GGML_ASSERT(nb00 == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(dst); - - GGML_ASSERT(n_dims <= ne0); - GGML_ASSERT(n_dims % 2 == 0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - // row index used to determine which thread to use - int ir = 0; - - const float theta_scale = powf(freq_base, -2.0f/n_dims); - - float corr_dims[2]; - ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); - - const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; - - const float * freq_factors = NULL; - if (src2 != NULL) { - GGML_ASSERT(src2->type == GGML_TYPE_F32); - GGML_ASSERT(src2->ne[0] >= n_dims / 2); - freq_factors = (const float *) src2->data; - } - - // backward process uses inverse rotation by cos and sin. - // cos and sin build a rotation matrix, where the inverse is the transpose. - // this essentially just switches the sign of sin. - const float sin_sign = forward ? 1.0f : -1.0f; - - const int32_t * pos = (const int32_t *) src1->data; - - for (int64_t i3 = 0; i3 < ne3; i3++) { - for (int64_t i2 = 0; i2 < ne2; i2++) { - const int64_t p = pos[i2]; - - float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith; - ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); - - for (int64_t i1 = 0; i1 < ne1; i1++) { - if (ir++ < ir0) continue; - if (ir > ir1) break; - - if (!is_neox) { - for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { - const float cos_theta = cache[i0 + 0]; - const float sin_theta = cache[i0 + 1]; - - const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - const float x0 = src[0]; - const float x1 = src[1]; - - dst_data[0] = x0*cos_theta - x1*sin_theta; - dst_data[1] = x0*sin_theta + x1*cos_theta; - } - } else { - for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { - const int64_t ic = i0/2; - - const float cos_theta = cache[i0 + 0]; - const float sin_theta = cache[i0 + 1]; - - const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); - float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); - - const float x0 = src[0]; - const float x1 = src[n_dims/2]; - - dst_data[0] = x0*cos_theta - x1*sin_theta; - dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta; - } - } - - for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) { - const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - dst_data[0] = src[0]; - dst_data[1] = src[1]; - } - } - } - } -} - -// TODO: deduplicate f16/f32 code -static void ggml_compute_forward_rope_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const bool forward) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - const struct ggml_tensor * src2 = dst->src[2]; - - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; - - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; - //const int n_ctx = ((int32_t *) dst->op_params)[3]; - const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; - memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); - memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); - memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); - memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); - memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); - memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); - - GGML_TENSOR_UNARY_OP_LOCALS - - //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); - //printf("n_past = %d, ne2 = %d\n", n_past, ne2); - - GGML_ASSERT(nb0 == sizeof(ggml_fp16_t)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(dst); - - GGML_ASSERT(n_dims <= ne0); - GGML_ASSERT(n_dims % 2 == 0); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - // row index used to determine which thread to use - int ir = 0; - - const float theta_scale = powf(freq_base, -2.0f/n_dims); - - float corr_dims[2]; - ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); - - const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; - - const float * freq_factors = NULL; - if (src2 != NULL) { - GGML_ASSERT(src2->type == GGML_TYPE_F32); - GGML_ASSERT(src2->ne[0] >= n_dims / 2); - freq_factors = (const float *) src2->data; - } - - // backward process uses inverse rotation by cos and sin. - // cos and sin build a rotation matrix, where the inverse is the transpose. - // this essentially just switches the sign of sin. - const float sin_sign = forward ? 1.0f : -1.0f; - - const int32_t * pos = (const int32_t *) src1->data; - - for (int64_t i3 = 0; i3 < ne3; i3++) { - for (int64_t i2 = 0; i2 < ne2; i2++) { - const int64_t p = pos[i2]; - - float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith; - ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); - - for (int64_t i1 = 0; i1 < ne1; i1++) { - if (ir++ < ir0) continue; - if (ir > ir1) break; - - if (!is_neox) { - for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { - const float cos_theta = cache[i0 + 0]; - const float sin_theta = cache[i0 + 1]; - - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - const float x0 = GGML_FP16_TO_FP32(src[0]); - const float x1 = GGML_FP16_TO_FP32(src[1]); - - dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); - dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); - } - } else { - for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { - const int64_t ic = i0/2; - - const float cos_theta = cache[i0 + 0]; - const float sin_theta = cache[i0 + 1]; - - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); - ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); - - const float x0 = GGML_FP16_TO_FP32(src[0]); - const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]); - - dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); - dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); - } - } - - for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) { - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - dst_data[0] = src[0]; - dst_data[1] = src[1]; - } - } - } - } -} - -static void ggml_compute_forward_rope( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_rope_f16(params, dst, true); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_rope_f32(params, dst, true); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_rope_back - -static void ggml_compute_forward_rope_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_rope_f16(params, dst, false); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_rope_f32(params, dst, false); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_conv_transpose_1d - -static void ggml_compute_forward_conv_transpose_1d_f16_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const int nk = ne00*ne01*ne02; - - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb10 == sizeof(float)); - - if (ith == 0) { - memset(params->wdata, 0, params->wsize); - - // permute kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout) - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i02*nb02 + i01*nb01); - ggml_fp16_t * dst_data = wdata + i01*ne00*ne02; - for (int64_t i00 = 0; i00 < ne00; i00++) { - dst_data[i00*ne02 + i02] = src[i00]; - } - } - } - } - - // permute source data (src1) from (L x Cin) to (Cin x L) - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + nk; - ggml_fp16_t * dst_data = wdata; - - for (int64_t i11 = 0; i11 < ne11; i11++) { - const float * const src = (float *)((char *) src1->data + i11*nb11); - for (int64_t i10 = 0; i10 < ne10; i10++) { - dst_data[i10*ne11 + i11] = GGML_FP32_TO_FP16(src[i10]); - } - } - } - - // need to zero dst since we are accumulating into it - memset(dst->data, 0, ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - - const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; - - // total rows in dst - const int nr = ne1; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - ggml_fp16_t * const wdata_src = wdata + nk; - - for (int i1 = ir0; i1 < ir1; i1++) { - float * dst_data = (float *)((char *) dst->data + i1*nb1); - ggml_fp16_t * wdata_kernel = wdata + i1*ne02*ne00; - for (int i10 = 0; i10 < ne10; i10++) { - const int i1n = i10*ne11; - for (int i00 = 0; i00 < ne00; i00++) { - float v = 0; - ggml_vec_dot_f16(ne02, &v, 0, - (ggml_fp16_t *) wdata_src + i1n, 0, - (ggml_fp16_t *) wdata_kernel + i00*ne02, 0, 1); - dst_data[i10*s0 + i00] += v; - } - } - } -} - -static void ggml_compute_forward_conv_transpose_1d_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const int nk = ne00*ne01*ne02; - - GGML_ASSERT(nb00 == sizeof(float)); - GGML_ASSERT(nb10 == sizeof(float)); - - if (ith == 0) { - memset(params->wdata, 0, params->wsize); - - // prepare kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout) - { - float * const wdata = (float *) params->wdata + 0; - - for (int64_t i02 = 0; i02 < ne02; i02++) { - for (int64_t i01 = 0; i01 < ne01; i01++) { - const float * const src = (float *)((char *) src0->data + i02*nb02 + i01*nb01); - float * dst_data = wdata + i01*ne00*ne02; - for (int64_t i00 = 0; i00 < ne00; i00++) { - dst_data[i00*ne02 + i02] = src[i00]; - } - } - } - } - - // prepare source data (src1) - { - float * const wdata = (float *) params->wdata + nk; - float * dst_data = wdata; - - for (int64_t i11 = 0; i11 < ne11; i11++) { - const float * const src = (float *)((char *) src1->data + i11*nb11); - for (int64_t i10 = 0; i10 < ne10; i10++) { - dst_data[i10*ne11 + i11] = src[i10]; - } - } - } - - // need to zero dst since we are accumulating into it - memset(dst->data, 0, ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - - const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; - - // total rows in dst - const int nr = ne1; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - float * const wdata = (float *) params->wdata + 0; - float * const wdata_src = wdata + nk; - - for (int i1 = ir0; i1 < ir1; i1++) { - float * dst_data = (float *)((char *) dst->data + i1*nb1); - float * wdata_kernel = wdata + i1*ne02*ne00; - for (int i10 = 0; i10 < ne10; i10++) { - const int i1n = i10*ne11; - for (int i00 = 0; i00 < ne00; i00++) { - float v = 0; - ggml_vec_dot_f32(ne02, &v, 0, - wdata_src + i1n, 0, - wdata_kernel + i00*ne02, 0, 1); - dst_data[i10*s0 + i00] += v; - } - } - } -} - -static void ggml_compute_forward_conv_transpose_1d( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_conv_transpose_1d_f16_f32(params, dst); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_conv_transpose_1d_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_im2col_f32 -// src0: kernel [OC, IC, KH, KW] -// src1: image [N, IC, IH, IW] -// dst: result [N, OH, OW, IC*KH*KW] -static void ggml_compute_forward_im2col_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - GGML_TENSOR_BINARY_OP_LOCALS; - - const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; - const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; - const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; - const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; - const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; - const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; - const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t N = is_2D ? ne13 : ne12; - const int64_t IC = is_2D ? ne12 : ne11; - const int64_t IH = is_2D ? ne11 : 1; - const int64_t IW = ne10; - - const int64_t KH = is_2D ? ne01 : 1; - const int64_t KW = ne00; - - const int64_t OH = is_2D ? ne2 : 1; - const int64_t OW = ne1; - - int ofs0 = is_2D ? nb13 : nb12; - int ofs1 = is_2D ? nb12 : nb11; - - GGML_ASSERT(nb10 == sizeof(float)); - - // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] - { - float * const wdata = (float *) dst->data; - - for (int64_t in = 0; in < N; in++) { - for (int64_t ioh = 0; ioh < OH; ioh++) { // 1 - for (int64_t iow = 0; iow < OW; iow++) { - for (int64_t iic = ith; iic < IC; iic += nth) { - - // micro kernel - float * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] - const float * const src_data = (float *)((char *) src1->data + in*ofs0 + iic*ofs1); // [IH, IW] - - for (int64_t ikh = 0; ikh < KH; ikh++) { // 1 - for (int64_t ikw = 0; ikw < KW; ikw++) { - const int64_t iiw = iow*s0 + ikw*d0 - p0; - const int64_t iih = ioh*s1 + ikh*d1 - p1; - - if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { - dst_data[iic*(KH*KW) + ikh*KW + ikw] = 0; - } else { - dst_data[iic*(KH*KW) + ikh*KW + ikw] = (src_data[iih*IW + iiw]); - } - } - } - } - } - } - } - } -} - - -// ggml_compute_forward_im2col_f16 -// src0: kernel [OC, IC, KH, KW] -// src1: image [N, IC, IH, IW] -// dst: result [N, OH, OW, IC*KH*KW] -static void ggml_compute_forward_im2col_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F16); - - GGML_TENSOR_BINARY_OP_LOCALS; - - const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; - const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; - const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; - const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; - const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; - const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; - const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t N = is_2D ? ne13 : ne12; - const int64_t IC = is_2D ? ne12 : ne11; - const int64_t IH = is_2D ? ne11 : 1; - const int64_t IW = ne10; - - const int64_t KH = is_2D ? ne01 : 1; - const int64_t KW = ne00; - - const int64_t OH = is_2D ? ne2 : 1; - const int64_t OW = ne1; - - int ofs0 = is_2D ? nb13 : nb12; - int ofs1 = is_2D ? nb12 : nb11; - - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb10 == sizeof(float)); - - // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) dst->data; - - for (int64_t in = 0; in < N; in++) { - for (int64_t ioh = 0; ioh < OH; ioh++) { // 1 - for (int64_t iow = 0; iow < OW; iow++) { - for (int64_t iic = ith; iic < IC; iic += nth) { - - // micro kernel - ggml_fp16_t * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] - const float * const src_data = (float *)((char *) src1->data + in*ofs0 + iic*ofs1); // [IH, IW] - - for (int64_t ikh = 0; ikh < KH; ikh++) { // 1 - for (int64_t ikw = 0; ikw < KW; ikw++) { - const int64_t iiw = iow*s0 + ikw*d0 - p0; - const int64_t iih = ioh*s1 + ikh*d1 - p1; - - if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { - dst_data[iic*(KH*KW) + ikh*KW + ikw] = 0; - } else { - dst_data[iic*(KH*KW) + ikh*KW + ikw] = GGML_FP32_TO_FP16(src_data[iih*IW + iiw]); - } - } - } - } - } - } - } - } -} - -static void ggml_compute_forward_im2col( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - switch (dst->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_im2col_f16(params, dst); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_im2col_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_im2col_back_f32 - -static void ggml_compute_forward_im2col_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - GGML_TENSOR_BINARY_OP_LOCALS; - - const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; - const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; - const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; - const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; - const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; - const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; - const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t N = is_2D ? ne3 : ne2; - const int64_t IC = is_2D ? ne2 : ne1; - const int64_t IH = is_2D ? ne1 : 1; - const int64_t IW = ne0; - - const int64_t KH = is_2D ? ne01 : 1; - const int64_t KW = ne00; - - const int64_t OH = is_2D ? ne12 : 1; - const int64_t OW = ne11; - - int ofs0 = is_2D ? nb3 : nb2; - int ofs1 = is_2D ? nb2 : nb1; - - GGML_ASSERT(nb0 == sizeof(float)); - - // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] - { - float * const wdata = (float *) dst->data; - - for (int64_t in = 0; in < N; in++) { - for (int64_t iic = ith; iic < IC; iic += nth) { - for (int64_t iih = 0; iih < IH; iih++) { - for (int64_t iiw = 0; iiw < IW; iiw++) { - - // micro kernel - float grad = 0.0f; - for (int64_t ikh = 0; ikh < KH; ikh++) { - for (int64_t ikw = 0; ikw < KW; ikw++) { - // For s0 > 1 some values were skipped over in the forward pass. - // These values have tmpw % s0 != 0 and need to be skipped in the backwards pass as well. - const int64_t tmpw = (iiw + p0 - ikw*d0); - if (tmpw % s0 != 0) { - continue; - } - const int64_t iow = tmpw / s0; - - // Equivalent logic as above except for s1. - int64_t ioh; - if (is_2D) { - const int64_t tmph = iih + p1 - ikh*d1; - - if (tmph % s1 != 0) { - continue; - } - - ioh = tmph / s1; - } else { - ioh = 0; - } - - if (iow < 0 || iow >= OW || ioh < 0 || ioh >= OH) { - continue; - } - - const float * const src_data = (const float *) src1->data - + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] - grad += src_data[iic*(KH*KW) + ikh*KW + ikw]; - } - } - float * dst_data = (float *)((char *) wdata + (in*ofs0 + iic*ofs1)); // [IH, IW] - dst_data[iih*IW + iiw] = grad; - } - } - } - } - } -} - -// ggml_compute_forward_conv_transpose_2d - -static void ggml_compute_forward_conv_transpose_2d( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - GGML_TENSOR_BINARY_OP_LOCALS - - const int ith = params->ith; - const int nth = params->nth; - - const int nk = ne00*ne01*ne02*ne03; - - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb10 == sizeof(float)); - - if (ith == 0) { - memset(params->wdata, 0, params->wsize); - - // permute kernel data (src0) from (Kw x Kh x Cout x Cin) to (Cin x Kw x Kh x Cout) - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i03*nb03 + i02*nb02); - ggml_fp16_t * dst_data = wdata + i02*ne01*ne00*ne03; - for (int64_t i01 = 0; i01 < ne01; i01++) { - for (int64_t i00 = 0; i00 < ne00; i00++) { - dst_data[i01*ne00*ne03 + i00*ne03 + i03] = src[i01 * ne00 + i00]; - } - } - } - } - } - - // permute source data (src1) from (Sw x Sh x Cin) to (Cin x Sw x Sh) - { - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + nk; - for (int i12 = 0; i12 < ne12; i12++) { - for (int i11 = 0; i11 < ne11; i11++) { - const float * const src = (float *)((char *) src1->data + i12*nb12 + i11*nb11); - ggml_fp16_t * dst_data = wdata + i11*ne10*ne12; - for (int i10 = 0; i10 < ne10; i10++) { - dst_data[i10*ne12 + i12] = GGML_FP32_TO_FP16(src[i10]); - } - } - } - } - - memset(dst->data, 0, ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - - const int32_t stride = ggml_get_op_params_i32(dst, 0); - - // total patches in dst - const int np = ne2; - - // patches per thread - const int dp = (np + nth - 1)/nth; - - // patch range for this thread - const int ip0 = dp*ith; - const int ip1 = MIN(ip0 + dp, np); - - ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - ggml_fp16_t * const wdata_src = wdata + nk; - - for (int i2 = ip0; i2 < ip1; i2++) { // Cout - float * dst_data = (float *)((char *) dst->data + i2*nb2); - ggml_fp16_t * wdata_kernel = wdata + i2*ne01*ne00*ne03; - for (int i11 = 0; i11 < ne11; i11++) { - for (int i10 = 0; i10 < ne10; i10++) { - const int i1n = i11*ne10*ne12 + i10*ne12; - for (int i01 = 0; i01 < ne01; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - float v = 0; - ggml_vec_dot_f16(ne03, &v, 0, - wdata_src + i1n, 0, - wdata_kernel + i01*ne00*ne03 + i00*ne03, 0, 1); - dst_data[(i11*stride + i01)*ne0 + i10*stride + i00] += v; - } - } - } - } - } -} - -// ggml_compute_forward_pool_1d_sk_p0 - -static void ggml_compute_forward_pool_1d_sk_p0( - const struct ggml_compute_params * params, - const enum ggml_op_pool op, - const int k, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src = dst->src[0]; - - assert(src->type == GGML_TYPE_F32 || src->type == GGML_TYPE_F16); - - if (params->ith != 0) { - return; - } - - const char * cdata = (const char *)src->data; - const char * const data_end = cdata + ggml_nbytes(src); - float * drow = (float *)dst->data; - - const int64_t rs = dst->ne[0]; - - while (cdata < data_end) { - const void * srow = (const void *)cdata; - int j = 0; - for (int64_t i = 0; i < rs; ++i) { - switch (op) { - case GGML_OP_POOL_AVG: drow[i] = 0; break; - case GGML_OP_POOL_MAX: drow[i] = -FLT_MAX; break; - case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); - } - for (int ki = 0; ki < k; ++ki) { - const float srow_j = (src->type == GGML_TYPE_F32) ? ((const float*)srow)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t*)srow)[j]); - switch (op) { - case GGML_OP_POOL_AVG: drow[i] += srow_j; break; - case GGML_OP_POOL_MAX: if (srow_j > drow[i]) drow[i] = srow_j; break; - case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); - } - ++j; - } - switch (op) { - case GGML_OP_POOL_AVG: drow[i] /= k; break; - case GGML_OP_POOL_MAX: break; - case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); - } - } - - cdata += src->nb[1]; - drow += rs; - } -} - -// ggml_compute_forward_pool_1d - -static void ggml_compute_forward_pool_1d( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const int32_t * opts = (const int32_t *)dst->op_params; - enum ggml_op_pool op = opts[0]; - const int k0 = opts[1]; - const int s0 = opts[2]; - const int p0 = opts[3]; - GGML_ASSERT(p0 == 0); // padding not supported - GGML_ASSERT(k0 == s0); // only s = k supported - - ggml_compute_forward_pool_1d_sk_p0(params, op, k0, dst); -} - -// ggml_compute_forward_pool_2d - -static void ggml_compute_forward_pool_2d( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src = dst->src[0]; - - assert(src->type == GGML_TYPE_F32 || src->type == GGML_TYPE_F16); - - if (params->ith != 0) { - return; - } - - const int32_t * opts = (const int32_t *)dst->op_params; - enum ggml_op_pool op = opts[0]; - const int k0 = opts[1]; - const int k1 = opts[2]; - const int s0 = opts[3]; - const int s1 = opts[4]; - const int p0 = opts[5]; - const int p1 = opts[6]; - const char * cdata = (const char*)src->data; - const char * const data_end = cdata + ggml_nbytes(src); - - const int64_t px = dst->ne[0]; - const int64_t py = dst->ne[1]; - const int64_t pa = px * py; - - float * dplane = (float *)dst->data; - - const int ka = k0 * k1; - const int offset0 = -p0; - const int offset1 = -p1; - - while (cdata < data_end) { - for (int oy = 0; oy < py; ++oy) { - float * const drow = dplane + oy * px; - for (int ox = 0; ox < px; ++ox) { - float * const out = drow + ox; - switch (op) { - case GGML_OP_POOL_AVG: *out = 0; break; - case GGML_OP_POOL_MAX: *out = -FLT_MAX; break; - case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); - } - - const int ix = offset0 + ox * s0; - const int iy = offset1 + oy * s1; - - for (int ky = 0; ky < k1; ++ky) { - if (iy + ky < 0 || iy + ky >= src->ne[1]) continue; - const void * srow = (const void *)(cdata + src->nb[1] * (iy + ky)); - for (int kx = 0; kx < k0; ++kx) { - int j = ix + kx; - if (j < 0 || j >= src->ne[0]) continue; - const float srow_j = (src->type == GGML_TYPE_F32) ? ((const float*)srow)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t*)srow)[j]); - switch (op) { - case GGML_OP_POOL_AVG: *out += srow_j; break; - case GGML_OP_POOL_MAX: if (srow_j > *out) *out = srow_j; break; - case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); - } - } - } - switch (op) { - case GGML_OP_POOL_AVG: *out /= ka; break; - case GGML_OP_POOL_MAX: break; - case GGML_OP_POOL_COUNT: GGML_ABORT("fatal error"); - } - } - } - - cdata += src->nb[2]; - dplane += pa; - } -} - -// ggml_compute_forward_pool_2d_back - -static void ggml_compute_forward_pool_2d_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src = dst->src[0]; - const struct ggml_tensor * dstf = dst->src[1]; // forward tensor of dst - - assert(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); - - if (params->ith != 0) { - return; - } - - const int32_t * opts = (const int32_t *)dst->op_params; - enum ggml_op_pool op = opts[0]; - const int k0 = opts[1]; - const int k1 = opts[2]; - const int s0 = opts[3]; - const int s1 = opts[4]; - const int p0 = opts[5]; - const int p1 = opts[6]; - - char * cdata = (char *) dst->data; - const char * cdataf = (const char *) dstf->data; - const char * const data_end = cdata + ggml_nbytes(dst); - - GGML_ASSERT(params->ith == 0); - memset(cdata, 0, ggml_nbytes(dst)); - - const int64_t px = src->ne[0]; - const int64_t py = src->ne[1]; - const int64_t pa = px * py; - - const float * splane = (const float *) src->data; - - const int ka = k0 * k1; - const int offset0 = -p0; - const int offset1 = -p1; - - while (cdata < data_end) { - for (int oy = 0; oy < py; ++oy) { - const float * const srow = splane + oy * px; - for (int ox = 0; ox < px; ++ox) { - const float grad0 = srow[ox]; - - const int ix = offset0 + ox * s0; - const int iy = offset1 + oy * s1; - - if (op == GGML_OP_POOL_MAX) { - float maxval = -FLT_MAX; - int kxmax = -1; - int kymax = -1; - - for (int ky = 0; ky < k1; ++ky) { - if (iy + ky < 0 || iy + ky >= dst->ne[1]) { - continue; - } - const void * drowf = (const void *)(cdataf + dst->nb[1] * (iy + ky)); - for (int kx = 0; kx < k0; ++kx) { - int j = ix + kx; - if (j < 0 || j >= dst->ne[0]) { - continue; - } - - const float val = dst->type == GGML_TYPE_F32 ? - ((const float *) drowf)[j] : GGML_FP16_TO_FP32(((const ggml_fp16_t *) drowf)[j]); - if (val <= maxval) { - continue; - } - - maxval = val; - kxmax = kx; - kymax = ky; - } - } - - if (kxmax == -1 || kymax == -1) { - continue; - } - - void * drow = (void *)(cdata + dst->nb[1] * (iy + kymax)); - const int j = ix + kxmax; - if (dst->type == GGML_TYPE_F32) { - ((float *) drow)[j] += grad0; - } else { - ((ggml_fp16_t *) drow)[j] = GGML_FP32_TO_FP16(grad0 + GGML_FP16_TO_FP32(((const ggml_fp16_t *) drow)[j])); - } - } else if (op == GGML_OP_POOL_AVG) { - const float grad = grad0 / ka; - - for (int ky = 0; ky < k1; ++ky) { - if (iy + ky < 0 || iy + ky >= dst->ne[1]) { - continue; - } - void * drow = (void *)(cdata + dst->nb[1] * (iy + ky)); - for (int kx = 0; kx < k0; ++kx) { - int j = ix + kx; - if (j < 0 || j >= dst->ne[0]) { - continue; - } - - if (dst->type == GGML_TYPE_F32) { - ((float *) drow)[j] += grad; - } else { - ((ggml_fp16_t *) drow)[j] += GGML_FP32_TO_FP16(grad); - } - } - } - } else { - GGML_ASSERT(false); - } - } - } - - cdata += dst->nb[2]; - cdataf += dst->nb[2]; - splane += pa; - } -} - -// ggml_compute_forward_upscale - -static void ggml_compute_forward_upscale_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - const float sf0 = (float)ne0/src0->ne[0]; - const float sf1 = (float)ne1/src0->ne[1]; - const float sf2 = (float)ne2/src0->ne[2]; - const float sf3 = (float)ne3/src0->ne[3]; - - // TODO: optimize - - for (int64_t i3 = 0; i3 < ne3; i3++) { - const int64_t i03 = i3 / sf3; - for (int64_t i2 = ith; i2 < ne2; i2 += nth) { - const int64_t i02 = i2 / sf2; - for (int64_t i1 = 0; i1 < ne1; i1++) { - const int64_t i01 = i1 / sf1; - for (int64_t i0 = 0; i0 < ne0; i0++) { - const int64_t i00 = i0 / sf0; - - const float * x = (float *)((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - float * y = (float *)((char *) dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); - - *y = *x; - } - } - } - } -} - -static void ggml_compute_forward_upscale( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_upscale_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - - -// ggml_compute_forward_pad - -static void ggml_compute_forward_pad_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - GGML_ASSERT( dst->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - float * dst_ptr = (float *) dst->data; - - // TODO: optimize - - for (int64_t i2 = 0; i2 < ne2; ++i2) { - for (int64_t i1 = ith; i1 < ne1; i1 += nth) { - for (int64_t i0 = 0; i0 < ne0; ++i0) { - for (int64_t i3 = 0; i3 < ne3; ++i3) { - const int64_t dst_idx = i3*(ne0*ne1*ne2) + i2*(ne0*ne1) + i1*ne0 + i0; - - const float * src_ptr = (const float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - - if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { - dst_ptr[dst_idx] = *src_ptr; - } else { - dst_ptr[dst_idx] = 0; - } - } - } - } - } -} - -static void ggml_compute_forward_pad( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_pad_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - - -// ggml_compute_forward_arange - -static void ggml_compute_forward_arange_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - GGML_ASSERT(dst->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - const float start = ggml_get_op_params_f32(dst, 0); - const float stop = ggml_get_op_params_f32(dst, 1); - const float step = ggml_get_op_params_f32(dst, 2); - - const int64_t steps = (int64_t) ceilf((stop - start) / step); - - GGML_ASSERT(ggml_nelements(dst) == steps); - - for (int64_t i = ith; i < steps; i+= nth) { - float value = start + step * i; - ((float *)dst->data)[i] = value; - } -} - -static void ggml_compute_forward_arange( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - switch (dst->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_arange_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -static void ggml_compute_forward_timestep_embedding_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_ASSERT(src0->nb[0] == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - GGML_TENSOR_UNARY_OP_LOCALS - - const int dim = ggml_get_op_params_i32(dst, 0); - const int max_period = ggml_get_op_params_i32(dst, 1); - - int half = dim / 2; - - for (int64_t i = 0; i < ne00; i++) { - float * embed_data = (float *)((char *) dst->data + i*nb1); - for (int64_t j = ith; j < half; j += nth) { - float timestep = ((float *)src0->data)[i]; - float freq = (float)expf(-logf(max_period) * j / half); - float arg = timestep * freq; - embed_data[j] = cosf(arg); - embed_data[j + half] = sinf(arg); - } - if (dim % 2 != 0 && ith == 0) { - embed_data[dim] = 0.f; - } - } -} - -static void ggml_compute_forward_timestep_embedding( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_timestep_embedding_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_argsort - -static void ggml_compute_forward_argsort_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_TENSOR_UNARY_OP_LOCALS - - GGML_ASSERT(nb0 == sizeof(float)); - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t nr = ggml_nrows(src0); - - enum ggml_sort_order order = (enum ggml_sort_order) ggml_get_op_params_i32(dst, 0); - - for (int64_t i = ith; i < nr; i += nth) { - int32_t * dst_data = (int32_t *)((char *) dst->data + i*nb1); - const float * src_data = (float *)((char *) src0->data + i*nb01); - - for (int64_t j = 0; j < ne0; j++) { - dst_data[j] = j; - } - - // C doesn't have a functional sort, so we do a bubble sort instead - for (int64_t j = 0; j < ne0; j++) { - for (int64_t k = j + 1; k < ne0; k++) { - if ((order == GGML_SORT_ORDER_ASC && src_data[dst_data[j]] > src_data[dst_data[k]]) || - (order == GGML_SORT_ORDER_DESC && src_data[dst_data[j]] < src_data[dst_data[k]])) { - int32_t tmp = dst_data[j]; - dst_data[j] = dst_data[k]; - dst_data[k] = tmp; - } - } - } - } -} - -static void ggml_compute_forward_argsort( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_argsort_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_flash_attn_ext - -static void ggml_compute_forward_flash_attn_ext_f16( - const struct ggml_compute_params * params, - const struct ggml_tensor * q, - const struct ggml_tensor * k, - const struct ggml_tensor * v, - const struct ggml_tensor * mask, - struct ggml_tensor * dst) { - - GGML_TENSOR_LOCALS(int64_t, neq, q, ne) - GGML_TENSOR_LOCALS(size_t, nbq, q, nb) - GGML_TENSOR_LOCALS(int64_t, nek, k, ne) - GGML_TENSOR_LOCALS(size_t, nbk, k, nb) - GGML_TENSOR_LOCALS(int64_t, nev, v, ne) - GGML_TENSOR_LOCALS(size_t, nbv, v, nb) - GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) - GGML_TENSOR_LOCALS(size_t, nb, dst, nb) - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t D = neq0; - const int64_t N = neq1; - - GGML_ASSERT(ne0 == D); - GGML_ASSERT(ne2 == N); - - // input tensor rows must be contiguous - GGML_ASSERT(nbq0 == ggml_type_size(q->type)); - GGML_ASSERT(nbk0 == ggml_type_size(k->type)); - GGML_ASSERT(nbv0 == ggml_type_size(v->type)); - - GGML_ASSERT(neq0 == D); - GGML_ASSERT(nek0 == D); - GGML_ASSERT(nev0 == D); - - GGML_ASSERT(neq1 == N); - GGML_ASSERT(nev0 == D); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - // broadcast factors - const int64_t rk2 = neq2/nek2; - const int64_t rk3 = neq3/nek3; - - const int64_t rv2 = neq2/nev2; - const int64_t rv3 = neq3/nev3; - - // parallelize by q rows using ggml_vec_dot_f32 - - // total rows in q - const int nr = neq1*neq2*neq3; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - float scale = 1.0f; - float max_bias = 0.0f; - float logit_softcap = 0.0f; - - memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); - memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); - memcpy(&logit_softcap, (float *) dst->op_params + 2, sizeof(float)); - - if (logit_softcap != 0) { - scale /= logit_softcap; - } - - const uint32_t n_head = neq2; - const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head)); - - const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - - enum ggml_type const k_vec_dot_type = type_traits[k->type].vec_dot_type; - ggml_from_float_t const q_to_vec_dot = type_traits[k_vec_dot_type].from_float; - ggml_vec_dot_t const kq_vec_dot = type_traits[k->type].vec_dot; - ggml_to_float_t const v_to_float = type_traits[v->type].to_float; - - GGML_ASSERT(q_to_vec_dot && "fattn: unsupported K-type"); - GGML_ASSERT(v_to_float && "fattn: unsupported V-type"); - - // loop over n_batch and n_head - for (int ir = ir0; ir < ir1; ++ir) { - // q indices - const int iq3 = ir/(neq2*neq1); - const int iq2 = (ir - iq3*neq2*neq1)/neq1; - const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1); - - const uint32_t h = iq2; // head index - const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f; - - float S = 0.0f; // sum - float M = -INFINITY; // maximum KQ value - - float * VKQ32 = (float *) params->wdata + ith*(3*D + CACHE_LINE_SIZE_F32); // FP32 VKQ accumulator - float * V32 = (VKQ32 + 1*D); // (temporary) FP32 V buffer - ggml_fp16_t * VKQ16 = (ggml_fp16_t *) (VKQ32 + 1*D); // (temporary) FP16 VKQ accumulator - ggml_fp16_t * Q_q = (ggml_fp16_t *) (VKQ32 + 2*D); // (temporary) buffer for Q converted to quantized/FP16 - - if (v->type == GGML_TYPE_F16) { - memset(VKQ16, 0, D*sizeof(ggml_fp16_t)); - } else { - memset(VKQ32, 0, D*sizeof(float)); - } - - const ggml_fp16_t * mp = mask ? (ggml_fp16_t *)((char *) mask->data + iq1*mask->nb[1]) : NULL; - - // k indices - const int ik3 = iq3 / rk3; - const int ik2 = iq2 / rk2; - - // v indices - const int iv3 = iq3 / rv3; - const int iv2 = iq2 / rv2; - - const float * pq = (const float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)); - q_to_vec_dot(pq, Q_q, D); - - // online softmax / attention - // loop over n_kv and n_head_kv - // ref: https://arxiv.org/pdf/2112.05682.pdf - for (int64_t ic = 0; ic < nek1; ++ic) { - const float mv = mp ? slope*GGML_FP16_TO_FP32(mp[ic]) : 0.0f; - if (mv == -INFINITY) { - continue; - } - - float s; // KQ value - - const char * k_data = (const char *) k->data + ( ic*nbk1 + ik2*nbk2 + ik3*nbk3); - kq_vec_dot(D, &s, 0, k_data, 0, Q_q, 0, 1); - - s = s*scale; // scale KQ value - - if (logit_softcap != 0.0f) { - s = logit_softcap*tanhf(s); - } - - s += mv; // apply mask - - const float Mold = M; - - float ms = 1.0f; // upon new higher max val, scale VKQ and KQ sum with this value - float vs = 1.0f; // post-softmax KQ value, expf(s - M) - - const char * v_data = ((const char *) v->data + (ic*nbv1 + iv2*nbv2 + iv3*nbv3)); - - if (v->type == GGML_TYPE_F16) { - if (s > M) { - // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f - M = s; - ms = expf(Mold - M); - - // V = V*expf(Mold - M) - ggml_vec_scale_f16(D, VKQ16, ms); - } else { - // no new maximum, ms == 1.0f, vs != 1.0f - vs = expf(s - M); - } - - // V += v*expf(s - M) - ggml_vec_mad_f16(D, VKQ16, (const ggml_fp16_t *) v_data, vs); - } else { - if (s > M) { - // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f - M = s; - ms = expf(Mold - M); - - // V = V*expf(Mold - M) - ggml_vec_scale_f32(D, VKQ32, ms); - } else { - // no new maximum, ms == 1.0f, vs != 1.0f - vs = expf(s - M); - } - - v_to_float(v_data, V32, D); - - // V += v*expf(s - M) - ggml_vec_mad_f32(D, VKQ32, V32, vs); - } - - S = S*ms + vs; // scale and increment sum with partial sum - } - - if (v->type == GGML_TYPE_F16) { - for (int64_t d = 0; d < D; ++d) { - VKQ32[d] = GGML_FP16_TO_FP32(VKQ16[d]); - } - } - - // V /= S - const float S_inv = 1.0f/S; - ggml_vec_scale_f32(D, VKQ32, S_inv); - - // dst indices - const int i1 = iq1; - const int i2 = iq2; - const int i3 = iq3; - - // original - //memcpy((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3), V, nev0*sizeof(float)); - - // permute(0, 2, 1, 3) - memcpy((char *) dst->data + (i3*ne2*ne1 + i2 + i1*ne1)*nb1, VKQ32, nb1); - } -} - -static void ggml_compute_forward_flash_attn_ext( - const struct ggml_compute_params * params, - const struct ggml_tensor * q, - const struct ggml_tensor * k, - const struct ggml_tensor * v, - const struct ggml_tensor * mask, - struct ggml_tensor * dst) { - switch (dst->op_params[3]) { - case GGML_PREC_DEFAULT: - case GGML_PREC_F32: - { - // uses F32 accumulators - ggml_compute_forward_flash_attn_ext_f16(params, q, k, v, mask, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_flash_attn_back - -static void ggml_compute_forward_flash_attn_back_f32( - const struct ggml_compute_params * params, - const bool masked, - struct ggml_tensor * dst) { - - const struct ggml_tensor * q = dst->src[0]; - const struct ggml_tensor * k = dst->src[1]; - const struct ggml_tensor * v = dst->src[2]; - const struct ggml_tensor * d = dst->src[3]; - - GGML_TENSOR_LOCALS(int64_t, neq, q, ne) - GGML_TENSOR_LOCALS(size_t, nbq, q, nb) - GGML_TENSOR_LOCALS(int64_t, nek, k, ne) - GGML_TENSOR_LOCALS(size_t, nbk, k, nb) - GGML_TENSOR_LOCALS(int64_t, nev, v, ne) - GGML_TENSOR_LOCALS(size_t, nbv, v, nb) - GGML_TENSOR_LOCALS(int64_t, ned, d, ne) - GGML_TENSOR_LOCALS(size_t, nbd, d, nb) - GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) - GGML_TENSOR_LOCALS(size_t, nb, dst, nb) - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t D = neq0; - const int64_t N = neq1; - const int64_t P = nek1 - N; - const int64_t M = P + N; - - const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL); - const int mxDM = MAX(D, Mup); - - // GGML_ASSERT(ne0 == D); - // GGML_ASSERT(ne1 == N); - GGML_ASSERT(P >= 0); - - GGML_ASSERT(nbq0 == sizeof(float)); - GGML_ASSERT(nbk0 == sizeof(float)); - GGML_ASSERT(nbv0 == sizeof(float)); - - GGML_ASSERT(neq0 == D); - GGML_ASSERT(nek0 == D); - GGML_ASSERT(nev1 == D); - GGML_ASSERT(ned0 == D); - - GGML_ASSERT(neq1 == N); - GGML_ASSERT(nek1 == N + P); - GGML_ASSERT(nev1 == D); - GGML_ASSERT(ned1 == N); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - if (ith == 0) { - memset(dst->data, 0, nb0*ne0*ne1*ne2*ne3); - } - ggml_barrier(params->threadpool); - - const int64_t elem_q = ggml_nelements(q); - const int64_t elem_k = ggml_nelements(k); - - enum ggml_type result_type = dst->type; - GGML_ASSERT(ggml_blck_size(result_type) == 1); - const size_t tsize = ggml_type_size(result_type); - - const size_t offs_q = 0; - const size_t offs_k = offs_q + GGML_PAD(elem_q * tsize, GGML_MEM_ALIGN); - const size_t offs_v = offs_k + GGML_PAD(elem_k * tsize, GGML_MEM_ALIGN); - - void * grad_q = (char *) dst->data; - void * grad_k = (char *) dst->data + offs_k; - void * grad_v = (char *) dst->data + offs_v; - - const size_t nbgq1 = nb0*neq0; - const size_t nbgq2 = nb0*neq0*neq1; - const size_t nbgq3 = nb0*neq0*neq1*neq2; - - const size_t nbgk1 = nb0*nek0; - const size_t nbgk2 = nb0*nek0*nek1; - const size_t nbgk3 = nb0*nek0*nek1*neq2; - - const size_t nbgv1 = nb0*nev0; - const size_t nbgv2 = nb0*nev0*nev1; - const size_t nbgv3 = nb0*nev0*nev1*neq2; - - // parallelize by k rows using ggml_vec_dot_f32 - - // total rows in k - const int nr = nek2*nek3; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - const float scale = 1.0f/sqrtf(D); - - //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale); - - // how often k2 (and v2) is repeated in q2 - int nrep = neq2/nek2; - - for (int ir = ir0; ir < ir1; ++ir) { - // q indices - const int ik3 = ir/(nek2); - const int ik2 = ir - ik3*nek2; - - const int iq3 = ik3; - const int id3 = ik3; - const int iv3 = ik3; - const int iv2 = ik2; - - for (int irep = 0; irep < nrep; ++irep) { - const int iq2 = ik2 + irep*nek2; - const int id2 = iq2; - - // (ik2 + irep*nek2) % nek2 == ik2 - for (int iq1 = 0; iq1 < neq1; ++iq1) { - const int id1 = iq1; - - // not sure about CACHE_LINE_SIZE_F32.. - // - maybe it must not be multiplied by 2 and excluded from .. in SM 1*(..) offset? - float * S = (float *) params->wdata + ith*2*(mxDM + CACHE_LINE_SIZE_F32) + 0*(mxDM+CACHE_LINE_SIZE_F32); - float * SM = (float *) params->wdata + ith*2*(mxDM + CACHE_LINE_SIZE_F32) + 1*(mxDM+CACHE_LINE_SIZE_F32); - - for (int i = M; i < Mup; ++i) { - S[i] = -INFINITY; - } - - const int64_t masked_begin = masked ? (P + iq1 + 1) : M; - for (int64_t ic = 0; ic < masked_begin; ++ic) { - // k indices - const int ik1 = ic; - - // S indices - const int i1 = ik1; - - ggml_vec_dot_f32(neq0, - S + i1, 0, - (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), 0, - (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), 0, 1); - } - - // scale - ggml_vec_scale_f32(masked_begin, S, scale); - - for (int64_t i = masked_begin; i < M; i++) { - S[i] = -INFINITY; - } - - // softmax - // exclude known -INF S[..] values from max and loop - // dont forget to set their SM values to zero - { - float max = -INFINITY; - ggml_vec_max_f32(masked_begin, &max, S); - - ggml_float sum = 0.0; - { -#ifdef GGML_SOFT_MAX_ACCELERATE - max = -max; - vDSP_vsadd(SM, 1, &max, SM, 1, Mup); - vvexpf(SM, SM, &Mup); - ggml_vec_sum_f32(Mup, &sum, SM); -#else - sum = ggml_vec_soft_max_f32(Mup, SM, S, max); -#endif - } - - assert(sum > 0.0); - - sum = 1.0/sum; - ggml_vec_scale_f32(masked_begin, SM, sum); - - } - - // step-by-step explanation - { - // forward-process shape grads from backward process - // parallel_for ik2,ik3: - // for irep: - // iq2 = ik2 + irep*nek2 - // k[:D,:M,:,:] [D,M,:,:] grad[k][:D,:M,ik2,ik3] += grad[kcur] - // q[:D,:N,:,:] [D,N,:,:] grad[q][:D,iq1,iq2,iq3] += grad[qcur] - // v[:M,:D,:,:] [M,D,:,:] grad[v][:M,:D,iv2,iv3] += grad[vcur] - // for iq1: - // kcur = k[:D,:M,ik2,ik3] [D,M,1,1] grad[kcur] = grad[S1].T @ qcur - // qcur = q[:D,iq1,iq2,iq3] [D,1,1,1] grad[qcur] = grad[S1] @ kcur - // vcur = v[:M,:D,iv2,iv3] [M,D,1,1] grad[vcur] = grad[S5].T @ S4 - // S0 = -Inf [D,1,1,1] - // ~S1[i] = dot(kcur[:D,i], qcur) - // S1 = qcur @ kcur.T [M,1,1,1] grad[S1] = grad[S2] * scale - // S2 = S1 * scale [M,1,1,1] grad[S2] = diag_mask_zero(grad[S3], P) - // S3 = diag_mask_inf(S2, P) [M,1,1,1] grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) - // S4 = softmax(S3) [M,1,1,1] grad[S4] = grad[S5] @ vcur - // ~S5[i] = dot(vcur[:,i], S4) - // S5 = S4 @ vcur.T [D,1,1,1] grad[S5] = d[:D,id1,id2,id3] - // ~dst[i,iq1,iq2,iq3] = S5[i] ^ - // dst[:D,iq1,iq2,iq3] = S5 | grad[dst[:D,iq1,iq2,iq3]] = d[:D,id1,id2,id3] - // dst backward-/ grad[dst] = d - // - // output gradients with their dependencies: - // - // grad[kcur] = grad[S1].T @ qcur - // grad[S1] = diag_mask_zero(grad[S3], P) * scale - // grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) - // grad[S4] = grad[S5] @ vcur - // grad[S4] = d[:D,id1,id2,id3] @ vcur - // grad[qcur] = grad[S1] @ kcur - // grad[vcur] = grad[S5].T @ S4 - // grad[vcur] = d[:D,id1,id2,id3].T @ S4 - // - // in post-order: - // - // S1 = qcur @ kcur.T - // S2 = S1 * scale - // S3 = diag_mask_inf(S2, P) - // S4 = softmax(S3) - // grad[S4] = d[:D,id1,id2,id3] @ vcur - // grad[S3] = S4 * (grad[S4] - dot(S4, grad[S4])) - // grad[S1] = diag_mask_zero(grad[S3], P) * scale - // grad[qcur] = grad[S1] @ kcur - // grad[kcur] = grad[S1].T @ qcur - // grad[vcur] = d[:D,id1,id2,id3].T @ S4 - // - // using less variables (SM=S4): - // - // S = diag_mask_inf(qcur @ kcur.T * scale, P) - // SM = softmax(S) - // S = d[:D,iq1,iq2,iq3] @ vcur - // dot_SM_gradSM = dot(SM, S) - // S = SM * (S - dot(SM, S)) - // S = diag_mask_zero(S, P) * scale - // - // grad[q][:D,iq1,iq2,iq3] += S @ kcur - // grad[k][:D,:M,ik2,ik3] += S.T @ qcur - // grad[v][:M,:D,iv2,iv3] += d[:D,id1,id2,id3].T @ SM - } - - // S = gradSM = d[:D,id1,id2,id3] @ vcur[:,:,iv2,iv3] - // S = d[:D,id1,id2,id3] @ vcur[:,:,iv2,iv3] - // for ic: - // S[:M] += vcur[:M,ic,iv2,iv3] * d[ic,id1,id2,id3] - // exclude known future zero S[..] values from operation - ggml_vec_set_f32(masked_begin, S, 0); - for (int64_t ic = 0; ic < D; ++ic) { - ggml_vec_mad_f32(masked_begin, - S, - (float *) ((char *) v->data + ( ic*nbv1 + iv2*nbv2 + iv3*nbv3)), - *(float *) ((char *) d->data + (ic*nbd0 + id1*nbd1 + id2*nbd2 + id3*nbd3))); - } - - // S = SM * (S - dot(SM, S)) - float dot_SM_gradSM = 0; - ggml_vec_dot_f32 (masked_begin, &dot_SM_gradSM, 0, SM, 0, S, 0, 1); - ggml_vec_acc1_f32(M, S, -dot_SM_gradSM); - ggml_vec_mul_f32 (masked_begin, S, S, SM); - - // S = diag_mask_zero(S, P) * scale - // already done by above ggml_vec_set_f32 - - // exclude known zero S[..] values from operation - ggml_vec_scale_f32(masked_begin, S, scale); - - // S shape [M,1] - // SM shape [M,1] - // kcur shape [D,M] - // qcur shape [D,1] - // vcur shape [M,D] - - // grad[q][:D,iq1,iq2,iq3] += S @ kcur - // grad[q][:D,iq1,iq2,iq3] += shape[M,1] @ shape[D,M] - // for ic: - // grad[q][:D,iq1,iq2,iq3] += S[ic] * kcur[:D,ic,ik2,ik3] - // exclude known zero S[..] values from loop - for (int64_t ic = 0; ic < masked_begin; ++ic) { - ggml_vec_mad_f32(D, - (float *) ((char *) grad_q + (iq1*nbgq1 + iq2*nbgq2 + iq3*nbgq3)), - (float *) ((char *) k->data + (ic*nbk1 + ik2*nbk2 + ik3*nbk3)), - S[ic]); - } - - // grad[k][:D,:M,iq2,iq3] += S.T @ qcur - // for ic: - // grad[k][:D,ic,iq2,iq3] += S.T[0,ic] * qcur[:D,0] - // grad[k][:D,ic,iq2,iq3] += S[ic] * qcur[:D,0] - // exclude known zero S[..] values from loop - for (int64_t ic = 0; ic < masked_begin; ++ic) { - ggml_vec_mad_f32(D, - (float *) ((char *) grad_k + (ic*nbgk1 + ik2*nbgk2 + ik3*nbgk3)), - (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), - S[ic]); - } - - // grad[v][:M,:D,iv2,iv3] += d[:D,id1,id2,id3].T @ SM - // for ic: - // grad[v][:M,ic,iv2,iv3] += d[:D,id1,id2,id3].T[0,ic] * SM[:M] - // grad[v][:M,ic,iv2,iv3] += d[ic,id1,id2,id3] * SM[:M] - // exclude known zero SM[..] values from mad - for (int64_t ic = 0; ic < D; ++ic) { - ggml_vec_mad_f32(masked_begin, - (float *) ((char *) grad_v + ( ic*nbgv1 + iv2*nbgv2 + iv3*nbgv3)), - SM, - *(float *) ((char *) d->data + (ic*nbd0 + id1*nbd1 + id2*nbd2 + id3*nbd3))); - } - } - } - } -} - -static void ggml_compute_forward_flash_attn_back( - const struct ggml_compute_params * params, - const bool masked, - struct ggml_tensor * dst) { - - const struct ggml_tensor * q = dst->src[0]; - - switch (q->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_flash_attn_back_f32(params, masked, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_ssm_conv - -static void ggml_compute_forward_ssm_conv_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - const struct ggml_tensor * src0 = dst->src[0]; // conv_x - const struct ggml_tensor * src1 = dst->src[1]; // conv1d.weight - - const int ith = params->ith; - const int nth = params->nth; - - const int nc = src1->ne[0]; // d_conv - const int ncs = src0->ne[0]; // d_conv - 1 + n_t - const int nr = src0->ne[1]; // d_inner - const int n_t = dst->ne[1]; // tokens per sequence - const int n_s = dst->ne[2]; // number of sequences in the batch - - GGML_ASSERT( dst->ne[0] == nr); - GGML_ASSERT(src0->nb[0] == sizeof(float)); - GGML_ASSERT(src1->nb[0] == sizeof(float)); - GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - const int ir = ir1 - ir0; - - for (int i3 = 0; i3 < n_s; ++i3) { - for (int i2 = 0; i2 < n_t; ++i2) { - // {d_conv - 1 + n_t, d_inner, n_seqs} - // sliding window - const float * s = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i2*(src0->nb[0]) + i3*(src0->nb[2])); // {d_conv, d_inner, n_s} - const float * c = (const float *) ((const char *) src1->data + ir0*(src1->nb[1])); // {d_conv, d_inner} - float * x = (float *) ((char *) dst->data + ir0*(dst->nb[0]) + i2*(dst->nb[1]) + i3*(dst->nb[2])); // {d_inner, n_t, n_s} - - // TODO: transpose the output for smaller strides for big batches? - // d_inner - for (int i1 = 0; i1 < ir; ++i1) { - // rowwise dot product - // NOTE: not using ggml_vec_dot_f32, because its sum is in double precision - float sumf = 0.0f; - - // d_conv - for (int i0 = 0; i0 < nc; ++i0) { - sumf += s[i0 + i1*ncs] * c[i0 + i1*nc]; - } - x[i1] = sumf; - } - } - } -} - -static void ggml_compute_forward_ssm_conv( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - switch (dst->src[0]->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_ssm_conv_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_ssm_scan - -static void ggml_compute_forward_ssm_scan_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - const struct ggml_tensor * src0 = dst->src[0]; // s - const struct ggml_tensor * src1 = dst->src[1]; // x - const struct ggml_tensor * src2 = dst->src[2]; // dt - const struct ggml_tensor * src3 = dst->src[3]; // A - const struct ggml_tensor * src4 = dst->src[4]; // B - const struct ggml_tensor * src5 = dst->src[5]; // C - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t nc = src0->ne[0]; // d_state - const int64_t nr = src0->ne[1]; // d_inner - const int64_t n_t = src1->ne[1]; // number of tokens per sequence - const int64_t n_s = src0->ne[2]; // number of sequences in the batch - - GGML_ASSERT(ggml_nelements(src1) + ggml_nelements(src0) == ggml_nelements(dst)); - GGML_ASSERT(src0->nb[0] == sizeof(float)); - GGML_ASSERT(src1->nb[0] == sizeof(float)); - GGML_ASSERT(src2->nb[0] == sizeof(float)); - GGML_ASSERT(src3->nb[0] == sizeof(float)); - GGML_ASSERT(src4->nb[0] == sizeof(float)); - GGML_ASSERT(src5->nb[0] == sizeof(float)); - // required for the dot product between s and C - GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float)); - // required for per-sequence offsets for states - GGML_ASSERT(src0->nb[2] == src0->ne[0]*src0->ne[1]*sizeof(float)); - // required to get correct offset for state destination (i.e. src1->nb[3]) - GGML_ASSERT(src1->nb[3] == src1->ne[0]*src1->ne[1]*src1->ne[2]*sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - const int ir = ir1 - ir0; - - for (int i3 = 0; i3 < n_s; ++i3) { - for (int i2 = 0; i2 < n_t; ++i2) { - const float * s0 = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2])); // {d_state, d_inner, n_s} - const float * x = (const float *) ((const char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s} - const float * dt = (const float *) ((const char *) src2->data + ir0*(src2->nb[0]) + i2*(src2->nb[1]) + i3*(src2->nb[2])); // {d_inner, n_t, n_s} - const float * A = (const float *) ((const char *) src3->data + ir0*(src3->nb[1])); // {d_state, d_inner} - const float * B = (const float *) ((const char *) src4->data + i2*(src4->nb[1]) + i3*(src4->nb[2])); // {d_state, n_t, n_s} - const float * C = (const float *) ((const char *) src5->data + i2*(src5->nb[1]) + i3*(src5->nb[2])); // {d_state, n_t, n_s} - float * y = ( float *) (( char *) dst->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s} - float * s = ( float *) (( char *) dst->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]) + src1->nb[3]); // {d_state, d_inner, n_s} - - // use the output as the source for the next token-wise iterations - if (i2 > 0) { s0 = s; } - - // d_inner - for (int i1 = 0; i1 < ir; ++i1) { - // ref: https://github.com/state-spaces/mamba/blob/34076d664838588a3c97727b263478ab9f621a07/mamba_ssm/ops/triton/selective_state_update.py#L78 - float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1]; - float x_dt = x[i1] * dt_soft_plus; - float sumf = 0.0f; - // d_state - for (int i0 = 0; i0 < nc; ++i0) { - int i = i0 + i1*nc; - // state = prev_state * dA + dB * x - float state = (s0[i] * expf(dt_soft_plus * A[i])) + (B[i0] * x_dt); - // y = rowwise_dotprod(state, C) - sumf += state * C[i0]; - s[i] = state; - } - y[i1] = sumf; - } - } - } -} - -static void ggml_compute_forward_ssm_scan( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - switch (dst->src[0]->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_ssm_scan_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_win_part - -static void ggml_compute_forward_win_part_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - UNUSED(params); - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) - GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) - - const int32_t nep0 = ((const int32_t *)(dst->op_params))[0]; - const int32_t nep1 = ((const int32_t *)(dst->op_params))[1]; - const int32_t w = ((const int32_t *)(dst->op_params))[2]; - - assert(ne00 == ne0); - assert(ne3 == nep0*nep1); - - // TODO: optimize / multi-thread - for (int py = 0; py < nep1; ++py) { - for (int px = 0; px < nep0; ++px) { - const int64_t i3 = py*nep0 + px; - for (int64_t i2 = 0; i2 < ne2; ++i2) { - for (int64_t i1 = 0; i1 < ne1; ++i1) { - for (int64_t i0 = 0; i0 < ne0; ++i0) { - const int64_t i02 = py*w + i2; - const int64_t i01 = px*w + i1; - const int64_t i00 = i0; - - const int64_t i = i3*ne2*ne1*ne0 + i2*ne1*ne0 + i1*ne0 + i0; - const int64_t j = i02*ne01*ne00 + i01*ne00 + i00; - - if (py*w + i2 >= ne02 || px*w + i1 >= ne01) { - ((float *) dst->data)[i] = 0.0f; - } else { - ((float *) dst->data)[i] = ((float *) src0->data)[j]; - } - } - } - } - } - } -} - -static void ggml_compute_forward_win_part( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_win_part_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_win_unpart - -static void ggml_compute_forward_win_unpart_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - UNUSED(params); - - const struct ggml_tensor * src0 = dst->src[0]; - - GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) - GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) - - const int32_t w = ((const int32_t *)(dst->op_params))[0]; - - // padding - const int px = (w - ne1%w)%w; - //const int py = (w - ne2%w)%w; - - const int npx = (px + ne1)/w; - //const int npy = (py + ne2)/w; - - assert(ne0 == ne00); - - // TODO: optimize / multi-thread - for (int64_t i2 = 0; i2 < ne2; ++i2) { - for (int64_t i1 = 0; i1 < ne1; ++i1) { - for (int64_t i0 = 0; i0 < ne0; ++i0) { - const int ip2 = i2/w; - const int ip1 = i1/w; - - const int64_t i02 = i2%w; - const int64_t i01 = i1%w; - const int64_t i00 = i0; - - const int64_t i = (ip2*npx + ip1)*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00 + i00; - const int64_t j = i2*ne1*ne0 + i1*ne0 + i0; - - ((float *) dst->data)[j] = ((float *) src0->data)[i]; - } - } - } -} - -static void ggml_compute_forward_win_unpart( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_win_unpart_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -//gmml_compute_forward_unary - -static void ggml_compute_forward_unary( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const enum ggml_unary_op op = ggml_get_unary_op(dst); - - switch (op) { - case GGML_UNARY_OP_ABS: - { - ggml_compute_forward_abs(params, dst); - } break; - case GGML_UNARY_OP_SGN: - { - ggml_compute_forward_sgn(params, dst); - } break; - case GGML_UNARY_OP_NEG: - { - ggml_compute_forward_neg(params, dst); - } break; - case GGML_UNARY_OP_STEP: - { - ggml_compute_forward_step(params, dst); - } break; - case GGML_UNARY_OP_TANH: - { - ggml_compute_forward_tanh(params, dst); - } break; - case GGML_UNARY_OP_ELU: - { - ggml_compute_forward_elu(params, dst); - } break; - case GGML_UNARY_OP_RELU: - { - ggml_compute_forward_relu(params, dst); - } break; - case GGML_UNARY_OP_SIGMOID: - { - ggml_compute_forward_sigmoid(params, dst); - } break; - case GGML_UNARY_OP_GELU: - { - ggml_compute_forward_gelu(params, dst); - } break; - case GGML_UNARY_OP_GELU_QUICK: - { - ggml_compute_forward_gelu_quick(params, dst); - } break; - case GGML_UNARY_OP_SILU: - { - ggml_compute_forward_silu(params, dst); - } break; - case GGML_UNARY_OP_HARDSWISH: - { - ggml_compute_forward_hardswish(params, dst); - } break; - case GGML_UNARY_OP_HARDSIGMOID: - { - ggml_compute_forward_hardsigmoid(params, dst); - } break; - case GGML_UNARY_OP_EXP: - { - ggml_compute_forward_exp(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_get_rel_pos - -static void ggml_compute_forward_get_rel_pos_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - UNUSED(params); - - const struct ggml_tensor * src0 = dst->src[0]; - - // ref: https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/modeling/image_encoder.py#L292-L322 - - GGML_TENSOR_UNARY_OP_LOCALS - - const int64_t w = ne1; - - ggml_fp16_t * src0_data = (ggml_fp16_t *) src0->data; - ggml_fp16_t * dst_data = (ggml_fp16_t *) dst->data; - - for (int64_t i2 = 0; i2 < ne2; ++i2) { - for (int64_t i1 = 0; i1 < ne1; ++i1) { - const int64_t pos = (w - i1 - 1) + i2; - for (int64_t i0 = 0; i0 < ne0; ++i0) { - dst_data[i2*ne1*ne0 + i1*ne0 + i0] = src0_data[pos*ne00 + i0]; - } - } - } -} - -static void ggml_compute_forward_get_rel_pos( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - case GGML_TYPE_BF16: - { - ggml_compute_forward_get_rel_pos_f16(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_add_rel_pos - -static void ggml_compute_forward_add_rel_pos_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - const struct ggml_tensor * src2 = dst->src[2]; - - const bool inplace = (bool) ((int32_t *) dst->op_params)[0]; - if (!inplace) { - if (params->ith == 0) { - memcpy((char *) dst->data, (char *) src0->data, ggml_nbytes(dst)); - } - ggml_barrier(params->threadpool); - } - // ref: https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/modeling/image_encoder.py#L357-L359 - - float * src1_data = (float *) src1->data; - float * src2_data = (float *) src2->data; - float * dst_data = (float *) dst->data; - - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - - const int ith = params->ith; - const int nth = params->nth; - - // total patches in dst - const int np = ne13; - - // patches per thread - const int dp = (np + nth - 1)/nth; - - // patch range for this thread - const int ip0 = dp*ith; - const int ip1 = MIN(ip0 + dp, np); - - for (int64_t i13 = ip0; i13 < ip1; ++i13) { - for (int64_t i12 = 0; i12 < ne12; ++i12) { - for (int64_t i11 = 0; i11 < ne11; ++i11) { - const int64_t jp1 = i13*ne12*ne11*ne10 + i12*ne11*ne10 + i11*ne10; - for (int64_t i10 = 0; i10 < ne10; ++i10) { - const int64_t jp0 = jp1 + i10; - const float src1_e = src1_data[jp0]; - const float src2_e = src2_data[jp0]; - - const int64_t jdh = jp0 * ne10; - const int64_t jdw = jdh - (ne10 - 1) * i10; - - for (int64_t j = 0; j < ne10; ++j) { - dst_data[jdh + j ] += src2_e; - dst_data[jdw + j*ne10] += src1_e; - } - } - } - } - } -} - -static void ggml_compute_forward_add_rel_pos( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_add_rel_pos_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_rwkv_wkv - -static void ggml_compute_forward_rwkv_wkv_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - const size_t T = dst->src[1]->ne[3]; - const size_t C = dst->ne[0]; - const size_t H = dst->src[1]->ne[2]; - const size_t n_seqs = dst->src[5]->ne[1]; - - float * dst_data = (float *) dst->data; - float * state = ((float *) dst->data) + C * T; - - if (params->ith != 0) { - return; - } - - memset(dst_data, 0, T * C * sizeof(float)); - - float * k = (float *) dst->src[0]->data; - float * v = (float *) dst->src[1]->data; - float * r = (float *) dst->src[2]->data; - float * time_faaaa = (float *) dst->src[3]->data; - float * time_decay = (float *) dst->src[4]->data; - - size_t t_stride = H * (C / H); - - size_t h_stride = C / H; - size_t h_stride_2d = (C / H) * (C / H); - - // basically fused operations: - // dst = r @ (time_faaaa * (k @ v) + state), - // state = time_decay * state + (k @ v), - // recursive through each token - for (size_t t = 0; t < T; t++) { - size_t t_offset = t * t_stride; - size_t state_offset = (C / H) * C * (t / (T / n_seqs)); - float * state_cur = state + state_offset; - float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[5]->data + state_offset; - - for (size_t h = 0; h < H; h++) { - size_t h_offset = h * h_stride; - size_t t_h_offset = t_offset + h_offset; - size_t h_2d_offset = h * h_stride_2d; - - for (size_t i = 0; i < C / H; i++) { - size_t t_h_i_offset = t_h_offset + i; - size_t h_i_offset = h_offset + i; - size_t h_2d_i_offset = h_2d_offset + i * h_stride; - - float k_val = k[t_h_i_offset]; - float r_val = r[t_h_i_offset]; - float time_faaaa_val = time_faaaa[h_i_offset]; - // RWKV v6: different time_decay for each token. - float time_decay_val = time_decay[t_h_i_offset]; - - for (size_t j = 0; j < C / H; j ++) { - size_t t_h_j_offset = t_h_offset + j; - size_t h_2d_i_j_offset = h_2d_i_offset + j; - - float v_val = v[t_h_j_offset]; - float kv_val = v_val * k_val; - float prev_state_val = state_prev[h_2d_i_j_offset]; - float temp_val = kv_val * time_faaaa_val + prev_state_val; - dst_data[t_h_j_offset] += temp_val * r_val; - state_cur[h_2d_i_j_offset] = prev_state_val * time_decay_val + kv_val; - } - } - } - } -} - -static void ggml_compute_forward_rwkv_wkv( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_rwkv_wkv_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_map_unary - -static void ggml_compute_forward_map_unary_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_unary_op_f32_t fun) { - - const struct ggml_tensor * src0 = dst->src[0]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - fun(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1]))); - } -} - -static void ggml_compute_forward_map_unary( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_unary_op_f32_t fun) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_map_unary_f32(params, dst, fun); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_map_binary - -static void ggml_compute_forward_map_binary_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_binary_op_f32_t fun) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - if (params->ith != 0) { - return; - } - - assert(ggml_is_contiguous_1(src0)); - assert(ggml_is_contiguous_1(src1)); - assert(ggml_is_contiguous_1(dst)); - assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - - const int n = ggml_nrows(src0); - const int nc = src0->ne[0]; - - for (int i = 0; i < n; i++) { - fun(nc, - (float *) ((char *) dst->data + i*( dst->nb[1])), - (float *) ((char *) src0->data + i*(src0->nb[1])), - (float *) ((char *) src1->data + i*(src1->nb[1]))); - } -} - -static void ggml_compute_forward_map_binary( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_binary_op_f32_t fun) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_map_binary_f32(params, dst, fun); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_map_custom1 - -static void ggml_compute_forward_map_custom1_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_custom1_op_f32_t fun) { - - const struct ggml_tensor * a = dst->src[0]; - - if (params->ith != 0) { - return; - } - - fun(dst, a); -} - -// ggml_compute_forward_map_custom2 - -static void ggml_compute_forward_map_custom2_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_custom2_op_f32_t fun) { - - const struct ggml_tensor * a = dst->src[0]; - const struct ggml_tensor * b = dst->src[1]; - - if (params->ith != 0) { - return; - } - - fun(dst, a, b); -} - -// ggml_compute_forward_map_custom3 - -static void ggml_compute_forward_map_custom3_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst, - const ggml_custom3_op_f32_t fun) { - - const struct ggml_tensor * a = dst->src[0]; - const struct ggml_tensor * b = dst->src[1]; - const struct ggml_tensor * c = dst->src[1]; - - if (params->ith != 0) { - return; - } - - fun(dst, a, b, c); -} - -// ggml_compute_forward_map_custom1 - -static void ggml_compute_forward_map_custom1( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * a = dst->src[0]; - - struct ggml_map_custom1_op_params p; - memcpy(&p, dst->op_params, sizeof(p)); - - p.fun(dst, a, params->ith, params->nth, p.userdata); -} - -// ggml_compute_forward_map_custom2 - -static void ggml_compute_forward_map_custom2( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * a = dst->src[0]; - const struct ggml_tensor * b = dst->src[1]; - - struct ggml_map_custom2_op_params p; - memcpy(&p, dst->op_params, sizeof(p)); - - p.fun(dst, a, b, params->ith, params->nth, p.userdata); -} - -// ggml_compute_forward_map_custom3 - -static void ggml_compute_forward_map_custom3( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * a = dst->src[0]; - const struct ggml_tensor * b = dst->src[1]; - const struct ggml_tensor * c = dst->src[2]; - - struct ggml_map_custom3_op_params p; - memcpy(&p, dst->op_params, sizeof(p)); - - p.fun(dst, a, b, c, params->ith, params->nth, p.userdata); -} - -// ggml_compute_forward_cross_entropy_loss - -static void ggml_compute_forward_cross_entropy_loss_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type)); - GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type)); - GGML_ASSERT(ggml_are_same_shape(src0, src1)); - GGML_ASSERT(ggml_is_scalar(dst)); - GGML_ASSERT(dst->type == GGML_TYPE_F32); - - // TODO: handle transposed/permuted matrices - const int64_t nc = src0->ne[0]; - const int64_t nr = ggml_nrows(src0); - - const int ith = params->ith; - const int nth = params->nth; - - float * sums = (float *) params->wdata; - float * st = ((float *) params->wdata) + nth + ith*nc; - float sum_thread = 0.0f; - - GGML_ASSERT(params->wsize >= sizeof(float) * (nth + nth * nc)); - - // rows per thread - const int64_t dr = (nr + nth - 1)/nth; - - // row range for this thread - const int64_t ir0 = dr*ith; - const int64_t ir1 = MIN(ir0 + dr, nr); - - for (int64_t i1 = ir0; i1 < ir1; ++i1) { - const float * s0 = (const float *)((const char *) src0->data + i1*src0->nb[1]); - const float * s1 = (const float *)((const char *) src1->data + i1*src1->nb[1]); - -#ifndef NDEBUG - for (int64_t i = 0; i < nc; ++i) { - //printf("p[%d] = %f\n", i, p[i]); - assert(!isnan(s0[i])); - assert(!isnan(s1[i])); - } -#endif - - float max = -INFINITY; - ggml_vec_max_f32(nc, &max, s0); - const ggml_float sum_softmax = ggml_vec_log_soft_max_f32(nc, st, s0, max); - assert(sum_softmax >= 0.0); - - ggml_vec_add1_f32(nc, st, st, -sum_softmax); - ggml_vec_mul_f32(nc, st, st, s1); - - float sum_st = 0.0f; - ggml_vec_sum_f32(nc, &sum_st, st); - sum_thread += sum_st; - -#ifndef NDEBUG - for (int64_t i = 0; i < nc; ++i) { - assert(!isnan(st[i])); - assert(!isinf(st[i])); - } -#endif - } - sums[ith] = sum_thread; - ggml_barrier(params->threadpool); - - if (ith == 0) { - float * dp = (float *) dst->data; - ggml_vec_sum_f32(nth, dp, sums); - dp[0] *= -1.0f / (float) nr; - } -} - -static void ggml_compute_forward_cross_entropy_loss( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_cross_entropy_loss_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -// ggml_compute_forward_cross_entropy_loss_back - -static void ggml_compute_forward_cross_entropy_loss_back_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - const struct ggml_tensor * opt0 = dst->src[2]; - - GGML_ASSERT(ggml_is_contiguous(dst)); - GGML_ASSERT(ggml_is_contiguous(src0)); - GGML_ASSERT(ggml_is_contiguous(src1)); - GGML_ASSERT(ggml_is_contiguous(opt0)); - GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); - - const int64_t ith = params->ith; - const int64_t nth = params->nth; - - // TODO: handle transposed/permuted matrices - const int64_t nc = src0->ne[0]; - const int64_t nr = ggml_nrows(src0); - - // rows per thread - const int64_t dr = (nr + nth - 1)/nth; - - // row range for this thread - const int64_t ir0 = dr*ith; - const int64_t ir1 = MIN(ir0 + dr, nr); - - const float d_by_nr = ((const float *) opt0->data)[0] / (float) nr; - - for (int64_t i1 = ir0; i1 < ir1; i1++) { - float * ds0 = (float *)((char *) dst->data + i1*dst->nb[1]); - float * s0 = (float *)((char *) src0->data + i1*src0->nb[1]); - float * s1 = (float *)((char *) src1->data + i1*src1->nb[1]); - -#ifndef NDEBUG - for (int64_t i = 0; i < nc; ++i) { - //printf("p[%d] = %f\n", i, p[i]); - assert(!isnan(s0[i])); - assert(!isnan(s1[i])); - } -#endif - - // soft_max - float max = -INFINITY; - ggml_vec_max_f32(nc, &max, s0); - ggml_float sum = ggml_vec_soft_max_f32(nc, ds0, s0, max); - assert(sum > 0.0); - ggml_vec_scale_f32(nc, ds0, 1.0/sum); - - // grad(src0) = (softmax(src0) - src1) * grad(cross_entropy_loss(src0, src1)) / nr - ggml_vec_sub_f32(nc, ds0, ds0, s1); - ggml_vec_scale_f32(nc, ds0, d_by_nr); - -#ifndef NDEBUG - for (int64_t i = 0; i < nc; ++i) { - assert(!isnan(ds0[i])); - assert(!isinf(ds0[i])); - } -#endif - } -} - -static void ggml_compute_forward_cross_entropy_loss_back( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_cross_entropy_loss_back_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} - -static void ggml_compute_forward_opt_step_adamw_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src0_grad = dst->src[1]; - const struct ggml_tensor * src0_grad_m = dst->src[2]; - const struct ggml_tensor * src0_grad_v = dst->src[3]; - GGML_ASSERT(ggml_are_same_shape(src0, src0_grad)); - - const int ith = params->ith; - const int nth = params->nth; - - const int nr = ggml_nrows(src0); - - GGML_TENSOR_UNARY_OP_LOCALS - GGML_ASSERT(nb00 == sizeof(float)); - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - /* const float gnorm = 1.0f; */ - int64_t iter; memcpy(&iter, &dst->op_params[0], sizeof(int64_t)); - const float alpha = ggml_get_op_params_f32(dst, 2); - const float beta1 = ggml_get_op_params_f32(dst, 3); - const float beta2 = ggml_get_op_params_f32(dst, 4); - const float eps = ggml_get_op_params_f32(dst, 5); - const float wd = ggml_get_op_params_f32(dst, 6); - - const float beta1h = alpha/(1.0f - powf(beta1, iter)); - const float beta2h = 1.0f/(1.0f - powf(beta2, iter)); - - for (int ir = ir0; ir < ir1; ++ir) { - const int64_t i03 = ir/(ne02*ne01); - const int64_t i02 = (ir - i03*ne02*ne01)/ne01; - const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const size_t offset = i03*nb03 + i02*nb02 + i01*nb01; - - float * w = (float *) ((char *) src0->data + offset); // weight - const float * g = (const float *) ((const char *) src0_grad->data + offset); // grad - float * m = (float *) ((char *) src0_grad_m->data + offset); - float * v = (float *) ((char *) src0_grad_v->data + offset); - - for (int i00 = 0; i00 < ne00; ++i00) { - m[i00] = m[i00]*beta1 + g[i00]*(1.0f - beta1); - v[i00] = v[i00]*beta2 + g[i00]*g[i00]*(1.0f - beta2); - - const float mh = m[i00]*beta1h; - const float vh = sqrtf(v[i00]*beta2h) + eps; - - // The weight decay is applied independently of the Adam momenta m and v. - // This is NOT equivalent to l2 regularization that adds w[i00]*w[i00] to the loss. - // See: https://arxiv.org/pdf/1711.05101v3.pdf - w[i00] = w[i00]*(1.0f - alpha*wd) - mh/vh; - } - } - - ggml_barrier(params->threadpool); - if (ith != 0) { - return; - } - - iter++; - memcpy(&dst->op_params[0], &iter, sizeof(int64_t)); -} - -static void ggml_compute_forward_opt_step_adamw( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F32: - { - ggml_compute_forward_opt_step_adamw_f32(params, dst); - } break; - default: - { - GGML_ABORT("fatal error"); - } - } -} -///////////////////////////////// - -static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { - GGML_ASSERT(params); - - if (tensor->op == GGML_OP_NONE || ggml_is_empty(tensor)) { - return; - } - - switch (tensor->op) { - case GGML_OP_DUP: - { - ggml_compute_forward_dup(params, tensor); - } break; - case GGML_OP_ADD: - { - ggml_compute_forward_add(params, tensor); - } break; - case GGML_OP_ADD1: - { - ggml_compute_forward_add1(params, tensor); - } break; - case GGML_OP_ACC: - { - ggml_compute_forward_acc(params, tensor); - } break; - case GGML_OP_SUB: - { - ggml_compute_forward_sub(params, tensor); - } break; - case GGML_OP_MUL: - { - ggml_compute_forward_mul(params, tensor); - } break; - case GGML_OP_DIV: - { - ggml_compute_forward_div(params, tensor); - } break; - case GGML_OP_SQR: - { - ggml_compute_forward_sqr(params, tensor); - } break; - case GGML_OP_SQRT: - { - ggml_compute_forward_sqrt(params, tensor); - } break; - case GGML_OP_LOG: - { - ggml_compute_forward_log(params, tensor); - } break; - case GGML_OP_SIN: - { - ggml_compute_forward_sin(params, tensor); - } break; - case GGML_OP_COS: - { - ggml_compute_forward_cos(params, tensor); - } break; - case GGML_OP_SUM: - { - ggml_compute_forward_sum(params, tensor); - } break; - case GGML_OP_SUM_ROWS: - { - ggml_compute_forward_sum_rows(params, tensor); - } break; - case GGML_OP_MEAN: - { - ggml_compute_forward_mean(params, tensor); - } break; - case GGML_OP_ARGMAX: - { - ggml_compute_forward_argmax(params, tensor); - } break; - case GGML_OP_COUNT_EQUAL: - { - ggml_compute_forward_count_equal(params, tensor); - } break; - case GGML_OP_REPEAT: - { - ggml_compute_forward_repeat(params, tensor); - } break; - case GGML_OP_REPEAT_BACK: - { - ggml_compute_forward_repeat_back(params, tensor); - } break; - case GGML_OP_CONCAT: - { - ggml_compute_forward_concat(params, tensor); - } break; - case GGML_OP_SILU_BACK: - { - ggml_compute_forward_silu_back(params, tensor); - } break; - case GGML_OP_NORM: - { - ggml_compute_forward_norm(params, tensor); - } break; - case GGML_OP_RMS_NORM: - { - ggml_compute_forward_rms_norm(params, tensor); - } break; - case GGML_OP_RMS_NORM_BACK: - { - ggml_compute_forward_rms_norm_back(params, tensor); - } break; - case GGML_OP_GROUP_NORM: - { - ggml_compute_forward_group_norm(params, tensor); - } break; - case GGML_OP_MUL_MAT: - { - ggml_compute_forward_mul_mat(params, tensor); - } break; - case GGML_OP_MUL_MAT_ID: - { - ggml_compute_forward_mul_mat_id(params, tensor); - } break; - case GGML_OP_OUT_PROD: - { - ggml_compute_forward_out_prod(params, tensor); - } break; - case GGML_OP_SCALE: - { - ggml_compute_forward_scale(params, tensor); - } break; - case GGML_OP_SET: - { - ggml_compute_forward_set(params, tensor); - } break; - case GGML_OP_CPY: - { - ggml_compute_forward_cpy(params, tensor); - } break; - case GGML_OP_CONT: - { - ggml_compute_forward_cont(params, tensor); - } break; - case GGML_OP_RESHAPE: - { - ggml_compute_forward_reshape(params, tensor); - } break; - case GGML_OP_VIEW: - { - ggml_compute_forward_view(params, tensor); - } break; - case GGML_OP_PERMUTE: - { - ggml_compute_forward_permute(params, tensor); - } break; - case GGML_OP_TRANSPOSE: - { - ggml_compute_forward_transpose(params, tensor); - } break; - case GGML_OP_GET_ROWS: - { - ggml_compute_forward_get_rows(params, tensor); - } break; - case GGML_OP_GET_ROWS_BACK: - { - ggml_compute_forward_get_rows_back(params, tensor); - } break; - case GGML_OP_DIAG: - { - ggml_compute_forward_diag(params, tensor); - } break; - case GGML_OP_DIAG_MASK_INF: - { - ggml_compute_forward_diag_mask_inf(params, tensor); - } break; - case GGML_OP_DIAG_MASK_ZERO: - { - ggml_compute_forward_diag_mask_zero(params, tensor); - } break; - case GGML_OP_SOFT_MAX: - { - ggml_compute_forward_soft_max(params, tensor); - } break; - case GGML_OP_SOFT_MAX_BACK: - { - ggml_compute_forward_soft_max_back(params, tensor); - } break; - case GGML_OP_ROPE: - { - ggml_compute_forward_rope(params, tensor); - } break; - case GGML_OP_ROPE_BACK: - { - ggml_compute_forward_rope_back(params, tensor); - } break; - case GGML_OP_CLAMP: - { - ggml_compute_forward_clamp(params, tensor); - } break; - case GGML_OP_CONV_TRANSPOSE_1D: - { - ggml_compute_forward_conv_transpose_1d(params, tensor); - } break; - case GGML_OP_IM2COL: - { - ggml_compute_forward_im2col(params, tensor); - } break; - case GGML_OP_IM2COL_BACK: - { - ggml_compute_forward_im2col_back_f32(params, tensor); - } break; - case GGML_OP_CONV_TRANSPOSE_2D: - { - ggml_compute_forward_conv_transpose_2d(params, tensor); - } break; - case GGML_OP_POOL_1D: - { - ggml_compute_forward_pool_1d(params, tensor); - } break; - case GGML_OP_POOL_2D: - { - ggml_compute_forward_pool_2d(params, tensor); - } break; - case GGML_OP_POOL_2D_BACK: - { - ggml_compute_forward_pool_2d_back(params, tensor); - } break; - case GGML_OP_UPSCALE: - { - ggml_compute_forward_upscale(params, tensor); - } break; - case GGML_OP_PAD: - { - ggml_compute_forward_pad(params, tensor); - } break; - case GGML_OP_ARANGE: - { - ggml_compute_forward_arange(params, tensor); - } break; - case GGML_OP_TIMESTEP_EMBEDDING: - { - ggml_compute_forward_timestep_embedding(params, tensor); - } break; - case GGML_OP_ARGSORT: - { - ggml_compute_forward_argsort(params, tensor); - } break; - case GGML_OP_LEAKY_RELU: - { - ggml_compute_forward_leaky_relu(params, tensor); - } break; - case GGML_OP_FLASH_ATTN_EXT: - { - ggml_compute_forward_flash_attn_ext(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], tensor); - } break; - case GGML_OP_FLASH_ATTN_BACK: - { - int32_t t = ggml_get_op_params_i32(tensor, 0); - GGML_ASSERT(t == 0 || t == 1); - bool masked = t != 0; - ggml_compute_forward_flash_attn_back(params, masked, tensor); - } break; - case GGML_OP_SSM_CONV: - { - ggml_compute_forward_ssm_conv(params, tensor); - } break; - case GGML_OP_SSM_SCAN: - { - ggml_compute_forward_ssm_scan(params, tensor); - } break; - case GGML_OP_WIN_PART: - { - ggml_compute_forward_win_part(params, tensor); - } break; - case GGML_OP_WIN_UNPART: - { - ggml_compute_forward_win_unpart(params, tensor); - } break; - case GGML_OP_UNARY: - { - ggml_compute_forward_unary(params, tensor); - } break; - case GGML_OP_GET_REL_POS: - { - ggml_compute_forward_get_rel_pos(params, tensor); - } break; - case GGML_OP_ADD_REL_POS: - { - ggml_compute_forward_add_rel_pos(params, tensor); - } break; - case GGML_OP_RWKV_WKV: - { - ggml_compute_forward_rwkv_wkv(params, tensor); - } break; - case GGML_OP_MAP_UNARY: - { - ggml_unary_op_f32_t fun; - memcpy(&fun, tensor->op_params, sizeof(fun)); - ggml_compute_forward_map_unary(params, tensor, fun); - } - break; - case GGML_OP_MAP_BINARY: - { - ggml_binary_op_f32_t fun; - memcpy(&fun, tensor->op_params, sizeof(fun)); - ggml_compute_forward_map_binary(params, tensor, fun); - } - break; - case GGML_OP_MAP_CUSTOM1_F32: - { - ggml_custom1_op_f32_t fun; - memcpy(&fun, tensor->op_params, sizeof(fun)); - ggml_compute_forward_map_custom1_f32(params, tensor, fun); - } - break; - case GGML_OP_MAP_CUSTOM2_F32: - { - ggml_custom2_op_f32_t fun; - memcpy(&fun, tensor->op_params, sizeof(fun)); - ggml_compute_forward_map_custom2_f32(params, tensor, fun); - } - break; - case GGML_OP_MAP_CUSTOM3_F32: - { - ggml_custom3_op_f32_t fun; - memcpy(&fun, tensor->op_params, sizeof(fun)); - ggml_compute_forward_map_custom3_f32(params, tensor, fun); - } - break; - case GGML_OP_MAP_CUSTOM1: - { - ggml_compute_forward_map_custom1(params, tensor); - } - break; - case GGML_OP_MAP_CUSTOM2: - { - ggml_compute_forward_map_custom2(params, tensor); - } - break; - case GGML_OP_MAP_CUSTOM3: - { - ggml_compute_forward_map_custom3(params, tensor); - } - break; - case GGML_OP_CROSS_ENTROPY_LOSS: - { - ggml_compute_forward_cross_entropy_loss(params, tensor); - } - break; - case GGML_OP_CROSS_ENTROPY_LOSS_BACK: - { - ggml_compute_forward_cross_entropy_loss_back(params, tensor); - } - break; - case GGML_OP_OPT_STEP_ADAMW: - { - ggml_compute_forward_opt_step_adamw(params, tensor); - } - break; - case GGML_OP_NONE: - { - // nop - } break; - case GGML_OP_COUNT: - { - GGML_ABORT("fatal error"); - } - } -} - -//////////////////////////////////////////////////////////////////////////////// - struct ggml_hash_set ggml_hash_set_new(size_t size) { size = ggml_hash_size(size); struct ggml_hash_set result; @@ -17671,1112 +5062,526 @@ static void ggml_hash_map_free(struct hash_map * map) { GGML_FREE(map); } -// gradient checkpointing +// utility functions to change gradients +// isrc is the index of tensor in cgraph->visited_has_set.keys +// the corresponding gradient (accumulators) are also at position isrc +// if tensor has a gradient accumulator, modify that accumulator in-place +// else if there is no gradient for tensor, set the corresponding value +// else, just add/subtract/etc. the gradients -static struct ggml_tensor * ggml_recompute_graph_node( +static void ggml_add_or_set( struct ggml_context * ctx, - struct ggml_cgraph * graph, - struct hash_map * replacements, - struct ggml_tensor * node) { - - if (node == NULL) { - return NULL; + struct ggml_cgraph * cgraph, + size_t isrc, + struct ggml_tensor * tensor) { + struct ggml_tensor * src = cgraph->visited_hash_set.keys[isrc]; + GGML_ASSERT(src); + if (cgraph->grads[isrc]) { + cgraph->grads[isrc] = ggml_add_impl(ctx, cgraph->grads[isrc], tensor, /*inplace =*/ cgraph->grad_accs[isrc]); + } else { + cgraph->grads[isrc] = tensor; } - - if (node->flags & GGML_TENSOR_FLAG_PARAM) { - return node; - } - - if (!ggml_hash_contains(&graph->visited_hash_set, node)) { - return node; - } - - int count_children = 0; - for (int k = 0; k < GGML_MAX_SRC; ++k) { - if (node->src[k]) { - ++count_children; - } - } - - if (count_children == 0) { - return node; - } - - size_t i = ggml_hash_find(&replacements->set, node); - GGML_ASSERT(i != GGML_HASHSET_FULL); // assert that not full - if (replacements->set.keys[i] == node) { - return replacements->vals[i]; - } - - struct ggml_tensor * clone = ggml_new_tensor(ctx, node->type, GGML_MAX_DIMS, node->ne); - - // insert clone into replacements - GGML_ASSERT(replacements->set.keys[i] == NULL); // assert that we don't overwrite - replacements->set.keys[i] = node; - replacements->vals[i] = clone; - - clone->op = node->op; - clone->grad = node->grad; - clone->flags = node->flags; - clone->extra = node->extra; - for (int k = 0; k < GGML_MAX_DIMS; ++k) { - clone->nb[k] = node->nb[k]; - } - for (int k = 0; k < GGML_MAX_SRC; ++k) { - clone->src[k] = ggml_recompute_graph_node(ctx, graph, replacements, node->src[k]); - } - if (node->view_src != NULL) { - clone->data = (node->view_src->data == NULL) - ? NULL // view_src not yet allocated - : (char *) node->view_src->data // view_src already allocated - + node->view_offs; - clone->view_src = node->view_src; - clone->view_offs = node->view_offs; - } - - GGML_ASSERT(sizeof(node->op_params) == sizeof(int32_t) * (GGML_MAX_OP_PARAMS / sizeof(int32_t))); - GGML_ASSERT(sizeof(node->name) == GGML_MAX_NAME); - memcpy(clone->op_params, node->op_params, sizeof(node->op_params)); - ggml_format_name(clone, "%s (clone)", ggml_get_name(node)); - - return clone; + ggml_format_name(cgraph->grads[isrc], "grad for %s", src->name); + ggml_build_forward_expand(cgraph, cgraph->grads[isrc]); } -void ggml_build_backward_gradient_checkpointing( - struct ggml_context * ctx, - struct ggml_cgraph * gf, - struct ggml_cgraph * gb, - struct ggml_cgraph * gb_tmp, - struct ggml_tensor * * checkpoints, - int n_checkpoints) { - ggml_graph_cpy(gf, gb_tmp); - ggml_build_backward_expand(ctx, gf, gb_tmp, false); +static void ggml_acc_or_set( + struct ggml_context * ctx, + struct ggml_cgraph * cgraph, + size_t isrc, + struct ggml_tensor * tensor, + const size_t nb1, + const size_t nb2, + const size_t nb3, + const size_t offset) { + struct ggml_tensor * src = cgraph->visited_hash_set.keys[isrc]; + GGML_ASSERT(src); + if (cgraph->grads[isrc]) { + cgraph->grads[isrc] = ggml_acc_impl(ctx, cgraph->grads[isrc], tensor, nb1, nb2, nb3, offset, cgraph->grad_accs[isrc]); + } else { + struct ggml_tensor * a_zero = ggml_scale(ctx, src, 0.0f); // FIXME this is going to produce NaN if a contains inf/NaN + cgraph->grads[isrc] = ggml_acc_impl(ctx, a_zero, tensor, nb1, nb2, nb3, offset, false); + } + ggml_format_name(cgraph->grads[isrc], "grad for %s", cgraph->visited_hash_set.keys[isrc]->name); + ggml_build_forward_expand(cgraph, cgraph->grads[isrc]); +} - if (n_checkpoints <= 0) { - ggml_graph_cpy(gb_tmp, gb); +static void ggml_add1_or_set( + struct ggml_context * ctx, + struct ggml_cgraph * cgraph, + size_t isrc, + struct ggml_tensor * tensor) { + struct ggml_tensor * src = cgraph->visited_hash_set.keys[isrc]; + GGML_ASSERT(src); + if (cgraph->grads[isrc]) { + cgraph->grads[isrc] = ggml_add1_impl(ctx, cgraph->grads[isrc], tensor, cgraph->grad_accs[isrc]); + } else { + cgraph->grads[isrc] = ggml_repeat(ctx, tensor, src); + } + ggml_format_name(cgraph->grads[isrc], "grad for %s", src->name); + ggml_build_forward_expand(cgraph, cgraph->grads[isrc]); +} + +static void ggml_sub_or_set( + struct ggml_context * ctx, + struct ggml_cgraph * cgraph, + size_t isrc, + struct ggml_tensor * tensor) { + struct ggml_tensor * src = cgraph->visited_hash_set.keys[isrc]; + GGML_ASSERT(src); + if (cgraph->grads[isrc]) { + cgraph->grads[isrc] = ggml_sub_impl(ctx, cgraph->grads[isrc], tensor, cgraph->grad_accs[isrc]); + } else { + cgraph->grads[isrc] = ggml_neg(ctx, tensor); + } + ggml_format_name(cgraph->grads[isrc], "grad for %s", src->name); + ggml_build_forward_expand(cgraph, cgraph->grads[isrc]); +} + +static void ggml_compute_backward( + struct ggml_context * ctx, struct ggml_cgraph * cgraph, int i, bool * grads_needed) { + struct ggml_tensor * tensor = cgraph->nodes[i]; + struct ggml_tensor * grad = ggml_graph_get_grad(cgraph, tensor); + + if (!grad) { return; } - struct hash_map * replacements = ggml_new_hash_map(gf->n_nodes + gf->n_leafs + n_checkpoints); - - // insert checkpoints in replacements - for (int i = 0; i < n_checkpoints; ++i) { - size_t k = ggml_hash_find(&replacements->set, checkpoints[i]); - GGML_ASSERT(k != GGML_HASHSET_FULL); // assert that not full - GGML_ASSERT(replacements->set.keys[k] == NULL); // assert that we don't overwrite - replacements->set.keys[k] = checkpoints[i]; - replacements->vals[k] = checkpoints[i]; - } - - ggml_graph_cpy(gf, gb); - // rewrite gb_tmp->nodes[gf->n_nodes:gb_tmp->n_nodes], - // replacing references to gb_tmp->nodes[0:gf->n_nodes] ( == gf->nodes[0:gf->n_nodes]), - // by recomputing them from checkpoints - for (int i = gf->n_nodes; in_nodes; ++i) { - struct ggml_tensor * node = gb_tmp->nodes[i]; - for (int k = 0; k < GGML_MAX_SRC; ++k) { - // insert new tensors recomputing src, reusing already made replacements, - // remember replacements: remember new tensors with mapping from corresponding gf nodes - // recurse for input tensors, - // unless (i.e. terminating when) input tensors are replacements (like checkpoints) - node->src[k] = ggml_recompute_graph_node(ctx, gf, replacements, node->src[k]); - } - // insert rewritten backward node with replacements made into resulting backward graph gb - ggml_build_forward_expand(gb, node); - } - - ggml_hash_map_free(replacements); -} - -// utility functions to change gradients -// if a is in acc_table, modify gradients in-place and mark result as gradient accumulator -// else if a is in zero_table, replace a -// else, just add/subtract/etc. the gradients - -static struct ggml_tensor * ggml_add_or_set( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - struct ggml_hash_set * zero_table, - struct ggml_hash_set * acc_table) { - if (ggml_hash_contains(acc_table, a)) { - struct ggml_tensor * ret = ggml_add_impl(ctx, a, b, true); - const size_t insert_result = ggml_hash_insert(acc_table, ret); - GGML_ASSERT(insert_result != GGML_HASHSET_FULL); - GGML_ASSERT(insert_result != GGML_HASHSET_ALREADY_EXISTS); - return ret; - } - if (ggml_hash_contains(zero_table, a)) { - return b; - } - return ggml_add_impl(ctx, a, b, false); -} - -static struct ggml_tensor * ggml_acc_or_set( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - const size_t nb1, - const size_t nb2, - const size_t nb3, - const size_t offset, - struct ggml_hash_set * zero_table, - struct ggml_hash_set * acc_table) { - if (ggml_hash_contains(acc_table, a)) { - struct ggml_tensor * ret = ggml_acc_impl(ctx, a, b, nb1, nb2, nb3, offset, true); - const size_t insert_result = ggml_hash_insert(acc_table, ret); - GGML_ASSERT(insert_result != GGML_HASHSET_FULL); - GGML_ASSERT(insert_result != GGML_HASHSET_ALREADY_EXISTS); - return ret; - } - if (ggml_hash_contains(zero_table, a)) { - struct ggml_tensor * a_zero = ggml_scale(ctx, a, 0.0f); // FIXME this is going to produce NaN if a contains inf/NaN - return ggml_acc_impl(ctx, a_zero, b, nb1, nb2, nb3, offset, false); - } - return ggml_acc_impl(ctx, a, b, nb1, nb2, nb3, offset, false); -} - -static struct ggml_tensor * ggml_add1_or_set( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - struct ggml_hash_set * zero_table, - struct ggml_hash_set * acc_table) { - if (ggml_hash_contains(acc_table, a)) { - struct ggml_tensor * ret = ggml_add1_impl(ctx, a, b, true); - const size_t insert_result = ggml_hash_insert(acc_table, ret); - GGML_ASSERT(insert_result != GGML_HASHSET_FULL); - GGML_ASSERT(insert_result != GGML_HASHSET_ALREADY_EXISTS); - return ret; - } - if (ggml_hash_contains(zero_table, a)) { - return ggml_repeat(ctx, b, a); - } - return ggml_add1_impl(ctx, a, b, false); -} - -static struct ggml_tensor * ggml_sub_or_set( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - struct ggml_hash_set * zero_table, - struct ggml_hash_set * acc_table) { - if (ggml_hash_contains(acc_table, a)) { - struct ggml_tensor * ret = ggml_sub_impl(ctx, a, b, true); - const size_t insert_result = ggml_hash_insert(acc_table, ret); - GGML_ASSERT(insert_result != GGML_HASHSET_FULL); - GGML_ASSERT(insert_result != GGML_HASHSET_ALREADY_EXISTS); - return ret; - } - if (ggml_hash_contains(zero_table, a)) { - return ggml_neg(ctx, b); - } - return ggml_sub_impl(ctx, a, b, false); -} - -static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor * tensor, struct ggml_hash_set * zero_table, struct ggml_hash_set * acc_table) { struct ggml_tensor * src0 = tensor->src[0]; struct ggml_tensor * src1 = tensor->src[1]; struct ggml_tensor * src2 = tensor->src[2]; + struct ggml_hash_set * hash_set = &cgraph->visited_hash_set; + const size_t isrc0 = src0 ? ggml_hash_find(hash_set, src0) : (size_t) -1; + const size_t isrc1 = src1 ? ggml_hash_find(hash_set, src1) : (size_t) -1; + const size_t isrc2 = src2 ? ggml_hash_find(hash_set, src2) : (size_t) -1; + const bool src0_needs_grads = src0 && isrc0 != GGML_HASHSET_FULL && ggml_bitset_get(hash_set->used, isrc0) && grads_needed[isrc0]; + const bool src1_needs_grads = src1 && isrc1 != GGML_HASHSET_FULL && ggml_bitset_get(hash_set->used, isrc1) && grads_needed[isrc1]; + const bool src2_needs_grads = src2 && isrc2 != GGML_HASHSET_FULL && ggml_bitset_get(hash_set->used, isrc2) && grads_needed[isrc2]; switch (tensor->op) { - case GGML_OP_DUP: - { - if (src0->grad) { - src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table, acc_table); - } - } break; - case GGML_OP_ADD: - { - if (src0->grad) { - src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table, acc_table); - } - if (src1->grad) { - if (ggml_are_same_shape(src0, src1)) { - src1->grad = ggml_add_or_set(ctx, src1->grad, tensor->grad, zero_table, acc_table); - } else { - src1->grad = ggml_add_or_set(ctx, src1->grad, ggml_repeat_back(ctx, tensor->grad, src1), zero_table, acc_table); - } - } - } break; - case GGML_OP_ADD1: - { - if (src0->grad) { - src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table, acc_table); - } - if (src1->grad) { - src1->grad = ggml_add_or_set(ctx, - src1->grad, - ggml_mean(ctx, tensor->grad), // TODO: should probably be sum instead of mean - zero_table, acc_table); - } - } break; - case GGML_OP_ACC: - { - if (src0->grad) { - src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table, acc_table); - } - if (src1->grad) { - const size_t nb1 = ((int32_t *) tensor->op_params)[0]; - const size_t nb2 = ((int32_t *) tensor->op_params)[1]; - const size_t nb3 = ((int32_t *) tensor->op_params)[2]; - const size_t offset = ((int32_t *) tensor->op_params)[3]; - - struct ggml_tensor * tensor_grad_view = ggml_view_4d(ctx, - tensor->grad, - src1->grad->ne[0], - src1->grad->ne[1], - src1->grad->ne[2], - src1->grad->ne[3], - nb1, nb2, nb3, offset); - - src1->grad = - ggml_add_or_set(ctx, - src1->grad, - ggml_reshape(ctx, - ggml_cont(ctx, tensor_grad_view), - src1->grad), - zero_table, acc_table); - } - } break; - case GGML_OP_SUB: - { - if (src0->grad) { - src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table, acc_table); - } - if (src1->grad) { - src1->grad = ggml_sub_or_set(ctx, src1->grad, tensor->grad, zero_table, acc_table); - } - } break; - case GGML_OP_MUL: - { - if (src0->grad) { - src0->grad = - ggml_add_or_set(ctx, - src0->grad, - ggml_mul(ctx, src1, tensor->grad), - zero_table, acc_table); - } - if (src1->grad) { - src1->grad = - ggml_add_or_set(ctx, - src1->grad, - ggml_mul(ctx, src0, tensor->grad), - zero_table, acc_table); - } - } break; - case GGML_OP_DIV: - { - if (src0->grad) { - src0->grad = - ggml_add_or_set(ctx, - src0->grad, - ggml_div(ctx, tensor->grad, src1), - zero_table, acc_table); - } - if (src1->grad) { - src1->grad = - ggml_sub_or_set(ctx, - src1->grad, - ggml_mul(ctx, - tensor->grad, - ggml_div(ctx, tensor, src1)), - zero_table, acc_table); - } - } break; - case GGML_OP_SQR: - { - if (src0->grad) { - src0->grad = - ggml_add_or_set(ctx, - src0->grad, - ggml_scale(ctx, - ggml_mul(ctx, src0, tensor->grad), - 2.0f), - zero_table, acc_table); - } - } break; - case GGML_OP_SQRT: - { - if (src0->grad) { - src0->grad = - ggml_add_or_set(ctx, - src0->grad, - ggml_scale(ctx, - ggml_div(ctx, - tensor->grad, - tensor), - 0.5f), - zero_table, acc_table); - } - } break; - case GGML_OP_LOG: - { - if (src0->grad) { - src0->grad = - ggml_add_or_set(ctx, - src0->grad, - ggml_div(ctx, - tensor->grad, - src0), - zero_table, acc_table); - } - } break; - case GGML_OP_SIN: - { - if (src0->grad) { - src0->grad = - ggml_add_or_set(ctx, - src0->grad, - ggml_mul(ctx, - tensor->grad, - ggml_cos(ctx, src0)), - zero_table, acc_table); - } - } break; - case GGML_OP_COS: - { - if (src0->grad) { - src0->grad = - ggml_sub_or_set(ctx, - src0->grad, - ggml_mul(ctx, - tensor->grad, - ggml_sin(ctx, src0)), - zero_table, acc_table); - } - } break; - case GGML_OP_SUM: - { - if (src0->grad) { - src0->grad = - ggml_add1_or_set(ctx, - src0->grad, - tensor->grad, - zero_table, acc_table); - } - } break; - case GGML_OP_SUM_ROWS: - { - if (src0->grad) { - src0->grad = - ggml_add_or_set(ctx, - src0->grad, - ggml_repeat(ctx, - tensor->grad, - src0->grad), - zero_table, acc_table); - } - } break; - case GGML_OP_MEAN: - case GGML_OP_ARGMAX: - case GGML_OP_COUNT_EQUAL: - { - GGML_ABORT("fatal error"); // TODO: implement + case GGML_OP_DUP: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, grad); } - case GGML_OP_REPEAT: - { - // necessary for llama - if (src0->grad) { - src0->grad = ggml_add_or_set(ctx, - src0->grad, - ggml_repeat_back(ctx, tensor->grad, src0->grad), - zero_table, acc_table); - } - } break; - case GGML_OP_REPEAT_BACK: - { - if (src0->grad) { - // TODO: test this - src0->grad = ggml_add_or_set(ctx, - src0->grad, - ggml_repeat(ctx, tensor->grad, src0->grad), - zero_table, acc_table); - } - } break; - case GGML_OP_CONCAT: - { - GGML_ABORT("fatal error"); // TODO: implement + } break; + case GGML_OP_ADD: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, grad); } - case GGML_OP_SILU_BACK: - { - GGML_ABORT("fatal error"); // TODO: not implemented + if (src1_needs_grads) { + struct ggml_tensor * tmp = grad; + if (!ggml_are_same_shape(src0, src1)) { + tmp = ggml_repeat_back(ctx, tmp, src1); + } + ggml_add_or_set(ctx, cgraph, isrc1, tmp); } - case GGML_OP_NORM: - { - GGML_ABORT("fatal error"); // TODO: not implemented + } break; + case GGML_OP_ADD1: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, grad); } - case GGML_OP_RMS_NORM: - { - // necessary for llama - if (src0->grad) { - float eps; - memcpy(&eps, tensor->op_params, sizeof(float)); - - src0->grad = ggml_add_or_set(ctx, - src0->grad, - ggml_rms_norm_back(ctx, src0, tensor->grad, eps), - zero_table, acc_table); - } - } break; - case GGML_OP_RMS_NORM_BACK: - { - GGML_ABORT("fatal error"); // TODO: not implemented + if (src1_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc1, ggml_mean(ctx, grad)); // TODO: should probably be sum instead of mean } - case GGML_OP_GROUP_NORM: - { - GGML_ABORT("fatal error"); // TODO: not implemented + } break; + case GGML_OP_ACC: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, grad); } - case GGML_OP_MUL_MAT: - { - // https://cs231n.github.io/optimization-2/#staged - // # forward pass - // s0 = np.random.randn(5, 10) - // s1 = np.random.randn(10, 3) - // t = s0.dot(s1) + if (src1_needs_grads) { + const size_t nb1 = ((int32_t *) tensor->op_params)[0]; + const size_t nb2 = ((int32_t *) tensor->op_params)[1]; + const size_t nb3 = ((int32_t *) tensor->op_params)[2]; + const size_t offset = ((int32_t *) tensor->op_params)[3]; - // # now suppose we had the gradient on t from above in the circuit - // dt = np.random.randn(*t.shape) # same shape as t - // ds0 = dt.dot(s1.T) #.T gives the transpose of the matrix - // ds1 = t.T.dot(dt) + struct ggml_tensor * tensor_grad_view = ggml_view_4d(ctx, + grad, src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3], + nb1, nb2, nb3, offset); - // tensor.shape [m,p,qq,rr] - // src0.shape [n,m,q1,r1] - // src1.shape [n,p,qq,rr] - - // necessary for llama - if (src0->grad) { - struct ggml_tensor * s1_tg = - ggml_out_prod(ctx, // [n,m,qq,rr] - src1, // [n,p,qq,rr] - tensor->grad); // [m,p,qq,rr] - const int64_t qq = s1_tg->ne[2]; - const int64_t rr = s1_tg->ne[3]; - const int64_t q1 = src0->ne[2]; - const int64_t r1 = src0->ne[3]; - const bool ne2_broadcasted = qq > q1; - const bool ne3_broadcasted = rr > r1; - if (ne2_broadcasted || ne3_broadcasted) { - // sum broadcast repetitions of s1_tg into shape of src0 - s1_tg = ggml_repeat_back(ctx, s1_tg, src0); - } - src0->grad = - ggml_add_or_set(ctx, - src0->grad, // [n,m,q1,r1] - s1_tg, // [n,m,q1,r1] - zero_table, acc_table); - } - if (src1->grad) { - src1->grad = - ggml_add_or_set(ctx, - src1->grad, // [n,p,qq,rr] - // ggml_mul_mat(ctx, // [n,p,qq,rr] - // ggml_cont(ctx, // [m,n,q1,r1] - // ggml_transpose(ctx, src0)), // [m,n,q1,r1] - // tensor->grad), // [m,p,qq,rr] - - // // when src0 is bigger than tensor->grad (this is mostly the case in llama), - // // avoid transpose of src0, rather transpose smaller tensor->grad - // // and then use ggml_out_prod - ggml_out_prod(ctx, // [n,p,qq,rr] - src0, // [n,m,q1,r1] - ggml_transpose(ctx, // [p,m,qq,rr] - tensor->grad)), // [m,p,qq,rr] - zero_table, acc_table); - } - } break; - case GGML_OP_MUL_MAT_ID: - { - GGML_ABORT("fatal error"); // TODO: not implemented + ggml_add_or_set(ctx, cgraph, isrc1, ggml_reshape(ctx, ggml_cont(ctx, tensor_grad_view), src1)); } - case GGML_OP_OUT_PROD: - { - GGML_ABORT("fatal error"); // TODO: not implemented + } break; + case GGML_OP_SUB: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, grad); } - case GGML_OP_SCALE: - { - // necessary for llama - if (src0->grad) { - float s; - memcpy(&s, tensor->op_params, sizeof(float)); - - src0->grad = - ggml_add_or_set(ctx, - src0->grad, - ggml_scale_impl(ctx, tensor->grad, s, false), - zero_table, acc_table); - } - } break; - case GGML_OP_SET: - { - const size_t nb1 = ((int32_t *) tensor->op_params)[0]; - const size_t nb2 = ((int32_t *) tensor->op_params)[1]; - const size_t nb3 = ((int32_t *) tensor->op_params)[2]; - const size_t offset = ((int32_t *) tensor->op_params)[3]; - - struct ggml_tensor * tensor_grad_view = NULL; - - if (src0->grad || src1->grad) { - GGML_ASSERT(src0->type == tensor->type); - GGML_ASSERT(tensor->grad->type == tensor->type); - GGML_ASSERT(!src1->grad || src1->grad->type == tensor->grad->type); - - tensor_grad_view = ggml_view_4d(ctx, - tensor->grad, src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3], - nb1, nb2, nb3, offset); - } - - if (src0->grad) { - src0->grad = ggml_add_or_set(ctx, - src0->grad, - ggml_acc_impl(ctx, - tensor->grad, - ggml_neg(ctx, tensor_grad_view), - nb1, nb2, nb3, offset, false), - zero_table, acc_table); - } - - if (src1->grad) { - src1->grad = - ggml_add_or_set(ctx, - src1->grad, - ggml_reshape(ctx, - ggml_cont(ctx, tensor_grad_view), - src1->grad), - zero_table, acc_table); - } - } break; - case GGML_OP_CPY: - { - // necessary for llama - // cpy overwrites value of src1 by src0 and returns view(src1) - // the overwriting is mathematically equivalent to: - // tensor = src0 * 1 + src1 * 0 - if (src0->grad) { - // dsrc0 = dtensor * 1 - src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table, acc_table); - } - if (src1->grad) { - // dsrc1 = dtensor * 0 -> noop - } - } break; - case GGML_OP_CONT: - { - // same as cpy - if (src0->grad) { - GGML_ASSERT(ggml_is_contiguous(src0->grad)); - GGML_ASSERT(ggml_is_contiguous(tensor->grad)); - src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table, acc_table); - } - } break; - case GGML_OP_RESHAPE: - { - // necessary for llama - if (src0->grad) { - src0->grad = - ggml_add_or_set(ctx, src0->grad, - ggml_reshape(ctx, - ggml_is_contiguous(tensor->grad) - ? tensor->grad - : ggml_cont(ctx, tensor->grad), - src0->grad), - zero_table, acc_table); - } - } break; - case GGML_OP_VIEW: - { - // necessary for llama - if (src0->grad) { - size_t offset; - - memcpy(&offset, tensor->op_params, sizeof(offset)); - - size_t nb1 = tensor->nb[1]; - size_t nb2 = tensor->nb[2]; - size_t nb3 = tensor->nb[3]; - - if (src0->type != src0->grad->type) { - // gradient is typically F32, but src0 could be other type - size_t ng = ggml_element_size(src0->grad); - size_t n0 = ggml_element_size(src0); - GGML_ASSERT(offset % n0 == 0); - GGML_ASSERT(nb1 % n0 == 0); - GGML_ASSERT(nb2 % n0 == 0); - GGML_ASSERT(nb3 % n0 == 0); - offset = (offset / n0) * ng; - nb1 = (nb1 / n0) * ng; - nb2 = (nb2 / n0) * ng; - nb3 = (nb3 / n0) * ng; - } - - src0->grad = ggml_acc_or_set(ctx, src0->grad, tensor->grad, nb1, nb2, nb3, offset, zero_table, acc_table); - } - } break; - case GGML_OP_PERMUTE: - { - // necessary for llama - if (src0->grad) { - int32_t * axes = (int32_t *) tensor->op_params; - int axis0 = axes[0] & 0x3; - int axis1 = axes[1] & 0x3; - int axis2 = axes[2] & 0x3; - int axis3 = axes[3] & 0x3; - int axes_backward[4] = {0,0,0,0}; - axes_backward[axis0] = 0; - axes_backward[axis1] = 1; - axes_backward[axis2] = 2; - axes_backward[axis3] = 3; - src0->grad = - ggml_add_or_set(ctx, src0->grad, - ggml_permute(ctx, - tensor->grad, - axes_backward[0], - axes_backward[1], - axes_backward[2], - axes_backward[3]), - zero_table, acc_table); - } - } break; - case GGML_OP_TRANSPOSE: - { - // necessary for llama - if (src0->grad) { - src0->grad = - ggml_add_or_set(ctx, src0->grad, - ggml_transpose(ctx, tensor->grad), - zero_table, acc_table); - } - } break; - case GGML_OP_GET_ROWS: - { - // necessary for llama (only for tokenizer) - if (src0->grad) { - src0->grad = - ggml_add_or_set(ctx, src0->grad, - // last ggml_get_rows_back argument src0->grad is only - // necessary to setup correct output shape - ggml_get_rows_back(ctx, tensor->grad, src1, src0->grad), - zero_table, acc_table); - } - if (src1->grad) { - // noop - } - } break; - case GGML_OP_GET_ROWS_BACK: - { - GGML_ABORT("fatal error"); // TODO: not implemented + if (src1_needs_grads) { + ggml_sub_or_set(ctx, cgraph, isrc1, grad); } - case GGML_OP_DIAG: - { - GGML_ABORT("fatal error"); // TODO: not implemented + } break; + case GGML_OP_MUL: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, src1, grad)); } - case GGML_OP_DIAG_MASK_INF: - { - // necessary for llama - if (src0->grad) { - const int n_past = ((int32_t *) tensor->op_params)[0]; - src0->grad = - ggml_add_or_set(ctx, src0->grad, - /* ggml_diag_mask_inf_impl() shouldn't be here */ - /* ref: https://github.com/ggerganov/llama.cpp/pull/4203#discussion_r1412377992 */ - ggml_diag_mask_zero_impl(ctx, tensor->grad, n_past, false), - zero_table, acc_table); + if (src1_needs_grads) { + struct ggml_tensor * tmp = ggml_mul(ctx, src0, grad); + if (!ggml_are_same_shape(src0, src1)) { + tmp = ggml_repeat_back(ctx, tmp, src1); } - } break; - case GGML_OP_DIAG_MASK_ZERO: - { - // necessary for llama - if (src0->grad) { - const int n_past = ((int32_t *) tensor->op_params)[0]; - src0->grad = - ggml_add_or_set(ctx, src0->grad, - ggml_diag_mask_zero_impl(ctx, tensor->grad, n_past, false), - zero_table, acc_table); - } - } break; - case GGML_OP_SOFT_MAX: - { - // necessary for llama - if (src0->grad) { - src0->grad = - ggml_add_or_set(ctx, src0->grad, - ggml_soft_max_back(ctx, tensor->grad, tensor), - zero_table, acc_table); - } - GGML_ASSERT((!src1 || !src1->grad) && "backward pass for softmax mask not implemented"); - } break; - case GGML_OP_SOFT_MAX_BACK: - { - GGML_ABORT("fatal error"); // TODO: not implemented + ggml_add_or_set(ctx, cgraph, isrc1, tmp); } - case GGML_OP_ROPE: - { - // necessary for llama - if (src0->grad) { - //const int n_past = ((int32_t *) tensor->op_params)[0]; - const int n_dims = ((int32_t *) tensor->op_params)[1]; - const int mode = ((int32_t *) tensor->op_params)[2]; - //const int n_ctx = ((int32_t *) tensor->op_params)[3]; - const int n_ctx_orig = ((int32_t *) tensor->op_params)[4]; - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + } break; + case GGML_OP_DIV: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_div(ctx, grad, src1)); + } + if (src1_needs_grads) { + ggml_sub_or_set(ctx, cgraph, isrc1, ggml_mul(ctx, grad, ggml_div(ctx, tensor, src1))); + } + } break; + case GGML_OP_SQR: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_scale(ctx, ggml_mul(ctx, src0, grad), 2.0f)); + } + } break; + case GGML_OP_SQRT: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_scale(ctx, ggml_div(ctx, grad, tensor), 0.5f)); + } + } break; + case GGML_OP_LOG: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_div(ctx, grad, src0)); + } + } break; + case GGML_OP_SIN: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, grad, ggml_cos(ctx, src0))); + } + } break; + case GGML_OP_COS: { + if (src0_needs_grads) { + ggml_sub_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, grad, ggml_sin(ctx, src0))); + } + } break; + case GGML_OP_SUM: { + if (src0_needs_grads) { + ggml_add1_or_set(ctx, cgraph, isrc0, grad); + } + } break; + case GGML_OP_SUM_ROWS: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_repeat(ctx, grad, src0)); + } + } break; + case GGML_OP_MEAN: { + if (src0_needs_grads) { + ggml_add1_or_set(ctx, cgraph, isrc0, ggml_scale_impl(ctx, grad, 1.0f/src0->ne[0], false)); + } + } break; + case GGML_OP_REPEAT: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_repeat_back(ctx, grad, src0)); + } + } break; + case GGML_OP_REPEAT_BACK: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_repeat(ctx, grad, src0)); + } + } break; + case GGML_OP_RMS_NORM: { + if (src0_needs_grads) { + float eps; + memcpy(&eps, tensor->op_params, sizeof(float)); + ggml_add_or_set(ctx, cgraph, isrc0, ggml_rms_norm_back(ctx, src0, grad, eps)); + } + } break; + case GGML_OP_MUL_MAT: { + // https://cs231n.github.io/optimization-2/#staged + // # forward pass + // s0 = np.random.randn(5, 10) + // s1 = np.random.randn(10, 3) + // t = s0.dot(s1) - memcpy(&freq_base, (int32_t *) tensor->op_params + 5, sizeof(float)); - memcpy(&freq_scale, (int32_t *) tensor->op_params + 6, sizeof(float)); - memcpy(&ext_factor, (int32_t *) tensor->op_params + 7, sizeof(float)); - memcpy(&attn_factor, (int32_t *) tensor->op_params + 8, sizeof(float)); - memcpy(&beta_fast, (int32_t *) tensor->op_params + 9, sizeof(float)); - memcpy(&beta_slow, (int32_t *) tensor->op_params + 10, sizeof(float)); + // # now suppose we had the gradient on t from above in the circuit + // dt = np.random.randn(*t.shape) # same shape as t + // ds0 = dt.dot(s1.T) #.T gives the transpose of the matrix + // ds1 = t.T.dot(dt) - src0->grad = ggml_add_or_set(ctx, - src0->grad, - ggml_rope_back(ctx, - tensor->grad, - src1, - src2, - n_dims, - mode, - n_ctx_orig, - freq_base, - freq_scale, - ext_factor, - attn_factor, - beta_fast, - beta_slow), - zero_table, acc_table); + // tensor.shape [m,p,qq,rr] + // src0.shape [n,m,q1,r1] + // src1.shape [n,p,qq,rr] + + if (src0_needs_grads) { + struct ggml_tensor * s1_tg = + ggml_out_prod(ctx, // [n,m,qq,rr] + src1, // [n,p,qq,rr] + grad); // [m,p,qq,rr] + const int64_t qq = s1_tg->ne[2]; + const int64_t rr = s1_tg->ne[3]; + const int64_t q1 = src0->ne[2]; + const int64_t r1 = src0->ne[3]; + const bool ne2_broadcasted = qq > q1; + const bool ne3_broadcasted = rr > r1; + if (ne2_broadcasted || ne3_broadcasted) { + // sum broadcast repetitions of s1_tg into shape of src0 + s1_tg = ggml_repeat_back(ctx, s1_tg, src0); } - GGML_ASSERT((!src2 || !src2->grad) && "gradients for freq factors not implemented"); - } break; - case GGML_OP_ROPE_BACK: - { - if (src0->grad) { - //const int n_past = ((int32_t *) tensor->op_params)[0]; - const int n_dims = ((int32_t *) tensor->op_params)[1]; - const int mode = ((int32_t *) tensor->op_params)[2]; - //const int n_ctx = ((int32_t *) tensor->op_params)[3]; - const int n_ctx_orig = ((int32_t *) tensor->op_params)[4]; - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + ggml_add_or_set(ctx, cgraph, isrc0, s1_tg /*= [n,m,q1,r1]*/); + } + if (src1_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc1, + // ggml_mul_mat(ctx, // [n,p,qq,rr] + // ggml_cont(ctx, // [m,n,q1,r1] + // ggml_transpose(ctx, src0)), // [m,n,q1,r1] + // grad), // [m,p,qq,rr] - memcpy(&freq_base, (int32_t *) tensor->op_params + 5, sizeof(float)); - memcpy(&freq_scale, (int32_t *) tensor->op_params + 6, sizeof(float)); - memcpy(&ext_factor, (int32_t *) tensor->op_params + 7, sizeof(float)); - memcpy(&attn_factor, (int32_t *) tensor->op_params + 8, sizeof(float)); - memcpy(&beta_fast, (int32_t *) tensor->op_params + 9, sizeof(float)); - memcpy(&beta_slow, (int32_t *) tensor->op_params + 10, sizeof(float)); + // when src0 is bigger than tensor->grad (this is mostly the case in llama), + // avoid transpose of src0, rather transpose smaller tensor->grad + // and then use ggml_out_prod + ggml_out_prod(ctx, // [n,p,qq,rr] + src0, // [n,m,q1,r1] + ggml_transpose(ctx, // [p,m,qq,rr] + grad))); // [m,p,qq,rr] + } + } break; + case GGML_OP_SCALE: { + if (src0_needs_grads) { + float s; + memcpy(&s, tensor->op_params, sizeof(float)); + ggml_add_or_set(ctx, cgraph, isrc0, ggml_scale_impl(ctx, grad, s, false)); + } + } break; + case GGML_OP_SET: { + const size_t nb1 = ((const int32_t *) tensor->op_params)[0]; + const size_t nb2 = ((const int32_t *) tensor->op_params)[1]; + const size_t nb3 = ((const int32_t *) tensor->op_params)[2]; + const size_t offset = ((const int32_t *) tensor->op_params)[3]; - src0->grad = ggml_add_or_set(ctx, - src0->grad, - ggml_rope_impl(ctx, - tensor->grad, - src1, - src2, - n_dims, - mode, - n_ctx_orig, - freq_base, - freq_scale, - ext_factor, - attn_factor, - beta_fast, - beta_slow, - false), - zero_table, acc_table); - } - } break; - case GGML_OP_CLAMP: - { - GGML_ABORT("fatal error"); // TODO: not implemented - } - case GGML_OP_CONV_TRANSPOSE_1D: - { - GGML_ABORT("fatal error"); // TODO: not implemented - } - case GGML_OP_IM2COL: - { - if (src1->grad) { - const int32_t s0 = ggml_get_op_params_i32(tensor, 0); - const int32_t s1 = ggml_get_op_params_i32(tensor, 1); - const int32_t p0 = ggml_get_op_params_i32(tensor, 2); - const int32_t p1 = ggml_get_op_params_i32(tensor, 3); - const int32_t d0 = ggml_get_op_params_i32(tensor, 4); - const int32_t d1 = ggml_get_op_params_i32(tensor, 5); - const bool is_2D = ggml_get_op_params_i32(tensor, 6) == 1; + struct ggml_tensor * tensor_grad_view = NULL; - src1->grad = ggml_add_or_set(ctx, - src1->grad, - ggml_im2col_back(ctx, src0, tensor->grad, src1->ne, s0, s1, p0, p1, d0, d1, is_2D), - zero_table, acc_table); - } - } break; - case GGML_OP_IM2COL_BACK: - { - GGML_ABORT("fatal error"); // TODO: not implemented - } - case GGML_OP_CONV_TRANSPOSE_2D: - { - GGML_ABORT("fatal error"); // TODO: not implemented - } - case GGML_OP_POOL_1D: - { - GGML_ABORT("fatal error"); // TODO: not implemented - } - case GGML_OP_POOL_2D: - { - if (src0->grad) { - const enum ggml_op_pool op = ggml_get_op_params_i32(tensor, 0); - const int32_t k0 = ggml_get_op_params_i32(tensor, 1); - const int32_t k1 = ggml_get_op_params_i32(tensor, 2); - const int32_t s0 = ggml_get_op_params_i32(tensor, 3); - const int32_t s1 = ggml_get_op_params_i32(tensor, 4); - const int32_t p0 = ggml_get_op_params_i32(tensor, 5); - const int32_t p1 = ggml_get_op_params_i32(tensor, 6); + if (src0_needs_grads || src1_needs_grads) { + GGML_ASSERT(src0->type == tensor->type); + GGML_ASSERT(!cgraph->grads[isrc0] || cgraph->grads[isrc0]->type == grad->type); + GGML_ASSERT(!cgraph->grads[isrc1] || !src1_needs_grads || cgraph->grads[isrc1]->type == grad->type); - src0->grad = ggml_add_or_set(ctx, - src0->grad, - ggml_pool_2d_back(ctx, tensor->grad, src0, op, k0, k1, s0, s1, p0, p1), - zero_table, acc_table); - } - } break; - case GGML_OP_POOL_2D_BACK: - { - GGML_ABORT("fatal error"); // TODO: not implemented + tensor_grad_view = ggml_view_4d(ctx, + grad, src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3], + nb1, nb2, nb3, offset); } - case GGML_OP_UPSCALE: - { - GGML_ABORT("fatal error"); // TODO: not implemented + + if (src0_needs_grads) { + struct ggml_tensor * tmp = ggml_neg(ctx, tensor_grad_view); + ggml_add_or_set(ctx, cgraph, isrc0, ggml_acc_impl(ctx, grad, tmp, nb1, nb2, nb3, offset, false)); } - case GGML_OP_PAD: - { - GGML_ABORT("fatal error"); // TODO: not implemented + + if (src1_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc1, ggml_reshape(ctx, ggml_cont(ctx, tensor_grad_view), src1)); } - case GGML_OP_ARANGE: - { - GGML_ABORT("fatal error"); // TODO: not implemented + } break; + case GGML_OP_CPY: { + // cpy overwrites value of src1 by src0 and returns view(src1) + // the overwriting is mathematically equivalent to: + // tensor = src0 * 1 + src1 * 0 + if (src0_needs_grads) { + // dsrc0 = dtensor * 1 + ggml_add_or_set(ctx, cgraph, isrc0, grad); } - case GGML_OP_TIMESTEP_EMBEDDING: - { - GGML_ABORT("fatal error"); // TODO: not implemented + if (src1_needs_grads) { + // dsrc1 = dtensor * 0 -> noop } - case GGML_OP_ARGSORT: - { - GGML_ABORT("fatal error"); // TODO: not implemented + } break; + case GGML_OP_CONT: { + // same as cpy + if (src0_needs_grads) { + GGML_ASSERT(!cgraph->grads[isrc0] || ggml_is_contiguous(cgraph->grads[isrc0])); + GGML_ASSERT(ggml_is_contiguous(grad)); + ggml_add_or_set(ctx, cgraph, isrc0, grad); } - case GGML_OP_LEAKY_RELU: - { - GGML_ABORT("fatal error"); // TODO: not implemented + } break; + case GGML_OP_RESHAPE: { + if (src0_needs_grads) { + struct ggml_tensor * grad_cont = ggml_is_contiguous(grad) ? grad : ggml_cont(ctx, grad); + ggml_add_or_set(ctx, cgraph, isrc0, ggml_reshape(ctx, grad_cont, src0)); } - case GGML_OP_FLASH_ATTN_EXT: - { - GGML_ABORT("FA backward pass not adapted after rework"); - struct ggml_tensor * flash_grad = NULL; - if (src0->grad || src1->grad || tensor->src[2]->grad) { - int32_t t = ggml_get_op_params_i32(tensor, 0); - GGML_ASSERT(t == 0 || t == 1); - bool masked = t != 0; - flash_grad = - ggml_flash_attn_back(ctx, - src0, - src1, - tensor->src[2], - tensor->grad, - masked); + } break; + case GGML_OP_VIEW: { + if (src0_needs_grads) { + size_t offset; + + memcpy(&offset, tensor->op_params, sizeof(offset)); + + size_t nb1 = tensor->nb[1]; + size_t nb2 = tensor->nb[2]; + size_t nb3 = tensor->nb[3]; + + if (cgraph->grads[isrc0] && src0->type != cgraph->grads[isrc0]->type) { + // gradient is typically F32, but src0 could be other type + size_t ng = ggml_element_size(cgraph->grads[isrc0]); + size_t n0 = ggml_element_size(src0); + GGML_ASSERT(offset % n0 == 0); + GGML_ASSERT(nb1 % n0 == 0); + GGML_ASSERT(nb2 % n0 == 0); + GGML_ASSERT(nb3 % n0 == 0); + offset = (offset / n0) * ng; + nb1 = (nb1 / n0) * ng; + nb2 = (nb2 / n0) * ng; + nb3 = (nb3 / n0) * ng; } - const int64_t elem_q = ggml_nelements(src0); - const int64_t elem_k = ggml_nelements(src1); - const int64_t elem_v = ggml_nelements(src2); - - enum ggml_type result_type = flash_grad->type; - GGML_ASSERT(ggml_blck_size(result_type) == 1); - const size_t tsize = ggml_type_size(result_type); - - const size_t offs_q = 0; - const size_t offs_k = offs_q + GGML_PAD(elem_q * tsize, GGML_MEM_ALIGN); - const size_t offs_v = offs_k + GGML_PAD(elem_k * tsize, GGML_MEM_ALIGN); - - if (src0->grad) { - struct ggml_tensor * view_q = ggml_view_1d(ctx, flash_grad, elem_q, offs_q); - struct ggml_tensor * grad_q = ggml_reshape(ctx, view_q, src0); - src0->grad = ggml_add_or_set(ctx, - src0->grad, - grad_q, - zero_table, acc_table); - } - if (src1->grad) { - struct ggml_tensor * view_k = ggml_view_1d(ctx, flash_grad, elem_k, offs_k); - struct ggml_tensor * grad_k = ggml_reshape(ctx, view_k, src1); - src1->grad = ggml_add_or_set(ctx, - src1->grad, - grad_k, - zero_table, acc_table); - } - if (src2->grad) { - struct ggml_tensor * view_v = ggml_view_1d(ctx, flash_grad, elem_v, offs_v); - struct ggml_tensor * grad_v = ggml_reshape(ctx, view_v, src2); - src2->grad = ggml_add_or_set(ctx, - src2->grad, - grad_v, - zero_table, acc_table); - } - } break; - case GGML_OP_FLASH_ATTN_BACK: - { - GGML_ABORT("fatal error"); // not supported + ggml_acc_or_set(ctx, cgraph, isrc0, grad, nb1, nb2, nb3, offset); } - case GGML_OP_SSM_CONV: - case GGML_OP_SSM_SCAN: - { - GGML_ABORT("fatal error"); // TODO: not implemented + } break; + case GGML_OP_PERMUTE: { + if (src0_needs_grads) { + const int32_t * axes = (const int32_t *) tensor->op_params; + const int axis0 = axes[0] & 0x3; + const int axis1 = axes[1] & 0x3; + const int axis2 = axes[2] & 0x3; + const int axis3 = axes[3] & 0x3; + int axb[4] = {0,0,0,0}; // axes backward + axb[axis0] = 0; + axb[axis1] = 1; + axb[axis2] = 2; + axb[axis3] = 3; + ggml_add_or_set(ctx, cgraph, isrc0, ggml_permute(ctx, grad, axb[0], axb[1], axb[2], axb[3])); } + } break; + case GGML_OP_TRANSPOSE: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_transpose(ctx, grad)); + } + } break; + case GGML_OP_GET_ROWS: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_get_rows_back(ctx, grad, src1, src0)); + } + if (src1_needs_grads) { + // noop + } + } break; + case GGML_OP_DIAG_MASK_INF: { + if (src0_needs_grads) { + /* ggml_diag_mask_inf_impl() shouldn't be here */ + /* ref: https://github.com/ggerganov/llama.cpp/pull/4203#discussion_r1412377992 */ + const int n_past = ((const int32_t *) tensor->op_params)[0]; + ggml_add_or_set(ctx, cgraph, isrc0, ggml_diag_mask_zero_impl(ctx, grad, n_past, false)); + } + } break; + case GGML_OP_DIAG_MASK_ZERO: { + if (src0_needs_grads) { + const int n_past = ((const int32_t *) tensor->op_params)[0]; + ggml_add_or_set(ctx, cgraph, isrc0, ggml_diag_mask_zero_impl(ctx, grad, n_past, false)); + } + } break; + case GGML_OP_SOFT_MAX: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_soft_max_back(ctx, grad, tensor)); + } + GGML_ASSERT((!src1 || !src1_needs_grads) && "backward pass for softmax mask not implemented"); + } break; + case GGML_OP_ROPE: { + if (src0_needs_grads) { + //const int n_past = ((int32_t *) tensor->op_params)[0]; + const int n_dims = ((const int32_t *) tensor->op_params)[1]; + const int mode = ((const int32_t *) tensor->op_params)[2]; + //const int n_ctx = ((int32_t *) tensor->op_params)[3]; + const int n_ctx_orig = ((const int32_t *) tensor->op_params)[4]; + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + + memcpy(&freq_base, (const float *) tensor->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (const float *) tensor->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (const float *) tensor->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (const float *) tensor->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (const float *) tensor->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (const float *) tensor->op_params + 10, sizeof(float)); + + ggml_add_or_set(ctx, cgraph, isrc0, + ggml_rope_back(ctx, grad, src1, src2, n_dims, mode, n_ctx_orig, freq_base, + freq_scale, ext_factor, attn_factor, beta_fast, beta_slow)); + } + GGML_ASSERT((!src2 || !src2_needs_grads) && "gradients for freq factors not implemented"); + } break; + case GGML_OP_IM2COL: { + if (src1_needs_grads) { + const int32_t s0 = ggml_get_op_params_i32(tensor, 0); + const int32_t s1 = ggml_get_op_params_i32(tensor, 1); + const int32_t p0 = ggml_get_op_params_i32(tensor, 2); + const int32_t p1 = ggml_get_op_params_i32(tensor, 3); + const int32_t d0 = ggml_get_op_params_i32(tensor, 4); + const int32_t d1 = ggml_get_op_params_i32(tensor, 5); + const bool is_2D = ggml_get_op_params_i32(tensor, 6) == 1; + + ggml_add_or_set(ctx, cgraph, isrc1, ggml_im2col_back(ctx, src0, grad, src1->ne, s0, s1, p0, p1, d0, d1, is_2D)); + } + } break; + case GGML_OP_POOL_2D: { + if (src0_needs_grads) { + const enum ggml_op_pool op = ggml_get_op_params_i32(tensor, 0); + const int32_t k0 = ggml_get_op_params_i32(tensor, 1); + const int32_t k1 = ggml_get_op_params_i32(tensor, 2); + const int32_t s0 = ggml_get_op_params_i32(tensor, 3); + const int32_t s1 = ggml_get_op_params_i32(tensor, 4); + const int32_t p0 = ggml_get_op_params_i32(tensor, 5); + const int32_t p1 = ggml_get_op_params_i32(tensor, 6); + + ggml_add_or_set(ctx, cgraph, isrc0, ggml_pool_2d_back(ctx, grad, src0, op, k0, k1, s0, s1, p0, p1)); + } + } break; case GGML_OP_WIN_PART: case GGML_OP_WIN_UNPART: - case GGML_OP_UNARY: - { - switch (ggml_get_unary_op(tensor)) { - case GGML_UNARY_OP_ABS: - { - if (src0->grad) { - src0->grad = - ggml_add_or_set(ctx, - src0->grad, - ggml_mul(ctx, - ggml_sgn(ctx, src0), - tensor->grad), - zero_table, acc_table); - } - } break; - case GGML_UNARY_OP_SGN: - { - if (src0->grad) { - // noop - } - } break; - case GGML_UNARY_OP_NEG: - { - if (src0->grad) { - src0->grad = ggml_sub_or_set(ctx, src0->grad, tensor->grad, zero_table, acc_table); - } - } break; - case GGML_UNARY_OP_STEP: - { - if (src0->grad) { - // noop - } - } break; - case GGML_UNARY_OP_TANH: - { - GGML_ABORT("fatal error"); // TODO: not implemented - } - case GGML_UNARY_OP_ELU: - { - GGML_ABORT("fatal error"); // TODO: not implemented - } - case GGML_UNARY_OP_RELU: - { - if (src0->grad) { - src0->grad = ggml_add_or_set(ctx, - src0->grad, - ggml_mul(ctx, - ggml_step(ctx, src0), - tensor->grad), - zero_table, acc_table); - } - } break; - case GGML_UNARY_OP_SIGMOID: - { - GGML_ABORT("fatal error"); // TODO: not implemented - } - case GGML_UNARY_OP_GELU: - { - GGML_ABORT("fatal error"); // TODO: not implemented - } - case GGML_UNARY_OP_GELU_QUICK: - { - GGML_ABORT("fatal error"); // TODO: not implemented - } - case GGML_UNARY_OP_SILU: - { - // necessary for llama - if (src0->grad) { - src0->grad = ggml_add_or_set(ctx, - src0->grad, - ggml_silu_back(ctx, src0, tensor->grad), - zero_table, acc_table); - } - } break; - case GGML_UNARY_OP_EXP: - { - if (src0->grad) { - src0->grad = ggml_add_or_set(ctx, - src0->grad, - ggml_mul(ctx, tensor, tensor->grad), - zero_table, acc_table); - } - } break; - default: - GGML_ABORT("fatal error"); - } - } break; - case GGML_OP_GET_REL_POS: - case GGML_OP_ADD_REL_POS: - case GGML_OP_RWKV_WKV: - case GGML_OP_MAP_UNARY: - case GGML_OP_MAP_BINARY: - case GGML_OP_MAP_CUSTOM1_F32: - case GGML_OP_MAP_CUSTOM2_F32: - case GGML_OP_MAP_CUSTOM3_F32: - case GGML_OP_MAP_CUSTOM1: - case GGML_OP_MAP_CUSTOM2: - case GGML_OP_MAP_CUSTOM3: - { - GGML_ABORT("fatal error"); // not supported + case GGML_OP_UNARY: { + switch (ggml_get_unary_op(tensor)) { + case GGML_UNARY_OP_ABS: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, ggml_sgn(ctx, src0), grad)); + } + } break; + case GGML_UNARY_OP_SGN: { + // noop + } break; + case GGML_UNARY_OP_NEG: { + if (src0_needs_grads) { + ggml_sub_or_set(ctx, cgraph, isrc0, grad); + } + } break; + case GGML_UNARY_OP_STEP: { + // noop + } break; + case GGML_UNARY_OP_RELU: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, ggml_step(ctx, src0), grad)); + } + } break; + case GGML_UNARY_OP_SILU: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_silu_back(ctx, src0, grad)); + } + } break; + case GGML_UNARY_OP_EXP: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, tensor, grad)); + } + } break; + default: { + fprintf(stderr, "%s: unsupported unary op for backward pass: %s\n", + __func__, ggml_unary_op_name(ggml_get_unary_op(tensor))); + GGML_ABORT("fatal error"); + } //break; } - case GGML_OP_CROSS_ENTROPY_LOSS: - { - if (src0->grad) { - src0->grad = ggml_add_or_set(ctx, - src0->grad, - ggml_cross_entropy_loss_back(ctx, - src0, - src1, - tensor->grad), - zero_table, acc_table); - } - GGML_ASSERT(!src1->grad && "backward pass for labels not implemented"); - } break; - case GGML_OP_CROSS_ENTROPY_LOSS_BACK: - { - GGML_ABORT("fatal error"); // not supported + } break; + case GGML_OP_CROSS_ENTROPY_LOSS: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_cross_entropy_loss_back(ctx, src0, src1, grad)); } - case GGML_OP_OPT_STEP_ADAMW: - { - GGML_ABORT("fatal error"); // not supported - } - case GGML_OP_NONE: - { - // nop - } break; + GGML_ASSERT(!src1_needs_grads && "backward pass for labels not implemented"); + } break; + case GGML_OP_NONE: { + // noop + } break; case GGML_OP_COUNT: - { - GGML_ABORT("fatal error"); - } + default: { + fprintf(stderr, "%s: unsupported ggml op for backward pass: %s\n", __func__, ggml_op_name(tensor->op)); + GGML_ABORT("fatal error"); + } //break; } - for (int i = 0; i < GGML_MAX_SRC; ++i) { - if (tensor->src[i] && tensor->src[i]->grad) { - GGML_ASSERT(ggml_are_same_shape(tensor->src[i], tensor->src[i]->grad)); - } - } + GGML_ASSERT(!src0_needs_grads || ggml_are_same_shape(src0, cgraph->grads[isrc0])); + GGML_ASSERT(!src1_needs_grads || ggml_are_same_shape(src1, cgraph->grads[isrc1])); + GGML_ASSERT(!src2_needs_grads || ggml_are_same_shape(src2, cgraph->grads[isrc2])); } static void ggml_visit_parents(struct ggml_cgraph * cgraph, struct ggml_tensor * node) { - if (node->grad == NULL) { - // this usually happens when we generate intermediate nodes from constants in the backward pass - // it can also happen during forward pass, if the user performs computations with constants - if (node->op != GGML_OP_NONE) { - //GGML_PRINT_DEBUG("%s: warning: node %p has no grad, but op %d\n", __func__, (void *) node, node->op); - } - } - // check if already visited if (ggml_hash_insert(&cgraph->visited_hash_set, node) == GGML_HASHSET_ALREADY_EXISTS) { return; @@ -18837,18 +5642,41 @@ void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * ggml_build_forward_impl(cgraph, tensor, true); } -void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool accumulate) { - GGML_ASSERT(gf->n_nodes > 0); - GGML_ASSERT(gf->grads); +void ggml_build_backward_expand( + struct ggml_context * ctx_static, + struct ggml_context * ctx_compute, + struct ggml_cgraph * cgraph, + bool accumulate) { + GGML_ASSERT(cgraph->n_nodes > 0); + GGML_ASSERT(cgraph->grads); + GGML_ASSERT(cgraph->grad_accs); - for (int i = 0; i < gf->n_nodes; ++i) { - struct ggml_tensor * node = gf->nodes[i]; + const int n_nodes_f = cgraph->n_nodes; + + memset(cgraph->grads, 0, cgraph->visited_hash_set.size*sizeof(struct ggml_tensor *)); + memset(cgraph->grad_accs, 0, cgraph->visited_hash_set.size*sizeof(struct ggml_tensor *)); + bool * grads_needed = calloc(cgraph->visited_hash_set.size, sizeof(bool)); + + { + bool any_params = false; + bool any_loss = false; + for (int i = 0; i < n_nodes_f; ++i) { + struct ggml_tensor * node = cgraph->nodes[i]; + any_params = any_params || (node->flags & GGML_TENSOR_FLAG_PARAM); + any_loss = any_loss || (node->flags & GGML_TENSOR_FLAG_LOSS); + } + GGML_ASSERT(any_params && "no trainable parameters found, did you forget to call ggml_set_param?"); + GGML_ASSERT(any_loss && "no training loss found, did you forget to call ggml_set_loss?"); + } + + for (int i = 0; i < n_nodes_f; ++i) { + struct ggml_tensor * node = cgraph->nodes[i]; if (node->type == GGML_TYPE_I32) { continue; } - bool needs_grad = node->flags & GGML_TENSOR_FLAG_PARAM; + bool node_needs_grad = (node->flags & GGML_TENSOR_FLAG_PARAM) || (node->flags & GGML_TENSOR_FLAG_LOSS); bool ignore_src[GGML_MAX_SRC] = {false}; switch (node->op) { // gradients in node->src[0] for one reason or another have no effect on output gradients @@ -18865,7 +5693,7 @@ void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * } break; // gradients in node->src[1] for one reason or another have no effect on output gradients - case GGML_OP_CPY: // gradients in CPY target are irrelevant + case GGML_OP_CPY: // gradients in CPY target are irrelevant case GGML_OP_GET_ROWS: // row indices not differentiable case GGML_OP_GET_ROWS_BACK: // same as for GET_ROWS case GGML_OP_ROPE: // positions not differentiable @@ -18876,14 +5704,14 @@ void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * break; } for (int j = 0; j < GGML_MAX_SRC; ++j) { - if (!node->src[j] || !node->src[j]->grad || ignore_src[j]) { + if (!node->src[j] || ignore_src[j] || !grads_needed[ggml_hash_find(&cgraph->visited_hash_set, node->src[j])]) { continue; } GGML_ASSERT(node->src[j]->type == GGML_TYPE_F32 || node->src[j]->type == GGML_TYPE_F16); - needs_grad = true; + node_needs_grad = true; break; } - if (!needs_grad) { + if (!node_needs_grad) { continue; } @@ -18891,76 +5719,26 @@ void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * GGML_ASSERT(!node->view_src || node->op == GGML_OP_CPY || node->op == GGML_OP_VIEW || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_TRANSPOSE); - // create a new tensor with the same type and shape as the node and set it as grad - node->grad = ggml_dup_tensor(ctx, node); - } - - // keep tables of original gradients for replacement/accumulation logic - struct ggml_hash_set zero_table = ggml_hash_set_new(gf->size); - struct ggml_hash_set acc_table = ggml_hash_set_new(gf->size); - for (int i = 0; i < gf->n_nodes; i++) { - struct ggml_tensor * node = gf->nodes[i]; - - if (node->grad) { - { - const size_t insert_result = ggml_hash_insert(&zero_table, node->grad); - GGML_ASSERT(insert_result != GGML_HASHSET_FULL); - GGML_ASSERT(insert_result != GGML_HASHSET_ALREADY_EXISTS); - } - - // only gradients of trainable parameters should be accumulated - if (accumulate && (node->flags & GGML_TENSOR_FLAG_PARAM)) { - const size_t insert_result = ggml_hash_insert(&acc_table, node->grad); - GGML_ASSERT(insert_result != GGML_HASHSET_FULL); - GGML_ASSERT(insert_result != GGML_HASHSET_ALREADY_EXISTS); - } + const size_t igrad = ggml_hash_find(&cgraph->visited_hash_set, node); + GGML_ASSERT(igrad != GGML_HASHSET_FULL); + GGML_ASSERT(ggml_bitset_get(cgraph->visited_hash_set.used, igrad)); + if ((accumulate && (node->flags & GGML_TENSOR_FLAG_PARAM)) || (node->flags & GGML_TENSOR_FLAG_LOSS)) { + cgraph->grad_accs[igrad] = ggml_dup_tensor(ctx_static, node); + cgraph->grads[igrad] = cgraph->grad_accs[igrad]; + ggml_format_name(cgraph->grad_accs[igrad], "grad acc for %s", node->name); } + grads_needed[igrad] = true; } - for (int i = gf->n_nodes - 1; i >= 0; i--) { - struct ggml_tensor * node = gf->nodes[i]; - + for (int i = n_nodes_f - 1; i >= 0; --i) { // inplace operations to add gradients are not created by ggml_compute_backward except for gradient accumulation // use allocator to automatically make inplace operations - if (node->grad) { - ggml_compute_backward(ctx, node, &zero_table, &acc_table); - } + ggml_compute_backward(ctx_compute, cgraph, i, grads_needed); } - for (int i = 0; i < gf->n_nodes; i++) { - struct ggml_tensor * node = gf->nodes[i]; - - if (node->flags & GGML_TENSOR_FLAG_PARAM) { - GGML_PRINT_DEBUG("%s: found root node %p\n", __func__, (void *) node); - ggml_build_forward_expand(gb, node->grad); - } - } - - ggml_hash_set_free(&zero_table); - ggml_hash_set_free(&acc_table); + free(grads_needed); } -void ggml_build_opt_adamw( - struct ggml_context * ctx, - struct ggml_cgraph * gf, - struct ggml_cgraph * gb, - float alpha, - float beta1, - float beta2, - float eps, - float wd) { - for (int i = 0; i < gf->n_nodes; i++) { - struct ggml_tensor * node = gf->nodes[i]; - - if (node->flags & GGML_TENSOR_FLAG_PARAM) { - GGML_PRINT_DEBUG("%s: found root node %p\n", __func__, (void *) node); - struct ggml_tensor * opt_step = ggml_opt_step_adamw(ctx, node, node->grad, alpha, beta1, beta2, eps, wd); - ggml_build_forward_expand(gb, opt_step); - } - } -} - - static void * incr_ptr_aligned(void ** p, size_t size, size_t align) { void * ptr = *p; ptr = (void *) GGML_PAD((uintptr_t) ptr, align); @@ -18976,7 +5754,8 @@ static size_t ggml_graph_nbytes(size_t size, bool grads) { incr_ptr_aligned(&p, size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); // leafs incr_ptr_aligned(&p, hash_size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); // hash keys if (grads) { - incr_ptr_aligned(&p, size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); // grads + incr_ptr_aligned(&p, hash_size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); // grads + incr_ptr_aligned(&p, hash_size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); // grad_accs } incr_ptr_aligned(&p, ggml_bitset_size(hash_size) * sizeof(ggml_bitset_t), sizeof(ggml_bitset_t)); @@ -19002,10 +5781,12 @@ struct ggml_cgraph * ggml_new_graph_custom(struct ggml_context * ctx, size_t siz void * p = cgraph + 1; - struct ggml_tensor ** nodes_ptr = incr_ptr_aligned(&p, size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); - struct ggml_tensor ** leafs_ptr = incr_ptr_aligned(&p, size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); - struct ggml_tensor ** hash_keys_ptr = incr_ptr_aligned(&p, hash_size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); - struct ggml_tensor ** grads_ptr = grads ? incr_ptr_aligned(&p, size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)) : NULL; + struct ggml_tensor ** nodes_ptr = incr_ptr_aligned(&p, size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); + struct ggml_tensor ** leafs_ptr = incr_ptr_aligned(&p, size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); + struct ggml_tensor ** hash_keys_ptr = incr_ptr_aligned(&p, hash_size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); + struct ggml_tensor ** grads_ptr = grads ? incr_ptr_aligned(&p, hash_size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)) : NULL; + struct ggml_tensor ** grad_accs_ptr = grads ? incr_ptr_aligned(&p, hash_size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)) : NULL; + ggml_bitset_t * hash_used = incr_ptr_aligned(&p, ggml_bitset_size(hash_size) * sizeof(ggml_bitset_t), sizeof(ggml_bitset_t)); // check that we allocated the correct amount of memory @@ -19017,12 +5798,17 @@ struct ggml_cgraph * ggml_new_graph_custom(struct ggml_context * ctx, size_t siz /*.n_leafs =*/ 0, /*.nodes =*/ nodes_ptr, /*.grads =*/ grads_ptr, + /*.grad_accs =*/ grad_accs_ptr, /*.leafs =*/ leafs_ptr, /*.hash_table =*/ { hash_size, hash_used, hash_keys_ptr }, /*.order =*/ GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT, }; ggml_hash_set_reset(&cgraph->visited_hash_set); + if (grads) { + memset(cgraph->grads, 0, hash_size*sizeof(struct ggml_tensor *)); + memset(cgraph->grad_accs, 0, hash_size*sizeof(struct ggml_tensor *)); + } return cgraph; } @@ -19033,14 +5819,15 @@ struct ggml_cgraph * ggml_new_graph(struct ggml_context * ctx) { struct ggml_cgraph ggml_graph_view(struct ggml_cgraph * cgraph0, int i0, int i1) { struct ggml_cgraph cgraph = { - /*.size =*/ 0, - /*.n_nodes =*/ i1 - i0, - /*.n_leafs =*/ 0, - /*.nodes =*/ cgraph0->nodes + i0, - /*.grads =*/ cgraph0->grads ? cgraph0->grads + i0 : NULL, - /*.leafs =*/ NULL, - /*.hash_table =*/ { 0, NULL, NULL }, - /*.order =*/ cgraph0->order, + /*.size =*/ 0, + /*.n_nodes =*/ i1 - i0, + /*.n_leafs =*/ 0, + /*.nodes =*/ cgraph0->nodes + i0, + /*.grads =*/ NULL, // gradients would need visited_hash_set + /*.grad_accs =*/ NULL, + /*.leafs =*/ NULL, + /*.visited_hash_set =*/ { 0, NULL, NULL }, + /*.order =*/ cgraph0->order, }; return cgraph; @@ -19063,19 +5850,33 @@ void ggml_graph_cpy(struct ggml_cgraph * src, struct ggml_cgraph * dst) { dst->nodes[i] = src->nodes[i]; } - if (src->grads) { - GGML_ASSERT(dst->grads != NULL); - for (int i = 0; i < src->n_nodes; ++i) { - dst->grads[i] = src->grads[i]; - } - } - for (size_t i = 0; i < src->visited_hash_set.size; ++i) { // copy all hashset keys (tensors) that are in use if (ggml_bitset_get(src->visited_hash_set.used, i)) { ggml_hash_insert(&dst->visited_hash_set, src->visited_hash_set.keys[i]); } } + + if (dst->grads) { + memset(dst->grads, 0, dst->visited_hash_set.size*sizeof(struct ggml_tensor *)); + memset(dst->grad_accs, 0, dst->visited_hash_set.size*sizeof(struct ggml_tensor *)); + } + if (src->grads) { + GGML_ASSERT(dst->grads != NULL); + GGML_ASSERT(dst->grad_accs != NULL); + for (int i = 0; i < src->n_nodes; ++i) { + const size_t igrad_src = ggml_hash_find(&src->visited_hash_set, src->nodes[i]); + const size_t igrad_dst = ggml_hash_find(&dst->visited_hash_set, dst->nodes[i]); + + GGML_ASSERT(igrad_src != GGML_HASHSET_FULL); + GGML_ASSERT(ggml_bitset_get(src->visited_hash_set.used, igrad_src)); + GGML_ASSERT(igrad_dst != GGML_HASHSET_FULL); + GGML_ASSERT(ggml_bitset_get(dst->visited_hash_set.used, igrad_dst)); + + dst->grads[igrad_dst] = src->grads[igrad_src]; + dst->grad_accs[igrad_dst] = src->grad_accs[igrad_src]; + } + } } struct ggml_cgraph * ggml_graph_dup(struct ggml_context * ctx, struct ggml_cgraph * cgraph) { @@ -19084,33 +5885,49 @@ struct ggml_cgraph * ggml_graph_dup(struct ggml_context * ctx, struct ggml_cgrap return result; } +struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) { + if (ggml_is_empty(tensor)) { + return tensor; + } + if (tensor->buffer) { + ggml_backend_tensor_memset(tensor, 0, 0, ggml_nbytes(tensor)); + } else { + GGML_ASSERT(tensor->data); + memset(tensor->data, 0, ggml_nbytes(tensor)); + } + return tensor; +} + void ggml_graph_reset(struct ggml_cgraph * cgraph) { GGML_ASSERT(cgraph->grads != NULL); for (int i = 0; i < cgraph->n_nodes; i++) { - struct ggml_tensor * node = cgraph->nodes[i]; + struct ggml_tensor * node = cgraph->nodes[i]; + struct ggml_tensor * grad_acc = ggml_graph_get_grad_acc(cgraph, node); - // initial gradients of loss should be 1, 0 otherwise - if (node->grad) { - if (node->flags & GGML_TENSOR_FLAG_LOSS) { - GGML_ASSERT(node->grad->buffer); - GGML_ASSERT(node->type == GGML_TYPE_F32); - GGML_ASSERT(ggml_is_scalar(node)); - - const float onef = 1.0f; - ggml_backend_tensor_set(node->grad, &onef, 0, ggml_nbytes(node->grad)); - } else { - ggml_set_zero(node->grad); - } - } - - GGML_ASSERT(node); if (node->op == GGML_OP_OPT_STEP_ADAMW) { - // set iteration to 1 and clear momenta - ggml_set_op_params_i32(node, 0, 1); + // clear momenta ggml_set_zero(node->src[2]); ggml_set_zero(node->src[3]); } + + // initial gradients of loss should be 1, 0 otherwise + if (grad_acc) { + if (node->flags & GGML_TENSOR_FLAG_LOSS) { + GGML_ASSERT(grad_acc->type == GGML_TYPE_F32); + GGML_ASSERT(ggml_is_scalar(grad_acc)); + + const float onef = 1.0f; + if (grad_acc->buffer) { + ggml_backend_tensor_set(grad_acc, &onef, 0, sizeof(float)); + } else { + GGML_ASSERT(grad_acc->data); + *((float *) grad_acc->data) = onef; + } + } else { + ggml_set_zero(grad_acc); + } + } } } @@ -19148,1097 +5965,7 @@ void ggml_graph_add_node(struct ggml_cgraph * cgraph, struct ggml_tensor * tenso cgraph->n_nodes++; } -// Android's libc implementation "bionic" does not support setting affinity -#if defined(__gnu_linux__) -static void set_numa_thread_affinity(int thread_n) { - if (!ggml_is_numa()) { - return; - } - - int node_num; - int rv; - size_t setsize = CPU_ALLOC_SIZE(g_state.numa.total_cpus); - - switch(g_state.numa.numa_strategy) { - case GGML_NUMA_STRATEGY_DISTRIBUTE: - // run thread on node_num thread_n / (threads per node) - node_num = thread_n % g_state.numa.n_nodes; - break; - case GGML_NUMA_STRATEGY_ISOLATE: - // run thread on current_node - node_num = g_state.numa.current_node; - break; - case GGML_NUMA_STRATEGY_NUMACTL: - // use the cpuset that numactl gave us - rv = pthread_setaffinity_np(pthread_self(), setsize, &g_state.numa.cpuset); - if (rv) { - fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n",strerror(rv)); - } - return; - default: - return; - } - - struct ggml_numa_node * node = &g_state.numa.nodes[node_num]; - - cpu_set_t * cpus = CPU_ALLOC(g_state.numa.total_cpus); - CPU_ZERO_S(setsize, cpus); - for (size_t i = 0; i < node->n_cpus; ++i) { - CPU_SET_S(node->cpus[i], setsize, cpus); - } - - rv = pthread_setaffinity_np(pthread_self(), setsize, cpus); - if (rv) { - fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", strerror(rv)); - } - - CPU_FREE(cpus); -} - -static void clear_numa_thread_affinity(void) { - if (!ggml_is_numa()) { - return; - } - - size_t setsize = CPU_ALLOC_SIZE(g_state.numa.total_cpus); - - cpu_set_t * cpus = CPU_ALLOC(g_state.numa.total_cpus); - CPU_ZERO_S(setsize, cpus); - for (unsigned i = 0; i < g_state.numa.total_cpus; ++i) { - CPU_SET_S(i, setsize, cpus); - } - - int rv = pthread_setaffinity_np(pthread_self(), setsize, cpus); - if (rv) { - fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", strerror(rv)); - } - - CPU_FREE(cpus); -} -#else -// TODO: Windows etc. -// (the linux implementation may also work on BSD, someone should test) -static void set_numa_thread_affinity(int thread_n) { UNUSED(thread_n); } -static void clear_numa_thread_affinity(void) {} -#endif - -static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { - int n_tasks = 0; - - if (ggml_is_empty(node)) { - // no need to multi-thread a no-op - n_tasks = 1; - return n_tasks; - } - - switch (node->op) { - case GGML_OP_CPY: - case GGML_OP_DUP: - case GGML_OP_CONT: - case GGML_OP_ADD: - case GGML_OP_ADD1: - case GGML_OP_ACC: - { - n_tasks = n_threads; - } break; - case GGML_OP_SUB: - case GGML_OP_SQR: - case GGML_OP_SQRT: - case GGML_OP_LOG: - case GGML_OP_SIN: - case GGML_OP_COS: - case GGML_OP_SUM: - case GGML_OP_SUM_ROWS: - case GGML_OP_MEAN: - case GGML_OP_ARGMAX: - { - n_tasks = 1; - } break; - case GGML_OP_COUNT_EQUAL: - { - n_tasks = n_threads; - } break; - case GGML_OP_REPEAT: - case GGML_OP_REPEAT_BACK: - case GGML_OP_LEAKY_RELU: - { - n_tasks = 1; - } break; - case GGML_OP_UNARY: - switch (ggml_get_unary_op(node)) { - case GGML_UNARY_OP_ABS: - case GGML_UNARY_OP_SGN: - case GGML_UNARY_OP_NEG: - case GGML_UNARY_OP_STEP: - case GGML_UNARY_OP_TANH: - case GGML_UNARY_OP_ELU: - case GGML_UNARY_OP_RELU: - case GGML_UNARY_OP_SIGMOID: - case GGML_UNARY_OP_HARDSWISH: - case GGML_UNARY_OP_HARDSIGMOID: - case GGML_UNARY_OP_EXP: - { - n_tasks = 1; - } break; - - case GGML_UNARY_OP_GELU: - case GGML_UNARY_OP_GELU_QUICK: - case GGML_UNARY_OP_SILU: - { - n_tasks = n_threads; - } break; - default: - GGML_ABORT("fatal error"); - } - break; - case GGML_OP_SILU_BACK: - case GGML_OP_MUL: - case GGML_OP_DIV: - case GGML_OP_NORM: - case GGML_OP_RMS_NORM: - case GGML_OP_RMS_NORM_BACK: - case GGML_OP_GROUP_NORM: - case GGML_OP_CONCAT: - case GGML_OP_MUL_MAT: - case GGML_OP_MUL_MAT_ID: - case GGML_OP_OUT_PROD: - { - n_tasks = n_threads; - } break; - case GGML_OP_GET_ROWS: - { - // FIXME: get_rows can use additional threads, but the cost of launching additional threads - // decreases performance with GPU offloading - //n_tasks = n_threads; - n_tasks = 1; - } break; - case GGML_OP_SCALE: - case GGML_OP_SET: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - case GGML_OP_GET_ROWS_BACK: - case GGML_OP_DIAG: - { - n_tasks = 1; - } break; - case GGML_OP_DIAG_MASK_ZERO: - case GGML_OP_DIAG_MASK_INF: - case GGML_OP_SOFT_MAX_BACK: - case GGML_OP_ROPE: - case GGML_OP_ROPE_BACK: - case GGML_OP_ADD_REL_POS: - { - n_tasks = n_threads; - } break; - case GGML_OP_CLAMP: - { - n_tasks = 1; //TODO - } break; - case GGML_OP_SOFT_MAX: - { - n_tasks = MIN(n_threads, ggml_nrows(node->src[0])); - } break; - case GGML_OP_IM2COL: - case GGML_OP_IM2COL_BACK: - case GGML_OP_CONV_TRANSPOSE_1D: - case GGML_OP_CONV_TRANSPOSE_2D: - { - n_tasks = n_threads; - } break; - case GGML_OP_POOL_1D: - case GGML_OP_POOL_2D: - case GGML_OP_POOL_2D_BACK: - { - n_tasks = 1; - } break; - case GGML_OP_UPSCALE: - case GGML_OP_PAD: - case GGML_OP_ARANGE: - case GGML_OP_TIMESTEP_EMBEDDING: - case GGML_OP_ARGSORT: - case GGML_OP_FLASH_ATTN_EXT: - case GGML_OP_FLASH_ATTN_BACK: - case GGML_OP_SSM_CONV: - case GGML_OP_SSM_SCAN: - { - n_tasks = n_threads; - } break; - case GGML_OP_WIN_PART: - case GGML_OP_WIN_UNPART: - case GGML_OP_GET_REL_POS: - case GGML_OP_RWKV_WKV: - case GGML_OP_MAP_UNARY: - case GGML_OP_MAP_BINARY: - case GGML_OP_MAP_CUSTOM1_F32: - case GGML_OP_MAP_CUSTOM2_F32: - case GGML_OP_MAP_CUSTOM3_F32: - { - n_tasks = 1; - } break; - case GGML_OP_MAP_CUSTOM1: - { - struct ggml_map_custom1_op_params p; - memcpy(&p, node->op_params, sizeof(p)); - if (p.n_tasks == GGML_N_TASKS_MAX) { - n_tasks = n_threads; - } else { - n_tasks = MIN(p.n_tasks, n_threads); - } - } break; - case GGML_OP_MAP_CUSTOM2: - { - struct ggml_map_custom2_op_params p; - memcpy(&p, node->op_params, sizeof(p)); - if (p.n_tasks == GGML_N_TASKS_MAX) { - n_tasks = n_threads; - } else { - n_tasks = MIN(p.n_tasks, n_threads); - } - } break; - case GGML_OP_MAP_CUSTOM3: - { - struct ggml_map_custom3_op_params p; - memcpy(&p, node->op_params, sizeof(p)); - if (p.n_tasks == GGML_N_TASKS_MAX) { - n_tasks = n_threads; - } else { - n_tasks = MIN(p.n_tasks, n_threads); - } - } break; - case GGML_OP_CROSS_ENTROPY_LOSS: - case GGML_OP_CROSS_ENTROPY_LOSS_BACK: - case GGML_OP_OPT_STEP_ADAMW: - { - n_tasks = n_threads; - } break; - case GGML_OP_NONE: - { - n_tasks = 1; - } break; - case GGML_OP_COUNT: - { - GGML_ABORT("fatal error"); - } - default: - { - fprintf(stderr, "%s: op not implemented: ", __func__); - if (node->op < GGML_OP_COUNT) { - fprintf(stderr, "%s\n", ggml_op_name(node->op)); - } else { - fprintf(stderr, "%d\n", node->op); - } - GGML_ABORT("fatal error"); - } - } - - assert(n_tasks > 0); - - return n_tasks; -} - -static thread_ret_t ggml_graph_compute_secondary_thread(void* data); - -#if defined(_WIN32) -#include "windows.h" - -// TODO: support > 64 CPUs -bool ggml_thread_apply_affinity(bool * mask) { - HANDLE h = GetCurrentThread(); - uint64_t bitmask = 0ULL; - - assert(GGML_MAX_N_THREADS >= 64); - - for (int32_t i = 0; i < 8; i++) { - int32_t idx = i * 8; - uint8_t val = 0; - val |= mask[idx + 0] << 0; - val |= mask[idx + 1] << 1; - val |= mask[idx + 2] << 2; - val |= mask[idx + 3] << 3; - val |= mask[idx + 4] << 4; - val |= mask[idx + 5] << 5; - val |= mask[idx + 6] << 6; - val |= mask[idx + 7] << 7; - bitmask |= (uint64_t)val << idx; - } - - for (int32_t i = 64; i < GGML_MAX_N_THREADS; i++) { - if (mask[i]) { - fprintf(stderr, "warn: setting thread-affinity for > 64 CPUs isn't supported on windows!\n"); - break; - } - } - - DWORD_PTR m = (DWORD_PTR)bitmask; - - m = SetThreadAffinityMask(h, m); - - return m != 0; -} - -static bool ggml_thread_apply_priority(int32_t prio) { - // Note that on Windows the Process Priority Class must be updated in order to set Thread priority. - // This is up to the applications. - DWORD p = THREAD_PRIORITY_NORMAL; - switch (prio) { - case GGML_SCHED_PRIO_NORMAL: p = THREAD_PRIORITY_NORMAL; break; - case GGML_SCHED_PRIO_MEDIUM: p = THREAD_PRIORITY_ABOVE_NORMAL; break; - case GGML_SCHED_PRIO_HIGH: p = THREAD_PRIORITY_HIGHEST; break; - case GGML_SCHED_PRIO_REALTIME: p = THREAD_PRIORITY_TIME_CRITICAL; break; - } - - if (prio == GGML_SCHED_PRIO_NORMAL) { - // Keep inherited policy/priority - return true; - } - - if (!SetThreadPriority(GetCurrentThread(), p)) { - fprintf(stderr, "warn: failed to set thread priority %d : (%d)\n", prio, (int) GetLastError()); - return false; - } - - return true; -} - -#elif defined(__APPLE__) -#include -#include - -static bool ggml_thread_apply_affinity(const bool * mask) { - // Not supported on Apple platforms - UNUSED(mask); - return true; -} - -static bool ggml_thread_apply_priority(int32_t prio) { - struct sched_param p; - int32_t policy = SCHED_OTHER; - switch (prio) { - case GGML_SCHED_PRIO_NORMAL: policy = SCHED_OTHER; p.sched_priority = 0; break; - case GGML_SCHED_PRIO_MEDIUM: policy = SCHED_FIFO; p.sched_priority = 40; break; - case GGML_SCHED_PRIO_HIGH: policy = SCHED_FIFO; p.sched_priority = 80; break; - case GGML_SCHED_PRIO_REALTIME: policy = SCHED_FIFO; p.sched_priority = 90; break; - } - - if (prio == GGML_SCHED_PRIO_NORMAL) { - // Keep inherited policy/priority - return true; - } - - int32_t err = pthread_setschedparam(pthread_self(), policy, &p); - if (err != 0) { - fprintf(stderr, "warn: failed to set thread priority %d : %s (%d)\n", prio, strerror(err), err); - return false; - } - - return true; -} - -#elif defined(__gnu_linux__) -// TODO: this may not work on BSD, to be verified - -static bool ggml_thread_apply_affinity(const bool * mask) { - cpu_set_t cpuset; - int err; - - CPU_ZERO(&cpuset); - - for (uint32_t i = 0; i < GGML_MAX_N_THREADS; i++) { - if (mask[i]) { - GGML_PRINT_DEBUG("Thread %lx: adding %d to cpuset\n", pthread_self(), i); - CPU_SET(i, &cpuset); - } - } - -#ifdef __ANDROID__ - err = sched_setaffinity(0, sizeof(cpuset), &cpuset); - if (err < 0) { - err = errno; - } -#else - err = pthread_setaffinity_np(pthread_self(), sizeof(cpuset), &cpuset); -#endif - if (err != 0) { - fprintf(stderr, "warn: failed to set affinity mask 0x%llx : %s (%d)\n", (unsigned long long)mask, strerror(err), err); - return false; - } - - return true; -} - -static bool ggml_thread_apply_priority(int32_t prio) { - struct sched_param p; - int32_t policy = SCHED_OTHER; - switch (prio) { - case GGML_SCHED_PRIO_NORMAL: policy = SCHED_OTHER; p.sched_priority = 0; break; - case GGML_SCHED_PRIO_MEDIUM: policy = SCHED_FIFO; p.sched_priority = 40; break; - case GGML_SCHED_PRIO_HIGH: policy = SCHED_FIFO; p.sched_priority = 80; break; - case GGML_SCHED_PRIO_REALTIME: policy = SCHED_FIFO; p.sched_priority = 90; break; - } - - if (prio == GGML_SCHED_PRIO_NORMAL) { - // Keep inherited policy/priority - return true; - } - - int32_t err = pthread_setschedparam(pthread_self(), policy, &p); - if (err != 0) { - fprintf(stderr, "warn: failed to set thread priority %d : %s (%d)\n", prio, strerror(err), err); - return false; - } - - return true; -} - -#else // unsupported platforms - -static bool ggml_thread_apply_affinity(const bool * mask) { - UNUSED(mask); - return true; -} - -static bool ggml_thread_apply_priority(int32_t prio) { - UNUSED(prio); - return true; -} - -#endif - -static bool ggml_thread_cpumask_is_valid(const bool * mask) { - for (int i = 0; i < GGML_MAX_N_THREADS; i++) { - if (mask[i]) { return true; } - } - return false; -} - -static void ggml_thread_cpumask_next(const bool * global_mask, bool * local_mask, bool strict, int32_t* iter) { - if (!strict) { - memcpy(local_mask, global_mask, GGML_MAX_N_THREADS); - return; - } else { - memset(local_mask, 0, GGML_MAX_N_THREADS); - int32_t base_idx = *iter; - for (int32_t i = 0; i < GGML_MAX_N_THREADS; i++) { - int32_t idx = base_idx + i; - if (idx >= GGML_MAX_N_THREADS) { - // Just a cheaper modulo - idx -= GGML_MAX_N_THREADS; - } - if (global_mask[idx]) { - local_mask[idx] = 1; - *iter = idx + 1; - return; - } - } - } -} - -void ggml_threadpool_free(struct ggml_threadpool* threadpool) { - if (!threadpool) return; - - const int n_threads = threadpool->n_threads_max; - -#ifndef GGML_USE_OPENMP - struct ggml_compute_state* workers = threadpool->workers; - - ggml_mutex_lock(&threadpool->mutex); - - threadpool->stop = true; - threadpool->pause = false; - - ggml_cond_broadcast(&threadpool->cond); - ggml_mutex_unlock(&threadpool->mutex); - - for (int j = 1; j < n_threads; j++) { - int32_t rc = ggml_thread_join(workers[j].thrd, NULL); - GGML_ASSERT(rc == GGML_EXIT_SUCCESS || rc == GGML_EXIT_ABORTED); - UNUSED(rc); - } - - ggml_mutex_destroy(&threadpool->mutex); - ggml_cond_destroy(&threadpool->cond); -#endif // GGML_USE_OPENMP - - const size_t workers_size = sizeof(struct ggml_compute_state) * n_threads; - ggml_aligned_free(threadpool->workers, workers_size); - ggml_aligned_free(threadpool, sizeof(struct ggml_threadpool)); -} - -#ifndef GGML_USE_OPENMP -// pause/resume must be called under mutex -static void ggml_threadpool_pause_locked(struct ggml_threadpool * threadpool) { - GGML_PRINT_DEBUG("Pausing threadpool\n"); - threadpool->pause = true; - ggml_cond_broadcast(&threadpool->cond); -} - -static void ggml_threadpool_resume_locked(struct ggml_threadpool * threadpool) { - GGML_PRINT_DEBUG("Resuming threadpool\n"); - threadpool->pause = false; - ggml_cond_broadcast(&threadpool->cond); -} -#endif - -void ggml_threadpool_pause(struct ggml_threadpool * threadpool) { -#ifndef GGML_USE_OPENMP - ggml_mutex_lock(&threadpool->mutex); - if (!threadpool->pause) { - ggml_threadpool_pause_locked(threadpool); - } - ggml_mutex_unlock(&threadpool->mutex); -#else - UNUSED(threadpool); -#endif -} - -void ggml_threadpool_resume(struct ggml_threadpool * threadpool) { -#ifndef GGML_USE_OPENMP - ggml_mutex_lock(&threadpool->mutex); - if (threadpool->pause) { - ggml_threadpool_resume_locked(threadpool); - } - ggml_mutex_unlock(&threadpool->mutex); -#else - UNUSED(threadpool); -#endif -} - -struct ggml_cplan ggml_graph_plan( - const struct ggml_cgraph * cgraph, - int n_threads, - struct ggml_threadpool * threadpool) { - - if (threadpool == NULL) { - GGML_PRINT_DEBUG("Threadpool is not specified. Will create a disposable threadpool : n_threads %d\n", n_threads); - } - if (n_threads <= 0) { - n_threads = threadpool ? threadpool->n_threads_max : GGML_DEFAULT_N_THREADS; - } - - size_t work_size = 0; - - struct ggml_cplan cplan; - memset(&cplan, 0, sizeof(struct ggml_cplan)); - - int max_tasks = 1; - - // thread scheduling for the different operations + work buffer size estimation - for (int i = 0; i < cgraph->n_nodes; i++) { - struct ggml_tensor * node = cgraph->nodes[i]; - - const int n_tasks = ggml_get_n_tasks(node, n_threads); - - max_tasks = MAX(max_tasks, n_tasks); - - size_t cur = 0; - - switch (node->op) { - case GGML_OP_CPY: - case GGML_OP_DUP: - { - if (ggml_is_quantized(node->type) || - // F16 -> BF16 and BF16 -> F16 copies go through intermediate F32 - (node->src[0]->type == GGML_TYPE_F16 && node->src[1] && node->src[1]->type == GGML_TYPE_BF16) || - (node->src[0]->type == GGML_TYPE_BF16 && node->src[1] && node->src[1]->type == GGML_TYPE_F16)) { - cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks; - } - } break; - case GGML_OP_ADD: - case GGML_OP_ADD1: - { - if (ggml_is_quantized(node->src[0]->type)) { - cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks; - } - } break; - case GGML_OP_ACC: - { - if (ggml_is_quantized(node->src[0]->type)) { - cur = ggml_type_size(GGML_TYPE_F32) * node->src[1]->ne[0] * n_tasks; - } - } break; - case GGML_OP_COUNT_EQUAL: - { - cur = ggml_type_size(node->type)*n_tasks; - } break; - case GGML_OP_MUL_MAT: - { - const enum ggml_type vec_dot_type = type_traits[node->src[0]->type].vec_dot_type; - - if (node->src[1]->type != vec_dot_type) { - cur = ggml_row_size(vec_dot_type, ggml_nelements(node->src[1])); - } - } break; - case GGML_OP_MUL_MAT_ID: - { - cur = 0; - const struct ggml_tensor * src0 = node->src[0]; - const struct ggml_tensor * src1 = node->src[1]; - const enum ggml_type vec_dot_type = type_traits[src0->type].vec_dot_type; - if (src1->type != vec_dot_type) { - cur += ggml_row_size(vec_dot_type, ggml_nelements(src1)); - } - const int n_as = src0->ne[2]; - cur += GGML_PAD(cur, sizeof(int64_t)); // align - cur += n_as * sizeof(int64_t); // matrix_row_counts - cur += n_as * src1->ne[2] * sizeof(int64_t); // matrix_rows - } break; - case GGML_OP_OUT_PROD: - { - if (ggml_is_quantized(node->src[0]->type)) { - cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks; - } - } break; - case GGML_OP_SOFT_MAX: - case GGML_OP_ROPE: - { - cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks; - } break; - case GGML_OP_CONV_TRANSPOSE_1D: - { - GGML_ASSERT(node->src[0]->ne[3] == 1); - GGML_ASSERT(node->src[1]->ne[2] == 1); - GGML_ASSERT(node->src[1]->ne[3] == 1); - - const int64_t ne00 = node->src[0]->ne[0]; // K - const int64_t ne01 = node->src[0]->ne[1]; // Cout - const int64_t ne02 = node->src[0]->ne[2]; // Cin - - const int64_t ne10 = node->src[1]->ne[0]; // L - const int64_t ne11 = node->src[1]->ne[1]; // Cin - - if ((node->src[0]->type == GGML_TYPE_F16 || - node->src[0]->type == GGML_TYPE_BF16) && - node->src[1]->type == GGML_TYPE_F32) { - cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02; - cur += sizeof(ggml_fp16_t)*ne10*ne11; - } else if (node->src[0]->type == GGML_TYPE_F32 && - node->src[1]->type == GGML_TYPE_F32) { - cur += sizeof(float)*ne00*ne01*ne02; - cur += sizeof(float)*ne10*ne11; - } else { - GGML_ABORT("fatal error"); - } - } break; - case GGML_OP_CONV_TRANSPOSE_2D: - { - const int64_t ne00 = node->src[0]->ne[0]; // W - const int64_t ne01 = node->src[0]->ne[1]; // H - const int64_t ne02 = node->src[0]->ne[2]; // Channels Out - const int64_t ne03 = node->src[0]->ne[3]; // Channels In - - const int64_t ne10 = node->src[1]->ne[0]; // W - const int64_t ne11 = node->src[1]->ne[1]; // H - const int64_t ne12 = node->src[1]->ne[2]; // Channels In - - cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02*ne03; - cur += sizeof(ggml_fp16_t)*ne10*ne11*ne12; - } break; - case GGML_OP_FLASH_ATTN_EXT: - { - const int64_t ne00 = node->src[0]->ne[0]; // D - - cur = 3*sizeof(float)*ne00*n_tasks; // 3x head size/thread - } break; - case GGML_OP_FLASH_ATTN_BACK: - { - const int64_t D = node->src[0]->ne[0]; - const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL); - const int64_t mxDn = MAX(D, ne11) * 2; // *2 because of S and SM in ggml_compute_forward_flash_attn_back - if (node->src[1]->type == GGML_TYPE_F32) { - cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) - cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 - } else if (node->src[1]->type == GGML_TYPE_F16) { - cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) - cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 - } else if (node->src[1]->type == GGML_TYPE_BF16) { - cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) - cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 - } - } break; - - case GGML_OP_CROSS_ENTROPY_LOSS: - { - cur = ggml_type_size(node->type)*(n_tasks + node->src[0]->ne[0]*n_tasks); - } break; - case GGML_OP_COUNT: - { - GGML_ABORT("fatal error"); - } - default: - break; - } - - work_size = MAX(work_size, cur); - } - - if (work_size > 0) { - work_size += CACHE_LINE_SIZE*(n_threads); - } - - cplan.threadpool = threadpool; - cplan.n_threads = MIN(max_tasks, n_threads); - cplan.work_size = work_size; - cplan.work_data = NULL; - - return cplan; -} - -static thread_ret_t ggml_graph_compute_thread(void * data) { - struct ggml_compute_state * state = (struct ggml_compute_state *) data; - struct ggml_threadpool * tp = state->threadpool; - - const struct ggml_cgraph * cgraph = tp->cgraph; - const struct ggml_cplan * cplan = tp->cplan; - - set_numa_thread_affinity(state->ith); - - struct ggml_compute_params params = { - /*.ith =*/ state->ith, - /*.nth =*/ atomic_load_explicit(&tp->n_threads_cur, memory_order_relaxed), - /*.wsize =*/ cplan->work_size, - /*.wdata =*/ cplan->work_data, - /*.threadpool=*/ tp, - }; - - for (int node_n = 0; node_n < cgraph->n_nodes && !tp->abort; node_n++) { - struct ggml_tensor * node = cgraph->nodes[node_n]; - - ggml_compute_forward(¶ms, node); - - if (state->ith == 0 && cplan->abort_callback && - cplan->abort_callback(cplan->abort_callback_data)) { - tp->abort = true; - tp->ec = GGML_STATUS_ABORTED; - } - - ggml_barrier(state->threadpool); - } - - return 0; -} - -#ifndef GGML_USE_OPENMP - -// check if thread is active -static inline bool ggml_graph_compute_thread_active(struct ggml_compute_state * state) { - struct ggml_threadpool * threadpool = state->threadpool; - int n_threads = atomic_load_explicit(&threadpool->n_threads_cur, memory_order_relaxed); - return (state->ith < n_threads); -} - -// check if thread is ready to proceed (exit from polling or sleeping) -static inline bool ggml_graph_compute_thread_ready(struct ggml_compute_state * state) { - struct ggml_threadpool * threadpool = state->threadpool; - - if (state->pending || threadpool->stop || threadpool->pause) { return true; } - - // check for new graph/work - int new_graph = atomic_load_explicit(&threadpool->n_graph, memory_order_relaxed); - if (new_graph != state->last_graph) { - state->pending = ggml_graph_compute_thread_active(state); - state->last_graph = new_graph; - } - - return state->pending; -} - -// sync thread state after polling -static inline void ggml_graph_compute_thread_sync(struct ggml_compute_state * state) { - // TSAN doesn't support standalone fence yet, we use a dummy read-modify-write instead - #ifdef GGML_TSAN_ENABLED - atomic_fetch_add_explicit(&state->threadpool->n_graph, 0, memory_order_seq_cst); - #else - atomic_thread_fence(memory_order_seq_cst); - #endif - UNUSED(state); -} - -static inline bool ggml_graph_compute_poll_for_work(struct ggml_compute_state * state) { - struct ggml_threadpool * threadpool = state->threadpool; - - // Skip polling for unused threads - if (!ggml_graph_compute_thread_active(state)) { - return state->pending; - } - - // This seems to make 0 ... 100 a decent range for polling level across modern processors. - // Perhaps, we can adjust it dynamically based on load and things. - const uint64_t n_rounds = 1024UL * 128 * threadpool->poll; - - for (uint64_t i=0; !ggml_graph_compute_thread_ready(state) && i < n_rounds; i++) { - // No new work. Keep polling. - ggml_thread_cpu_relax(); - } - - return state->pending; -} - -static inline bool ggml_graph_compute_check_for_work(struct ggml_compute_state * state) { - struct ggml_threadpool * threadpool = state->threadpool; - - if (ggml_graph_compute_poll_for_work(state)) { - ggml_graph_compute_thread_sync(state); - return state->pending; - } - - ggml_mutex_lock_shared(&threadpool->mutex); - while (!ggml_graph_compute_thread_ready(state)) { - // No new work. Wait for the signal. - GGML_PRINT_DEBUG("thread #%d waiting for work (sleeping)\n", state->ith); - ggml_cond_wait(&threadpool->cond, &threadpool->mutex); - } - ggml_mutex_unlock_shared(&threadpool->mutex); - - return state->pending; -} - -static thread_ret_t ggml_graph_compute_secondary_thread(void* data) { - struct ggml_compute_state * state = (struct ggml_compute_state *) data; - struct ggml_threadpool * threadpool = state->threadpool; - - ggml_thread_apply_priority(threadpool->prio); - if (ggml_thread_cpumask_is_valid(state->cpumask)) { - ggml_thread_apply_affinity(state->cpumask); - } - - while (true) { - // Check if we need to sleep - while (threadpool->pause) { - GGML_PRINT_DEBUG("thread #%d inside pause loop\n", state->ith); - ggml_mutex_lock_shared(&threadpool->mutex); - if (threadpool->pause) { - ggml_cond_wait(&threadpool->cond, &threadpool->mutex); - } - GGML_PRINT_DEBUG("thread #%d resuming after wait\n", state->ith); - ggml_mutex_unlock_shared(&threadpool->mutex); - } - - // This needs to be checked for after the cond_wait - if (threadpool->stop) break; - - // Check if there is new work - // The main thread is the only one that can dispatch new work - - ggml_graph_compute_check_for_work(state); - if (state->pending) { - state->pending = false; - - ggml_graph_compute_thread(state); - } - } - - return (thread_ret_t) 0; -} - -// Start processing new graph -static void ggml_graph_compute_kickoff(struct ggml_threadpool * threadpool, int n_threads) -{ - // Always take the mutex here because the worker threads are doing hybrid poll/wait - - ggml_mutex_lock(&threadpool->mutex); - - GGML_PRINT_DEBUG("threadpool: n_threads_cur %d n_threads %d\n", threadpool->n_threads_cur, n_threads); - - // Update the number of active threads - atomic_store_explicit(&threadpool->n_threads_cur, n_threads, memory_order_relaxed); - - // Indicate the graph is ready to be processed - // We need the full seq-cst fence here because of the polling threads (used in thread_sync) - atomic_fetch_add_explicit(&threadpool->n_graph, 1, memory_order_seq_cst); - - if (threadpool->pause) { - // Update main thread prio and affinity to match the threadpool settings - ggml_thread_apply_priority(threadpool->prio); - if (ggml_thread_cpumask_is_valid(threadpool->workers[0].cpumask)) { - ggml_thread_apply_affinity(threadpool->workers[0].cpumask); - } - - // resume does cond broadcast - ggml_threadpool_resume_locked(threadpool); - } else { - ggml_cond_broadcast(&threadpool->cond); - } - - ggml_mutex_unlock(&threadpool->mutex); -} - -#endif // GGML_USE_OPENMP - -void ggml_threadpool_params_init(struct ggml_threadpool_params * p, int n_threads) { - p->n_threads = n_threads; - p->prio = 0; // default priority (usually means normal or inherited) - p->poll = 50; // hybrid-polling enabled - p->strict_cpu = false; // no strict placement (all threads share same cpumask) - p->paused = false; // threads are ready to go - memset(p->cpumask, 0, GGML_MAX_N_THREADS); // all-zero means use the default affinity (usually inherited) -} - -struct ggml_threadpool_params ggml_threadpool_params_default(int n_threads) { - struct ggml_threadpool_params p; - ggml_threadpool_params_init(&p, n_threads); - return p; -} - -bool ggml_threadpool_params_match(const struct ggml_threadpool_params * p0, const struct ggml_threadpool_params * p1) { - if (p0->n_threads != p1->n_threads ) return false; - if (p0->prio != p1->prio ) return false; - if (p0->poll != p1->poll ) return false; - if (p0->strict_cpu != p1->strict_cpu ) return false; - return memcmp(p0->cpumask, p1->cpumask, GGML_MAX_N_THREADS) == 0; -} - -static struct ggml_threadpool * ggml_threadpool_new_impl( - struct ggml_threadpool_params * tpp, - struct ggml_cgraph * cgraph, - struct ggml_cplan * cplan) { - - struct ggml_threadpool * threadpool = - ggml_aligned_malloc(sizeof(struct ggml_threadpool)); - { - threadpool->cgraph = cgraph; - threadpool->cplan = cplan; - threadpool->n_graph = 0; - threadpool->n_barrier = 0; - threadpool->n_barrier_passed = 0; - threadpool->current_chunk = 0; - threadpool->stop = false; - threadpool->pause = tpp->paused; - threadpool->abort = false; - threadpool->workers = NULL; - threadpool->n_threads_max = tpp->n_threads; - threadpool->n_threads_cur = tpp->n_threads; - threadpool->poll = tpp->poll; - threadpool->prio = tpp->prio; - threadpool->ec = GGML_STATUS_SUCCESS; - } - - // Allocate and init workers state - const size_t workers_size = sizeof(struct ggml_compute_state) * tpp->n_threads; - struct ggml_compute_state * workers = ggml_aligned_malloc(workers_size); - - memset(workers, 0, workers_size); - for (int j = 0; j < tpp->n_threads; j++) { - workers[j].threadpool = threadpool; - workers[j].ith = j; - } - - threadpool->workers = workers; - -#ifndef GGML_USE_OPENMP - ggml_mutex_init(&threadpool->mutex); - ggml_cond_init(&threadpool->cond); - - // Spin the threads for all workers, and update CPU placements. - // Place the main thread last (towards the higher numbered CPU cores). - - int32_t cpumask_iter = 0; - - for (int j = 1; j < tpp->n_threads; j++) { - ggml_thread_cpumask_next(tpp->cpumask, workers[j].cpumask, tpp->strict_cpu, &cpumask_iter); - - int32_t rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_secondary_thread, &workers[j]); - GGML_ASSERT(rc == 0); - } - - ggml_thread_cpumask_next(tpp->cpumask, workers[0].cpumask, tpp->strict_cpu, &cpumask_iter); - - if (!threadpool->pause) { - // Update main thread prio and affinity at the start, otherwise we'll do it in resume - ggml_thread_apply_priority(threadpool->prio); - if (ggml_thread_cpumask_is_valid(threadpool->workers[0].cpumask)) { - ggml_thread_apply_affinity(threadpool->workers[0].cpumask); - } - } -#endif // GGML_USE_OPENMP - - return threadpool; -} - -struct ggml_threadpool * ggml_threadpool_new(struct ggml_threadpool_params * tpp) { - return ggml_threadpool_new_impl(tpp, NULL, NULL); -} - -enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { - GGML_ASSERT(cplan); - GGML_ASSERT(cplan->n_threads > 0); - GGML_ASSERT(cplan->work_size == 0 || cplan->work_data != NULL); - - int n_threads = cplan->n_threads; - struct ggml_threadpool * threadpool = cplan->threadpool; - - bool disposable_threadpool = false; - - if (threadpool == NULL) { - GGML_PRINT_DEBUG("Threadpool is not specified. Will create a disposable threadpool : n_threads %d\n", n_threads); - disposable_threadpool = true; - - struct ggml_threadpool_params ttp = ggml_threadpool_params_default(n_threads); - threadpool = ggml_threadpool_new_impl(&ttp, cgraph, cplan); - } else { - // Reset some of the parameters that need resetting - // No worker threads should be accessing the parameters below at this stage - threadpool->cgraph = cgraph; - threadpool->cplan = cplan; - threadpool->current_chunk = 0; - threadpool->abort = false; - threadpool->ec = GGML_STATUS_SUCCESS; - } - -#ifdef GGML_USE_OPENMP - if (n_threads > 1) { - #pragma omp parallel num_threads(n_threads) - { - #pragma omp single - { - // update the number of threads from the actual number of threads that we got from OpenMP - n_threads = omp_get_num_threads(); - atomic_store_explicit(&threadpool->n_threads_cur, n_threads, memory_order_relaxed); - } - - ggml_graph_compute_thread(&threadpool->workers[omp_get_thread_num()]); - } - } else { - atomic_store_explicit(&threadpool->n_threads_cur, 1, memory_order_relaxed); - ggml_graph_compute_thread(&threadpool->workers[0]); - } -#else - if (n_threads > threadpool->n_threads_max) { - GGML_LOG_WARN("cplan requested more threads (%d) than available (%d)\n", n_threads, threadpool->n_threads_max); - n_threads = threadpool->n_threads_max; - } - - // Kick all threads to start the new graph - ggml_graph_compute_kickoff(threadpool, n_threads); - - // This is a work thread too - ggml_graph_compute_thread(&threadpool->workers[0]); -#endif - - // don't leave affinity set on the main thread - clear_numa_thread_affinity(); - - enum ggml_status ret = threadpool->ec; - - if (disposable_threadpool) { - ggml_threadpool_free(threadpool); - } - - return ret; -} - -enum ggml_status ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads) { - struct ggml_cplan cplan = ggml_graph_plan(cgraph, n_threads, NULL); - - struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, cplan.work_size); - - cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs; - - return ggml_graph_compute(cgraph, &cplan); -} - -struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name) { +struct ggml_tensor * ggml_graph_get_tensor(const struct ggml_cgraph * cgraph, const char * name) { for (int i = 0; i < cgraph->n_leafs; i++) { struct ggml_tensor * leaf = cgraph->leafs[i]; @@ -20258,489 +5985,14 @@ struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const ch return NULL; } -static void ggml_graph_export_leaf(const struct ggml_tensor * tensor, FILE * fout) { - const int64_t * ne = tensor->ne; - const size_t * nb = tensor->nb; - - fprintf(fout, "%-6s %-12s %8d %" PRId64 " %" PRId64 " %" PRId64 " %" PRId64 " %16zu %16zu %16zu %16zu %16p %32s\n", - ggml_type_name(tensor->type), - ggml_op_name (tensor->op), - ggml_n_dims(tensor), - ne[0], ne[1], ne[2], ne[3], - nb[0], nb[1], nb[2], nb[3], - tensor->data, - tensor->name); +struct ggml_tensor * ggml_graph_get_grad(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) { + const size_t igrad = ggml_hash_find(&cgraph->visited_hash_set, node); + return igrad != GGML_HASHSET_FULL && ggml_bitset_get(cgraph->visited_hash_set.used, igrad) ? cgraph->grads[igrad] : NULL; } -static void ggml_graph_export_node(const struct ggml_tensor * tensor, const char * arg, FILE * fout) { - const int64_t * ne = tensor->ne; - const size_t * nb = tensor->nb; - - fprintf(fout, "%-6s %-6s %-12s %8d %" PRId64 " %" PRId64 " %" PRId64 " %" PRId64 " %16zu %16zu %16zu %16zu %16p %32s\n", - arg, - ggml_type_name(tensor->type), - ggml_op_name (tensor->op), - ggml_n_dims(tensor), - ne[0], ne[1], ne[2], ne[3], - nb[0], nb[1], nb[2], nb[3], - tensor->data, - tensor->name); -} - -void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) { - uint64_t size_eval = 0; - - // compute size of intermediate results - // TODO: does not take into account scratch buffers !!!! - for (int i = 0; i < cgraph->n_nodes; ++i) { - size_eval += ggml_nbytes_pad(cgraph->nodes[i]); - } - - // print - { - FILE * fout = stdout; - - fprintf(fout, "\n"); - fprintf(fout, "%-16s %8x\n", "magic", GGML_FILE_MAGIC); - fprintf(fout, "%-16s %8d\n", "version", GGML_FILE_VERSION); - fprintf(fout, "%-16s %8d\n", "leafs", cgraph->n_leafs); - fprintf(fout, "%-16s %8d\n", "nodes", cgraph->n_nodes); - fprintf(fout, "%-16s %" PRIu64 "\n", "eval", size_eval); - - // header - fprintf(fout, "\n"); - fprintf(fout, "%-6s %-12s %8s %8s %8s %8s %8s %16s %16s %16s %16s %16s %16s\n", - "TYPE", "OP", "NDIMS", "NE0", "NE1", "NE2", "NE3", "NB0", "NB1", "NB2", "NB3", "DATA", "NAME"); - - for (int i = 0; i < cgraph->n_leafs; ++i) { - ggml_graph_export_leaf(cgraph->leafs[i], fout); - - GGML_ASSERT(cgraph->leafs[i]->op == GGML_OP_NONE); - GGML_ASSERT(cgraph->leafs[i]->src[0] == NULL); - GGML_ASSERT(cgraph->leafs[i]->src[1] == NULL); - } - - // header - fprintf(fout, "\n"); - fprintf(fout, "%-6s %-6s %-12s %8s %8s %8s %8s %8s %16s %16s %16s %16s %8s %16s %16s\n", - "ARG", "TYPE", "OP", "NDIMS", "NE0", "NE1", "NE2", "NE3", "NB0", "NB1", "NB2", "NB3", "NTASKS", "DATA", "NAME"); - - for (int i = 0; i < cgraph->n_nodes; ++i) { - ggml_graph_export_node(cgraph->nodes[i], "DST", fout); - - for (int j = 0; j < GGML_MAX_SRC; ++j) { - if (cgraph->nodes[i]->src[j]) { - ggml_graph_export_node(cgraph->nodes[i]->src[j], "SRC", fout); - } - } - - fprintf(fout, "\n"); - } - - fprintf(fout, "\n"); - } - - // write binary data - { - FILE * fout = ggml_fopen(fname, "wb"); - - if (!fout) { - fprintf(stderr, "%s: failed to open %s: %s\n", __func__, fname, strerror(errno)); - return; - } - - // header - { - const uint32_t magic = GGML_FILE_MAGIC; - const uint32_t version = GGML_FILE_VERSION; - const uint32_t n_leafs = cgraph->n_leafs; - const uint32_t n_nodes = cgraph->n_nodes; - - fwrite(&magic, sizeof(uint32_t), 1, fout); - fwrite(&version, sizeof(uint32_t), 1, fout); - fwrite(&n_leafs, sizeof(uint32_t), 1, fout); - fwrite(&n_nodes, sizeof(uint32_t), 1, fout); - fwrite(&size_eval, sizeof(uint64_t), 1, fout); - } - - // leafs - { - for (int i = 0; i < cgraph->n_leafs; ++i) { - const struct ggml_tensor * tensor = cgraph->leafs[i]; - - const uint32_t type = tensor->type; - const uint32_t op = tensor->op; - const int32_t flags = tensor->flags; - - fwrite(&type, sizeof(uint32_t), 1, fout); - fwrite(&op, sizeof(uint32_t), 1, fout); - fwrite(&flags, sizeof(int32_t), 1, fout); - - for (int j = 0; j < GGML_MAX_DIMS; ++j) { - const uint64_t ne = tensor->ne[j]; - const uint64_t nb = tensor->nb[j]; - - fwrite(&ne, sizeof(uint64_t), 1, fout); - fwrite(&nb, sizeof(uint64_t), 1, fout); - } - - fwrite(tensor->name, sizeof(char), GGML_MAX_NAME, fout); - fwrite(tensor->op_params, sizeof(char), GGML_MAX_OP_PARAMS, fout); - - // dump the data - // TODO: pad this to 32 byte boundary - { - const size_t size = ggml_nbytes(tensor); - - fwrite(tensor->data, sizeof(char), size, fout); - } - } - } - - // nodes - { - for (int i = 0; i < cgraph->n_nodes; ++i) { - const struct ggml_tensor * tensor = cgraph->nodes[i]; - - const uint32_t type = tensor->type; - const uint32_t op = tensor->op; - const int32_t flags = tensor->flags; - - fwrite(&type, sizeof(uint32_t), 1, fout); - fwrite(&op, sizeof(uint32_t), 1, fout); - fwrite(&flags, sizeof(int32_t), 1, fout); - - for (int j = 0; j < GGML_MAX_DIMS; ++j) { - const uint64_t ne = tensor->ne[j]; - const uint64_t nb = tensor->nb[j]; - - fwrite(&ne, sizeof(uint64_t), 1, fout); - fwrite(&nb, sizeof(uint64_t), 1, fout); - } - - fwrite(tensor->name, sizeof(char), GGML_MAX_NAME, fout); - fwrite(tensor->op_params, sizeof(char), GGML_MAX_OP_PARAMS, fout); - - // output the op arguments - { - struct ggml_tensor * args[GGML_MAX_SRC] = { NULL }; - - for (int j = 0; j < GGML_MAX_SRC; ++j) { - args[j] = tensor->src[j]; - } - - for (int j = 0; j < GGML_MAX_SRC; ++j) { - if (args[j]) { - int32_t idx = -1; - - // check if leaf - { - for (int k = 0; k < cgraph->n_leafs; ++k) { - if (args[j] == cgraph->leafs[k]) { - idx = k; - break; - } - } - } - - // check if node - if (idx == -1) { - for (int k = 0; k < cgraph->n_nodes; ++k) { - if (args[j] == cgraph->nodes[k]) { - idx = cgraph->n_leafs + k; - break; - } - } - } - - if (idx == -1) { - fprintf(stderr, "%s: failed to find tensor, arg = %d, node = %d\n", __func__, j, i); - fclose(fout); - return; - } - - fwrite(&idx, sizeof(int32_t), 1, fout); - } else { - const int32_t nul = -1; - - fwrite(&nul, sizeof(int32_t), 1, fout); - } - } - } - - // dump the data - // TODO: pad this to 32 byte boundary - if ((flags & GGML_TENSOR_FLAG_PARAM)) { - const size_t size = ggml_nbytes(tensor); - - fwrite(tensor->data, sizeof(char), size, fout); - } - } - } - - fclose(fout); - } -} - -struct ggml_cgraph * ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval) { - assert(*ctx_data == NULL); - assert(*ctx_eval == NULL); - - struct ggml_cgraph * result = NULL; - - struct ggml_tensor * data = NULL; - - // read file into data - { - FILE * fin = ggml_fopen(fname, "rb"); - if (!fin) { - fprintf(stderr, "%s: failed to open %s: %s\n", __func__, fname, strerror(errno)); - return result; - } - - size_t fsize = 0; - - fseek(fin, 0, SEEK_END); - fsize = ftell(fin); - fseek(fin, 0, SEEK_SET); - - // create the data context - { - const size_t overhead = 1*ggml_tensor_overhead(); - - struct ggml_init_params params = { - .mem_size = fsize + overhead, - .mem_buffer = NULL, - .no_alloc = false, - }; - - *ctx_data = ggml_init(params); - - if (!*ctx_data) { - fprintf(stderr, "%s: failed to create ggml context\n", __func__); - fclose(fin); - return result; - } - } - - data = ggml_new_tensor_1d(*ctx_data, GGML_TYPE_I8, fsize); - - { - const size_t ret = fread(data->data, sizeof(char), fsize, fin); - if (ret != fsize) { - fprintf(stderr, "%s: failed to read %s\n", __func__, fname); - fclose(fin); - return result; - } - } - - fclose(fin); - } - - // populate result - { - char * ptr = (char *) data->data; - - const uint32_t magic = *(const uint32_t *) ptr; ptr += sizeof(magic); - - if (magic != GGML_FILE_MAGIC) { - fprintf(stderr, "%s: invalid magic number, got %08x\n", __func__, magic); - return result; - } - - const uint32_t version = *(const uint32_t *) ptr; ptr += sizeof(version); - - if (version != GGML_FILE_VERSION) { - fprintf(stderr, "%s: invalid version number\n", __func__); - return result; - } - - const uint32_t n_leafs = *(const uint32_t *) ptr; ptr += sizeof(n_leafs); - const uint32_t n_nodes = *(const uint32_t *) ptr; ptr += sizeof(n_nodes); - const uint64_t size_eval = *(const uint64_t *) ptr; ptr += sizeof(size_eval); - const int graph_size = MAX(n_leafs, n_nodes); - - // create the data context - { - const size_t overhead = (n_leafs + n_nodes)*ggml_tensor_overhead() + ggml_graph_overhead_custom(graph_size, false); - - struct ggml_init_params params = { - .mem_size = size_eval + overhead, - .mem_buffer = NULL, - .no_alloc = true, - }; - - *ctx_eval = ggml_init(params); - - if (!*ctx_eval) { - fprintf(stderr, "%s: failed to create ggml context\n", __func__); - return result; - } - } - - result = ggml_new_graph_custom(*ctx_eval, graph_size, false); - - result->n_leafs = n_leafs; - result->n_nodes = n_nodes; - - - // leafs - { - uint32_t type; - uint32_t op; - int32_t flags; - - for (uint32_t i = 0; i < n_leafs; ++i) { - type = *(const uint32_t *) ptr; ptr += sizeof(type); - op = *(const uint32_t *) ptr; ptr += sizeof(op); - flags = *(const int32_t *) ptr; ptr += sizeof(flags); - - int64_t ne[GGML_MAX_DIMS]; - size_t nb[GGML_MAX_DIMS]; - - for (int j = 0; j < GGML_MAX_DIMS; ++j) { - uint64_t ne_cur; - uint64_t nb_cur; - - ne_cur = *(const uint64_t *) ptr; ptr += sizeof(ne_cur); - nb_cur = *(const uint64_t *) ptr; ptr += sizeof(nb_cur); - - ne[j] = ne_cur; - nb[j] = nb_cur; - } - - struct ggml_tensor * tensor = ggml_new_tensor(*ctx_eval, (enum ggml_type) type, GGML_MAX_DIMS, ne); - - tensor->op = (enum ggml_op) op; - tensor->flags = flags; - - memcpy(tensor->name, ptr, GGML_MAX_NAME); ptr += GGML_MAX_NAME; - memcpy(tensor->op_params, ptr, GGML_MAX_OP_PARAMS); ptr += GGML_MAX_OP_PARAMS; - - for (int j = 0; j < GGML_MAX_DIMS; ++j) { - tensor->nb[j] = nb[j]; - } - - tensor->data = (void *) ptr; ptr += ggml_nbytes(tensor); - - result->leafs[i] = tensor; - - fprintf(stderr, "%s: loaded leaf %u: '%16s', %9zu bytes\n", __func__, i, tensor->name, ggml_nbytes(tensor)); - } - } - - ggml_set_no_alloc(*ctx_eval, false); - - // nodes - { - uint32_t type; - uint32_t op; - int32_t flags; - - for (uint32_t i = 0; i < n_nodes; ++i) { - type = *(const uint32_t *) ptr; ptr += sizeof(type); - op = *(const uint32_t *) ptr; ptr += sizeof(op); - flags = *(const int32_t *) ptr; ptr += sizeof(flags); - - enum ggml_op eop = (enum ggml_op) op; - - int64_t ne[GGML_MAX_DIMS]; - size_t nb[GGML_MAX_DIMS]; - - for (int j = 0; j < GGML_MAX_DIMS; ++j) { - uint64_t ne_cur; - uint64_t nb_cur; - - ne_cur = *(const uint64_t *) ptr; ptr += sizeof(ne_cur); - nb_cur = *(const uint64_t *) ptr; ptr += sizeof(nb_cur); - - ne[j] = ne_cur; - nb[j] = nb_cur; - } - - const char * ptr_name = ptr; ptr += GGML_MAX_NAME; - const char * ptr_op_params = ptr; ptr += GGML_MAX_OP_PARAMS; - - const int32_t * ptr_arg_idx = (const int32_t *) ptr; ptr += GGML_MAX_SRC*sizeof(int32_t); - - struct ggml_tensor * args[GGML_MAX_SRC] = { NULL }; - - // parse args - for (int j = 0; j < GGML_MAX_SRC; ++j) { - const int32_t arg_idx = ptr_arg_idx[j]; - - if (arg_idx == -1) { - continue; - } - - if (arg_idx < result->n_leafs) { - args[j] = result->leafs[arg_idx]; - } else { - args[j] = result->nodes[arg_idx - result->n_leafs]; - } - } - - // create the tensor - // "view" operations are handled differently - // TODO: handle inplace ops - currently a copy is always made - - struct ggml_tensor * tensor = NULL; - - switch (eop) { - // TODO: implement other view ops - case GGML_OP_RESHAPE: - { - tensor = ggml_reshape_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3]); - } break; - case GGML_OP_VIEW: - { - tensor = ggml_view_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3], 0, 0, 0, 0); - - size_t offs; - memcpy(&offs, ptr_op_params, sizeof(offs)); - - tensor->data = ((char *) tensor->data) + offs; - } break; - case GGML_OP_TRANSPOSE: - { - tensor = ggml_transpose(*ctx_eval, args[0]); - } break; - case GGML_OP_PERMUTE: - { - tensor = ggml_view_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3], 0, 0, 0, 0); - } break; - default: - { - tensor = ggml_new_tensor(*ctx_eval, (enum ggml_type) type, GGML_MAX_DIMS, ne); - - tensor->op = eop; - } break; - } - - memcpy(tensor->name, ptr_name, GGML_MAX_NAME); - memcpy(tensor->op_params, ptr_op_params, GGML_MAX_OP_PARAMS); - - for (int j = 0; j < GGML_MAX_DIMS; ++j) { - tensor->nb[j] = nb[j]; - } - - for (int j = 0; j < GGML_MAX_SRC; ++j) { - tensor->src[j] = args[j]; - } - - result->nodes[i] = tensor; - - // TODO tensor data is be duplicated due to ggml_new_tensor call above - if (flags & GGML_TENSOR_FLAG_PARAM) { - tensor->data = (void *) ptr; ptr += ggml_nbytes(tensor); - } - - fprintf(stderr, "%s: loaded node %u: '%16s', %9zu bytes\n", __func__, i, tensor->name, ggml_nbytes(tensor)); - } - } - } - - return result; +struct ggml_tensor * ggml_graph_get_grad_acc(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) { + const size_t igrad = ggml_hash_find(&cgraph->visited_hash_set, node); + return igrad != GGML_HASHSET_FULL && ggml_bitset_get(cgraph->visited_hash_set.used, igrad) ? cgraph->grad_accs[igrad] : NULL; } void ggml_graph_print(const struct ggml_cgraph * cgraph) { @@ -20753,7 +6005,8 @@ void ggml_graph_print(const struct ggml_cgraph * cgraph) { GGML_LOG_INFO(" - %3d: [ %5" PRId64 ", %5" PRId64 ", %5" PRId64 "] %16s %s\n", i, node->ne[0], node->ne[1], node->ne[2], - ggml_op_name(node->op), (node->flags & GGML_TENSOR_FLAG_PARAM) ? "x" : node->grad ? "g" : " "); + ggml_op_name(node->op), (node->flags & GGML_TENSOR_FLAG_PARAM) ? "x" : + ggml_graph_get_grad(cgraph, node) ? "g" : " "); } GGML_LOG_INFO("n_leafs = %d\n", cgraph->n_leafs); @@ -20788,8 +6041,9 @@ static bool ggml_graph_find(const struct ggml_cgraph * cgraph, const struct ggml static struct ggml_tensor * ggml_graph_get_parent(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) { for (int i = 0; i < cgraph->n_nodes; i++) { struct ggml_tensor * parent = cgraph->nodes[i]; + struct ggml_tensor * grad = ggml_graph_get_grad(cgraph, parent); - if (parent->grad == node) { + if (grad == node) { return parent; } } @@ -20829,6 +6083,7 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph for (int i = 0; i < gb->n_nodes; i++) { struct ggml_tensor * node = gb->nodes[i]; + struct ggml_tensor * grad = ggml_graph_get_grad(gb, node); if (ggml_graph_get_parent(gb, node) != NULL) { continue; @@ -20836,7 +6091,7 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph if (node->flags & GGML_TENSOR_FLAG_PARAM) { snprintf(color, sizeof(color), "yellow"); - } else if (node->grad) { + } else if (grad) { if (ggml_graph_find(gf, node)) { snprintf(color, sizeof(color), "green"); } else { @@ -20863,8 +6118,8 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph fprintf(fp, "%d [%" PRId64 ", %" PRId64 ", %" PRId64 "] | %s", i, node->ne[0], node->ne[1], node->ne[2], ggml_op_symbol(node->op)); } - if (node->grad) { - fprintf(fp, " | %s\"; ]\n", ggml_op_symbol(node->grad->op)); + if (grad) { + fprintf(fp, " | %s\"; ]\n", ggml_op_symbol(grad->op)); } else { fprintf(fp, "\"; ]\n"); } @@ -20890,15 +6145,17 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph if (ggml_nelements(node) < 5 && node->data != NULL) { fprintf(fp, " | ("); for (int j = 0; j < ggml_nelements(node); j++) { - if (node->type == GGML_TYPE_I8 || node->type == GGML_TYPE_I16 || node->type == GGML_TYPE_I32) { - fprintf(fp, "%d", ggml_get_i32_1d(node, j)); - } - else if (node->type == GGML_TYPE_F32 || - node->type == GGML_TYPE_F16 || - node->type == GGML_TYPE_BF16) { - fprintf(fp, "%.1e", (double)ggml_get_f32_1d(node, j)); - } - else { + // FIXME: use ggml-backend to obtain the tensor data + //if (node->type == GGML_TYPE_I8 || node->type == GGML_TYPE_I16 || node->type == GGML_TYPE_I32) { + // fprintf(fp, "%d", ggml_get_i32_1d(node, j)); + //} + //else if (node->type == GGML_TYPE_F32 || + // node->type == GGML_TYPE_F16 || + // node->type == GGML_TYPE_BF16) { + // fprintf(fp, "%.1e", (double)ggml_get_f32_1d(node, j)); + //} + //else + { fprintf(fp, "#"); } if (j < ggml_nelements(node) - 1) { @@ -20943,918 +6200,6 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph //////////////////////////////////////////////////////////////////////////////// -static void ggml_opt_set_params(int np, struct ggml_tensor * const ps[], const float * x) { - int i = 0; - for (int p = 0; p < np; ++p) { - const int64_t ne = ggml_nelements(ps[p]) ; - // TODO: add function to set tensor from array - for (int64_t j = 0; j < ne; ++j) { - ggml_set_f32_1d(ps[p], j, x[i++]); - } - } -} - -static void ggml_opt_get_params(int np, struct ggml_tensor * const ps[], float * x) { - int i = 0; - for (int p = 0; p < np; ++p) { - const int64_t ne = ggml_nelements(ps[p]) ; - // TODO: add function to get all elements at once - for (int64_t j = 0; j < ne; ++j) { - x[i++] = ggml_get_f32_1d(ps[p], j); - } - } -} - -static void ggml_opt_get_grad(int np, struct ggml_tensor * const ps[], float * g) { - int64_t i = 0; - for (int p = 0; p < np; ++p) { - const int64_t ne = ggml_nelements(ps[p]) ; - // TODO: add function to get all elements at once - for (int64_t j = 0; j < ne; ++j) { - g[i++] = ggml_get_f32_1d(ps[p]->grad, j); - } - } -} - -static void ggml_opt_acc_grad(int np, struct ggml_tensor * const ps[], float * g, float scale) { - int64_t i = 0; - for (int p = 0; p < np; ++p) { - const int64_t ne = ggml_nelements(ps[p]) ; - // TODO: add function to get all elements at once - for (int64_t j = 0; j < ne; ++j) { - g[i++] += ggml_get_f32_1d(ps[p]->grad, j) * scale; - } - } -} - -// -// Using AdamW - ref: https://arxiv.org/pdf/1711.05101v3.pdf -// -// (Original Adam - ref: https://arxiv.org/pdf/1412.6980.pdf) -// - -static enum ggml_opt_result ggml_opt_adam( - struct ggml_context * ctx, - struct ggml_opt_context * opt, - struct ggml_opt_params params, - struct ggml_tensor * f, - struct ggml_cgraph * gf, - struct ggml_cgraph * gb, - ggml_opt_callback callback, - void * callback_data) { - GGML_ASSERT(ggml_is_scalar(f)); - GGML_ASSERT(f->type == GGML_TYPE_F32); - - // these will store the parameters we want to optimize - struct ggml_tensor * ps[GGML_MAX_PARAMS]; - - int np = 0; - int64_t nx = 0; - for (int i = 0; i < gf->n_nodes; ++i) { - if (gf->nodes[i]->flags & GGML_TENSOR_FLAG_PARAM) { - GGML_PRINT_DEBUG("found param %d: grad->op = %d\n", np, gf->nodes[i]->grad->op); - - GGML_ASSERT(np < GGML_MAX_PARAMS); - - ps[np++] = gf->nodes[i]; - nx += ggml_nelements(gf->nodes[i]); - } - } - - if ((opt->params.type != params.type) || (opt->nx != nx) || (opt->params.past != params.past)) { - int iter = opt->iter; - ggml_opt_init(opt->ctx, opt, params, nx); - opt->iter = iter; - } - - // constants - float sched = params.adam.sched; - const float alpha = params.adam.alpha; - const float decay = params.adam.decay * alpha; - const float beta1 = params.adam.beta1; - const float beta2 = params.adam.beta2; - const float eps = params.adam.eps; - const float gclip = params.adam.gclip; - const int decay_min_ndim = params.adam.decay_min_ndim; - const int n_accum = MAX(1, params.n_gradient_accumulation); - const float accum_norm = 1.0f / (float) n_accum; - - float * g = opt->adam.g->data; // gradients - float * m = opt->adam.m->data; // first moment - float * v = opt->adam.v->data; // second moment - - float * pf = params.past > 0 ? opt->adam.pf->data : NULL; // past function values - - struct ggml_cplan cplan = ggml_graph_plan(gb, params.n_threads, NULL); - struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, cplan.work_size); - cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs; - - bool cancel = false; - - // compute the function value - float fx = 0; - ggml_set_zero(opt->adam.g); - for (int accum_step = 0; accum_step < n_accum; ++accum_step) { - if (callback) { - callback(callback_data, accum_step, &sched, &cancel); - if (cancel) { - return GGML_OPT_RESULT_CANCEL; - } - } - // ggml_graph_reset (gf); - ggml_set_f32 (f->grad, 1.0f); - ggml_graph_compute(gb, &cplan); - ggml_opt_acc_grad(np, ps, g, accum_norm); - fx += ggml_get_f32_1d(f, 0); - } - fx *= accum_norm; - - opt->adam.fx_prev = fx; - opt->adam.fx_best = opt->adam.fx_prev; - if (pf) { - pf[opt->iter % params.past] = opt->adam.fx_prev; - } - - opt->loss_before = opt->adam.fx_prev; - opt->loss_after = opt->adam.fx_prev; - - // initialize - if (opt->just_initialized) { - opt->adam.n_no_improvement = 0; - opt->just_initialized = false; - } - - float * fx_best = &opt->adam.fx_best; - float * fx_prev = &opt->adam.fx_prev; - int * n_no_improvement = &opt->adam.n_no_improvement; - - int iter0 = opt->iter; - - // run the optimizer - for (int t = 0; t < params.adam.n_iter; ++t) { - opt->iter = iter0 + t + 1; - GGML_PRINT_DEBUG ("=== iter %d ===\n", t); - - GGML_PRINT_DEBUG ("f = %10.6f\n", ggml_get_f32_1d(f, 0)); - GGML_PRINT_DEBUG_5("df/dx0 = %10.6f\n", ggml_get_f32_1d(ps[0]->grad, 0)); - GGML_PRINT_DEBUG_5("df/dx1 = %10.6f\n", ggml_get_f32_1d(ps[1]->grad, 0)); - - for (int i = 0; i < np; ++i) { - GGML_PRINT_DEBUG("param %d: %10.6f, g = %10.6f\n", i, - ggml_get_f32_1d(ps[i], 0), ggml_get_f32_1d(ps[i]->grad, 0)); - } - - const int64_t t_start_wall = ggml_time_us(); - const int64_t t_start_cpu = ggml_cycles(); - UNUSED(t_start_wall); - UNUSED(t_start_cpu); - - { - float gnorm = 1.0f; - if (gclip > 0.0f) { - // gradient clipping - ggml_float sum = 0.0; - for (int64_t i = 0; i < nx; ++i) { - sum += (ggml_float)(g[i]*g[i]); - } - ggml_float norm = sqrt(sum); - if (norm > (ggml_float) gclip) { - gnorm = (float) ((ggml_float) gclip / norm); - } - } - const float beta1h = alpha*sched/(1.0f - powf(beta1, opt->iter)); - const float beta2h = 1.0f/(1.0f - powf(beta2, opt->iter)); - int64_t i = 0; - for (int p = 0; p < np; ++p) { - const int64_t ne = ggml_nelements(ps[p]); - const float p_decay = ((ggml_n_dims(ps[p]) >= decay_min_ndim) ? decay : 0.0f) * sched; - for (int64_t j = 0; j < ne; ++j) { - float x = ggml_get_f32_1d(ps[p], j); - float g_ = g[i]*gnorm; - m[i] = m[i]*beta1 + g_*(1.0f - beta1); - v[i] = v[i]*beta2 + g_*g_*(1.0f - beta2); - float mh = m[i]*beta1h; - float vh = v[i]*beta2h; - vh = sqrtf(vh) + eps; - x = x*(1.0f - p_decay) - mh/vh; - ggml_set_f32_1d(ps[p], j, x); - ++i; - } - } - } - - fx = 0; - ggml_set_zero(opt->adam.g); - for (int accum_step = 0; accum_step < n_accum; ++accum_step) { - if (callback) { - callback(callback_data, accum_step, &sched, &cancel); - if (cancel) { - return GGML_OPT_RESULT_CANCEL;; - } - } - // ggml_graph_reset (gf); - ggml_set_f32 (f->grad, 1.0f); - ggml_graph_compute(gb, &cplan); - ggml_opt_acc_grad(np, ps, g, accum_norm); - fx += ggml_get_f32_1d(f, 0); - } - fx *= accum_norm; - - opt->loss_after = fx; - - // check convergence - if (fabsf(fx - fx_prev[0])/fx < params.adam.eps_f) { - GGML_PRINT_DEBUG("converged\n"); - - return GGML_OPT_RESULT_OK; - } - - // delta-based convergence test - if (pf != NULL) { - // need at least params.past iterations to start checking for convergence - if (params.past <= iter0 + t) { - const float rate = (pf[(iter0 + t)%params.past] - fx)/fx; - - if (fabsf(rate) < params.delta) { - return GGML_OPT_RESULT_OK; - } - } - - pf[(iter0 + t)%params.past] = fx; - } - - // check for improvement - if (params.max_no_improvement > 0) { - if (fx_best[0] > fx) { - fx_best[0] = fx; - n_no_improvement[0] = 0; - } else { - ++n_no_improvement[0]; - - if (n_no_improvement[0] >= params.max_no_improvement) { - return GGML_OPT_RESULT_OK; - } - } - } - - fx_prev[0] = fx; - - { - const int64_t t_end_cpu = ggml_cycles(); - GGML_PRINT_DEBUG("time iter: %5.3f s\n", ((float)(t_end_cpu - t_start_cpu))/CLOCKS_PER_SEC); - UNUSED(t_end_cpu); - - const int64_t t_end_wall = ggml_time_us(); - GGML_PRINT_DEBUG("wall time iter: %5.3f s\n", (t_end_wall - t_start_wall)/1e6); - UNUSED(t_end_wall); - } - } - - return GGML_OPT_RESULT_DID_NOT_CONVERGE; -} - -// -// L-BFGS -// -// the L-BFGS implementation below is based on the following implementation: -// -// https://github.com/chokkan/liblbfgs -// - -struct ggml_lbfgs_iteration_data { - float alpha; - float ys; - float * s; - float * y; -}; - -static enum ggml_opt_result linesearch_backtracking( - const struct ggml_opt_params * params, - int nx, - float * x, - float * fx, - float * g, - float * d, - float * step, - const float * xp, - struct ggml_tensor * f, - struct ggml_cgraph * gb, - struct ggml_cplan * cplan, - const int np, - struct ggml_tensor * ps[], - bool * cancel, - ggml_opt_callback callback, - void * callback_data) { - int count = 0; - - float width = 0.0f; - float dg = 0.0f; - float finit = 0.0f; - float dginit = 0.0f; - float dgtest = 0.0f; - - const float dec = 0.5f; - const float inc = 2.1f; - - const int n_accum = MAX(1, params->n_gradient_accumulation); - const float accum_norm = 1.0f / (float) n_accum; - - if (*step <= 0.f) { - return GGML_LINESEARCH_INVALID_PARAMETERS; - } - - // compute the initial gradient in the search direction - ggml_vec_dot_f32(nx, &dginit, 0, g, 0, d, 0, 1); - - // make sure that d points to a descent direction - if (0 < dginit) { - return GGML_LINESEARCH_FAIL; - } - - // initialize local variables - finit = *fx; - dgtest = params->lbfgs.ftol*dginit; - - while (true) { - ggml_vec_cpy_f32(nx, x, xp); - ggml_vec_mad_f32(nx, x, d, *step); - - // evaluate the function and gradient values - { - ggml_opt_set_params(np, ps, x); - - *fx = 0; - memset(g, 0, sizeof(float)*nx); - for (int accum_step = 0; accum_step < n_accum; ++accum_step) { - if (callback) { - // LBFG-S does not support learning rate -> ignore learning schedule - float sched = 0; - callback(callback_data, accum_step, &sched, cancel); - if (*cancel) { - return GGML_OPT_RESULT_CANCEL; - } - } - // ggml_graph_reset (gf); - ggml_set_f32 (f->grad, 1.0f); - ggml_graph_compute(gb, cplan); - ggml_opt_acc_grad(np, ps, g, accum_norm); - *fx += ggml_get_f32_1d(f, 0); - } - *fx *= accum_norm; - - } - - ++count; - - if (*fx > finit + (*step)*dgtest) { - width = dec; - } else { - // Armijo condition is satisfied - if (params->lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_ARMIJO) { - return count; - } - - ggml_vec_dot_f32(nx, &dg, 0, g, 0, d, 0, 1); - - // check the Wolfe condition - if (dg < params->lbfgs.wolfe * dginit) { - width = inc; - } else { - if(params->lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_WOLFE) { - // regular Wolfe conditions - return count; - } - - if(dg > -params->lbfgs.wolfe*dginit) { - width = dec; - } else { - // strong Wolfe condition (GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE) - return count; - } - } - } - - if (*step < params->lbfgs.min_step) { - return GGML_LINESEARCH_MINIMUM_STEP; - } - if (*step > params->lbfgs.max_step) { - return GGML_LINESEARCH_MAXIMUM_STEP; - } - if (params->lbfgs.max_linesearch <= count) { - return GGML_LINESEARCH_MAXIMUM_ITERATIONS; - } - - (*step) *= width; - } - - GGML_ABORT("line search failed"); - - //return GGML_LINESEARCH_FAIL; -} - -static enum ggml_opt_result ggml_opt_lbfgs( - struct ggml_context * ctx, - struct ggml_opt_context * opt, - struct ggml_opt_params params, - struct ggml_tensor * f, - struct ggml_cgraph * gf, - struct ggml_cgraph * gb, - ggml_opt_callback callback, - void * callback_data) { - if (params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_WOLFE || - params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE) { - if (params.lbfgs.wolfe <= params.lbfgs.ftol || 1.f <= params.lbfgs.wolfe) { - return GGML_OPT_RESULT_INVALID_WOLFE; - } - } - - const int m = params.lbfgs.m; - - // these will store the parameters we want to optimize - struct ggml_tensor * ps[GGML_MAX_PARAMS]; - - int np = 0; - int nx = 0; - for (int i = 0; i < gf->n_nodes; ++i) { - if (gf->nodes[i]->flags & GGML_TENSOR_FLAG_PARAM) { - GGML_PRINT_DEBUG("found param %d: grad->op = %d\n", np, gf->nodes[i]->grad->op); - - GGML_ASSERT(np < GGML_MAX_PARAMS); - - ps[np++] = gf->nodes[i]; - nx += ggml_nelements(gf->nodes[i]); - } - } - - if ((opt->params.type != params.type) || (opt->nx != nx) || (opt->params.past != params.past) || (opt->params.lbfgs.m != params.lbfgs.m)) { - int iter = opt->iter; - ggml_opt_init(ctx, opt, params, nx); - opt->iter = iter; - } - - struct ggml_cplan cplan = ggml_graph_plan(gb, params.n_threads, NULL); - struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, cplan.work_size); - cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs; - - float * x = opt->lbfgs.x->data; // current parameters - float * xp = opt->lbfgs.xp->data; // previous parameters - float * g = opt->lbfgs.g->data; // current gradient - float * gp = opt->lbfgs.gp->data; // previous gradient - float * d = opt->lbfgs.d->data; // search direction - - float * pf = params.past > 0 ? opt->lbfgs.pf->data : NULL; // past function values - - const int n_accum = MAX(1, params.n_gradient_accumulation); - const float accum_norm = 1.0f / (float) n_accum; - - float fx = 0.0f; // cost function value - float xnorm = 0.0f; // ||x|| - float gnorm = 0.0f; // ||g|| - - // initialize x from the graph nodes - ggml_opt_get_params(np, ps, x); - - // the L-BFGS memory - float * lm_alpha = opt->lbfgs.lmal->data; - float * lm_ys = opt->lbfgs.lmys->data; - float * lm_s = opt->lbfgs.lms->data; - float * lm_y = opt->lbfgs.lmy->data; - - bool cancel = false; - - // evaluate the function value and its gradient - { - ggml_opt_set_params(np, ps, x); - - fx = 0; - memset(g, 0, sizeof(float)*nx); - for (int accum_step = 0; accum_step < n_accum; ++accum_step) { - if (callback) { - // LBFG-S does not support learning rate -> ignore learning schedule - float sched = 0; - callback(callback_data, accum_step, &sched, &cancel); - if (cancel) { - return GGML_OPT_RESULT_CANCEL; - } - } - // ggml_graph_reset (gf); - ggml_set_f32 (f->grad, 1.0f); - ggml_graph_compute(gb, &cplan); - ggml_opt_acc_grad(np, ps, g, accum_norm); - fx += ggml_get_f32_1d(f, 0); - } - fx *= accum_norm; - - opt->loss_before = fx; - opt->loss_after = fx; - } - - // search direction = -gradient - ggml_vec_neg_f32(nx, d, g); - - // ||x||, ||g|| - ggml_vec_norm_f32(nx, &xnorm, x); - ggml_vec_norm_f32(nx, &gnorm, g); - - if (xnorm < 1.0f) { - xnorm = 1.0f; - } - - // already optimized - if (gnorm/xnorm <= params.lbfgs.eps) { - return GGML_OPT_RESULT_OK; - } - - if (opt->just_initialized) { - if (pf) { - pf[0] = fx; - } - opt->lbfgs.fx_best = fx; - - // initial step - ggml_vec_norm_inv_f32(nx, &opt->lbfgs.step, d); - opt->lbfgs.j = 0; - opt->lbfgs.k = 1; - opt->lbfgs.end = 0; - opt->lbfgs.n_no_improvement = 0; - opt->just_initialized = false; - } - - float * fx_best = &opt->lbfgs.fx_best; - float * step = &opt->lbfgs.step; - int * j = &opt->lbfgs.j; - int * k = &opt->lbfgs.k; - int * end = &opt->lbfgs.end; - int * n_no_improvement = &opt->lbfgs.n_no_improvement; - - int ls = 0; - int bound = 0; - - float ys = 0.0f; - float yy = 0.0f; - float beta = 0.0f; - - int it = 0; - - while (true) { - // store the current position and gradient vectors - ggml_vec_cpy_f32(nx, xp, x); - ggml_vec_cpy_f32(nx, gp, g); - - // TODO: instead of passing &cancel here, use the return code of the linesearch - // to determine if the optimization should be cancelled - // this is a simple change, but not doing this atm, since I don't have a nice - // way to test and don't want to break something with so many changes lined up - ls = linesearch_backtracking(¶ms, nx, x, &fx, g, d, step, xp, f, gb, &cplan, np, ps, &cancel, callback, callback_data); - if (cancel) { - return GGML_OPT_RESULT_CANCEL; - } - - if (ls < 0) { - // linesearch failed - go back to the previous point and return - ggml_vec_cpy_f32(nx, x, xp); - ggml_vec_cpy_f32(nx, g, gp); - - return ls; - } - - opt->loss_after = fx; - - ggml_vec_norm_f32(nx, &xnorm, x); - ggml_vec_norm_f32(nx, &gnorm, g); - - GGML_PRINT_DEBUG("f = %10.6f\n", ggml_get_f32_1d(f, 0)); - - if (xnorm < 1.0f) { - xnorm = 1.0f; - } - if (gnorm/xnorm <= params.lbfgs.eps) { - // converged - return GGML_OPT_RESULT_OK; - } - - // delta-based convergence test - if (pf != NULL) { - // need at least params.past iterations to start checking for convergence - if (params.past <= k[0]) { - const float rate = (pf[k[0]%params.past] - fx)/fx; - - if (fabsf(rate) < params.delta) { - return GGML_OPT_RESULT_OK; - } - } - - pf[k[0]%params.past] = fx; - } - - // check for improvement - if (params.max_no_improvement > 0) { - if (fx < fx_best[0]) { - fx_best[0] = fx; - n_no_improvement[0] = 0; - } else { - n_no_improvement[0]++; - - if (n_no_improvement[0] >= params.max_no_improvement) { - return GGML_OPT_RESULT_OK; - } - } - } - - if (params.lbfgs.n_iter != 0 && params.lbfgs.n_iter < it + 1) { - // reached the maximum number of iterations - return GGML_OPT_RESULT_DID_NOT_CONVERGE; - } - - // update vectors s and y: - // s_{k+1} = x_{k+1} - x_{k} = \step * d_{k}. - // y_{k+1} = g_{k+1} - g_{k}. - // - ggml_vec_sub_f32(nx, &lm_s[end[0]*nx], x, xp); - ggml_vec_sub_f32(nx, &lm_y[end[0]*nx], g, gp); - - // compute scalars ys and yy: - // ys = y^t \cdot s -> 1 / \rho. - // yy = y^t \cdot y. - // - ggml_vec_dot_f32(nx, &ys, 0, &lm_y[end[0]*nx], 0, &lm_s[end[0]*nx], 0, 1); - ggml_vec_dot_f32(nx, &yy, 0, &lm_y[end[0]*nx], 0, &lm_y[end[0]*nx], 0, 1); - - lm_ys[end[0]] = ys; - - // find new search direction - // ref: https://en.wikipedia.org/wiki/Limited-memory_BFGS - - bound = (m <= k[0]) ? m : k[0]; - k[0]++; - it++; - end[0] = (end[0] + 1)%m; - - // initialize search direction with -g - ggml_vec_neg_f32(nx, d, g); - - j[0] = end[0]; - for (int i = 0; i < bound; ++i) { - j[0] = (j[0] + m - 1) % m; - // \alpha_{j} = \rho_{j} s^{t}_{j} \cdot q_{k+1} - ggml_vec_dot_f32(nx, &lm_alpha[j[0]], 0, &lm_s[j[0]*nx], 0, d, 0, 1); - lm_alpha[j[0]] /= lm_ys[j[0]]; - // q_{i} = q_{i+1} - \alpha_{i} y_{i} - ggml_vec_mad_f32(nx, d, &lm_y[j[0]*nx], -lm_alpha[j[0]]); - } - - ggml_vec_scale_f32(nx, d, ys/yy); - - for (int i = 0; i < bound; ++i) { - // \beta_{j} = \rho_{j} y^t_{j} \cdot \gamma_{i} - ggml_vec_dot_f32(nx, &beta, 0, &lm_y[j[0]*nx], 0, d, 0, 1); - beta /= lm_ys[j[0]]; - // \gamma_{i+1} = \gamma_{i} + (\alpha_{j} - \beta_{j}) s_{j} - ggml_vec_mad_f32(nx, d, &lm_s[j[0]*nx], lm_alpha[j[0]] - beta); - j[0] = (j[0] + 1)%m; - } - - step[0] = 1.0; - } - - GGML_ABORT("lbfgs failed"); - - //return GGML_OPT_RESULT_DID_NOT_CONVERGE; -} - -struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) { - struct ggml_opt_params result; - - switch (type) { - case GGML_OPT_TYPE_ADAM: - { - result = (struct ggml_opt_params) { - .type = GGML_OPT_TYPE_ADAM, - .graph_size = GGML_DEFAULT_GRAPH_SIZE, - .n_threads = 1, // FIXME: GGML_DEFAULT_N_THREADS ? - .past = 0, - .delta = 1e-5f, - - .max_no_improvement = 100, - - .print_forward_graph = true, - .print_backward_graph = true, - - .n_gradient_accumulation = 1, - - .adam = { - .n_iter = 10000, - .sched = 1.000f, - .decay = 0.0f, - .decay_min_ndim = 2, - .alpha = 0.001f, - .beta1 = 0.9f, - .beta2 = 0.999f, - .eps = 1e-8f, - .eps_f = 1e-5f, - .eps_g = 1e-3f, - .gclip = 0.0f, - }, - }; - } break; - case GGML_OPT_TYPE_LBFGS: - { - result = (struct ggml_opt_params) { - .type = GGML_OPT_TYPE_LBFGS, - .graph_size = GGML_DEFAULT_GRAPH_SIZE, - .n_threads = 1, - .past = 0, - .delta = 1e-5f, - - .max_no_improvement = 0, - - .print_forward_graph = true, - .print_backward_graph = true, - - .n_gradient_accumulation = 1, - - .lbfgs = { - .m = 6, - .n_iter = 100, - .max_linesearch = 20, - - .eps = 1e-5f, - .ftol = 1e-4f, - .wolfe = 0.9f, - .min_step = 1e-20f, - .max_step = 1e+20f, - - .linesearch = GGML_LINESEARCH_DEFAULT, - }, - }; - } break; - } - - return result; -} - -GGML_API void ggml_opt_init( - struct ggml_context * ctx, - struct ggml_opt_context * opt, - struct ggml_opt_params params, - int64_t nx) { - opt->ctx = ctx; - opt->params = params; - opt->iter = 0; - opt->nx = nx; - opt->just_initialized = true; - if (opt->ctx == NULL) { - struct ggml_init_params ctx_opt_params; - if (opt->params.type == GGML_OPT_TYPE_ADAM) { - ctx_opt_params.mem_size = GGML_MEM_ALIGN*3 + ggml_tensor_overhead()*3 + ggml_type_size(GGML_TYPE_F32)*nx*3; - if (opt->params.past > 0) { - ctx_opt_params.mem_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_size(GGML_TYPE_F32)*opt->params.past; - } - } else if (opt->params.type == GGML_OPT_TYPE_LBFGS) { - ctx_opt_params.mem_size = GGML_MEM_ALIGN*9 + ggml_tensor_overhead()*9 + ggml_type_size(GGML_TYPE_F32)*(nx*5 + opt->params.lbfgs.m*2 + nx*opt->params.lbfgs.m*2); - if (opt->params.past > 0) { - ctx_opt_params.mem_size += GGML_MEM_ALIGN + ggml_tensor_overhead() + ggml_type_size(GGML_TYPE_F32)*opt->params.past; - } - } - ctx_opt_params.mem_buffer = NULL; - ctx_opt_params.no_alloc = false; - - opt->ctx = ggml_init(ctx_opt_params); - } - switch (opt->params.type) { - case GGML_OPT_TYPE_ADAM: - { - opt->adam.g = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); - opt->adam.m = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); - opt->adam.v = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); - opt->adam.pf = params.past > 0 - ? ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, params.past) - : NULL; - ggml_set_zero(opt->adam.m); - ggml_set_zero(opt->adam.v); - if (opt->adam.pf) { - ggml_set_zero(opt->adam.pf); - } - } break; - case GGML_OPT_TYPE_LBFGS: - { - opt->lbfgs.x = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); - opt->lbfgs.xp = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); - opt->lbfgs.g = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); - opt->lbfgs.gp = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); - opt->lbfgs.d = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, nx); - opt->lbfgs.pf = params.past > 0 - ? ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, params.past) - : NULL; - opt->lbfgs.lmal = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, params.lbfgs.m); - opt->lbfgs.lmys = ggml_new_tensor_1d(opt->ctx, GGML_TYPE_F32, params.lbfgs.m); - opt->lbfgs.lms = ggml_new_tensor_2d(opt->ctx, GGML_TYPE_F32, nx, params.lbfgs.m); - opt->lbfgs.lmy = ggml_new_tensor_2d(opt->ctx, GGML_TYPE_F32, nx, params.lbfgs.m); - ggml_set_zero(opt->lbfgs.x); - ggml_set_zero(opt->lbfgs.xp); - ggml_set_zero(opt->lbfgs.g); - ggml_set_zero(opt->lbfgs.gp); - ggml_set_zero(opt->lbfgs.d); - if (opt->lbfgs.pf) { - ggml_set_zero(opt->lbfgs.pf); - } - ggml_set_zero(opt->lbfgs.lmal); - ggml_set_zero(opt->lbfgs.lmys); - ggml_set_zero(opt->lbfgs.lms); - ggml_set_zero(opt->lbfgs.lmy); - } break; - } -} - -enum ggml_opt_result ggml_opt( - struct ggml_context * ctx, - struct ggml_opt_params params, - struct ggml_tensor * f) { - bool free_ctx = false; - if (ctx == NULL) { - struct ggml_init_params params_ctx = { - .mem_size = 16*1024*1024, - .mem_buffer = NULL, - .no_alloc = false, - }; - - ctx = ggml_init(params_ctx); - if (ctx == NULL) { - return GGML_OPT_RESULT_NO_CONTEXT; - } - - free_ctx = true; - } - - enum ggml_opt_result result = GGML_OPT_RESULT_OK; - - struct ggml_opt_context * opt = (struct ggml_opt_context *) alloca(sizeof(struct ggml_opt_context)); - - ggml_opt_init(ctx, opt, params, 0); - result = ggml_opt_resume(ctx, opt, f); - - if (free_ctx) { - ggml_free(ctx); - } - - return result; -} - -enum ggml_opt_result ggml_opt_resume( - struct ggml_context * ctx, - struct ggml_opt_context * opt, - struct ggml_tensor * f) { - - // build forward + backward compute graphs - struct ggml_cgraph * gf = ggml_new_graph_custom(ctx, opt->params.graph_size, true); - ggml_build_forward_expand(gf, f); - - struct ggml_cgraph * gb = ggml_graph_dup(ctx, gf); - ggml_build_backward_expand(ctx, gf, gb, false); - - return ggml_opt_resume_g(ctx, opt, f, gf, gb, NULL, NULL); -} - -enum ggml_opt_result ggml_opt_resume_g( - struct ggml_context * ctx, - struct ggml_opt_context * opt, - struct ggml_tensor * f, - struct ggml_cgraph * gf, - struct ggml_cgraph * gb, - ggml_opt_callback callback, - void * callback_data) { - - GGML_ASSERT(f->grad && "ggml_set_param must be called for at least one ancestor"); - - // build forward + backward compute graphs - enum ggml_opt_result result = GGML_OPT_RESULT_OK; - - switch (opt->params.type) { - case GGML_OPT_TYPE_ADAM: - { - result = ggml_opt_adam(ctx, opt, opt->params, f, gf, gb, callback, callback_data); - } break; - case GGML_OPT_TYPE_LBFGS: - { - result = ggml_opt_lbfgs(ctx, opt, opt->params, f, gf, gb, callback, callback_data); - } break; - } - - if (opt->params.print_forward_graph) { - ggml_graph_print (gf); - ggml_graph_dump_dot(gf, NULL, "opt-forward.dot"); - } - - if (opt->params.print_backward_graph) { - ggml_graph_print (gb); - ggml_graph_dump_dot(gb, gf, "opt-backward.dot"); - } - - return result; -} - -//////////////////////////////////////////////////////////////////////////////// - void ggml_set_input(struct ggml_tensor * tensor) { tensor->flags |= GGML_TENSOR_FLAG_INPUT; } @@ -22102,18 +6447,46 @@ static size_t gguf_type_size(enum gguf_type type) { return GGUF_TYPE_SIZE[type]; } -static void gguf_tensor_info_sanitize(struct gguf_tensor_info * info) { - GGML_ASSERT(info->n_dims <= GGML_MAX_DIMS); - GGML_ASSERT(0 <= info->type && info->type < GGML_TYPE_COUNT); +static bool gguf_tensor_info_sanitize(struct gguf_tensor_info * info) { + if (info->n_dims > GGML_MAX_DIMS) { + fprintf(stderr, "%s: invalid number of dimensions (%" PRIu32 ")\n", __func__, info->n_dims); + return false; + } + + if (info->type < 0 || info->type >= GGML_TYPE_COUNT) { + fprintf(stderr, "%s: invalid type (%d)\n", __func__, info->type); + return false; + } + + if (strlen(info->name.data) >= GGML_MAX_NAME) { + fprintf(stderr, "%s: tensor '%s' name is too long\n", __func__, info->name.data); + return false; + } for (uint32_t i = 0; i < info->n_dims; ++i) { - GGML_ASSERT(info->ne[i] > 0); + if (info->ne[i] <= 0) { + fprintf(stderr, "%s: invalid number of elements (%" PRIu64 ")\n", __func__, info->ne[i]); + return false; + } } // prevent overflow for total number of elements - GGML_ASSERT(INT64_MAX/info->ne[1] > info->ne[0]); - GGML_ASSERT(INT64_MAX/info->ne[2] > info->ne[0]*info->ne[1]); - GGML_ASSERT(INT64_MAX/info->ne[3] > info->ne[0]*info->ne[1]*info->ne[2]); + if (INT64_MAX/info->ne[1] <= info->ne[0]) { + fprintf(stderr, "%s: invalid number of elements (%" PRIu64 ")\n", __func__, info->ne[1]); + return false; + } + + if (INT64_MAX/info->ne[2] <= info->ne[0]*info->ne[1]) { + fprintf(stderr, "%s: invalid number of elements (%" PRIu64 ")\n", __func__, info->ne[2]); + return false; + } + + if (INT64_MAX/info->ne[3] <= info->ne[0]*info->ne[1]*info->ne[2]) { + fprintf(stderr, "%s: invalid number of elements (%" PRIu64 ")\n", __func__, info->ne[3]); + return false; + } + + return true; } static bool gguf_fread_el(FILE * file, void * dst, size_t size, size_t * offset) { @@ -22414,8 +6787,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p ok = ok && gguf_fread_el (file, &info->type, sizeof(info->type), &offset); ok = ok && gguf_fread_el (file, &info->offset, sizeof(info->offset), &offset); - // TODO: return an error instead of crashing with GGML_ASSERT - gguf_tensor_info_sanitize(info); + ok = ok && gguf_tensor_info_sanitize(info); // make sure there is no duplicated tensor names for (uint64_t j = 0; j < i && ok; ++j) { @@ -23237,254 +7609,30 @@ void gguf_get_meta_data(const struct gguf_context * ctx, void * data) { gguf_buf_free(buf); } -//////////////////////////////////////////////////////////////////////////////// - -int ggml_cpu_has_avx(void) { -#if defined(__AVX__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_avx_vnni(void) { -#if defined(__AVXVNNI__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_avx2(void) { -#if defined(__AVX2__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_avx512(void) { -#if defined(__AVX512F__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_avx512_vbmi(void) { -#if defined(__AVX512VBMI__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_avx512_vnni(void) { -#if defined(__AVX512VNNI__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_avx512_bf16(void) { -#if defined(__AVX512BF16__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_amx_int8(void) { -#if defined(__AMX_INT8__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_fma(void) { -#if defined(__FMA__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_neon(void) { -#if defined(__ARM_ARCH) - return ggml_arm_arch_features.has_neon; -#else - return 0; -#endif -} - -int ggml_cpu_has_sve(void) { -#if defined(__ARM_ARCH) - return ggml_arm_arch_features.has_sve; -#else - return 0; -#endif -} - -int ggml_cpu_has_arm_fma(void) { -#if defined(__ARM_FEATURE_FMA) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_riscv_v(void) { -#if defined(__riscv_v_intrinsic) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_metal(void) { -#if defined(GGML_USE_METAL) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_f16c(void) { -#if defined(__F16C__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_fp16_va(void) { -#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_wasm_simd(void) { -#if defined(__wasm_simd128__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_blas(void) { -#if defined(GGML_USE_BLAS) || defined(GGML_USE_CUDA) || defined(GGML_USE_VULKAN) || defined(GGML_USE_SYCL) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_cuda(void) { -#if defined(GGML_USE_CUDA) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_vulkan(void) { -#if defined(GGML_USE_VULKAN) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_kompute(void) { -#if defined(GGML_USE_KOMPUTE) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_sycl(void) { -#if defined(GGML_USE_SYCL) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_rpc(void) { -#if defined(GGML_USE_RPC) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_cann(void) { -#if defined(GGML_USE_CANN) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_llamafile(void) { -#if defined(GGML_USE_LLAMAFILE) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_gpublas(void) { - return ggml_cpu_has_cuda() || ggml_cpu_has_vulkan() || ggml_cpu_has_kompute() || ggml_cpu_has_sycl(); -} - -int ggml_cpu_has_sse3(void) { -#if defined(__SSE3__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_ssse3(void) { -#if defined(__SSSE3__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_vsx(void) { -#if defined(__POWER9_VECTOR__) - return 1; -#else - return 0; -#endif -} - -int ggml_cpu_has_matmul_int8(void) { -#if defined(__ARM_ARCH) - return ggml_arm_arch_features.has_i8mm; -#else - return 0; -#endif -} - -int ggml_cpu_get_sve_cnt(void) { -#if defined(__ARM_ARCH) - return ggml_arm_arch_features.sve_cnt; -#else - return 0; -#endif -} - void ggml_log_set(ggml_log_callback log_callback, void * user_data) { g_logger_state.log_callback = log_callback ? log_callback : ggml_log_callback_default; g_logger_state.log_callback_user_data = user_data; } -//////////////////////////////////////////////////////////////////////////////// + +void ggml_threadpool_params_init(struct ggml_threadpool_params * p, int n_threads) { + p->n_threads = n_threads; + p->prio = 0; // default priority (usually means normal or inherited) + p->poll = 50; // hybrid-polling enabled + p->strict_cpu = false; // no strict placement (all threads share same cpumask) + p->paused = false; // threads are ready to go + memset(p->cpumask, 0, GGML_MAX_N_THREADS); // all-zero means use the default affinity (usually inherited) +} + +struct ggml_threadpool_params ggml_threadpool_params_default(int n_threads) { + struct ggml_threadpool_params p; + ggml_threadpool_params_init(&p, n_threads); + return p; +} + +bool ggml_threadpool_params_match(const struct ggml_threadpool_params * p0, const struct ggml_threadpool_params * p1) { + if (p0->n_threads != p1->n_threads ) return false; + if (p0->prio != p1->prio ) return false; + if (p0->poll != p1->poll ) return false; + if (p0->strict_cpu != p1->strict_cpu ) return false; + return memcmp(p0->cpumask, p1->cpumask, GGML_MAX_N_THREADS) == 0; +} diff --git a/ggml/src/kompute-shaders/op_rope_f16.comp b/ggml/src/kompute-shaders/op_rope_f16.comp deleted file mode 100644 index 0ecfb2eab..000000000 --- a/ggml/src/kompute-shaders/op_rope_f16.comp +++ /dev/null @@ -1,73 +0,0 @@ -#version 450 - -#include "rope_common.comp" - -layout(binding = 0) buffer restrict readonly tensorInA { float16_t inA[]; }; -layout(binding = 1) buffer restrict readonly tensorInB { int inB[]; }; -layout(binding = 2) buffer restrict writeonly tensorOut { float16_t out_[]; }; - -void main() { - const uint i3 = gl_WorkGroupID.z; - const uint i2 = gl_WorkGroupID.y; - const uint i1 = gl_WorkGroupID.x; - - const bool is_neox = (pcs.mode & GGML_ROPE_TYPE_NEOX) != 0; - - float corr_dims[2]; - rope_yarn_corr_dims(pcs.n_dims, pcs.n_ctx_orig, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims); - - const float theta_scale = pow(pcs.freq_base, -2.0/pcs.n_dims); - - const int p = inB[pcs.inBOff + i2]; - - float theta = float(p); - - if (!is_neox) { - for (uint i0 = 0; i0 < pcs.ne0; i0 += 2) { - float cos_theta, sin_theta; - rope_yarn(theta, pcs.freq_scale, corr_dims, i0, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta); - - theta *= theta_scale; - - const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 2) + pcs.inAOff; // Based from in - const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 2) + pcs.outOff; // Based from out_ - - const float x0 = float(inA[src]); - const float x1 = float(inA[src+1]); - - out_[dst_data] = float16_t(x0*cos_theta - x1*sin_theta); - out_[dst_data+1] = float16_t(x0*sin_theta + x1*cos_theta); - } - } else { - const float inv_ndims = -1.f/pcs.n_dims; - for (uint ic = 0; ic < pcs.n_dims; ic += 2) { - const uint cur_rot = ic; - - float cos_theta, sin_theta; - rope_yarn(theta, pcs.freq_scale, corr_dims, cur_rot, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta); - - theta *= theta_scale; - - const uint i0 = ic/2; - - const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 2) + pcs.inAOff; // Based from in - const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 2) + pcs.outOff; // Based from out_ - - const float x0 = float(inA[src]); - const float x1 = float(inA[src+pcs.n_dims/2]); - - out_[dst_data] = float16_t(x0*cos_theta - x1*sin_theta); - out_[dst_data+pcs.n_dims/2] = float16_t(x0*sin_theta + x1*cos_theta); - } - - for (uint ic = pcs.n_dims; ic < pcs.ne0; ic += 2) { - const uint i0 = ic; - - const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 2) + pcs.inAOff; // Based from in - const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 2) + pcs.outOff; // Based from out_ - - out_[dst_data + 0] = inA[src + 0]; - out_[dst_data + 1] = inA[src + 1]; - } - } -} diff --git a/ggml/src/kompute-shaders/op_rope_f32.comp b/ggml/src/kompute-shaders/op_rope_f32.comp deleted file mode 100644 index cec0fd9a5..000000000 --- a/ggml/src/kompute-shaders/op_rope_f32.comp +++ /dev/null @@ -1,73 +0,0 @@ -#version 450 - -#include "rope_common.comp" - -layout(binding = 0) buffer restrict readonly tensorInA { float inA[]; }; -layout(binding = 1) buffer restrict readonly tensorInB { int inB[]; }; -layout(binding = 2) buffer restrict writeonly tensorOut { float out_[]; }; - -void main() { - const uint i3 = gl_WorkGroupID.z; - const uint i2 = gl_WorkGroupID.y; - const uint i1 = gl_WorkGroupID.x; - - const bool is_neox = (pcs.mode & GGML_ROPE_TYPE_NEOX) != 0; - - float corr_dims[2]; - rope_yarn_corr_dims(pcs.n_dims, pcs.n_ctx_orig, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims); - - const float theta_scale = pow(pcs.freq_base, -2.0/pcs.n_dims); - - const int p = inB[pcs.inBOff + i2]; - - float theta = float(p); - - if (!is_neox) { - for (uint i0 = 0; i0 < pcs.ne0; i0 += 2) { - float cos_theta, sin_theta; - rope_yarn(theta, pcs.freq_scale, corr_dims, i0, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta); - - theta *= theta_scale; - - const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 4) + pcs.inAOff; // Based from in - const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 4) + pcs.outOff; // Based from out_ - - const float x0 = inA[src]; - const float x1 = inA[src+1]; - - out_[dst_data] = x0*cos_theta - x1*sin_theta; - out_[dst_data+1] = x0*sin_theta + x1*cos_theta; - } - } else { - const float inv_ndims = -1.f/pcs.n_dims; - for (uint ic = 0; ic < pcs.n_dims; ic += 2) { - const uint cur_rot = ic; - - float cos_theta, sin_theta; - rope_yarn(theta, pcs.freq_scale, corr_dims, cur_rot, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta); - - theta *= theta_scale; - - const uint i0 = ic/2; - - const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 4) + pcs.inAOff; // Based from in - const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 4) + pcs.outOff; // Based from out_ - - const float x0 = inA[src]; - const float x1 = inA[src+pcs.n_dims/2]; - - out_[dst_data] = x0*cos_theta - x1*sin_theta; - out_[dst_data+pcs.n_dims/2] = x0*sin_theta + x1*cos_theta; - } - - for (uint ic = pcs.n_dims; ic < pcs.ne0; ic += 2) { - const uint i0 = ic; - - const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 4) + pcs.inAOff; // Based from in - const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 4) + pcs.outOff; // Based from out_ - - out_[dst_data + 0] = inA[src + 0]; - out_[dst_data + 1] = inA[src + 1]; - } - } -} diff --git a/ggml/src/vulkan-shaders/add.comp b/ggml/src/vulkan-shaders/add.comp deleted file mode 100644 index 3974845d6..000000000 --- a/ggml/src/vulkan-shaders/add.comp +++ /dev/null @@ -1,14 +0,0 @@ -#version 450 - -#include "types.comp" -#include "generic_binary_head.comp" - -void main() { - const uint idx = get_idx(); - - if (idx >= p.ne) { - return; - } - - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(idx)]) + FLOAT_TYPE(data_b[src1_idx(idx)])); -} diff --git a/ggml/src/vulkan-shaders/dequant_funcs.comp b/ggml/src/vulkan-shaders/dequant_funcs.comp deleted file mode 100644 index d5b989735..000000000 --- a/ggml/src/vulkan-shaders/dequant_funcs.comp +++ /dev/null @@ -1,68 +0,0 @@ -#if !defined(DATA_A_F32) && !defined(DATA_A_F16) -#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require -#endif - -#if defined(DATA_A_F32) -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - return vec2(data_a[a_offset + ib], data_a[a_offset + ib + 1]); -} -#endif - -#if defined(DATA_A_F16) -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - return vec2(data_a[a_offset + ib], data_a[a_offset + ib + 1]); -} -#endif - -#if defined(DATA_A_Q4_0) -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - const float d = float(data_a[a_offset + ib].d); - const uint vui = uint(data_a[a_offset + ib].qs[iqs]); - return (vec2(vui & 0xF, vui >> 4) - 8.0f) * d; -} -#endif - -#if defined(DATA_A_Q4_1) -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - const float d = float(data_a[a_offset + ib].d); - const float m = float(data_a[a_offset + ib].m); - const uint vui = uint(data_a[a_offset + ib].qs[iqs]); - return vec2(vui & 0xF, vui >> 4) * d + m; -} -#endif - -#if defined(DATA_A_Q5_0) -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - const float d = float(data_a[a_offset + ib].d); - const uint uint_qh = uint(data_a[a_offset + ib].qh[1]) << 16 | data_a[a_offset + ib].qh[0]; - const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); - const uint vui = uint(data_a[a_offset + ib].qs[iqs]); - return (vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) - 16.0f) * d; -} -#endif - -#if defined(DATA_A_Q5_1) -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - const float d = float(data_a[a_offset + ib].d); - const float m = float(data_a[a_offset + ib].m); - const uint uint_qh = data_a[a_offset + ib].qh; - const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); - const uint vui = uint(data_a[a_offset + ib].qs[iqs]); - return vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) * d + m; -} -#endif - -#if defined(DATA_A_Q8_0) -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - const float d = float(data_a[a_offset + ib].d); - return vec2(int(data_a[a_offset + ib].qs[iqs]), int(data_a[a_offset + ib].qs[iqs + 1])) * d; -} -#endif - -#if defined(DATA_A_IQ4_NL) -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - const float d = float(data_a[a_offset + ib].d); - const uint vui = uint(data_a[a_offset + ib].qs[iqs]); - return vec2(kvalues_iq4nl[vui & 0xF], kvalues_iq4nl[vui >> 4]) * d; -} -#endif diff --git a/ggml/src/vulkan-shaders/div.comp b/ggml/src/vulkan-shaders/div.comp deleted file mode 100644 index 8cfce58b1..000000000 --- a/ggml/src/vulkan-shaders/div.comp +++ /dev/null @@ -1,14 +0,0 @@ -#version 450 - -#include "types.comp" -#include "generic_binary_head.comp" - -void main() { - const uint idx = get_idx(); - - if (idx >= p.ne) { - return; - } - - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(idx)]) / FLOAT_TYPE(data_b[src1_idx(idx)])); -} diff --git a/ggml/src/vulkan-shaders/generic_binary_head.comp b/ggml/src/vulkan-shaders/generic_binary_head.comp deleted file mode 100644 index b6beaff1c..000000000 --- a/ggml/src/vulkan-shaders/generic_binary_head.comp +++ /dev/null @@ -1,52 +0,0 @@ -#extension GL_EXT_shader_16bit_storage : require - -layout (push_constant) uniform parameter -{ - uint ne; - uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03; - uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13; - uint ne20; uint ne21; uint ne22; uint ne23; uint nb20; uint nb21; uint nb22; uint nb23; - uint d_offset; - float param1; float param2; int param3; -} p; - -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; - -uint get_idx() { - return gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; -} - -uint src0_idx(uint idx) { - const uint i03 = idx / (p.ne02*p.ne01*p.ne00); - const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00; - const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00); - const uint i02_offset = i02*p.ne01*p.ne00; - const uint i01 = (idx - i03_offset - i02_offset) / p.ne00; - const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00; - return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00; -} - -uint src1_idx(uint idx) { - const uint i03 = idx / (p.ne02*p.ne01*p.ne00); - const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00; - const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00); - const uint i02_offset = i02*p.ne01*p.ne00; - const uint i01 = (idx - i03_offset - i02_offset) / p.ne00; - const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00; - - return (i03 % p.ne13)*p.nb13 + (i02 % p.ne12)*p.nb12 + (i01 % p.ne11)*p.nb11 + (i00 % p.ne10)*p.nb10; -} - -uint dst_idx(uint idx) { - const uint i23 = idx / (p.ne22*p.ne21*p.ne20); - const uint i23_offset = i23 * p.ne22*p.ne21*p.ne20; - const uint i22 = (idx - i23_offset) / (p.ne21*p.ne20); - const uint i22_offset = i22*p.ne21*p.ne20; - const uint i21 = (idx - i23_offset - i22_offset) / p.ne20; - const uint i20 = idx - i23_offset - i22_offset - i21*p.ne20; - return i23*p.nb23 + i22*p.nb22 + i21*p.nb21 + i20*p.nb20; -} diff --git a/ggml/src/vulkan-shaders/mul.comp b/ggml/src/vulkan-shaders/mul.comp deleted file mode 100644 index bfb61c92d..000000000 --- a/ggml/src/vulkan-shaders/mul.comp +++ /dev/null @@ -1,14 +0,0 @@ -#version 450 - -#include "types.comp" -#include "generic_binary_head.comp" - -void main() { - const uint idx = get_idx(); - - if (idx >= p.ne) { - return; - } - - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(idx)]) * FLOAT_TYPE(data_b[src1_idx(idx)])); -} diff --git a/ggml/src/vulkan-shaders/mul_mat_split_k_reduce.comp b/ggml/src/vulkan-shaders/mul_mat_split_k_reduce.comp deleted file mode 100644 index 825b91031..000000000 --- a/ggml/src/vulkan-shaders/mul_mat_split_k_reduce.comp +++ /dev/null @@ -1,29 +0,0 @@ -#version 450 - -#extension GL_EXT_control_flow_attributes : enable - -layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {float data_a[];}; -layout (binding = 1) writeonly buffer D {float data_d[];}; - -layout (push_constant) uniform parameter { - uint ne; - uint k_num; -} p; - -void main() { - const uint idx = gl_GlobalInvocationID.x; - - if (idx >= p.ne) { - return; - } - - float result = 0.0f; - - [[unroll]] for (uint i = 0; i < p.k_num; i++) { - result += data_a[i * p.ne + idx]; - } - - data_d[idx] = result; -} diff --git a/ggml/src/vulkan-shaders/mul_mat_vec.comp b/ggml/src/vulkan-shaders/mul_mat_vec.comp deleted file mode 100644 index d3ccba7fc..000000000 --- a/ggml/src/vulkan-shaders/mul_mat_vec.comp +++ /dev/null @@ -1,56 +0,0 @@ -#version 450 - -#ifdef FLOAT16 -#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require -#endif - -#include "mul_mat_vec_base.comp" - -layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; - -layout (constant_id = 0) const uint BLOCK_SIZE = 32; - -shared FLOAT_TYPE tmp[BLOCK_SIZE]; - -void main() { - const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; - const uint tid = gl_LocalInvocationID.x; - - // There are not enough cols to use all threads - if (tid >= p.ncols) { - return; - } - - const uint block_size = min(p.ncols, BLOCK_SIZE); - - uint a_offset, b_offset, d_offset; - get_offsets(a_offset, b_offset, d_offset); - - const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2; - - tmp[tid] = FLOAT_TYPE(0.0f); - - [[unroll]] for (uint i = 0; i < p.ncols/block_size; i += 2) { - const uint col = i*block_size + 2*tid; - const uint ib = (row*p.ncols + col)/QUANT_K; // block index - const uint iqs = (col%QUANT_K)/QUANT_R; // quant index - const uint iybs = col - col%QUANT_K; // y block start index - - vec2 v = dequantize(ib, iqs, a_offset / QUANT_K); - - // matrix multiplication - tmp[tid] = fma(FLOAT_TYPE(v.x), FLOAT_TYPE(data_b[b_offset + iybs + iqs]), fma(FLOAT_TYPE(v.y), FLOAT_TYPE(data_b[b_offset + iybs + iqs + y_offset]), tmp[tid])); - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (uint s = block_size/2; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); - } -} diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_q2_k.comp b/ggml/src/vulkan-shaders/mul_mat_vec_q2_k.comp deleted file mode 100644 index ec8eadcd5..000000000 --- a/ggml/src/vulkan-shaders/mul_mat_vec_q2_k.comp +++ /dev/null @@ -1,74 +0,0 @@ -#version 450 - -#include "mul_mat_vec_base.comp" - -layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; - -shared FLOAT_TYPE tmp[32]; - -void main() { - const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; - - uint a_offset, b_offset, d_offset; - get_offsets(a_offset, b_offset, d_offset); - - const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; - - const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - - const uint step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 - - const uint v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const uint v_in = tid - step*v_im; // 0...15 or 0...7 - - const uint l0 = K_QUANTS_PER_ITERATION*v_in; // 0...15 - const uint q_offset = 32*v_im + l0; - const uint s_offset = 8*v_im; - const uint y_offset = 128*v_im + l0; - - tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - - [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - const uint y_idx = i * QUANT_K + y_offset; - - const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x); - const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y); - - FLOAT_TYPE sum1 = FLOAT_TYPE(0.0); - FLOAT_TYPE sum2 = FLOAT_TYPE(0.0); - for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { - sum1 = fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 0]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 0) & 3), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 0) & 3), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 2) & 3), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 2) & 3), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 4) & 3), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 4) & 3), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 6) & 3), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l +112]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 6) & 3), sum1)))))))); - sum2 = fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 0]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 0] >> 4) & 0xF), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 1] >> 4) & 0xF), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 2] >> 4) & 0xF), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 3] >> 4) & 0xF), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 4] >> 4) & 0xF), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 5] >> 4) & 0xF), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 6] >> 4) & 0xF), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l +112]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 7] >> 4) & 0xF), sum2)))))))); - } - const uint tmp_idx = 16 * ix + tid; - tmp[tmp_idx] = fma(dall, sum1, fma(-dmin, sum2, tmp[tmp_idx])); - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (uint s = 16; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); - } -} diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_q3_k.comp b/ggml/src/vulkan-shaders/mul_mat_vec_q3_k.comp deleted file mode 100644 index 3ca4ad85a..000000000 --- a/ggml/src/vulkan-shaders/mul_mat_vec_q3_k.comp +++ /dev/null @@ -1,67 +0,0 @@ -#version 450 - -#include "mul_mat_vec_base.comp" - -layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; - -shared FLOAT_TYPE tmp[32]; - -void main() { - const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; - - uint a_offset, b_offset, d_offset; - get_offsets(a_offset, b_offset, d_offset); - - const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; - - const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - - const uint step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 - - const uint v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const uint v_in = tid - step*v_im; // 0...15 or 0...7 - - const uint8_t m = uint8_t(1 << (4 * v_im)); - - const uint l0 = K_QUANTS_PER_ITERATION*v_in; // 0...15 - const uint q_offset = 32*v_im + l0; - const uint y_offset = 128*v_im + l0; - - tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - - const uint s_shift = 4 * v_im; - - [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - const uint y_idx = i * QUANT_K + y_offset; - - const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); - - FLOAT_TYPE sum = FLOAT_TYPE(0.0); - for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { - sum = fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 0]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[0] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 0)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[2] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 1)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[4] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 2)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[6] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 3)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[1] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 0)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[3] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 0) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 1)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[5] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 2)) != 0) ? 0 : 4)), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l +112]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[7] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 2) & 0x3) << 4)) - 32), FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 3)) != 0) ? 0 : 4)), sum)))))))); - } - const uint tmp_idx = 16 * ix + tid; - tmp[tmp_idx] = fma(d, sum, tmp[tmp_idx]); - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (uint s = 16; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); - } -} diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_q4_k.comp b/ggml/src/vulkan-shaders/mul_mat_vec_q4_k.comp deleted file mode 100644 index d91e00e10..000000000 --- a/ggml/src/vulkan-shaders/mul_mat_vec_q4_k.comp +++ /dev/null @@ -1,118 +0,0 @@ -#version 450 - -#include "mul_mat_vec_base.comp" - -layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; - -shared FLOAT_TYPE tmp[32]; - -void main() { - const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; - - uint a_offset, b_offset, d_offset; - get_offsets(a_offset, b_offset, d_offset); - - const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; - - const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - - const uint step = 8/K_QUANTS_PER_ITERATION; // 8 or 4 - - const uint il = tid/step; // 0...3 - const uint ir = tid - step*il; // 0...7 or 0...3 - const uint n = 2 * K_QUANTS_PER_ITERATION; // 2 or 4 - - const uint v_im = il / 2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 - const uint v_in = il % 2; - - const uint l0 = n * (2 * ir + v_in); // 0...15 - const uint q_offset = 32*v_im + l0; - const uint y_offset = 64*v_im + l0; - - tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - - [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - const uint y1_idx = i * QUANT_K + y_offset; - const uint y2_idx = y1_idx + 128; - - const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x); - const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y); - - const uint8_t sc0 = uint8_t( data_a[ib0 + i].scales[v_im * 2 ] & 0x3f); - const uint8_t sc1 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 1] & 0x3f); - const uint8_t sc2 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 4] & 0x3f); - const uint8_t sc3 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 5] & 0x3f); - const uint8_t sc4 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 8] & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 ] & 0xc0) >> 2)); - const uint8_t sc5 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 9] & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 1] & 0xc0) >> 2)); - const uint8_t sc6 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 8] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 4] & 0xc0) >> 2)); - const uint8_t sc7 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 9] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 5] & 0xc0) >> 2)); - -#if K_QUANTS_PER_ITERATION == 2 - const uint8_t q4_0 = uint8_t(data_a[ib0 + i].qs[q_offset ] & 0xf); - const uint8_t q4_1 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] & 0xf); - const uint8_t q4_2 = uint8_t(data_a[ib0 + i].qs[q_offset + 2] & 0xf); - const uint8_t q4_3 = uint8_t(data_a[ib0 + i].qs[q_offset + 3] & 0xf); - const uint8_t q4_4 = uint8_t(data_a[ib0 + i].qs[q_offset ] >> 4); - const uint8_t q4_5 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] >> 4); - const uint8_t q4_6 = uint8_t(data_a[ib0 + i].qs[q_offset + 2] >> 4); - const uint8_t q4_7 = uint8_t(data_a[ib0 + i].qs[q_offset + 3] >> 4); - const uint8_t q4_8 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf); - const uint8_t q4_9 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf); - const uint8_t q4_10 = uint8_t(data_a[ib0 + i].qs[q_offset + 66] & 0xf); - const uint8_t q4_11 = uint8_t(data_a[ib0 + i].qs[q_offset + 67] & 0xf); - const uint8_t q4_12 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] >> 4); - const uint8_t q4_13 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] >> 4); - const uint8_t q4_14 = uint8_t(data_a[ib0 + i].qs[q_offset + 66] >> 4); - const uint8_t q4_15 = uint8_t(data_a[ib0 + i].qs[q_offset + 67] >> 4); - - const FLOAT_TYPE sx = fma(FLOAT_TYPE(data_b[b_offset + y1_idx]), q4_0, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 1]), q4_1, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 2]), q4_2, FLOAT_TYPE(data_b[b_offset + y1_idx + 3]) * q4_3))); - const FLOAT_TYPE sy = fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), q4_4, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 33]), q4_5, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 34]), q4_6, FLOAT_TYPE(data_b[b_offset + y1_idx + 35]) * q4_7))); - const FLOAT_TYPE sz = fma(FLOAT_TYPE(data_b[b_offset + y2_idx]), q4_8, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 1]), q4_9, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 2]), q4_10, FLOAT_TYPE(data_b[b_offset + y2_idx + 3]) * q4_11))); - const FLOAT_TYPE sw = fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), q4_12, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 33]), q4_13, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 34]), q4_14, FLOAT_TYPE(data_b[b_offset + y2_idx + 35]) * q4_15))); - const FLOAT_TYPE smin = - fma(FLOAT_TYPE(data_b[b_offset + y1_idx ]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx ]), sc6, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), sc7, - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 1]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 33]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 1]), sc6, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 33]), sc7, - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 2]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 34]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 2]), sc6, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 34]), sc7, - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 3]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 35]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 3]), sc6, FLOAT_TYPE(data_b[b_offset + y2_idx + 35]) * sc7))))))))))))))); - const uint tmp_idx = 16 * ix + tid; - tmp[tmp_idx] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, tmp[tmp_idx])); -#else - const uint8_t q4_0 = uint8_t(data_a[ib0 + i].qs[q_offset ] & 0xf); - const uint8_t q4_1 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] & 0xf); - const uint8_t q4_2 = uint8_t(data_a[ib0 + i].qs[q_offset ] >> 4); - const uint8_t q4_3 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] >> 4); - const uint8_t q4_4 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf); - const uint8_t q4_5 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf); - const uint8_t q4_6 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] >> 4); - const uint8_t q4_7 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] >> 4); - - const FLOAT_TYPE sx = fma(FLOAT_TYPE(data_b[b_offset + y1_idx ]), q4_0, FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * q4_1); - const FLOAT_TYPE sy = fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), q4_2, FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * q4_3); - const FLOAT_TYPE sz = fma(FLOAT_TYPE(data_b[b_offset + y2_idx ]), q4_4, FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * q4_5); - const FLOAT_TYPE sw = fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), q4_6, FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * q4_7); - const FLOAT_TYPE smin = - fma(FLOAT_TYPE(data_b[b_offset + y1_idx ]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx ]), sc6, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), sc7, - + fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 1]), sc2, fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 33]), sc3, fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 1]), sc6, FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * sc7))))))); - - tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * FLOAT_TYPE(data_a[ib0 + i].scales[v_im] & 0x3f) + sy * FLOAT_TYPE(data_a[ib0 + i].scales[v_im + 1] & 0x3f) + - sz * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 4] & 0x0f) | ((data_a[ib0 + i].scales[v_im] & 0xc0) >> 2)) + sw * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 5] & 0x0f) | ((data_a[ib0 + i].scales[v_im + 1] & 0xc0) >> 2))) - dmin * smin); - const uint tmp_idx = 16 * ix + tid; - tmp[tmp_idx] = fma(dall, (fma(sx, FLOAT_TYPE(data_a[ib0 + i].scales[v_im] & 0x3f), fma(sy, FLOAT_TYPE(data_a[ib0 + i].scales[v_im + 1] & 0x3f), - fma(sz, FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 4] & 0x0f) | ((data_a[ib0 + i].scales[v_im] & 0xc0) >> 2)), fma(sw, FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 5] & 0x0f) | ((data_a[ib0 + i].scales[v_im + 1] & 0xc0) >> 2))))))), fma(-dmin, smin, tmp[tmp_idx])); -#endif - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (uint s = 16; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); - } -} diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_q5_k.comp b/ggml/src/vulkan-shaders/mul_mat_vec_q5_k.comp deleted file mode 100644 index 2306785af..000000000 --- a/ggml/src/vulkan-shaders/mul_mat_vec_q5_k.comp +++ /dev/null @@ -1,109 +0,0 @@ -#version 450 - -#include "mul_mat_vec_base.comp" - -layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; - -shared FLOAT_TYPE tmp[32]; - -void main() { - const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; - - uint a_offset, b_offset, d_offset; - get_offsets(a_offset, b_offset, d_offset); - - const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; - - const uint tid = gl_LocalInvocationID.x/2; // 0...31 or 0...16 - const uint ix = gl_LocalInvocationID.x%2; // 0 or 0, 1 - - const uint il = tid/4; // 0...3 - const uint ir = tid - 4*il; // 0...7 or 0...3 - - const uint v_im = il / 2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 - const uint v_in = il % 2; - - const uint l0 = 4*ir + 2*v_in; // 0...15 - const uint q_offset = 32*v_im + l0; - const uint y_offset = 64*v_im + l0; - - const uint8_t hm1 = uint8_t(1 << (2*v_im)); - const uint8_t hm2 = uint8_t(hm1 << 4); - - tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - - [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += 2) { - const uint y1_idx = i * QUANT_K + y_offset; - const uint y2_idx = y1_idx + 128; - - const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x); - const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y); - - const uint8_t sc0 = uint8_t( data_a[ib0 + i].scales[v_im * 2 ] & 0x3f); - const uint8_t sc1 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 1] & 0x3f); - const uint8_t sc2 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 4] & 0x3f); - const uint8_t sc3 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 5] & 0x3f); - const uint8_t sc4 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 8] & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 ] & 0xc0) >> 2)); - const uint8_t sc5 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 9] & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 1] & 0xc0) >> 2)); - const uint8_t sc6 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 8] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 4] & 0xc0) >> 2)); - const uint8_t sc7 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 9] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 5] & 0xc0) >> 2)); - - const uint8_t q4_0 = uint8_t(data_a[ib0 + i].qs[q_offset ] & 0xf); - const uint8_t q4_1 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] & 0xf); - const uint8_t q4_2 = uint8_t(data_a[ib0 + i].qs[q_offset + 16] & 0xf); - const uint8_t q4_3 = uint8_t(data_a[ib0 + i].qs[q_offset + 17] & 0xf); - const uint8_t q4_4 = uint8_t(data_a[ib0 + i].qs[q_offset ] >> 4); - const uint8_t q4_5 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] >> 4); - const uint8_t q4_6 = uint8_t(data_a[ib0 + i].qs[q_offset + 16] >> 4); - const uint8_t q4_7 = uint8_t(data_a[ib0 + i].qs[q_offset + 17] >> 4); - const uint8_t q4_8 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf); - const uint8_t q4_9 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf); - const uint8_t q4_10 = uint8_t(data_a[ib0 + i].qs[q_offset + 80] & 0xf); - const uint8_t q4_11 = uint8_t(data_a[ib0 + i].qs[q_offset + 81] & 0xf); - const uint8_t q4_12 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] >> 4); - const uint8_t q4_13 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] >> 4); - const uint8_t q4_14 = uint8_t(data_a[ib0 + i].qs[q_offset + 80] >> 4); - const uint8_t q4_15 = uint8_t(data_a[ib0 + i].qs[q_offset + 81] >> 4); - - const FLOAT_TYPE sx = - fma(FLOAT_TYPE(data_b[b_offset + y1_idx ]), (q4_0 + (((data_a[ib0 + i].qh[l0 ] & hm1) != 0) ? 16 : 0)), - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 1]), (q4_1 + (((data_a[ib0 + i].qh[l0 + 1] & hm1) != 0) ? 16 : 0)), - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 16]), (q4_2 + (((data_a[ib0 + i].qh[l0 + 16] & hm1) != 0) ? 16 : 0)), - FLOAT_TYPE(data_b[b_offset + y1_idx + 17]) * (q4_3 + (((data_a[ib0 + i].qh[l0 + 17] & hm1) != 0) ? 16 : 0))))); - const FLOAT_TYPE sy = - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]), (q4_4 + (((data_a[ib0 + i].qh[l0 ] & (hm1 << 1)) != 0) ? 16 : 0)), - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 33]), (q4_5 + (((data_a[ib0 + i].qh[l0 + 1] & (hm1 << 1)) != 0) ? 16 : 0)), - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 48]), (q4_6 + (((data_a[ib0 + i].qh[l0 + 16] & (hm1 << 1)) != 0) ? 16 : 0)), - FLOAT_TYPE(data_b[b_offset + y1_idx + 49]) * (q4_7 + (((data_a[ib0 + i].qh[l0 + 17] & (hm1 << 1)) != 0) ? 16 : 0))))); - const FLOAT_TYPE sz = - fma(FLOAT_TYPE(data_b[b_offset + y2_idx ]), (q4_8 + (((data_a[ib0 + i].qh[l0 ] & hm2) != 0) ? 16 : 0)), - fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 1]), (q4_9 + (((data_a[ib0 + i].qh[l0 + 1] & hm2) != 0) ? 16 : 0)), - fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 16]), (q4_10 + (((data_a[ib0 + i].qh[l0 + 16] & hm2) != 0) ? 16 : 0)), - FLOAT_TYPE(data_b[b_offset + y2_idx + 17]) * (q4_11 + (((data_a[ib0 + i].qh[l0 + 17] & hm2) != 0) ? 16 : 0))))); - const FLOAT_TYPE sw = - fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]), (q4_12 + (((data_a[ib0 + i].qh[l0 ] & (hm2 << 1)) != 0) ? 16 : 0)), - fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 33]), (q4_13 + (((data_a[ib0 + i].qh[l0 + 1] & (hm2 << 1)) != 0) ? 16 : 0)), - fma(FLOAT_TYPE(data_b[b_offset + y2_idx + 48]), (q4_14 + (((data_a[ib0 + i].qh[l0 + 16] & (hm2 << 1)) != 0) ? 16 : 0)), - FLOAT_TYPE(data_b[b_offset + y2_idx + 49]) * (q4_15 + (((data_a[ib0 + i].qh[l0 + 17] & (hm2 << 1)) != 0) ? 16 : 0))))); - const FLOAT_TYPE smin = - fma(FLOAT_TYPE(data_b[b_offset + y1_idx ]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 1 ]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 16]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 17]), sc2, - fma(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 48]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 49]), sc3, - fma(FLOAT_TYPE(data_b[b_offset + y2_idx ]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 1 ]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 16]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 17]), sc6, - (FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 48]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 49])) * sc7))); - const uint tmp_idx = 16 * ix + tid; - tmp[tmp_idx] = fma(dall, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dmin, smin, tmp[tmp_idx])); - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (uint s = 16; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); - } -} diff --git a/ggml/src/vulkan-shaders/mul_mat_vec_q6_k.comp b/ggml/src/vulkan-shaders/mul_mat_vec_q6_k.comp deleted file mode 100644 index 95c286eeb..000000000 --- a/ggml/src/vulkan-shaders/mul_mat_vec_q6_k.comp +++ /dev/null @@ -1,79 +0,0 @@ -#version 450 - -#include "mul_mat_vec_base.comp" - -layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; - -shared FLOAT_TYPE tmp[32]; - -void main() { - const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; - - uint a_offset, b_offset, d_offset; - get_offsets(a_offset, b_offset, d_offset); - - const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; - - const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - - const uint step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 - - const uint v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const uint v_in = tid - step*v_im; // 0...15 or 0...7 - -#if K_QUANTS_PER_ITERATION == 1 - const uint l0 = v_in; // 0...15 - const uint is = 0; -#else - const uint l0 = 4 * v_in; // 0, 4, 8, ..., 28 - const uint is = v_in / 4; -#endif - - const uint ql_offset = 64*v_im + l0; - const uint qh_offset = 32*v_im + l0; - const uint s_offset = 8*v_im + is; - const uint y_offset = 128*v_im + l0; - - tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - - [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - const uint y_idx = i * QUANT_K + y_offset; - - const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); - -#if K_QUANTS_PER_ITERATION == 1 - const uint tmp_idx = 16 * ix + tid; - tmp[tmp_idx] = fma(FLOAT_TYPE(data_b[b_offset + y_idx + 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 0] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x03) << 4)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x03) << 4)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x0c) << 2)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x0c) << 2)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 0] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x30) >> 0)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x30) >> 0)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0xc0) >> 2)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0xc0) >> 2)) - 32), tmp[tmp_idx])))))))); -#else - FLOAT_TYPE sum = FLOAT_TYPE(0.0); - [[unroll]] for (int l = 0; l < 4; ++l) { - sum = fma(FLOAT_TYPE(data_b[b_offset + y_idx + l+ 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 0) & 3) << 4)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l+32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 2) & 3) << 4)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l+64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 4) & 3) << 4)) - 32), - fma(FLOAT_TYPE(data_b[b_offset + y_idx + l+96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d, FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 6) & 3) << 4)) - 32), sum)))); - } - tmp[16 * ix + tid] += sum; -#endif - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (uint s = 16; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); - } -} diff --git a/ggml/src/vulkan-shaders/scale.comp b/ggml/src/vulkan-shaders/scale.comp deleted file mode 100644 index 5cd2f668d..000000000 --- a/ggml/src/vulkan-shaders/scale.comp +++ /dev/null @@ -1,14 +0,0 @@ -#version 450 - -#include "types.comp" -#include "generic_unary_head.comp" - -void main() { - const uint idx = get_idx(); - - if (idx >= p.ne) { - return; - } - - data_d[p.d_offset + dst_idx(idx)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(idx)]) * FLOAT_TYPE(p.param1)); -} diff --git a/ggml/src/vulkan-shaders/soft_max.comp b/ggml/src/vulkan-shaders/soft_max.comp deleted file mode 100644 index 0bd51ecab..000000000 --- a/ggml/src/vulkan-shaders/soft_max.comp +++ /dev/null @@ -1,106 +0,0 @@ -#version 450 - -#extension GL_EXT_shader_16bit_storage : require - -layout (push_constant) uniform parameter -{ - uint KX; - uint KY; - float scale; - float max_bias; - float m0; - float m1; - uint n_head_log2; -} p; - -#include "types.comp" - -#extension GL_EXT_control_flow_attributes : enable -#define BLOCK_SIZE 512 - -layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer Y {B_TYPE data_b[];}; -layout (binding = 2) buffer D {D_TYPE data_d[];}; - -shared FLOAT_TYPE vals[BLOCK_SIZE]; - -void main() { - const uint tid = gl_LocalInvocationID.x; - const uint rowx = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x; - const uint rowy = rowx % p.KY; - - float slope = 1.0f; - - // ALiBi - if (p.max_bias > 0.0f) { - const uint h = rowx/p.KY; // head index - - const float base = h < p.n_head_log2 ? p.m0 : p.m1; - const uint exp = h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1; - - slope = pow(base, exp); - } - - // Find max - FLOAT_TYPE max_val = uintBitsToFloat(0xFF800000); - - [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) { - const uint col = col0 + tid; - - if (col >= p.KX) { - break; - } - - max_val = max(max_val, FLOAT_TYPE(data_a[rowx * p.KX + col]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f))); - } - vals[tid] = max_val; - - barrier(); - [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { - if (tid < s) { - vals[tid] = max(vals[tid], vals[tid + s]); - } - barrier(); - } - - max_val = vals[0]; - barrier(); - - // Sum up values - vals[tid] = FLOAT_TYPE(0.0f); - - [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) { - const uint col = col0 + tid; - - if (col >= p.KX) { - break; - } - - const uint i = rowx * p.KX + col; - const FLOAT_TYPE val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val); - vals[tid] += val; - data_d[i] = D_TYPE(val); - } - - barrier(); - [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { - if (tid < s) { - vals[tid] += vals[tid + s]; - } - barrier(); - } - - const D_TYPE divisor = D_TYPE(vals[0]); - - [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) { - const uint col = col0 + tid; - - if (col >= p.KX) { - break; - } - - data_d[rowx*p.KX + col] /= divisor; - } -} diff --git a/ggml/src/vulkan-shaders/types.comp b/ggml/src/vulkan-shaders/types.comp deleted file mode 100644 index 21dce72fc..000000000 --- a/ggml/src/vulkan-shaders/types.comp +++ /dev/null @@ -1,200 +0,0 @@ -#if !defined(DATA_A_F32) && !defined(DATA_A_F16) -#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require -#endif - -#if defined(DATA_A_F32) -#define QUANT_K 1 -#define QUANT_R 1 - -#if !defined(LOAD_VEC_A) || LOAD_VEC_A == 1 -#define A_TYPE float -#elif LOAD_VEC_A == 4 -#define A_TYPE vec4 -#elif LOAD_VEC_A == 8 -#define A_TYPE mat2x4 -#endif -#endif - -#if defined(DATA_A_F16) -#define QUANT_K 1 -#define QUANT_R 1 - -#if !defined(LOAD_VEC_A) || LOAD_VEC_A == 1 -#define A_TYPE float16_t -#elif LOAD_VEC_A == 4 -#define A_TYPE f16vec4 -#elif LOAD_VEC_A == 8 -#define A_TYPE f16mat2x4 -#endif -#endif - -#if defined(DATA_A_Q4_0) -#extension GL_EXT_shader_16bit_storage : require -#define QUANT_K 32 -#define QUANT_R 2 - -struct block_q4_0 -{ - float16_t d; - uint8_t qs[16]; -}; - -#define A_TYPE block_q4_0 -#endif - -#if defined(DATA_A_Q4_1) -#extension GL_EXT_shader_16bit_storage : require -#define QUANT_K 32 -#define QUANT_R 2 - -struct block_q4_1 -{ - float16_t d; - float16_t m; - uint8_t qs[16]; -}; - -#define A_TYPE block_q4_1 -#endif - -#if defined(DATA_A_Q5_0) -#extension GL_EXT_shader_16bit_storage : require -#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require -#define QUANT_K 32 -#define QUANT_R 2 - -struct block_q5_0 -{ - float16_t d; - uint16_t qh[2]; - uint8_t qs[16]; -}; - -#define A_TYPE block_q5_0 -#endif - -#if defined(DATA_A_Q5_1) -#extension GL_EXT_shader_16bit_storage : require -#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require -#define QUANT_K 32 -#define QUANT_R 2 - -struct block_q5_1 -{ - float16_t d; - float16_t m; - uint qh; - uint8_t qs[16]; -}; - -#define A_TYPE block_q5_1 -#endif - -#if defined(DATA_A_Q8_0) -#extension GL_EXT_shader_16bit_storage : require -#define QUANT_K 32 -#define QUANT_R 1 - -struct block_q8_0 -{ - float16_t d; - int8_t qs[32]; -}; - -#define A_TYPE block_q8_0 -#endif - -// K-quants -#if defined(DATA_A_Q2_K) -#extension GL_EXT_shader_16bit_storage : require -#define QUANT_K 256 - -struct block_q2_K -{ - uint8_t scales[QUANT_K/16]; - uint8_t qs[QUANT_K/4]; - f16vec2 d; -}; - -#define A_TYPE block_q2_K -#endif - -#if defined(DATA_A_Q3_K) -#extension GL_EXT_shader_16bit_storage : require -#define QUANT_K 256 - -struct block_q3_K -{ - uint8_t hmask[QUANT_K/8]; - uint8_t qs[QUANT_K/4]; - uint8_t scales[12]; - float16_t d; -}; - -#define A_TYPE block_q3_K -#endif - -#if defined(DATA_A_Q4_K) -#extension GL_EXT_shader_16bit_storage : require -#define QUANT_K 256 - -struct block_q4_K -{ - f16vec2 d; - uint8_t scales[3*QUANT_K/64]; - uint8_t qs[QUANT_K/2]; -}; - -#define A_TYPE block_q4_K -#endif - -#if defined(DATA_A_Q5_K) -#extension GL_EXT_shader_16bit_storage : require -#define QUANT_K 256 - -struct block_q5_K -{ - f16vec2 d; - uint8_t scales[12]; - uint8_t qh[QUANT_K/8]; - uint8_t qs[QUANT_K/2]; -}; - -#define A_TYPE block_q5_K -#endif - -#if defined(DATA_A_Q6_K) -#extension GL_EXT_shader_16bit_storage : require -#define QUANT_K 256 - -struct block_q6_K -{ - uint8_t ql[QUANT_K/2]; - uint8_t qh[QUANT_K/4]; - int8_t scales[QUANT_K/16]; - float16_t d; -}; - -#define A_TYPE block_q6_K -#endif - -// IQuants - -#if defined(DATA_A_IQ4_NL) -#extension GL_EXT_shader_16bit_storage : require -#define QUANT_K 32 -#define QUANT_R 2 - -struct block_iq4_nl -{ - float16_t d; - uint8_t qs[QUANT_K/2]; -}; - -#define A_TYPE block_iq4_nl - -const int8_t kvalues_iq4nl[16] = { - int8_t(-127), int8_t(-104), int8_t(-83), int8_t(-65), int8_t(-49), int8_t(-35), int8_t(-22), int8_t(-10), - int8_t(1), int8_t(13), int8_t(25), int8_t(38), int8_t(53), int8_t(69), int8_t(89), int8_t(113) -}; -#endif diff --git a/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp deleted file mode 100644 index 49759c593..000000000 --- a/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp +++ /dev/null @@ -1,604 +0,0 @@ - - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -#ifdef _WIN32 - #include - #include // For _mkdir on Windows - #include // For std::replace on w64devkit -#else - #include - #include - #include -#endif - -#define ASYNCIO_CONCURRENCY 64 - -std::mutex lock; -std::vector> shader_fnames; - -std::string GLSLC = "glslc"; -std::string input_dir = "vulkan-shaders"; -std::string output_dir = "/tmp"; -std::string target_hpp = "ggml-vulkan-shaders.hpp"; -std::string target_cpp = "ggml-vulkan-shaders.cpp"; -bool no_clean = false; - -const std::vector type_names = { - "f32", - "f16", - "q4_0", - "q4_1", - "q5_0", - "q5_1", - "q8_0", - "q2_k", - "q3_k", - "q4_k", - "q5_k", - "q6_k", - "iq4_nl" -}; - -void execute_command(const std::string& command, std::string& stdout_str, std::string& stderr_str) { -#ifdef _WIN32 - HANDLE stdout_read, stdout_write; - HANDLE stderr_read, stderr_write; - SECURITY_ATTRIBUTES sa = { sizeof(SECURITY_ATTRIBUTES), NULL, TRUE }; - - if (!CreatePipe(&stdout_read, &stdout_write, &sa, 0) || - !SetHandleInformation(stdout_read, HANDLE_FLAG_INHERIT, 0)) { - throw std::runtime_error("Failed to create stdout pipe"); - } - - if (!CreatePipe(&stderr_read, &stderr_write, &sa, 0) || - !SetHandleInformation(stderr_read, HANDLE_FLAG_INHERIT, 0)) { - throw std::runtime_error("Failed to create stderr pipe"); - } - - PROCESS_INFORMATION pi; - STARTUPINFOA si = { sizeof(STARTUPINFOA) }; - si.dwFlags = STARTF_USESTDHANDLES; - si.hStdOutput = stdout_write; - si.hStdError = stderr_write; - - std::vector cmd(command.begin(), command.end()); - cmd.push_back('\0'); - - if (!CreateProcessA(NULL, cmd.data(), NULL, NULL, TRUE, 0, NULL, NULL, &si, &pi)) { - throw std::runtime_error("Failed to create process"); - } - - CloseHandle(stdout_write); - CloseHandle(stderr_write); - - std::array buffer; - DWORD bytes_read; - - while (ReadFile(stdout_read, buffer.data(), buffer.size(), &bytes_read, NULL) && bytes_read > 0) { - stdout_str.append(buffer.data(), bytes_read); - } - - while (ReadFile(stderr_read, buffer.data(), buffer.size(), &bytes_read, NULL) && bytes_read > 0) { - stderr_str.append(buffer.data(), bytes_read); - } - - CloseHandle(stdout_read); - CloseHandle(stderr_read); - WaitForSingleObject(pi.hProcess, INFINITE); - CloseHandle(pi.hProcess); - CloseHandle(pi.hThread); -#else -int stdout_pipe[2]; - int stderr_pipe[2]; - - if (pipe(stdout_pipe) != 0 || pipe(stderr_pipe) != 0) { - throw std::runtime_error("Failed to create pipes"); - } - - pid_t pid = fork(); - if (pid < 0) { - throw std::runtime_error("Failed to fork process"); - } - - if (pid == 0) { - close(stdout_pipe[0]); - close(stderr_pipe[0]); - dup2(stdout_pipe[1], STDOUT_FILENO); - dup2(stderr_pipe[1], STDERR_FILENO); - close(stdout_pipe[1]); - close(stderr_pipe[1]); - execl("/bin/sh", "sh", "-c", command.c_str(), (char*) nullptr); - _exit(EXIT_FAILURE); - } else { - close(stdout_pipe[1]); - close(stderr_pipe[1]); - - std::array buffer; - ssize_t bytes_read; - - while ((bytes_read = read(stdout_pipe[0], buffer.data(), buffer.size())) > 0) { - stdout_str.append(buffer.data(), bytes_read); - } - - while ((bytes_read = read(stderr_pipe[0], buffer.data(), buffer.size())) > 0) { - stderr_str.append(buffer.data(), bytes_read); - } - - close(stdout_pipe[0]); - close(stderr_pipe[0]); - waitpid(pid, nullptr, 0); - } -#endif -} - -bool directory_exists(const std::string& path) { - struct stat info; - if (stat(path.c_str(), &info) != 0) { - return false; // Path doesn't exist or can't be accessed - } - return (info.st_mode & S_IFDIR) != 0; // Check if it is a directory -} - -bool create_directory(const std::string& path) { -#ifdef _WIN32 - return _mkdir(path.c_str()) == 0 || errno == EEXIST; // EEXIST means the directory already exists -#else - return mkdir(path.c_str(), 0755) == 0 || errno == EEXIST; // 0755 is the directory permissions -#endif -} - -std::string to_uppercase(const std::string& input) { - std::string result = input; - for (char& c : result) { - c = std::toupper(c); - } - return result; -} - -bool string_ends_with(const std::string& str, const std::string& suffix) { - if (suffix.size() > str.size()) { - return false; - } - return std::equal(suffix.rbegin(), suffix.rend(), str.rbegin()); -} - -static const char path_separator = '/'; - -std::string join_paths(const std::string& path1, const std::string& path2) { - return path1 + path_separator + path2; -} - -std::string basename(const std::string &path) { - return path.substr(path.find_last_of("/\\") + 1); -} - -void string_to_spv(const std::string& _name, const std::string& in_fname, const std::map& defines, bool fp16 = true) { - std::string name = _name + (fp16 ? "" : "_fp32"); - std::string out_fname = join_paths(output_dir, name + ".spv"); - std::string in_path = join_paths(input_dir, in_fname); - - #ifdef _WIN32 - std::vector cmd = {GLSLC, "-fshader-stage=compute", "--target-env=vulkan1.2", "-O", "\"" + in_path + "\"", "-o", "\"" + out_fname + "\""}; - #else - std::vector cmd = {GLSLC, "-fshader-stage=compute", "--target-env=vulkan1.2", "-O", in_path, "-o", out_fname}; - #endif - - #ifdef GGML_VULKAN_SHADER_DEBUG_INFO - cmd.push_back("-g"); - #endif - - for (const auto& define : defines) { - cmd.push_back("-D" + define.first + "=" + define.second); - } - - std::string command; - for (const auto& part : cmd) { - command += part + " "; - } - - std::string stdout_str, stderr_str; - try { - // std::cout << "Executing command: "; - // for (const auto& part : cmd) { - // std::cout << part << " "; - // } - // std::cout << std::endl; - - execute_command(command, stdout_str, stderr_str); - if (!stderr_str.empty()) { - std::cerr << "cannot compile " << name << "\n\n" << command << "\n\n" << stderr_str << std::endl; - return; - } - - std::lock_guard guard(lock); - shader_fnames.push_back(std::make_pair(name, out_fname)); - } catch (const std::exception& e) { - std::cerr << "Error executing command for " << name << ": " << e.what() << std::endl; - } -} - -std::map merge_maps(const std::map& a, const std::map& b) { - std::map result = a; - result.insert(b.begin(), b.end()); - return result; -} - -void matmul_shaders(std::vector>& tasks, bool fp16, bool matmul_id) { - std::string load_vec = fp16 ? "8" : "4"; - std::string aligned_b_type_f32 = fp16 ? "mat2x4" : "vec4"; - std::string aligned_b_type_f16 = fp16 ? "f16mat2x4" : "f16vec4"; - - std::map base_dict = {{"FLOAT_TYPE", fp16 ? "float16_t" : "float"}}; - std::string shader_name = "matmul"; - - if (matmul_id) { - base_dict["MUL_MAT_ID"] = "1"; - shader_name = "matmul_id"; - } - - if (fp16) { - base_dict["FLOAT16"] = "1"; - } - - // Shaders with f16 B_TYPE - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv(shader_name + "_f32_f16", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F32", "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}), fp16); - })); - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv(shader_name + "_f32_f16_aligned", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F32", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}}), fp16); - })); - - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv(shader_name + "_f16", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F16", "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}), fp16); - })); - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv(shader_name + "_f16_aligned", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F16", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}}), fp16); - })); - - for (const auto& tname : type_names) { - std::string data_a_key = "DATA_A_" + to_uppercase(tname); - // For unaligned, load one at a time for f32/f16, or two at a time for quants - std::string load_vec_a_unaligned = (tname == "f32" || tname == "f16") ? "1" : "2"; - // For aligned matmul loads - std::string load_vec_a = (tname == "f32" || tname == "f16") ? load_vec : "2"; - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv(shader_name + "_" + tname + "_f32", "mul_mm.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a_unaligned}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}), fp16); - })); - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv(shader_name + "_" + tname + "_f32_aligned", "mul_mm.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f32}, {"D_TYPE", "float"}}), fp16); - })); - } -} - -void process_shaders(std::vector>& tasks) { - std::cout << "ggml_vulkan: Generating and compiling shaders to SPIR-V" << std::endl; - std::map base_dict = {{"FLOAT_TYPE", "float"}}; - - for (const auto& fp16 : {false, true}) { - matmul_shaders(tasks, fp16, false); - matmul_shaders(tasks, fp16, true); - } - - for (const auto& tname : type_names) { - // mul mat vec - std::string data_a_key = "DATA_A_" + to_uppercase(tname); - std::string shader = (string_ends_with(tname, "_k")) ? "mul_mat_vec_" + tname + ".comp" : "mul_mat_vec.comp"; - - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("mul_mat_vec_" + tname + "_f32_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); - })); - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("mul_mat_vec_" + tname + "_f16_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}})); - })); - - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("mul_mat_vec_id_" + tname + "_f32", shader, merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); - })); - - // Dequant shaders - if (tname != "f16") { - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("dequant_" + tname, "dequant_" + tname + ".comp", merge_maps(base_dict, {{data_a_key, "1"}, {"D_TYPE", "float16_t"}})); - })); - } - - if (!string_ends_with(tname, "_k")) { - shader = (tname == "f32" || tname == "f16") ? "get_rows.comp" : "get_rows_quant.comp"; - - if (tname == "f16") { - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("get_rows_" + tname, shader, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); - })); - } else { - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("get_rows_" + tname, shader, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}}); - })); - } - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("get_rows_" + tname + "_f32", shader, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float"}}); - })); - } - } - - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("mul_mat_vec_p021_f16_f32", "mul_mat_vec_p021.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("mul_mat_vec_nc_f16_f32", "mul_mat_vec_nc.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - - // Norms - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("norm_f32", "norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); - })); - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("group_norm_f32", "group_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); - })); - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("rms_norm_f32", "rms_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); - })); - - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("cpy_f32_f32", "copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("cpy_f32_f16", "copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("cpy_f16_f16", "copy.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); - })); - - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("add_f32", "add.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("add_f16_f32_f16", "add.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float16_t"}, {"FLOAT_TYPE", "float"}}); - })); - - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("acc_f32", "acc.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - })); - - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("split_k_reduce", "mul_mat_split_k_reduce.comp", {}); - })); - - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("mul_f32", "mul.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - })); - - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("div_f32", "div.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - })); - - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("repeat_f32", "repeat.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("scale_f32", "scale.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - })); - - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("sqr_f32", "square.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - })); - - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("sin_f32", "sin.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - })); - - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("cos_f32", "cos.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - })); - - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("clamp_f32", "clamp.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); - })); - - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("pad_f32", "pad.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("concat_f32", "concat.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("concat_f16", "concat.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("concat_i32", "concat.comp", {{"A_TYPE", "int"}, {"B_TYPE", "int"}, {"D_TYPE", "int"}}); - })); - - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("upscale_f32", "upscale.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("gelu_f32", "gelu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("gelu_quick_f32", "gelu_quick.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("silu_f32", "silu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("relu_f32", "relu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("leaky_relu_f32", "leaky_relu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("tanh_f32", "tanh.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("diag_mask_inf_f32", "diag_mask_inf.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("soft_max_f32", "soft_max.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); - })); - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("soft_max_f32_f16", "soft_max.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}})); - })); - - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("rope_norm_f32", "rope_norm.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("rope_norm_f16", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); - })); - - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("rope_neox_f32", "rope_neox.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); - })); - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("rope_neox_f16", "rope_neox.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); - })); - - tasks.push_back(std::async(std::launch::async, [] { - string_to_spv("argsort_f32", "argsort.comp", {{"A_TYPE", "float"}}); - })); - - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("sum_rows_f32", "sum_rows.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); - })); - - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("im2col_f32", "im2col.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); - })); - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("im2col_f32_f16", "im2col.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}})); - })); - - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("timestep_embedding_f32", "timestep_embedding.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); - })); - - tasks.push_back(std::async(std::launch::async, [=] { - string_to_spv("pool2d_f32", "pool2d.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); - })); -} - -void write_output_files() { - FILE* hdr = fopen(target_hpp.c_str(), "w"); - FILE* src = fopen(target_cpp.c_str(), "w"); - - fprintf(hdr, "#include \n\n"); - fprintf(src, "#include \"%s\"\n\n", basename(target_hpp).c_str()); - - for (const auto& pair : shader_fnames) { - const std::string& name = pair.first; - #ifdef _WIN32 - std::string path = pair.second; - std::replace(path.begin(), path.end(), '/', '\\' ); - #else - const std::string& path = pair.second; - #endif - - FILE* spv = fopen(path.c_str(), "rb"); - if (!spv) { - std::cerr << "Error opening SPIR-V file: " << path << " (" << strerror(errno) << ")\n"; - continue; - } - - fseek(spv, 0, SEEK_END); - size_t size = ftell(spv); - fseek(spv, 0, SEEK_SET); - - std::vector data(size); - size_t read_size = fread(data.data(), 1, size, spv); - fclose(spv); - if (read_size != size) { - std::cerr << "Error reading SPIR-V file: " << path << " (" << strerror(errno) << ")\n"; - continue; - } - - fprintf(hdr, "extern unsigned char %s_data[%zu];\n", name.c_str(), size); - fprintf(hdr, "const uint64_t %s_len = %zu;\n\n", name.c_str(), size); - - fprintf(src, "unsigned char %s_data[%zu] = {\n", name.c_str(), size); - for (size_t i = 0; i < size; ++i) { - fprintf(src, "0x%02x,", data[i]); - if ((i + 1) % 12 == 0) fprintf(src, "\n"); - } - fprintf(src, "\n};\n\n"); - - if (!no_clean) { - std::remove(path.c_str()); - } - } - - fclose(hdr); - fclose(src); -} - -int main(int argc, char** argv) { - std::map args; - for (int i = 1; i < argc; i += 2) { - if (i + 1 < argc) { - args[argv[i]] = argv[i + 1]; - } - } - - if (args.find("--glslc") != args.end()) { - GLSLC = args["--glslc"]; // Path to glslc - } - if (args.find("--input-dir") != args.end()) { - input_dir = args["--input-dir"]; // Directory containing shader sources - } - if (args.find("--output-dir") != args.end()) { - output_dir = args["--output-dir"]; // Directory for containing SPIR-V output - } - if (args.find("--target-hpp") != args.end()) { - target_hpp = args["--target-hpp"]; // Path to generated header file - } - if (args.find("--target-cpp") != args.end()) { - target_cpp = args["--target-cpp"]; // Path to generated cpp file - } - if (args.find("--no-clean") != args.end()) { - no_clean = true; // Keep temporary SPIR-V files in output-dir after build - } - - if (!directory_exists(input_dir)) { - std::cerr << "\"" << input_dir << "\" must be a valid directory containing shader sources" << std::endl; - return EXIT_FAILURE; - } - - if (!directory_exists(output_dir)) { - if (!create_directory(output_dir)) { - std::cerr << "Error creating output directory: " << output_dir << "\n"; - return EXIT_FAILURE; - } - } - - std::vector> tasks; - process_shaders(tasks); - - for (auto& task : tasks) { - task.get(); - } - - write_output_files(); - - return EXIT_SUCCESS; -} diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 7ab08b036..703199fcb 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -64,15 +64,27 @@ class Keys: BASE_MODEL_AUTHOR = "general.base_model.{id}.author" BASE_MODEL_VERSION = "general.base_model.{id}.version" BASE_MODEL_ORGANIZATION = "general.base_model.{id}.organization" + BASE_MODEL_DESCRIPTION = "general.base_model.{id}.description" BASE_MODEL_URL = "general.base_model.{id}.url" # Model Website/Paper BASE_MODEL_DOI = "general.base_model.{id}.doi" BASE_MODEL_UUID = "general.base_model.{id}.uuid" BASE_MODEL_REPO_URL = "general.base_model.{id}.repo_url" # Model Source Repository (git/svn/etc...) + # Dataset Source + DATASET_COUNT = "general.dataset.count" + DATASET_NAME = "general.dataset.{id}.name" + DATASET_AUTHOR = "general.dataset.{id}.author" + DATASET_VERSION = "general.dataset.{id}.version" + DATASET_ORGANIZATION = "general.dataset.{id}.organization" + DATASET_DESCRIPTION = "general.dataset.{id}.description" + DATASET_URL = "general.dataset.{id}.url" # Model Website/Paper + DATASET_DOI = "general.dataset.{id}.doi" + DATASET_UUID = "general.dataset.{id}.uuid" + DATASET_REPO_URL = "general.dataset.{id}.repo_url" # Model Source Repository (git/svn/etc...) + # Array based KV stores TAGS = "general.tags" LANGUAGES = "general.languages" - DATASETS = "general.datasets" class LLM: VOCAB_SIZE = "{arch}.vocab_size" @@ -231,6 +243,7 @@ class MODEL_ARCH(IntEnum): COMMAND_R = auto() DBRX = auto() OLMO = auto() + OLMO2 = auto() OLMOE = auto() OPENELM = auto() ARCTIC = auto() @@ -392,6 +405,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.COMMAND_R: "command-r", MODEL_ARCH.DBRX: "dbrx", MODEL_ARCH.OLMO: "olmo", + MODEL_ARCH.OLMO2: "olmo2", MODEL_ARCH.OLMOE: "olmoe", MODEL_ARCH.OPENELM: "openelm", MODEL_ARCH.ARCTIC: "arctic", @@ -882,6 +896,8 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.OUTPUT, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ROPE_FACTORS_LONG, + MODEL_TENSOR.ROPE_FACTORS_SHORT, MODEL_TENSOR.ATTN_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_K, @@ -1057,6 +1073,22 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.OLMO2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_POST_NORM, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.FFN_POST_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], MODEL_ARCH.OLMOE: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, @@ -1358,9 +1390,10 @@ class TokenType(IntEnum): class RopeScalingType(Enum): - NONE = 'none' - LINEAR = 'linear' - YARN = 'yarn' + NONE = 'none' + LINEAR = 'linear' + YARN = 'yarn' + LONGROPE = 'longrope' class PoolingType(IntEnum): diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 0d8d8a0b0..7a55d1296 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -568,6 +568,9 @@ class GGUFWriter: def add_base_model_organization(self, source_id: int, organization: str) -> None: self.add_string(Keys.General.BASE_MODEL_ORGANIZATION.format(id=source_id), organization) + def add_base_model_description(self, source_id: int, description: str) -> None: + self.add_string(Keys.General.BASE_MODEL_DESCRIPTION.format(id=source_id), description) + def add_base_model_url(self, source_id: int, url: str) -> None: self.add_string(Keys.General.BASE_MODEL_URL.format(id=source_id), url) @@ -580,15 +583,42 @@ class GGUFWriter: def add_base_model_repo_url(self, source_id: int, repo_url: str) -> None: self.add_string(Keys.General.BASE_MODEL_REPO_URL.format(id=source_id), repo_url) + def add_dataset_count(self, source_count: int) -> None: + self.add_uint32(Keys.General.DATASET_COUNT, source_count) + + def add_dataset_name(self, source_id: int, name: str) -> None: + self.add_string(Keys.General.DATASET_NAME.format(id=source_id), name) + + def add_dataset_author(self, source_id: int, author: str) -> None: + self.add_string(Keys.General.DATASET_AUTHOR.format(id=source_id), author) + + def add_dataset_version(self, source_id: int, version: str) -> None: + self.add_string(Keys.General.DATASET_VERSION.format(id=source_id), version) + + def add_dataset_organization(self, source_id: int, organization: str) -> None: + self.add_string(Keys.General.DATASET_ORGANIZATION.format(id=source_id), organization) + + def add_dataset_description(self, source_id: int, description: str) -> None: + self.add_string(Keys.General.DATASET_DESCRIPTION.format(id=source_id), description) + + def add_dataset_url(self, source_id: int, url: str) -> None: + self.add_string(Keys.General.DATASET_URL.format(id=source_id), url) + + def add_dataset_doi(self, source_id: int, doi: str) -> None: + self.add_string(Keys.General.DATASET_DOI.format(id=source_id), doi) + + def add_dataset_uuid(self, source_id: int, uuid: str) -> None: + self.add_string(Keys.General.DATASET_UUID.format(id=source_id), uuid) + + def add_dataset_repo_url(self, source_id: int, repo_url: str) -> None: + self.add_string(Keys.General.DATASET_REPO_URL.format(id=source_id), repo_url) + def add_tags(self, tags: Sequence[str]) -> None: self.add_array(Keys.General.TAGS, tags) def add_languages(self, languages: Sequence[str]) -> None: self.add_array(Keys.General.LANGUAGES, languages) - def add_datasets(self, datasets: Sequence[str]) -> None: - self.add_array(Keys.General.DATASETS, datasets) - def add_tensor_data_layout(self, layout: str) -> None: self.add_string(Keys.LLM.TENSOR_DATA_LAYOUT.format(arch=self.arch), layout) diff --git a/gguf-py/gguf/metadata.py b/gguf-py/gguf/metadata.py index db318542a..962c27b20 100644 --- a/gguf-py/gguf/metadata.py +++ b/gguf-py/gguf/metadata.py @@ -41,7 +41,7 @@ class Metadata: base_models: Optional[list[dict]] = None tags: Optional[list[str]] = None languages: Optional[list[str]] = None - datasets: Optional[list[str]] = None + datasets: Optional[list[dict]] = None @staticmethod def load(metadata_override_path: Optional[Path] = None, model_path: Optional[Path] = None, model_name: Optional[str] = None, total_params: int = 0) -> Metadata: @@ -91,9 +91,11 @@ class Metadata: # Base Models is received here as an array of models metadata.base_models = metadata_override.get("general.base_models", metadata.base_models) + # Datasets is received here as an array of datasets + metadata.datasets = metadata_override.get("general.datasets", metadata.datasets) + metadata.tags = metadata_override.get(Keys.General.TAGS, metadata.tags) metadata.languages = metadata_override.get(Keys.General.LANGUAGES, metadata.languages) - metadata.datasets = metadata_override.get(Keys.General.DATASETS, metadata.datasets) # Direct Metadata Override (via direct cli argument) if model_name is not None: @@ -346,12 +348,12 @@ class Metadata: use_model_card_metadata("author", "model_creator") use_model_card_metadata("basename", "model_type") - if "base_model" in model_card: + if "base_model" in model_card or "base_models" in model_card or "base_model_sources" in model_card: # This represents the parent models that this is based on # Example: stabilityai/stable-diffusion-xl-base-1.0. Can also be a list (for merges) # Example of merges: https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.1/blob/main/README.md metadata_base_models = [] - base_model_value = model_card.get("base_model", None) + base_model_value = model_card.get("base_model", model_card.get("base_models", model_card.get("base_model_sources", None))) if base_model_value is not None: if isinstance(base_model_value, str): @@ -364,18 +366,106 @@ class Metadata: for model_id in metadata_base_models: # NOTE: model size of base model is assumed to be similar to the size of the current model - model_full_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(model_id, total_params) base_model = {} - if model_full_name_component is not None: - base_model["name"] = Metadata.id_to_title(model_full_name_component) - if org_component is not None: - base_model["organization"] = Metadata.id_to_title(org_component) - if version is not None: - base_model["version"] = version - if org_component is not None and model_full_name_component is not None: - base_model["repo_url"] = f"https://huggingface.co/{org_component}/{model_full_name_component}" + if isinstance(model_id, str): + if model_id.startswith("http://") or model_id.startswith("https://") or model_id.startswith("ssh://"): + base_model["repo_url"] = model_id + + # Check if Hugging Face ID is present in URL + if "huggingface.co" in model_id: + match = re.match(r"https?://huggingface.co/([^/]+/[^/]+)$", model_id) + if match: + model_id_component = match.group(1) + model_full_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(model_id_component, total_params) + + # Populate model dictionary with extracted components + if model_full_name_component is not None: + base_model["name"] = Metadata.id_to_title(model_full_name_component) + if org_component is not None: + base_model["organization"] = Metadata.id_to_title(org_component) + if version is not None: + base_model["version"] = version + + else: + # Likely a Hugging Face ID + model_full_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(model_id, total_params) + + # Populate model dictionary with extracted components + if model_full_name_component is not None: + base_model["name"] = Metadata.id_to_title(model_full_name_component) + if org_component is not None: + base_model["organization"] = Metadata.id_to_title(org_component) + if version is not None: + base_model["version"] = version + if org_component is not None and model_full_name_component is not None: + base_model["repo_url"] = f"https://huggingface.co/{org_component}/{model_full_name_component}" + + elif isinstance(model_id, dict): + base_model = model_id + + else: + logger.error(f"base model entry '{str(model_id)}' not in a known format") + metadata.base_models.append(base_model) + if "datasets" in model_card or "dataset" in model_card or "dataset_sources" in model_card: + # This represents the datasets that this was trained from + metadata_datasets = [] + dataset_value = model_card.get("datasets", model_card.get("dataset", model_card.get("dataset_sources", None))) + + if dataset_value is not None: + if isinstance(dataset_value, str): + metadata_datasets.append(dataset_value) + elif isinstance(dataset_value, list): + metadata_datasets.extend(dataset_value) + + if metadata.datasets is None: + metadata.datasets = [] + + for dataset_id in metadata_datasets: + # NOTE: model size of base model is assumed to be similar to the size of the current model + dataset = {} + if isinstance(dataset_id, str): + if dataset_id.startswith(("http://", "https://", "ssh://")): + dataset["repo_url"] = dataset_id + + # Check if Hugging Face ID is present in URL + if "huggingface.co" in dataset_id: + match = re.match(r"https?://huggingface.co/([^/]+/[^/]+)$", dataset_id) + if match: + dataset_id_component = match.group(1) + dataset_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(dataset_id_component, total_params) + + # Populate dataset dictionary with extracted components + if dataset_name_component is not None: + dataset["name"] = Metadata.id_to_title(dataset_name_component) + if org_component is not None: + dataset["organization"] = Metadata.id_to_title(org_component) + if version is not None: + dataset["version"] = version + + else: + # Likely a Hugging Face ID + dataset_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(dataset_id, total_params) + + # Populate dataset dictionary with extracted components + if dataset_name_component is not None: + dataset["name"] = Metadata.id_to_title(dataset_name_component) + if org_component is not None: + dataset["organization"] = Metadata.id_to_title(org_component) + if version is not None: + dataset["version"] = version + if org_component is not None and dataset_name_component is not None: + dataset["repo_url"] = f"https://huggingface.co/{org_component}/{dataset_name_component}" + + elif isinstance(dataset_id, dict): + dataset = dataset_id + + else: + logger.error(f"dataset entry '{str(dataset_id)}' not in a known format") + + metadata.datasets.append(dataset) + use_model_card_metadata("license", "license") use_model_card_metadata("license_name", "license_name") use_model_card_metadata("license_link", "license_link") @@ -386,9 +476,6 @@ class Metadata: use_array_model_card_metadata("languages", "languages") use_array_model_card_metadata("languages", "language") - use_array_model_card_metadata("datasets", "datasets") - use_array_model_card_metadata("datasets", "dataset") - # Hugging Face Parameter Heuristics #################################### @@ -458,7 +545,10 @@ class Metadata: gguf_writer.add_size_label(self.size_label) if self.license is not None: - gguf_writer.add_license(self.license) + if isinstance(self.license, list): + gguf_writer.add_license(",".join(self.license)) + else: + gguf_writer.add_license(self.license) if self.license_name is not None: gguf_writer.add_license_name(self.license_name) if self.license_link is not None: @@ -493,6 +583,8 @@ class Metadata: gguf_writer.add_base_model_version(key, base_model_entry["version"]) if "organization" in base_model_entry: gguf_writer.add_base_model_organization(key, base_model_entry["organization"]) + if "description" in base_model_entry: + gguf_writer.add_base_model_description(key, base_model_entry["description"]) if "url" in base_model_entry: gguf_writer.add_base_model_url(key, base_model_entry["url"]) if "doi" in base_model_entry: @@ -502,9 +594,29 @@ class Metadata: if "repo_url" in base_model_entry: gguf_writer.add_base_model_repo_url(key, base_model_entry["repo_url"]) + if self.datasets is not None: + gguf_writer.add_dataset_count(len(self.datasets)) + for key, dataset_entry in enumerate(self.datasets): + if "name" in dataset_entry: + gguf_writer.add_dataset_name(key, dataset_entry["name"]) + if "author" in dataset_entry: + gguf_writer.add_dataset_author(key, dataset_entry["author"]) + if "version" in dataset_entry: + gguf_writer.add_dataset_version(key, dataset_entry["version"]) + if "organization" in dataset_entry: + gguf_writer.add_dataset_organization(key, dataset_entry["organization"]) + if "description" in dataset_entry: + gguf_writer.add_dataset_description(key, dataset_entry["description"]) + if "url" in dataset_entry: + gguf_writer.add_dataset_url(key, dataset_entry["url"]) + if "doi" in dataset_entry: + gguf_writer.add_dataset_doi(key, dataset_entry["doi"]) + if "uuid" in dataset_entry: + gguf_writer.add_dataset_uuid(key, dataset_entry["uuid"]) + if "repo_url" in dataset_entry: + gguf_writer.add_dataset_repo_url(key, dataset_entry["repo_url"]) + if self.tags is not None: gguf_writer.add_tags(self.tags) if self.languages is not None: gguf_writer.add_languages(self.languages) - if self.datasets is not None: - gguf_writer.add_datasets(self.datasets) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index f4a787c56..1b6a3f4ad 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -13,7 +13,7 @@ class TensorNameMap: "transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx jais exaone "transformer.word_embeddings", # falcon "word_embeddings", # bloom - "model.embed_tokens", # llama-hf nemotron olmoe + "model.embed_tokens", # llama-hf nemotron olmoe olmo2 "tok_embeddings", # llama-pth "embeddings.word_embeddings", # bert nomic-bert "language_model.embedding.word_embeddings", # persimmon @@ -54,7 +54,7 @@ class TensorNameMap: # Output MODEL_TENSOR.OUTPUT: ( "embed_out", # gptneox - "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx jais nemotron exaone olmoe + "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx jais nemotron exaone olmoe olmo2 "output", # llama-pth bloom internlm2 "word_embeddings_for_head", # persimmon "lm_head.linear", # phi2 @@ -66,7 +66,7 @@ class TensorNameMap: MODEL_TENSOR.OUTPUT_NORM: ( "gpt_neox.final_layer_norm", # gptneox "transformer.ln_f", # gpt2 gpt-j falcon jais exaone - "model.norm", # llama-hf baichuan internlm2 olmoe + "model.norm", # llama-hf baichuan internlm2 olmoe olmo2 "norm", # llama-pth "transformer.norm_f", # mpt dbrx "ln_f", # refact bloom qwen gpt2 @@ -145,7 +145,7 @@ class TensorNameMap: # Attention query MODEL_TENSOR.ATTN_Q: ( - "model.layers.{bid}.self_attn.q_proj", # llama-hf nemotron olmoe + "model.layers.{bid}.self_attn.q_proj", # llama-hf nemotron olmoe olmo2 "layers.{bid}.attention.wq", # llama-pth "encoder.layer.{bid}.attention.self.query", # bert "transformer.h.{bid}.attn.q_proj", # gpt-j @@ -157,7 +157,7 @@ class TensorNameMap: # Attention key MODEL_TENSOR.ATTN_K: ( - "model.layers.{bid}.self_attn.k_proj", # llama-hf nemotron olmoe + "model.layers.{bid}.self_attn.k_proj", # llama-hf nemotron olmoe olmo2 "layers.{bid}.attention.wk", # llama-pth "encoder.layer.{bid}.attention.self.key", # bert "transformer.h.{bid}.attn.k_proj", # gpt-j @@ -170,7 +170,7 @@ class TensorNameMap: # Attention value MODEL_TENSOR.ATTN_V: ( - "model.layers.{bid}.self_attn.v_proj", # llama-hf nemotron olmoe + "model.layers.{bid}.self_attn.v_proj", # llama-hf nemotron olmoe olmo2 "layers.{bid}.attention.wv", # llama-pth "encoder.layer.{bid}.attention.self.value", # bert "transformer.h.{bid}.attn.v_proj", # gpt-j @@ -188,7 +188,7 @@ class TensorNameMap: "transformer.blocks.{bid}.attn.out_proj", # mpt "transformer.h.{bid}.self_attention.dense", # falcon "h.{bid}.self_attention.dense", # bloom - "model.layers.{bid}.self_attn.o_proj", # llama-hf nemotron olmoe + "model.layers.{bid}.self_attn.o_proj", # llama-hf nemotron olmoe olmo2 "layers.{bid}.attention.wo", # llama-pth "encoder.layer.{bid}.attention.output.dense", # bert "transformer.h.{bid}.attn.out_proj", # gpt-j @@ -215,7 +215,7 @@ class TensorNameMap: ), MODEL_TENSOR.ATTN_POST_NORM: ( - "model.layers.{bid}.post_attention_layernorm", # gemma2 + "model.layers.{bid}.post_attention_layernorm", # gemma2 olmo2 ), # Rotary embeddings @@ -250,7 +250,7 @@ class TensorNameMap: # Post feed-forward norm MODEL_TENSOR.FFN_POST_NORM: ( - "model.layers.{bid}.post_feedforward_layernorm", # gemma2 + "model.layers.{bid}.post_feedforward_layernorm", # gemma2 olmo2 ), MODEL_TENSOR.FFN_GATE_INP: ( @@ -273,7 +273,7 @@ class TensorNameMap: "transformer.blocks.{bid}.ffn.up_proj", # mpt "transformer.h.{bid}.mlp.dense_h_to_4h", # falcon "h.{bid}.mlp.dense_h_to_4h", # bloom - "model.layers.{bid}.mlp.up_proj", # llama-hf refact nemotron + "model.layers.{bid}.mlp.up_proj", # llama-hf refact nemotron olmo2 "layers.{bid}.feed_forward.w3", # llama-pth "encoder.layer.{bid}.intermediate.dense", # bert "transformer.h.{bid}.mlp.fc_in", # gpt-j @@ -314,7 +314,7 @@ class TensorNameMap: # Feed-forward gate MODEL_TENSOR.FFN_GATE: ( - "model.layers.{bid}.mlp.gate_proj", # llama-hf refact + "model.layers.{bid}.mlp.gate_proj", # llama-hf refact olmo2 "layers.{bid}.feed_forward.w1", # llama-pth "transformer.h.{bid}.mlp.w2", # qwen "transformer.h.{bid}.mlp.c_fc2", # jais @@ -346,7 +346,7 @@ class TensorNameMap: "transformer.blocks.{bid}.ffn.down_proj", # mpt "transformer.h.{bid}.mlp.dense_4h_to_h", # falcon "h.{bid}.mlp.dense_4h_to_h", # bloom - "model.layers.{bid}.mlp.down_proj", # llama-hf nemotron + "model.layers.{bid}.mlp.down_proj", # llama-hf nemotron olmo2 "layers.{bid}.feed_forward.w2", # llama-pth "encoder.layer.{bid}.output.dense", # bert "transformer.h.{bid}.mlp.fc_out", # gpt-j @@ -383,7 +383,7 @@ class TensorNameMap: MODEL_TENSOR.ATTN_Q_NORM: ( "language_model.encoder.layers.{bid}.self_attention.q_layernorm", "model.layers.{bid}.self_attn.q_layernorm", # persimmon - "model.layers.{bid}.self_attn.q_norm", # cohere olmoe chameleon + "model.layers.{bid}.self_attn.q_norm", # cohere olmoe chameleon olmo2 "transformer.blocks.{bid}.attn.q_ln", # sea-lion "encoder.layer.{bid}.attention.self.layer_norm_q", # jina-bert-v2 "transformer.layers.{bid}.attn.q_norm", # openelm @@ -392,7 +392,7 @@ class TensorNameMap: MODEL_TENSOR.ATTN_K_NORM: ( "language_model.encoder.layers.{bid}.self_attention.k_layernorm", "model.layers.{bid}.self_attn.k_layernorm", # persimmon - "model.layers.{bid}.self_attn.k_norm", # cohere olmoe chameleon + "model.layers.{bid}.self_attn.k_norm", # cohere olmoe chameleon olmo2 "transformer.blocks.{bid}.attn.k_ln", # sea-lion "encoder.layer.{bid}.attention.self.layer_norm_k", # jina-bert-v2 "transformer.layers.{bid}.attn.k_norm", # openelm diff --git a/gguf-py/tests/test_metadata.py b/gguf-py/tests/test_metadata.py index 81a2a30ae..40d484f4e 100755 --- a/gguf-py/tests/test_metadata.py +++ b/gguf-py/tests/test_metadata.py @@ -182,8 +182,43 @@ class TestMetadataMethod(unittest.TestCase): expect.base_models=[{'name': 'Mistral 7B Merge 14 v0', 'organization': 'EmbeddedLLM', 'version': '14-v0', 'repo_url': 'https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0'}, {'name': 'Trinity v1', 'organization': 'Janai Hq', 'version': 'v1', 'repo_url': 'https://huggingface.co/janai-hq/trinity-v1'}] expect.tags=['Llama-3', 'instruct', 'finetune', 'chatml', 'DPO', 'RLHF', 'gpt4', 'synthetic data', 'distillation', 'function calling', 'json mode', 'axolotl'] expect.languages=['en'] - expect.datasets=['teknium/OpenHermes-2.5'] + expect.datasets=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}] + self.assertEqual(got, expect) + # Base Model spec is inferred from model id + model_card = {'base_models': 'teknium/OpenHermes-2.5'} + expect = gguf.Metadata(base_models=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]) + got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None) + self.assertEqual(got, expect) + + # Base Model spec is only url + model_card = {'base_models': ['https://huggingface.co/teknium/OpenHermes-2.5']} + expect = gguf.Metadata(base_models=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]) + got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None) + self.assertEqual(got, expect) + + # Base Model spec is given directly + model_card = {'base_models': [{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]} + expect = gguf.Metadata(base_models=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]) + got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None) + self.assertEqual(got, expect) + + # Dataset spec is inferred from model id + model_card = {'datasets': 'teknium/OpenHermes-2.5'} + expect = gguf.Metadata(datasets=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]) + got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None) + self.assertEqual(got, expect) + + # Dataset spec is only url + model_card = {'datasets': ['https://huggingface.co/teknium/OpenHermes-2.5']} + expect = gguf.Metadata(datasets=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]) + got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None) + self.assertEqual(got, expect) + + # Dataset spec is given directly + model_card = {'datasets': [{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]} + expect = gguf.Metadata(datasets=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]) + got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None) self.assertEqual(got, expect) def test_apply_metadata_heuristic_from_hf_parameters(self): diff --git a/grammars/README.md b/grammars/README.md index 4e8b4e2fc..976954091 100644 --- a/grammars/README.md +++ b/grammars/README.md @@ -46,7 +46,7 @@ Terminals support the full range of Unicode. Unicode characters can be specified Character ranges can be negated with `^`: ``` -single-line ::= [^\n]+ "\n"` +single-line ::= [^\n]+ "\n" ``` ## Sequences and Alternatives @@ -124,7 +124,7 @@ You can use GBNF grammars: - In [llama-cli](../examples/main), passed as the `--json` / `-j` flag - To convert to a grammar ahead of time: - in CLI, with [examples/json_schema_to_grammar.py](../examples/json_schema_to_grammar.py) - - in JavaScript with [json-schema-to-grammar.mjs](../examples/server/public/json-schema-to-grammar.mjs) (this is used by the [server](../examples/server)'s Web UI) + - in JavaScript with [json-schema-to-grammar.mjs](../examples/server/public_legacy/json-schema-to-grammar.mjs) (this is used by the [server](../examples/server)'s Web UI) Take a look at [tests](../tests/test-json-schema-to-grammar.cpp) to see which features are likely supported (you'll also find usage examples in https://github.com/ggerganov/llama.cpp/pull/5978, https://github.com/ggerganov/llama.cpp/pull/6659 & https://github.com/ggerganov/llama.cpp/pull/6555). diff --git a/grammars/english.gbnf b/grammars/english.gbnf new file mode 100644 index 000000000..2e53686c8 --- /dev/null +++ b/grammars/english.gbnf @@ -0,0 +1,6 @@ +# note: this might be incomplete, mostly an example +root ::= en-char+ ([ \t\n] en-char+)* +en-char ::= letter | digit | punctuation +letter ::= [a-zA-Z] +digit ::= [0-9] +punctuation ::= [!"#$%&'()*+,-./:;<=>?@[\\\]^_`{|}~] diff --git a/include/llama-cpp.h b/include/llama-cpp.h new file mode 100644 index 000000000..daa04d4d8 --- /dev/null +++ b/include/llama-cpp.h @@ -0,0 +1,25 @@ +#pragma once + +#ifndef __cplusplus +#error "This header is for C++ only" +#endif + +#include + +#include "llama.h" + +struct llama_model_deleter { + void operator()(llama_model * model) { llama_free_model(model); } +}; + +struct llama_context_deleter { + void operator()(llama_context * context) { llama_free(context); } +}; + +struct llama_sampler_deleter { + void operator()(llama_sampler * sampler) { llama_sampler_free(sampler); } +}; + +typedef std::unique_ptr llama_model_ptr; +typedef std::unique_ptr llama_context_ptr; +typedef std::unique_ptr llama_sampler_ptr; diff --git a/include/llama.h b/include/llama.h index ec07eeab9..aa5880096 100644 --- a/include/llama.h +++ b/include/llama.h @@ -2,6 +2,7 @@ #define LLAMA_H #include "ggml.h" +#include "ggml-cpu.h" #include "ggml-backend.h" #include @@ -103,6 +104,7 @@ extern "C" { LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH = 24, LLAMA_VOCAB_PRE_TYPE_EXAONE = 25, LLAMA_VOCAB_PRE_TYPE_CHAMELEON = 26, + LLAMA_VOCAB_PRE_TYPE_MINERVA = 27, }; enum llama_rope_type { @@ -184,7 +186,8 @@ extern "C" { LLAMA_ROPE_SCALING_TYPE_NONE = 0, LLAMA_ROPE_SCALING_TYPE_LINEAR = 1, LLAMA_ROPE_SCALING_TYPE_YARN = 2, - LLAMA_ROPE_SCALING_TYPE_MAX_VALUE = LLAMA_ROPE_SCALING_TYPE_YARN, + LLAMA_ROPE_SCALING_TYPE_LONGROPE = 3, + LLAMA_ROPE_SCALING_TYPE_MAX_VALUE = LLAMA_ROPE_SCALING_TYPE_LONGROPE, }; enum llama_pooling_type { @@ -271,6 +274,9 @@ extern "C" { }; struct llama_model_params { + // NULL-terminated list of devices to use for offloading (if NULL, all available devices are used) + ggml_backend_dev_t * devices; + int32_t n_gpu_layers; // number of layers to store in VRAM enum llama_split_mode split_mode; // how to split the model across multiple GPUs @@ -666,6 +672,9 @@ extern "C" { // Apply the KV cache updates (such as K-shifts, defragmentation, etc.) LLAMA_API void llama_kv_cache_update(struct llama_context * ctx); + // Check if the context supports KV cache shifting + LLAMA_API bool llama_kv_cache_can_shift(struct llama_context * ctx); + // // State / sessions // @@ -796,7 +805,7 @@ extern "C" { // Processes a batch of tokens with the ecoder part of the encoder-decoder model. // Stores the encoder output internally for later use by the decoder cross-attention layers. // 0 - success - // < 0 - error + // < 0 - error. the KV cache state is restored to the state before this call LLAMA_API int32_t llama_encode( struct llama_context * ctx, struct llama_batch batch); @@ -804,7 +813,7 @@ extern "C" { // Positive return values does not mean a fatal error, but rather a warning. // 0 - success // 1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context) - // < 0 - error + // < 0 - error. the KV cache state is restored to the state before this call LLAMA_API int32_t llama_decode( struct llama_context * ctx, struct llama_batch batch); @@ -983,6 +992,9 @@ extern "C" { char * buf, int32_t length); + // Get list of built-in chat templates + LLAMA_API int32_t llama_chat_builtin_templates(const char ** output, size_t len); + // // Sampling API // @@ -1247,8 +1259,6 @@ extern "C" { LLAMA_API void llama_perf_sampler_print(const struct llama_sampler * chain); LLAMA_API void llama_perf_sampler_reset( struct llama_sampler * chain); - LLAMA_API void llama_perf_dump_yaml(FILE * stream, const struct llama_context * ctx); - #ifdef __cplusplus } #endif diff --git a/pocs/CMakeLists.txt b/pocs/CMakeLists.txt index 03e1d2c04..d49d14dee 100644 --- a/pocs/CMakeLists.txt +++ b/pocs/CMakeLists.txt @@ -8,5 +8,7 @@ include_directories(${CMAKE_CURRENT_SOURCE_DIR}) if (EMSCRIPTEN) else() - add_subdirectory(vdot) + if (NOT GGML_BACKEND_DL) + add_subdirectory(vdot) + endif() endif() diff --git a/pocs/vdot/CMakeLists.txt b/pocs/vdot/CMakeLists.txt index d5405ad29..6235aec1f 100644 --- a/pocs/vdot/CMakeLists.txt +++ b/pocs/vdot/CMakeLists.txt @@ -1,9 +1,9 @@ set(TARGET llama-vdot) add_executable(${TARGET} vdot.cpp) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) set(TARGET llama-q8dot) add_executable(${TARGET} q8dot.cpp) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/pocs/vdot/q8dot.cpp b/pocs/vdot/q8dot.cpp index 131d7c177..3df6e1f42 100644 --- a/pocs/vdot/q8dot.cpp +++ b/pocs/vdot/q8dot.cpp @@ -11,6 +11,7 @@ #include #include +#include constexpr int kVecSize = 1 << 16; @@ -136,7 +137,7 @@ int main(int argc, char** argv) { auto ggml_type = type == 0 ? GGML_TYPE_Q4_0 : GGML_TYPE_Q4_1; - const auto * funcs = ggml_get_type_traits(ggml_type); + const auto * funcs = ggml_get_type_traits_cpu(ggml_type); Stat simple, ggml; diff --git a/pocs/vdot/vdot.cpp b/pocs/vdot/vdot.cpp index 88e66ea13..2dca62848 100644 --- a/pocs/vdot/vdot.cpp +++ b/pocs/vdot/vdot.cpp @@ -9,6 +9,7 @@ #include #include +#include #if defined(_MSC_VER) #pragma warning(disable: 4244 4267) // possible loss of data @@ -236,7 +237,7 @@ int main(int argc, char** argv) { int n4 = useQ4_1 ? kVecSize / QK4_1 : kVecSize / QK4_0; n4 = 64*((n4 + 63)/64); int n8 = kVecSize / QK8_0; n8 = 64*((n8 + 63)/64); - const auto * funcs = useQ4_1 ? ggml_get_type_traits(GGML_TYPE_Q4_1) : ggml_get_type_traits(GGML_TYPE_Q4_0); + const auto * funcs_cpu = ggml_get_type_traits_cpu(useQ4_1 ? GGML_TYPE_Q4_1 : GGML_TYPE_Q4_0); std::vector q40; std::vector q41; @@ -261,9 +262,9 @@ int main(int argc, char** argv) { // Note, we do not include this in the timing as in practical application // we already have the quantized model weights. if (useQ4_1) { - funcs->from_float(x1.data(), q41.data(), kVecSize); + funcs_cpu->from_float(x1.data(), q41.data(), kVecSize); } else { - funcs->from_float(x1.data(), q40.data(), kVecSize); + funcs_cpu->from_float(x1.data(), q40.data(), kVecSize); } // Now measure time the dot product needs using the "scalar" version above @@ -282,10 +283,10 @@ int main(int argc, char** argv) { dot_q4_q8(kVecSize, &result, q40.data(), q8.data()); } else { - const auto * vdot = ggml_get_type_traits(funcs->vec_dot_type); + const auto * vdot = ggml_get_type_traits_cpu(funcs_cpu->vec_dot_type); vdot->from_float(y1.data(), q8.data(), kVecSize); - if (useQ4_1) funcs->vec_dot(kVecSize, &result, 0, q41.data(), 0, q8.data(), 0, 1); - else funcs->vec_dot(kVecSize, &result, 0, q40.data(), 0, q8.data(), 0, 1); + if (useQ4_1) funcs_cpu->vec_dot(kVecSize, &result, 0, q41.data(), 0, q8.data(), 0, 1); + else funcs_cpu->vec_dot(kVecSize, &result, 0, q40.data(), 0, q8.data(), 0, 1); } sumq += result; t2 = std::chrono::high_resolution_clock::now(); diff --git a/scripts/compare-commits.sh b/scripts/compare-commits.sh index 8b9b1ad39..143d98729 100755 --- a/scripts/compare-commits.sh +++ b/scripts/compare-commits.sh @@ -16,15 +16,21 @@ bench_args="${@:3}" rm -f llama-bench.sqlite > /dev/null # to test a backend, call the script with the corresponding environment variable (e.g. GGML_CUDA=1 ./scripts/compare-commits.sh ...) +if [ -n "$GGML_CUDA" ]; then + cmake_opts="-DGGML_CUDA=ON" +fi + +function run { + rm -fr build > /dev/null + cmake -B build -S . $cmake_opts > /dev/null + cmake --build build -t llama-bench > /dev/null + build/bin/llama-bench -o sql -oe md $bench_args | sqlite3 llama-bench.sqlite +} git checkout $1 > /dev/null -make clean > /dev/null -make -j$(nproc) $make_opts llama-bench > /dev/null -./llama-bench -o sql -oe md $bench_args | sqlite3 llama-bench.sqlite +run git checkout $2 > /dev/null -make clean > /dev/null -make -j$(nproc) $make_opts llama-bench > /dev/null -./llama-bench -o sql -oe md $bench_args | sqlite3 llama-bench.sqlite +run ./scripts/compare-llama-bench.py -b $1 -c $2 diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py index 4ac6b5fc0..5069ae638 100755 --- a/scripts/compare-llama-bench.py +++ b/scripts/compare-llama-bench.py @@ -19,22 +19,22 @@ logger = logging.getLogger("compare-llama-bench") # Properties by which to differentiate results per commit: KEY_PROPERTIES = [ - "cpu_info", "gpu_info", "n_gpu_layers", "cuda", "vulkan", "kompute", "metal", "sycl", "rpc", "gpu_blas", - "blas", "model_filename", "model_type", "n_batch", "n_ubatch", "embeddings", "n_threads", - "type_k", "type_v", "use_mmap", "no_kv_offload", "split_mode", "main_gpu", "tensor_split", "flash_attn", "n_prompt", "n_gen" + "cpu_info", "gpu_info", "backends", "n_gpu_layers", "model_filename", "model_type", "n_batch", "n_ubatch", + "embeddings", "cpu_mask", "cpu_strict", "poll", "n_threads", "type_k", "type_v", "use_mmap", "no_kv_offload", + "split_mode", "main_gpu", "tensor_split", "flash_attn", "n_prompt", "n_gen" ] # Properties that are boolean and are converted to Yes/No for the table: -BOOL_PROPERTIES = ["cuda", "vulkan", "kompute", "metal", "sycl", "gpu_blas", "blas", "embeddings", "use_mmap", "no_kv_offload", "flash_attn"] +BOOL_PROPERTIES = ["embeddings", "cpu_strict", "use_mmap", "no_kv_offload", "flash_attn"] # Header names for the table: PRETTY_NAMES = { - "cuda": "CUDA", "vulkan": "Vulkan", "kompute": "Kompute", "metal": "Metal", "sycl": "SYCL", "rpc": "RPC", - "gpu_blas": "GPU BLAS", "blas": "BLAS", "cpu_info": "CPU", "gpu_info": "GPU", "model_filename": "File", "model_type": "Model", - "model_size": "Model Size [GiB]", "model_n_params": "Num. of Par.", "n_batch": "Batch size", "n_ubatch": "Microbatch size", - "n_threads": "Threads", "type_k": "K type", "type_v": "V type", "n_gpu_layers": "GPU layers", "split_mode": "Split mode", - "main_gpu": "Main GPU", "no_kv_offload": "NKVO", "flash_attn": "FlashAttention", "tensor_split": "Tensor split", - "use_mmap": "Use mmap", "embeddings": "Embeddings", + "cpu_info": "CPU", "gpu_info": "GPU", "backends": "Backends", "n_gpu_layers": "GPU layers", + "model_filename": "File", "model_type": "Model", "model_size": "Model size [GiB]", + "model_n_params": "Num. of par.", "n_batch": "Batch size", "n_ubatch": "Microbatch size", + "embeddings": "Embeddings", "cpu_mask": "CPU mask", "cpu_strict": "CPU strict", "poll": "Poll", + "n_threads": "Threads", "type_k": "K type", "type_v": "V type", "split_mode": "Split mode", "main_gpu": "Main GPU", + "no_kv_offload": "NKVO", "flash_attn": "FlashAttention", "tensor_split": "Tensor split", "use_mmap": "Use mmap", } DEFAULT_SHOW = ["model_type"] # Always show these properties by default. @@ -303,14 +303,11 @@ else: show = [] # Show CPU and/or GPU by default even if the hardware for all results is the same: - if "gpu_blas" not in properties_different and "n_gpu_layers" not in properties_different: - gpu_blas = bool(rows_full[0][KEY_PROPERTIES.index("gpu_blas")]) + if "n_gpu_layers" not in properties_different: ngl = int(rows_full[0][KEY_PROPERTIES.index("n_gpu_layers")]) - if not gpu_blas or ngl != 99 and "cpu_info" not in properties_different: + if ngl != 99 and "cpu_info" not in properties_different: show.append("cpu_info") - if gpu_blas and "gpu_info" not in properties_different: - show.append("gpu_info") show += properties_different diff --git a/scripts/pod-llama.sh b/scripts/pod-llama.sh deleted file mode 100644 index 6e56e1ed0..000000000 --- a/scripts/pod-llama.sh +++ /dev/null @@ -1,212 +0,0 @@ -#!/bin/bash -# -# Use this script only on fresh pods (runpod.io)! -# Otherwise, it can break your environment! -# - -if [ -z "$1" ]; then - echo "Usage: $0 " - echo " 0: no models" - echo " 1: tinyllama-1b" - echo " 2: codellama-7b" - echo " 3: codellama-13b" - echo " 4: codellama-34b" - echo " 5: codellama-7b-instruct" - echo " 6: codellama-13b-instruct" - echo " 7: codellama-34b-instruct" - - exit 1 -fi - -set -x - -# setup deps -apt-get update -apt-get install -y git-lfs cmake cmake-curses-gui vim ruby -git-lfs install - -if [ ! -d "/workspace" ]; then - ln -sfn $(pwd) /workspace -fi - -# download data -cd /workspace - -# this is useful to git clone repos without doubling the disk size due to .git -git clone https://github.com/iboB/git-lfs-download -ln -sfn /workspace/git-lfs-download/git-lfs-download /usr/local/bin/git-lfs-download - -# llama.cpp -cd /workspace -git clone https://github.com/ggerganov/llama.cpp - -cd llama.cpp - -GGML_CUDA=1 make -j - -ln -sfn /workspace/TinyLlama-1.1B-Chat-v0.3 ./models/tinyllama-1b -ln -sfn /workspace/CodeLlama-7b-hf ./models/codellama-7b -ln -sfn /workspace/CodeLlama-13b-hf ./models/codellama-13b -ln -sfn /workspace/CodeLlama-34b-hf ./models/codellama-34b -ln -sfn /workspace/CodeLlama-7b-Instruct-hf ./models/codellama-7b-instruct -ln -sfn /workspace/CodeLlama-13b-Instruct-hf ./models/codellama-13b-instruct -ln -sfn /workspace/CodeLlama-34b-Instruct-hf ./models/codellama-34b-instruct - -pip install -r requirements.txt - -# cmake -cd /workspace/llama.cpp - -mkdir build-cublas -cd build-cublas - -cmake -DGGML_CUDA=1 ../ -make -j - -if [ "$1" -eq "0" ]; then - exit 0 -fi - -# more models -if [ "$1" -eq "1" ]; then - cd /workspace - - git-lfs-download https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3 - - cd /workspace/llama.cpp - - python3 examples/convert_legacy_llama.py ./models/tinyllama-1b --outfile ./models/tinyllama-1b/ggml-model-f16.gguf --outtype f16 - - ./llama-quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_0.gguf q4_0 - ./llama-quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_k.gguf q4_k - ./llama-quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q8_0.gguf q8_0 -fi - -if [ "$1" -eq "2" ]; then - cd /workspace - - git-lfs-download https://huggingface.co/codellama/CodeLlama-7b-hf --without *safetensors* - rm -v ./CodeLlama-7b-hf/*safetensors* - - cd /workspace/llama.cpp - - python3 examples/convert_legacy_llama.py ./models/codellama-7b --outfile ./models/codellama-7b/ggml-model-f16.gguf --outtype f16 - - ./llama-quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_0.gguf q4_0 - ./llama-quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_k.gguf q4_k - ./llama-quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q8_0.gguf q8_0 -fi - -if [ "$1" -eq "3" ]; then - cd /workspace - - git-lfs-download https://huggingface.co/codellama/CodeLlama-13b-hf --without *safetensors* - rm -v ./CodeLlama-13b-hf/*safetensors* - - cd /workspace/llama.cpp - - python3 examples/convert_legacy_llama.py ./models/codellama-13b --outfile ./models/codellama-13b/ggml-model-f16.gguf --outtype f16 - - ./llama-quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_0.gguf q4_0 - ./llama-quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_k.gguf q4_k - ./llama-quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q8_0.gguf q8_0 -fi - -if [ "$1" -eq "4" ]; then - cd /workspace - - git-lfs-download https://huggingface.co/codellama/CodeLlama-34b-hf --without *safetensors* - rm -v ./CodeLlama-34b-hf/*safetensors* - - cd /workspace/llama.cpp - - python3 examples/convert_legacy_llama.py ./models/codellama-34b --outfile ./models/codellama-34b/ggml-model-f16.gguf --outtype f16 - - ./llama-quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_0.gguf q4_0 - ./llama-quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_k.gguf q4_k - ./llama-quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q8_0.gguf q8_0 -fi - -if [ "$1" -eq "5" ]; then - cd /workspace - - git-lfs-download https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf --without *safetensors* - rm -v ./CodeLlama-7b-Instruct-hf/*safetensors* - - cd /workspace/llama.cpp - - python3 examples/convert_legacy_llama.py ./models/codellama-7b-instruct --outfile ./models/codellama-7b-instruct/ggml-model-f16.gguf --outtype f16 - - ./llama-quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_0.gguf q4_0 - ./llama-quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_k.gguf q4_k - ./llama-quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q8_0.gguf q8_0 -fi - -if [ "$1" -eq "6" ]; then - cd /workspace - - git-lfs-download https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf --without *safetensors* - rm -v ./CodeLlama-13b-Instruct-hf/*safetensors* - - cd /workspace/llama.cpp - - python3 examples/convert_legacy_llama.py ./models/codellama-13b-instruct --outfile ./models/codellama-13b-instruct/ggml-model-f16.gguf --outtype f16 - - ./llama-quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_0.gguf q4_0 - ./llama-quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_k.gguf q4_k - ./llama-quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q8_0.gguf q8_0 -fi - -if [ "$1" -eq "7" ]; then - cd /workspace - - git-lfs-download https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf --without *safetensors* - rm -v ./CodeLlama-34b-Instruct-hf/*safetensors* - - cd /workspace/llama.cpp - - python3 examples/convert_legacy_llama.py ./models/codellama-34b-instruct --outfile ./models/codellama-34b-instruct/ggml-model-f16.gguf --outtype f16 - - ./llama-quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_0.gguf q4_0 - ./llama-quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_k.gguf q4_k - ./llama-quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q8_0.gguf q8_0 -fi - -if [ "$1" -eq "1" ]; then - # perf + perplexity - cd /workspace/llama.cpp/build-cublas - - make -j && ../scripts/run-all-perf.sh tinyllama-1b "f16" "-ngl 99 -t 1 -p 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,32,64,128,256,512,1024,2048 -n 128" - - ../scripts/get-wikitext-2.sh - unzip wikitext-2-raw-v1.zip - - make -j && ./bin/llama-perplexity -m ../models/tinyllama-1b/ggml-model-f16.gguf -f ./wikitext-2-raw/wiki.test.raw -ngl 100 --chunks 32 - - # batched - cd /workspace/llama.cpp - - GGML_CUDA=1 make -j && ./llama-batched ./models/tinyllama-1b/ggml-model-f16.gguf "Hello, my name is" 8 128 999 - - # batched-bench - cd /workspace/llama.cpp - - GGML_CUDA=1 make -j && ./llama-batched-bench ./models/tinyllama-1b/ggml-model-f16.gguf 4608 1 99 0 512 128 1,2,3,4,5,6,7,8,16,32 - - # parallel - cd /workspace/llama.cpp - - GGML_CUDA=1 make -j && ./llama-parallel -m ./models/tinyllama-1b/ggml-model-f16.gguf -t 1 -ngl 100 -c 4096 -b 512 -s 1 -np 8 -ns 128 -n 100 -cb - -fi - -# speculative -#if [ "$1" -eq "7" ]; then -# cd /workspace/llama.cpp -# -# GGML_CUDA=1 make -j && ./llama-speculative -m ./models/codellama-34b-instruct/ggml-model-f16.gguf -md ./models/codellama-7b-instruct/ggml-model-q4_0.gguf -p "# Dijkstra's shortest path algorithm in Python (4 spaces indentation) + complexity analysis:\n\n" -e -ngl 999 -ngld 999 -t 4 -n 512 -c 4096 -s 21 --draft 16 -np 1 --temp 0.0 -#fi - -# more benches -#GGML_CUDA=1 make -j && ./llama-batched-bench ./models/codellama-7b/ggml-model-q4_k.gguf 4096 1 99 1 512,3200 128,128,800 1 -#GGML_CUDA=1 make -j && ./llama-batched-bench ./models/codellama-13b/ggml-model-q4_k.gguf 4096 1 99 1 512,3200 128,128,800 1 diff --git a/scripts/run-with-preset.py b/scripts/run-with-preset.py deleted file mode 100755 index 8f0bf8ca8..000000000 --- a/scripts/run-with-preset.py +++ /dev/null @@ -1,146 +0,0 @@ -#!/usr/bin/env python3 - -import logging -import argparse -import os -import subprocess -import sys - -import yaml - -logger = logging.getLogger("run-with-preset") - -CLI_ARGS_LLAMA_CLI_PERPLEXITY = [ - "batch-size", "cfg-negative-prompt", "cfg-scale", "chunks", "color", "ctx-size", "escape", - "export", "file", "frequency-penalty", "grammar", "grammar-file", "hellaswag", - "hellaswag-tasks", "ignore-eos", "in-prefix", "in-prefix-bos", "in-suffix", - "interactive", "interactive-first", "keep", "logdir", "logit-bias", "lora", "lora-base", - "low-vram", "main-gpu", "mirostat", "mirostat-ent", "mirostat-lr", "mlock", - "model", "multiline-input", "n-gpu-layers", "n-predict", "no-mmap", "no-mul-mat-q", - "np-penalize-nl", "numa", "ppl-output-type", "ppl-stride", "presence-penalty", "prompt", - "prompt-cache", "prompt-cache-all", "prompt-cache-ro", "repeat-last-n", - "repeat-penalty", "reverse-prompt", "rope-freq-base", "rope-freq-scale", "rope-scale", "seed", - "simple-io", "tensor-split", "threads", "temp", "top-k", "top-p", "typical", - "verbose-prompt" -] - -CLI_ARGS_LLAMA_BENCH = [ - "batch-size", "low-vram", "model", "mul-mat-q", "n-gen", "n-gpu-layers", - "n-prompt", "output", "repetitions", "tensor-split", "threads", "verbose" -] - -CLI_ARGS_LLAMA_SERVER = [ - "alias", "batch-size", "ctx-size", "embedding", "host", "lora", "lora-base", - "low-vram", "main-gpu", "mlock", "model", "n-gpu-layers", "n-probs", "no-mmap", "no-mul-mat-q", - "numa", "path", "port", "rope-freq-base", "timeout", "rope-freq-scale", "tensor-split", - "threads", "verbose" -] - -description = """Run llama.cpp binaries with presets from YAML file(s). -To specify which binary should be run, specify the "binary" property (llama-cli, llama-perplexity, llama-bench, and llama-server are supported). -To get a preset file template, run a llama.cpp binary with the "--logdir" CLI argument. - -Formatting considerations: -- The YAML property names are the same as the CLI argument names of the corresponding binary. -- Properties must use the long name of their corresponding llama.cpp CLI arguments. -- Like the llama.cpp binaries the property names do not differentiate between hyphens and underscores. -- Flags must be defined as ": true" to be effective. -- To define the logit_bias property, the expected format is ": " in the "logit_bias" namespace. -- To define multiple "reverse_prompt" properties simultaneously the expected format is a list of strings. -- To define a tensor split, pass a list of floats. -""" -usage = "run-with-preset.py [-h] [yaml_files ...] [-- ...]" -epilog = (" -- specify additional CLI ars to be passed to the binary (override all preset files). " - "Unknown args will be ignored.") - -parser = argparse.ArgumentParser( - description=description, usage=usage, epilog=epilog, formatter_class=argparse.RawTextHelpFormatter) -parser.add_argument("-bin", "--binary", help="The binary to run.") -parser.add_argument("yaml_files", nargs="*", - help="Arbitrary number of YAML files from which to read preset values. " - "If two files specify the same values the later one will be used.") -parser.add_argument("--verbose", action="store_true", help="increase output verbosity") - -known_args, unknown_args = parser.parse_known_args() - -if not known_args.yaml_files and not unknown_args: - parser.print_help() - sys.exit(0) - -logging.basicConfig(level=logging.DEBUG if known_args.verbose else logging.INFO) - -props = dict() - -for yaml_file in known_args.yaml_files: - with open(yaml_file, "r") as f: - props.update(yaml.load(f, yaml.SafeLoader)) - -props = {prop.replace("_", "-"): val for prop, val in props.items()} - -binary = props.pop("binary", "llama-cli") -if known_args.binary: - binary = known_args.binary - -if os.path.exists(f"./{binary}"): - binary = f"./{binary}" - -if binary.lower().endswith("llama-cli") or binary.lower().endswith("llama-perplexity"): - cli_args = CLI_ARGS_LLAMA_CLI_PERPLEXITY -elif binary.lower().endswith("llama-bench"): - cli_args = CLI_ARGS_LLAMA_BENCH -elif binary.lower().endswith("llama-server"): - cli_args = CLI_ARGS_LLAMA_SERVER -else: - logger.error(f"Unknown binary: {binary}") - sys.exit(1) - -command_list = [binary] - -for cli_arg in cli_args: - value = props.pop(cli_arg, None) - - if not value or value == -1: - continue - - if cli_arg == "logit-bias": - for token, bias in value.items(): - command_list.append("--logit-bias") - command_list.append(f"{token}{bias:+}") - continue - - if cli_arg == "reverse-prompt" and not isinstance(value, str): - for rp in value: - command_list.append("--reverse-prompt") - command_list.append(str(rp)) - continue - - command_list.append(f"--{cli_arg}") - - if cli_arg == "tensor-split": - command_list.append(",".join([str(v) for v in value])) - continue - - value = str(value) - - if value != "True": - command_list.append(str(value)) - -num_unused = len(props) -if num_unused > 10: - logger.info(f"The preset file contained a total of {num_unused} unused properties.") -elif num_unused > 0: - logger.info("The preset file contained the following unused properties:") - for prop, value in props.items(): - logger.info(f" {prop}: {value}") - -command_list += unknown_args - -sp = subprocess.Popen(command_list) - -while sp.returncode is None: - try: - sp.wait() - except KeyboardInterrupt: - pass - -sys.exit(sp.returncode) diff --git a/scripts/server-llm.sh b/scripts/server-llm.sh deleted file mode 100644 index 802592a3e..000000000 --- a/scripts/server-llm.sh +++ /dev/null @@ -1,418 +0,0 @@ -#!/bin/bash -# -# Helper script for deploying llama.cpp server with a single Bash command -# -# - Works on Linux and macOS -# - Supports: CPU, CUDA, Metal -# - Can run all GGUF models from HuggingFace -# - Can serve requests in parallel -# - Always builds latest llama.cpp from GitHub -# -# Limitations -# -# - Chat templates are poorly supported (base models recommended) -# - Might be unstable! -# -# Usage: -# ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose] [-non-interactive] -# -# --port: port number, default is 8888 -# --repo: path to a repo containing GGUF model files -# --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input -# --backend: cpu, cuda, metal, depends on the OS -# --gpu-id: gpu id, default is 0 -# --n-parallel: number of parallel requests, default is 8 -# --n-kv: KV cache size, default is 4096 -# --verbose: verbose output -# --non-interactive: run without asking a permission to run -# -# Example: -# -# bash -c "$(curl -s https://ggml.ai/server-llm.sh)" -# - -set -e - -# required utils: curl, git, make -if ! command -v curl &> /dev/null; then - printf "[-] curl not found\n" - exit 1 -fi -if ! command -v git &> /dev/null; then - printf "[-] git not found\n" - exit 1 -fi -if ! command -v make &> /dev/null; then - printf "[-] make not found\n" - exit 1 -fi - -# parse arguments -is_interactive=1 -port=8888 -repo="" -wtype="" -backend="cpu" - -# if macOS, use metal backend by default -if [[ "$OSTYPE" == "darwin"* ]]; then - backend="metal" -elif command -v nvcc &> /dev/null; then - backend="cuda" -fi - -gpu_id=0 -n_parallel=8 -n_kv=4096 -verbose=0 - -function print_usage { - printf "Usage:\n" - printf " ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose] [-non-interactive]\n\n" - printf " --port: port number, default is 8888\n" - printf " --repo: path to a repo containing GGUF model files\n" - printf " --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input\n" - printf " --backend: cpu, cuda, metal, depends on the OS\n" - printf " --gpu-id: gpu id, default is 0\n" - printf " --n-parallel: number of parallel requests, default is 8\n" - printf " --n-kv: KV cache size, default is 4096\n" - printf " --verbose: verbose output\n\n" - printf " --non-interactive: run without asking a permission to run\n" - printf "Example:\n\n" - printf ' bash -c "$(curl -s https://ggml.ai/server-llm.sh)"\n\n' -} - -while [[ $# -gt 0 ]]; do - key="$1" - case $key in - --non-interactive) - is_interactive=0 - shift - ;; - --port) - port="$2" - shift - shift - ;; - --repo) - repo="$2" - shift - shift - ;; - --wtype) - wtype="$2" - shift - shift - ;; - --backend) - backend="$2" - shift - shift - ;; - --gpu-id) - gpu_id="$2" - shift - shift - ;; - --n-parallel) - n_parallel="$2" - shift - shift - ;; - --n-kv) - n_kv="$2" - shift - shift - ;; - --verbose) - verbose=1 - shift - ;; - --help) - print_usage - exit 0 - ;; - *) - echo "Unknown argument: $key" - print_usage - exit 1 - ;; - esac -done - -# available weights types -wtypes=("F16" "Q8_0" "Q4_0" "Q4_1" "Q5_0" "Q5_1" "Q6_K" "Q5_K_M" "Q5_K_S" "Q4_K_M" "Q4_K_S" "Q3_K_L" "Q3_K_M" "Q3_K_S" "Q2_K") - -wfiles=() -for wt in "${wtypes[@]}"; do - wfiles+=("") -done - -# map wtype input to index -if [[ ! -z "$wtype" ]]; then - iw=-1 - is=0 - for wt in "${wtypes[@]}"; do - # uppercase - uwt=$(echo "$wt" | tr '[:lower:]' '[:upper:]') - if [[ "$uwt" == "$wtype" ]]; then - iw=$is - break - fi - is=$((is+1)) - done - - if [[ $iw -eq -1 ]]; then - printf "[-] Invalid weight type: %s\n" "$wtype" - exit 1 - fi - - wtype="$iw" -fi - -# sample repos -repos=( - "https://huggingface.co/TheBloke/Llama-2-7B-GGUF" - "https://huggingface.co/TheBloke/Llama-2-13B-GGUF" - "https://huggingface.co/TheBloke/Llama-2-70B-GGUF" - "https://huggingface.co/TheBloke/CodeLlama-7B-GGUF" - "https://huggingface.co/TheBloke/CodeLlama-13B-GGUF" - "https://huggingface.co/TheBloke/CodeLlama-34B-GGUF" - "https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF" - "https://huggingface.co/TheBloke/zephyr-7B-beta-GGUF" - "https://huggingface.co/TheBloke/OpenHermes-2-Mistral-7B-GGUF" - "https://huggingface.co/TheBloke/CausalLM-7B-GGUF" -) -if [ $is_interactive -eq 1 ]; then - printf "\n" - printf "[I] This is a helper script for deploying llama.cpp's server on this machine.\n\n" - printf " Based on the options that follow, the script might download a model file\n" - printf " from the internet, which can be a few GBs in size. The script will also\n" - printf " build the latest llama.cpp source code from GitHub, which can be unstable.\n" - printf "\n" - printf " Upon success, an HTTP server will be started and it will serve the selected\n" - printf " model using llama.cpp for demonstration purposes.\n" - printf "\n" - printf " Please note:\n" - printf "\n" - printf " - All new data will be stored in the current folder\n" - printf " - The server will be listening on all network interfaces\n" - printf " - The server will run with default settings which are not always optimal\n" - printf " - Do not judge the quality of a model based on the results from this script\n" - printf " - Do not use this script to benchmark llama.cpp\n" - printf " - Do not use this script in production\n" - printf " - This script is only for demonstration purposes\n" - printf "\n" - printf " If you don't know what you are doing, please press Ctrl-C to abort now\n" - printf "\n" - printf " Press Enter to continue ...\n\n" - - read -fi - -if [[ -z "$repo" ]]; then - printf "[+] No repo provided from the command line\n" - printf " Please select a number from the list below or enter an URL:\n\n" - - is=0 - for r in "${repos[@]}"; do - printf " %2d) %s\n" $is "$r" - is=$((is+1)) - done - - # ask for repo until index of sample repo is provided or an URL - while [[ -z "$repo" ]]; do - printf "\n Or choose one from: https://huggingface.co/models?sort=trending&search=gguf\n\n" - read -p "[+] Select repo: " repo - - # check if the input is a number - if [[ "$repo" =~ ^[0-9]+$ ]]; then - if [[ "$repo" -ge 0 && "$repo" -lt ${#repos[@]} ]]; then - repo="${repos[$repo]}" - else - printf "[-] Invalid repo index: %s\n" "$repo" - repo="" - fi - elif [[ "$repo" =~ ^https?:// ]]; then - repo="$repo" - else - printf "[-] Invalid repo URL: %s\n" "$repo" - repo="" - fi - done -fi - -# remove suffix -repo=$(echo "$repo" | sed -E 's/\/tree\/main$//g') - -printf "[+] Checking for GGUF model files in %s\n" "$repo" - -# find GGUF files in the source -# TODO: better logic -model_tree="${repo%/}/tree/main" -model_files=$(curl -s "$model_tree" | grep -i "\\.gguf" | sed -E 's/.*(.*)<\/span><\/a>/\1/g') - -# list all files in the provided git repo -printf "[+] Model files:\n\n" -for file in $model_files; do - # determine iw by grepping the filename with wtypes - iw=-1 - is=0 - for wt in "${wtypes[@]}"; do - # uppercase - ufile=$(echo "$file" | tr '[:lower:]' '[:upper:]') - if [[ "$ufile" =~ "$wt" ]]; then - iw=$is - break - fi - is=$((is+1)) - done - - if [[ $iw -eq -1 ]]; then - continue - fi - - wfiles[$iw]="$file" - - have=" " - if [[ -f "$file" ]]; then - have="*" - fi - - printf " %2d) %s %s\n" $iw "$have" "$file" -done - -wfile="${wfiles[$wtype]}" - -# ask for weights type until provided and available -while [[ -z "$wfile" ]]; do - printf "\n" - read -p "[+] Select weight type: " wtype - wfile="${wfiles[$wtype]}" - - if [[ -z "$wfile" ]]; then - printf "[-] Invalid weight type: %s\n" "$wtype" - wtype="" - fi -done - -printf "[+] Selected weight type: %s (%s)\n" "$wtype" "$wfile" - -url="${repo%/}/resolve/main/$wfile" - -# check file if the model has been downloaded before -chk="$wfile.chk" - -# check if we should download the file -# - if $wfile does not exist -# - if $wfile exists but $chk does not exist -# - if $wfile exists and $chk exists but $wfile is newer than $chk -# TODO: better logic using git lfs info - -do_download=0 - -if [[ ! -f "$wfile" ]]; then - do_download=1 -elif [[ ! -f "$chk" ]]; then - do_download=1 -elif [[ "$wfile" -nt "$chk" ]]; then - do_download=1 -fi - -if [[ $do_download -eq 1 ]]; then - printf "[+] Downloading weights from %s\n" "$url" - - # download the weights file - curl -o "$wfile" -# -L "$url" - - # create a check file if successful - if [[ $? -eq 0 ]]; then - printf "[+] Creating check file %s\n" "$chk" - touch "$chk" - fi -else - printf "[+] Using cached weights %s\n" "$wfile" -fi - -# get latest llama.cpp and build - -printf "[+] Downloading latest llama.cpp\n" - -llama_cpp_dir="__llama_cpp_port_${port}__" - -if [[ -d "$llama_cpp_dir" && ! -f "$llama_cpp_dir/__ggml_script__" ]]; then - # if the dir exists and there isn't a file "__ggml_script__" in it, abort - printf "[-] Directory %s already exists\n" "$llama_cpp_dir" - printf "[-] Please remove it and try again\n" - exit 1 -elif [[ -d "$llama_cpp_dir" ]]; then - printf "[+] Directory %s already exists\n" "$llama_cpp_dir" - printf "[+] Using cached llama.cpp\n" - - cd "$llama_cpp_dir" - git reset --hard - git fetch - git checkout origin/master - - cd .. -else - printf "[+] Cloning llama.cpp\n" - - git clone https://github.com/ggerganov/llama.cpp "$llama_cpp_dir" -fi - -# mark that that the directory is made by this script -touch "$llama_cpp_dir/__ggml_script__" - -if [[ $verbose -eq 1 ]]; then - set -x -fi - -# build -cd "$llama_cpp_dir" - -make clean - -log="--silent" -if [[ $verbose -eq 1 ]]; then - log="" -fi - -if [[ "$backend" == "cuda" ]]; then - printf "[+] Building with CUDA backend\n" - GGML_CUDA=1 make -j llama-server $log -elif [[ "$backend" == "cpu" ]]; then - printf "[+] Building with CPU backend\n" - make -j llama-server $log -elif [[ "$backend" == "metal" ]]; then - printf "[+] Building with Metal backend\n" - make -j llama-server $log -else - printf "[-] Unknown backend: %s\n" "$backend" - exit 1 -fi - -# run the server - -printf "[+] Running server\n" - -args="" -if [[ "$backend" == "cuda" ]]; then - export CUDA_VISIBLE_DEVICES=$gpu_id - args="-ngl 999" -elif [[ "$backend" == "cpu" ]]; then - args="-ngl 0" -elif [[ "$backend" == "metal" ]]; then - args="-ngl 999" -else - printf "[-] Unknown backend: %s\n" "$backend" - exit 1 -fi - -if [[ $verbose -eq 1 ]]; then - args="$args --verbose" -fi - -./llama-server -m "../$wfile" --host 0.0.0.0 --port "$port" -c $n_kv -np "$n_parallel" $args - -exit 0 diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh index fba29b935..8cf25b77f 100755 --- a/scripts/sync-ggml-am.sh +++ b/scripts/sync-ggml-am.sh @@ -73,17 +73,19 @@ while read c; do src/ggml*.h \ src/ggml*.c \ src/ggml*.cpp \ - src/ggml*.m \ - src/ggml*.metal \ - src/ggml*.cu \ - src/ggml-amx/* \ + src/ggml-blas/* \ src/ggml-cann/* \ + src/ggml-cpu/* \ src/ggml-cuda/* \ + src/ggml-hip/* \ + src/ggml-kompute/* \ + src/ggml-metal/* \ + src/ggml-musa/* \ + src/ggml-rpc/* \ src/ggml-sycl/* \ - src/vulkan-shaders/* \ + src/ggml-vulkan/* \ include/ggml*.h \ tests/test-opt.cpp \ - tests/test-grad0.cpp \ tests/test-quantize-fns.cpp \ tests/test-quantize-perf.cpp \ tests/test-backend-ops.cpp \ @@ -114,52 +116,28 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then # replace filenames: # - # CMakelists.txt -> ggml/CMakeLists.txt - # src/CMakeLists.txt -> ggml/src/CMakeLists.txt - # cmake/FindSIMD.cmake -> ggml/cmake/FindSIMD.cmake + # CMakelists.txt -> ggml/CMakeLists.txt + # src/CMakeLists.txt -> ggml/src/CMakeLists.txt + # cmake/FindSIMD.cmake -> ggml/cmake/FindSIMD.cmake # - # src/ggml.c -> ggml/src/ggml.c - # src/ggml-aarch64.c -> ggml/src/ggml-aarch64.c - # src/ggml-aarch64.h -> ggml/src/ggml-aarch64.h - # src/ggml-alloc.c -> ggml/src/ggml-alloc.c - # src/ggml-amx/* -> ggml/src/ggml-amx/ - # src/ggml-amx.cpp -> ggml/src/ggml-amx.cpp - # src/ggml-backend-impl.h -> ggml/src/ggml-backend-impl.h - # src/ggml-backend.cpp -> ggml/src/ggml-backend.cpp - # src/ggml-cann/* -> ggml/src/ggml-cann/ - # src/ggml-cann.cpp -> ggml/src/ggml-cann.cpp - # src/ggml-common.h -> ggml/src/ggml-common.h - # src/ggml-cuda/* -> ggml/src/ggml-cuda/ - # src/ggml-cuda.cu -> ggml/src/ggml-cuda.cu - # src/ggml-impl.h -> ggml/src/ggml-impl.h - # src/ggml-kompute.cpp -> ggml/src/ggml-kompute.cpp - # src/ggml-metal.m -> ggml/src/ggml-metal.m - # src/ggml-quants.c -> ggml/src/ggml-quants.c - # src/ggml-quants.h -> ggml/src/ggml-quants.h - # src/ggml-rpc.cpp -> ggml/src/ggml-rpc.cpp - # src/ggml-sycl/* -> ggml/src/ggml-sycl/ - # src/ggml-sycl.cpp -> ggml/src/ggml-sycl.cpp - # src/ggml-vulkan.cpp -> ggml/src/ggml-vulkan.cpp - # src/vulkan-shaders/* -> ggml/src/vulkan-shaders/ + # src/ggml*.c -> ggml/src/ggml*.c + # src/ggml*.cpp -> ggml/src/ggml*.cpp + # src/ggml*.h -> ggml/src/ggml*.h + # src/ggml-blas/* -> ggml/src/ggml-blas/* + # src/ggml-cann/* -> ggml/src/ggml-cann/* + # src/ggml-cpu/* -> ggml/src/ggml-cpu/* + # src/ggml-cuda/* -> ggml/src/ggml-cuda/* + # src/ggml-hip/* -> ggml/src/ggml-hip/* + # src/ggml-kompute/* -> ggml/src/ggml-kompute/* + # src/ggml-metal/* -> ggml/src/ggml-metal/* + # src/ggml-musa/* -> ggml/src/ggml-musa/* + # src/ggml-rpc/* -> ggml/src/ggml-rpc/* + # src/ggml-sycl/* -> ggml/src/ggml-sycl/* + # src/ggml-vulkan/* -> ggml/src/ggml-vulkan/* # - # include/ggml.h -> ggml/include/ggml.h - # include/ggml-alloc.h -> ggml/include/ggml-alloc.h - # include/ggml-amx.h -> ggml/include/ggml-amx.h - # include/ggml-backend.h -> ggml/include/ggml-backend.h - # include/ggml-blas.h -> ggml/include/ggml-blas.h - # include/ggml-cann.h -> ggml/include/ggml-cann.h - # include/ggml-cuda.h -> ggml/include/ggml-cuda.h - # include/ggml-kompute.h -> ggml/include/ggml-kompute.h - # include/ggml-metal.h -> ggml/include/ggml-metal.h - # include/ggml-rpc.h -> ggml/include/ggml-rpc.h - # include/ggml-sycl.h -> ggml/include/ggml-sycl.h - # include/ggml-vulkan.h -> ggml/include/ggml-vulkan.h + # include/ggml*.h -> ggml/include/ggml*.h # - # tests/test-opt.cpp -> tests/test-opt.cpp - # tests/test-grad0.cpp -> tests/test-grad0.cpp - # tests/test-quantize-fns.cpp -> tests/test-quantize-fns.cpp - # tests/test-quantize-perf.cpp -> tests/test-quantize-perf.cpp - # tests/test-backend-ops.cpp -> tests/test-backend-ops.cpp + # tests/test*.cpp -> tests/ # # LICENSE -> LICENSE # scripts/gen-authors.sh -> scripts/gen-authors.sh @@ -168,45 +146,22 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then -e 's/([[:space:]]|[ab]\/)CMakeLists.txt/\1ggml\/CMakeLists.txt/g' \ -e 's/([[:space:]]|[ab]\/)src\/CMakeLists.txt/\1ggml\/src\/CMakeLists.txt/g' \ -e 's/([[:space:]]|[ab]\/)cmake\/FindSIMD.cmake/\1ggml\/cmake\/FindSIMD.cmake/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml\.c/\1ggml\/src\/ggml.c/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-aarch64\.c/\1ggml\/src\/ggml-aarch64.c/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-aarch64\.h/\1ggml\/src\/ggml-aarch64.h/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-alloc\.c/\1ggml\/src\/ggml-alloc.c/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-amx\//\1ggml\/src\/ggml-amx\//g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-amx\.cpp/\1ggml\/src\/ggml-amx.cpp/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-backend-impl\.h/\1ggml\/src\/ggml-backend-impl.h/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-backend\.cpp/\1ggml\/src\/ggml-backend.cpp/g' \ + -e 's/([[:space:]]|[ab]\/)src\/ggml(.*)\.c/\1ggml\/src\/ggml\2.c/g' \ + -e 's/([[:space:]]|[ab]\/)src\/ggml(.*)\.cpp/\1ggml\/src\/ggml\2.cpp/g' \ + -e 's/([[:space:]]|[ab]\/)src\/ggml(.*)\.h/\1ggml\/src\/ggml\2.h/g' \ + -e 's/([[:space:]]|[ab]\/)src\/ggml-blas\//\1ggml\/src\/ggml-blas\//g' \ -e 's/([[:space:]]|[ab]\/)src\/ggml-cann\//\1ggml\/src\/ggml-cann\//g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-cann\.cpp/\1ggml\/src\/ggml-cann.cpp/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-common\.h/\1ggml\/src\/ggml-common.h/g' \ + -e 's/([[:space:]]|[ab]\/)src\/ggml-cpu\//\1ggml\/src\/ggml-cpu\//g' \ -e 's/([[:space:]]|[ab]\/)src\/ggml-cuda\//\1ggml\/src\/ggml-cuda\//g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-cuda\.cu/\1ggml\/src\/ggml-cuda.cu/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-impl\.h/\1ggml\/src\/ggml-impl.h/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-kompute\.cpp/\1ggml\/src\/ggml-kompute.cpp/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-metal\.m/\1ggml\/src\/ggml-metal.m/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-quants\.c/\1ggml\/src\/ggml-quants.c/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-quants\.h/\1ggml\/src\/ggml-quants.h/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-rpc\.cpp/\1ggml\/src\/ggml-rpc.cpp/g' \ + -e 's/([[:space:]]|[ab]\/)src\/ggml-hip\//\1ggml\/src\/ggml-hip\//g' \ + -e 's/([[:space:]]|[ab]\/)src\/ggml-kompute\//\1ggml\/src\/ggml-kompute\//g' \ + -e 's/([[:space:]]|[ab]\/)src\/ggml-metal\//\1ggml\/src\/ggml-metal\//g' \ + -e 's/([[:space:]]|[ab]\/)src\/ggml-musa\//\1ggml\/src\/ggml-musa\//g' \ + -e 's/([[:space:]]|[ab]\/)src\/ggml-rpc\//\1ggml\/src\/ggml-rpc\//g' \ -e 's/([[:space:]]|[ab]\/)src\/ggml-sycl\//\1ggml\/src\/ggml-sycl\//g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-sycl\.cpp/\1ggml\/src\/ggml-sycl.cpp/g' \ - -e 's/([[:space:]]|[ab]\/)src\/ggml-vulkan\.cpp/\1ggml\/src\/ggml-vulkan.cpp/g' \ - -e 's/([[:space:]]|[ab]\/)src\/vulkan-shaders\//\1ggml\/src\/vulkan-shaders\//g' \ - -e 's/([[:space:]]|[ab]\/)include\/ggml\.h/\1ggml\/include\/ggml.h/g' \ - -e 's/([[:space:]]|[ab]\/)include\/ggml-alloc\.h/\1ggml\/include\/ggml-alloc.h/g' \ - -e 's/([[:space:]]|[ab]\/)include\/ggml-amx\.h/\1ggml\/include\/ggml-amx.h/g' \ - -e 's/([[:space:]]|[ab]\/)include\/ggml-backend\.h/\1ggml\/include\/ggml-backend.h/g' \ - -e 's/([[:space:]]|[ab]\/)include\/ggml-blas\.h/\1ggml\/include\/ggml-blas.h/g' \ - -e 's/([[:space:]]|[ab]\/)include\/ggml-cann\.h/\1ggml\/include\/ggml-cann.h/g' \ - -e 's/([[:space:]]|[ab]\/)include\/ggml-cuda\.h/\1ggml\/include\/ggml-cuda.h/g' \ - -e 's/([[:space:]]|[ab]\/)include\/ggml-kompute\.h/\1ggml\/include\/ggml-kompute.h/g' \ - -e 's/([[:space:]]|[ab]\/)include\/ggml-metal\.h/\1ggml\/include\/ggml-metal.h/g' \ - -e 's/([[:space:]]|[ab]\/)include\/ggml-rpc\.h/\1ggml\/include\/ggml-rpc.h/g' \ - -e 's/([[:space:]]|[ab]\/)include\/ggml-sycl\.h/\1ggml\/include\/ggml-sycl.h/g' \ - -e 's/([[:space:]]|[ab]\/)include\/ggml-vulkan\.h/\1ggml\/include\/ggml-vulkan.h/g' \ - -e 's/([[:space:]]|[ab]\/)examples\/common\.h/\1examples\/common.h/g' \ - -e 's/([[:space:]]|[ab]\/)examples\/common\.cpp/\1examples\/common.cpp/g' \ - -e 's/([[:space:]]|[ab]\/)examples\/common-ggml\.h/\1examples\/common-ggml.h/g' \ - -e 's/([[:space:]]|[ab]\/)examples\/common-ggml\.cpp/\1examples\/common-ggml.cpp/g' \ + -e 's/([[:space:]]|[ab]\/)src\/ggml-vulkan\//\1ggml\/src\/ggml-vulkan\//g' \ + -e 's/([[:space:]]|[ab]\/)include\/ggml(.*)\.h/\1ggml\/include\/ggml\2.h/g' \ + -e 's/([[:space:]]|[ab]\/)tests\/(.*)\.cpp/\1tests\/\2.cpp/g' \ -e 's/([[:space:]]|[ab]\/)LICENSE/\1LICENSE/g' \ -e 's/([[:space:]]|[ab]\/)scripts\/gen-authors\.sh/\1scripts\/gen-authors.sh/g' \ > ggml-src.patch.tmp diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index da40927e1..47eae44f7 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -162e232411ee98ceb0cccfa84886118d917d2123 +74d66b63eaf207a24f3e93bb922aba131cbf2906 diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh index f5d87324a..f81615bb6 100755 --- a/scripts/sync-ggml.sh +++ b/scripts/sync-ggml.sh @@ -4,46 +4,24 @@ cp -rpv ../ggml/CMakeLists.txt ./ggml/CMakeLists.txt cp -rpv ../ggml/src/CMakeLists.txt ./ggml/src/CMakeLists.txt cp -rpv ../ggml/cmake/FindSIMD.cmake ./ggml/cmake/FindSIMD.cmake -cp -rpv ../ggml/src/ggml.c ./ggml/src/ggml.c -cp -rpv ../ggml/src/ggml-aarch64.c ./ggml/src/ggml-aarch64.c -cp -rpv ../ggml/src/ggml-aarch64.h ./ggml/src/ggml-aarch64.h -cp -rpv ../ggml/src/ggml-alloc.c ./ggml/src/ggml-alloc.c -cp -rpv ../ggml/src/ggml-amx/* ./ggml/src/ggml-amx/ -cp -rpv ../ggml/src/ggml-amx.cpp ./ggml/src/ggml-amx.cpp -cp -rpv ../ggml/src/ggml-backend-impl.h ./ggml/src/ggml-backend-impl.h -cp -rpv ../ggml/src/ggml-backend.cpp ./ggml/src/ggml-backend.cpp -cp -rpv ../ggml/src/ggml-cann/* ./ggml/src/ggml-cann/ -cp -rpv ../ggml/src/ggml-cann.cpp ./ggml/src/ggml-cann.cpp -cp -rpv ../ggml/src/ggml-common.h ./ggml/src/ggml-common.h -cp -rpv ../ggml/src/ggml-cuda/* ./ggml/src/ggml-cuda/ -cp -rpv ../ggml/src/ggml-cuda.cu ./ggml/src/ggml-cuda.cu -cp -rpv ../ggml/src/ggml-impl.h ./ggml/src/ggml-impl.h -cp -rpv ../ggml/src/ggml-kompute.cpp ./ggml/src/ggml-kompute.cpp -cp -rpv ../ggml/src/ggml-metal.m ./ggml/src/ggml-metal.m -cp -rpv ../ggml/src/ggml-metal.metal ./ggml/src/ggml-metal.metal -cp -rpv ../ggml/src/ggml-quants.c ./ggml/src/ggml-quants.c -cp -rpv ../ggml/src/ggml-quants.h ./ggml/src/ggml-quants.h -cp -rpv ../ggml/src/ggml-rpc.cpp ./ggml/src/ggml-rpc.cpp -cp -rpv ../ggml/src/ggml-sycl/* ./ggml/src/ggml-sycl/ -cp -rpv ../ggml/src/ggml-sycl.cpp ./ggml/src/ggml-sycl.cpp -cp -rpv ../ggml/src/ggml-vulkan.cpp ./ggml/src/ggml-vulkan.cpp -cp -rpv ../ggml/src/vulkan-shaders/* ./ggml/src/vulkan-shaders/ +cp -rpv ../ggml/src/ggml*.c ./ggml/src/ +cp -rpv ../ggml/src/ggml*.cpp ./ggml/src/ +cp -rpv ../ggml/src/ggml*.h ./ggml/src/ +cp -rpv ../ggml/src/ggml-blas/* ./ggml/src/ggml-blas/ +cp -rpv ../ggml/src/ggml-cann/* ./ggml/src/ggml-cann/ +cp -rpv ../ggml/src/ggml-cpu/* ./ggml/src/ggml-cpu/ +cp -rpv ../ggml/src/ggml-cuda/* ./ggml/src/ggml-cuda/ +cp -rpv ../ggml/src/ggml-hip/* ./ggml/src/ggml-hip/ +cp -rpv ../ggml/src/ggml-kompute/* ./ggml/src/ggml-kompute/ +cp -rpv ../ggml/src/ggml-metal/* ./ggml/src/ggml-metal/ +cp -rpv ../ggml/src/ggml-musa/* ./ggml/src/ggml-musa/ +cp -rpv ../ggml/src/ggml-rpc/* ./ggml/src/ggml-rpc/ +cp -rpv ../ggml/src/ggml-sycl/* ./ggml/src/ggml-sycl/ +cp -rpv ../ggml/src/ggml-vulkan/* ./ggml/src/ggml-vulkan/ -cp -rpv ../ggml/include/ggml.h ./ggml/include/ggml.h -cp -rpv ../ggml/include/ggml-alloc.h ./ggml/include/ggml-alloc.h -cp -rpv ../ggml/include/ggml-amx.h ./ggml/include/ggml-amx.h -cp -rpv ../ggml/include/ggml-backend.h ./ggml/include/ggml-backend.h -cp -rpv ../ggml/include/ggml-blas.h ./ggml/include/ggml-blas.h -cp -rpv ../ggml/include/ggml-cann.h ./ggml/include/ggml-cann.h -cp -rpv ../ggml/include/ggml-cuda.h ./ggml/include/ggml-cuda.h -cp -rpv ../ggml/include/ggml-kompute.h ./ggml/include/ggml-kompute.h -cp -rpv ../ggml/include/ggml-metal.h ./ggml/include/ggml-metal.h -cp -rpv ../ggml/include/ggml-rpc.h ./ggml/include/ggml-rpc.h -cp -rpv ../ggml/include/ggml-sycl.h ./ggml/include/ggml-sycl.h -cp -rpv ../ggml/include/ggml-vulkan.h ./ggml/include/ggml-vulkan.h +cp -rpv ../ggml/include/ggml*.h ./ggml/include/ cp -rpv ../ggml/tests/test-opt.cpp ./tests/test-opt.cpp -cp -rpv ../ggml/tests/test-grad0.cpp ./tests/test-grad0.cpp cp -rpv ../ggml/tests/test-quantize-fns.cpp ./tests/test-quantize-fns.cpp cp -rpv ../ggml/tests/test-quantize-perf.cpp ./tests/test-quantize-perf.cpp cp -rpv ../ggml/tests/test-backend-ops.cpp ./tests/test-backend-ops.cpp diff --git a/spm-headers/ggml-cpp.h b/spm-headers/ggml-cpp.h new file mode 120000 index 000000000..8a8604cc2 --- /dev/null +++ b/spm-headers/ggml-cpp.h @@ -0,0 +1 @@ +../ggml/include/ggml-cpp.h \ No newline at end of file diff --git a/spm-headers/ggml-cpu.h b/spm-headers/ggml-cpu.h new file mode 120000 index 000000000..66e629607 --- /dev/null +++ b/spm-headers/ggml-cpu.h @@ -0,0 +1 @@ +../ggml/include/ggml-cpu.h \ No newline at end of file diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt index 04a564012..de5c36cc6 100644 --- a/src/CMakeLists.txt +++ b/src/CMakeLists.txt @@ -5,6 +5,8 @@ if (WIN32) endif() endif() +llama_add_compile_flags() + # # libraries # @@ -23,11 +25,12 @@ add_library(llama ) target_include_directories(llama PUBLIC . ../include ../common) -target_compile_features (llama PUBLIC cxx_std_11) # don't bump +target_compile_features (llama PUBLIC cxx_std_17) # don't bump target_link_libraries(llama PUBLIC ggml) if (BUILD_SHARED_LIBS) set_target_properties(llama PROPERTIES POSITION_INDEPENDENT_CODE ON) - target_compile_definitions(llama PRIVATE LLAMA_SHARED LLAMA_BUILD) + target_compile_definitions(llama PRIVATE LLAMA_BUILD) + target_compile_definitions(llama PUBLIC LLAMA_SHARED) endif() diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp index 7d12bee1d..f5e4c56d1 100644 --- a/src/llama-sampling.cpp +++ b/src/llama-sampling.cpp @@ -1909,8 +1909,11 @@ static void llama_sampler_dry_reset(struct llama_sampler * smpl) { static struct llama_sampler * llama_sampler_dry_clone(const struct llama_sampler * smpl) { const auto * ctx = (llama_sampler_dry *) smpl->ctx; - // nullptr is passed as vocab because it is only needed for raw sequence breaker processing, which we have already done and will be copying - auto * result = llama_sampler_init_dry(nullptr, ctx->dry_multiplier, ctx->dry_base, ctx->dry_allowed_length, ctx->dry_penalty_last_n, NULL, 0); + llama_vocab dummy_vocab; + + // dummy vocab is passed because it is only needed for raw sequence breaker processing, which we have already done and will simply be copying + auto * result = llama_sampler_init_dry_impl(dummy_vocab, ctx->total_context_size, ctx->dry_multiplier, ctx->dry_base, ctx->dry_allowed_length, ctx->dry_penalty_last_n, NULL, 0); + // Copy the state, including the processed breakers { auto * result_ctx = (llama_sampler_dry *) result->ctx; diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index d1dc96276..8c9aaf5a0 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -418,6 +418,7 @@ struct llm_tokenizer_bpe : llm_tokenizer { case LLAMA_VOCAB_PRE_TYPE_SMOLLM: case LLAMA_VOCAB_PRE_TYPE_CODESHELL: case LLAMA_VOCAB_PRE_TYPE_EXAONE: + case LLAMA_VOCAB_PRE_TYPE_MINERVA: regex_exprs = { "\\p{N}", "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)", diff --git a/src/llama.cpp b/src/llama.cpp index de7eb56ca..7b43359de 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -7,6 +7,7 @@ #include "ggml.h" #include "ggml-alloc.h" #include "ggml-backend.h" +#include "ggml-cpp.h" // TODO: replace with ggml API call #define QK_K 256 @@ -178,6 +179,7 @@ enum llm_arch { LLM_ARCH_COMMAND_R, LLM_ARCH_DBRX, LLM_ARCH_OLMO, + LLM_ARCH_OLMO2, LLM_ARCH_OLMOE, LLM_ARCH_OPENELM, LLM_ARCH_ARCTIC, @@ -231,6 +233,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_COMMAND_R, "command-r" }, { LLM_ARCH_DBRX, "dbrx" }, { LLM_ARCH_OLMO, "olmo" }, + { LLM_ARCH_OLMO2, "olmo2" }, { LLM_ARCH_OLMOE, "olmoe" }, { LLM_ARCH_OPENELM, "openelm" }, { LLM_ARCH_ARCTIC, "arctic" }, @@ -1033,6 +1036,8 @@ static const std::map> LLM_TENSOR_N { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, { LLM_TENSOR_OUTPUT, "output" }, { LLM_TENSOR_ROPE_FREQS, "rope_freqs" }, + { LLM_TENSOR_ROPE_FACTORS_LONG, "rope_factors_long" }, + { LLM_TENSOR_ROPE_FACTORS_SHORT, "rope_factors_short" }, { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, @@ -1206,6 +1211,25 @@ static const std::map> LLM_TENSOR_N { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_OLMO2, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_ATTN_POST_NORM, "blk.%d.post_attention_norm" }, + { LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" }, + { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" }, + { LLM_TENSOR_FFN_POST_NORM, "blk.%d.post_ffw_norm" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, { LLM_ARCH_OLMOE, { @@ -1527,6 +1551,67 @@ static const std::map> LLM_TENSOR_N }, }; +enum llm_chat_template { + LLM_CHAT_TEMPLATE_CHATML, + LLM_CHAT_TEMPLATE_LLAMA_2, + LLM_CHAT_TEMPLATE_LLAMA_2_SYS, + LLM_CHAT_TEMPLATE_LLAMA_2_SYS_BOS, + LLM_CHAT_TEMPLATE_LLAMA_2_SYS_STRIP, + LLM_CHAT_TEMPLATE_MISTRAL_V1, + LLM_CHAT_TEMPLATE_MISTRAL_V3, + LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN, + LLM_CHAT_TEMPLATE_MISTRAL_V7, + LLM_CHAT_TEMPLATE_PHI_3, + LLM_CHAT_TEMPLATE_ZEPHYR, + LLM_CHAT_TEMPLATE_MONARCH, + LLM_CHAT_TEMPLATE_GEMMA, + LLM_CHAT_TEMPLATE_ORION, + LLM_CHAT_TEMPLATE_OPENCHAT, + LLM_CHAT_TEMPLATE_VICUNA, + LLM_CHAT_TEMPLATE_VICUNA_ORCA, + LLM_CHAT_TEMPLATE_DEEPSEEK, + LLM_CHAT_TEMPLATE_DEEPSEEK_2, + LLM_CHAT_TEMPLATE_COMMAND_R, + LLM_CHAT_TEMPLATE_LLAMA_3, + LLM_CHAT_TEMPLATE_CHATGML_3, + LLM_CHAT_TEMPLATE_CHATGML_4, + LLM_CHAT_TEMPLATE_MINICPM, + LLM_CHAT_TEMPLATE_EXAONE_3, + LLM_CHAT_TEMPLATE_RWKV_WORLD, + LLM_CHAT_TEMPLATE_GRANITE, + LLM_CHAT_TEMPLATE_UNKNOWN, +}; + +static const std::map LLM_CHAT_TEMPLATES = { + { "chatml", LLM_CHAT_TEMPLATE_CHATML }, + { "llama2", LLM_CHAT_TEMPLATE_LLAMA_2 }, + { "llama2-sys", LLM_CHAT_TEMPLATE_LLAMA_2_SYS }, + { "llama2-sys-bos", LLM_CHAT_TEMPLATE_LLAMA_2_SYS_BOS }, + { "llama2-sys-strip", LLM_CHAT_TEMPLATE_LLAMA_2_SYS_STRIP }, + { "mistral-v1", LLM_CHAT_TEMPLATE_MISTRAL_V1 }, + { "mistral-v3", LLM_CHAT_TEMPLATE_MISTRAL_V3 }, + { "mistral-v3-tekken", LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN }, + { "mistral-v7", LLM_CHAT_TEMPLATE_MISTRAL_V7 }, + { "phi3", LLM_CHAT_TEMPLATE_PHI_3 }, + { "zephyr", LLM_CHAT_TEMPLATE_ZEPHYR }, + { "monarch", LLM_CHAT_TEMPLATE_MONARCH }, + { "gemma", LLM_CHAT_TEMPLATE_GEMMA }, + { "orion", LLM_CHAT_TEMPLATE_ORION }, + { "openchat", LLM_CHAT_TEMPLATE_OPENCHAT }, + { "vicuna", LLM_CHAT_TEMPLATE_VICUNA }, + { "vicuna-orca", LLM_CHAT_TEMPLATE_VICUNA_ORCA }, + { "deepseek", LLM_CHAT_TEMPLATE_DEEPSEEK }, + { "deepseek2", LLM_CHAT_TEMPLATE_DEEPSEEK_2 }, + { "command-r", LLM_CHAT_TEMPLATE_COMMAND_R }, + { "llama3", LLM_CHAT_TEMPLATE_LLAMA_3 }, + { "chatglm3", LLM_CHAT_TEMPLATE_CHATGML_3 }, + { "chatglm4", LLM_CHAT_TEMPLATE_CHATGML_4 }, + { "minicpm", LLM_CHAT_TEMPLATE_MINICPM }, + { "exaone3", LLM_CHAT_TEMPLATE_EXAONE_3 }, + { "rwkv-world", LLM_CHAT_TEMPLATE_RWKV_WORLD }, + { "granite", LLM_CHAT_TEMPLATE_GRANITE }, +}; + static llm_arch llm_arch_from_string(const std::string & name) { for (const auto & kv : LLM_ARCH_NAMES) { // NOLINT if (kv.second == name) { @@ -1600,9 +1685,10 @@ struct LLM_TN { // static const std::map LLAMA_ROPE_SCALING_TYPES = { - { LLAMA_ROPE_SCALING_TYPE_NONE, "none" }, - { LLAMA_ROPE_SCALING_TYPE_LINEAR, "linear" }, - { LLAMA_ROPE_SCALING_TYPE_YARN, "yarn" }, + { LLAMA_ROPE_SCALING_TYPE_NONE, "none" }, + { LLAMA_ROPE_SCALING_TYPE_LINEAR, "linear" }, + { LLAMA_ROPE_SCALING_TYPE_YARN, "yarn" }, + { LLAMA_ROPE_SCALING_TYPE_LONGROPE, "longrope" }, }; static llama_rope_scaling_type llama_rope_scaling_type_from_string(const std::string & name) { @@ -2300,6 +2386,7 @@ enum e_model { MODEL_1B, MODEL_1_3B, MODEL_1_4B, + MODEL_1_5B, MODEL_1_6B, MODEL_2B, MODEL_2_8B, @@ -2318,6 +2405,7 @@ enum e_model { MODEL_16B, MODEL_20B, MODEL_30B, + MODEL_32B, MODEL_34B, MODEL_35B, MODEL_40B, @@ -2797,31 +2885,22 @@ struct llama_kv_cache { std::vector k_l; // per layer std::vector v_l; - std::vector ctxs; - std::vector bufs; + std::vector ctxs; + std::vector bufs; - size_t total_size() const { + size_t total_size() { size_t size = 0; - for (ggml_backend_buffer_t buf : bufs) { - size += ggml_backend_buffer_get_size(buf); + for (auto & buf : bufs) { + size += ggml_backend_buffer_get_size(buf.get()); } return size; } - - ~llama_kv_cache() { - for (struct ggml_context * ctx : ctxs) { - ggml_free(ctx); - } - for (ggml_backend_buffer_t buf : bufs) { - ggml_backend_buffer_free(buf); - } - } }; struct llama_control_vector { std::vector tensors; // per layer - std::vector ctxs; - std::vector bufs; + std::vector ctxs; + std::vector bufs; int32_t layer_start = -1; int32_t layer_end = -1; @@ -2840,15 +2919,6 @@ struct llama_control_vector { } return cur; } - - ~llama_control_vector() { - for (struct ggml_context * ctx : ctxs) { - ggml_free(ctx); - } - for (ggml_backend_buffer_t buf : bufs) { - ggml_backend_buffer_free(buf); - } - } }; struct llama_model { @@ -2908,10 +2978,10 @@ struct llama_model { std::vector dev_layer; // contexts where the model tensors metadata is stored - std::vector ctxs; + std::vector ctxs; // the model memory buffers for the tensor data - std::vector bufs; + std::vector bufs; // model memory mapped files llama_mmaps mappings; @@ -2923,20 +2993,20 @@ struct llama_model { // for quantize-stats only std::vector> tensors_by_name; - int64_t t_load_us = 0; + int64_t t_load_us = 0; int64_t t_start_us = 0; + // total number of parameters in the model + uint64_t n_elements = 0; + + // total size of all the tensors in the model in bytes + size_t n_bytes = 0; + // keep track of loaded lora adapters std::set lora_adapters; ~llama_model() { - for (struct ggml_context * ctx : ctxs) { - ggml_free(ctx); - } - for (ggml_backend_buffer_t buf : bufs) { - ggml_backend_buffer_free(buf); - } - while (!lora_adapters.empty()) { + while (!lora_adapters.empty()) { llama_lora_adapter_free(*lora_adapters.begin()); } } @@ -3253,16 +3323,6 @@ struct llama_context { , t_start_us(model.t_start_us) , t_load_us(model.t_load_us) {} - ~llama_context() { - ggml_backend_sched_free(sched); - - for (ggml_backend_t backend : backends) { - ggml_backend_free(backend); - } - - ggml_backend_buffer_free(buf_output); - } - const struct llama_model & model; struct llama_cparams cparams; @@ -3272,7 +3332,7 @@ struct llama_context { std::unordered_map lora_adapters; - std::vector backends; + std::vector backends; std::vector> set_n_threads_fns; ggml_backend_t backend_cpu = nullptr; @@ -3294,7 +3354,7 @@ struct llama_context { mutable int32_t n_eval = 0; // number of eval calls // host buffer for the model output (logits and embeddings) - ggml_backend_buffer_t buf_output = nullptr; + ggml_backend_buffer_ptr buf_output; // decode output (2-dimensional array: [n_outputs][n_vocab]) size_t logits_size = 0; // capacity (of floats) for logits @@ -3324,7 +3384,7 @@ struct llama_context { // memory buffers used to evaluate the model std::vector buf_compute_meta; - ggml_backend_sched_t sched = nullptr; + ggml_backend_sched_ptr sched; ggml_abort_callback abort_callback = nullptr; void * abort_callback_data = nullptr; @@ -3358,8 +3418,8 @@ struct llama_lora_adapter { struct llama_model * base_model; // map tensor name to lora_a_b std::unordered_map ab_map; - std::vector ctxs; - std::vector bufs; + std::vector ctxs; + std::vector bufs; float alpha; @@ -3377,12 +3437,6 @@ struct llama_lora_adapter { } ~llama_lora_adapter() { - for (struct ggml_context * ctx : ctxs) { - ggml_free(ctx); - } - for (ggml_backend_buffer_t buf : bufs) { - ggml_backend_buffer_free(buf); - } auto pos = base_model->lora_adapters.find(this); if (pos != base_model->lora_adapters.end()) { base_model->lora_adapters.erase(pos); @@ -3401,24 +3455,21 @@ static bool buft_supported(ggml_backend_buffer_type_t buft, ggml_backend_dev_t d /*.mem_buffer =*/ NULL, /*.no_alloc =*/ true, }; - ggml_context * ctx = ggml_init(params); + ggml_context_ptr ctx { ggml_init(params) }; if (!ctx) { throw std::runtime_error(format("failed to create ggml context")); } - ggml_backend_buffer_t buf = ggml_backend_buft_alloc_buffer(buft, 0); - ggml_tensor * op_tensor = fn(ctx); + ggml_backend_buffer_ptr buf { ggml_backend_buft_alloc_buffer(buft, 0) }; + ggml_tensor * op_tensor = fn(ctx.get()); for (int i = 0; i < GGML_MAX_SRC; i++) { if (op_tensor->src[i] != nullptr) { assert(op_tensor->src[i]->buffer == nullptr); - op_tensor->src[i]->buffer = buf; + op_tensor->src[i]->buffer = buf.get(); } } bool op_supported = ggml_backend_dev_supports_op(dev, op_tensor); - ggml_free(ctx); - ggml_backend_buffer_free(buf); - return op_supported; } @@ -3470,7 +3521,8 @@ static bool llama_kv_cache_init( // create a context for each buffer type std::map ctx_map; auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * { - if (ctx_map.count(buft) == 0) { + auto it = ctx_map.find(buft); + if (it == ctx_map.end()) { struct ggml_init_params params = { /*.mem_size =*/ size_t(2u*n_layer*ggml_tensor_overhead()), /*.mem_buffer =*/ NULL, @@ -3481,9 +3533,10 @@ static bool llama_kv_cache_init( return nullptr; } ctx_map[buft] = ctx; - cache.ctxs.push_back(ctx); + cache.ctxs.emplace_back(ctx); + return ctx; } - return ctx_map.at(buft); + return it->second; }; cache.k_l.reserve(n_layer); @@ -3493,21 +3546,13 @@ static bool llama_kv_cache_init( const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s(); const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s(); - const llama_model::buft_list_t * buft_list; + ggml_backend_buffer_type_t buft; if (offload) { - buft_list = model.dev_layer.at(i).buft_list; + auto * dev = model.dev_layer.at(i).dev; + buft = ggml_backend_dev_buffer_type(dev); } else { - buft_list = &model.cpu_buft_list; + buft = ggml_backend_cpu_buffer_type(); } - ggml_backend_buffer_type_t buft = select_buft(*buft_list, - [&](ggml_context * ctx) { - ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size); - if (hparams.rope_type == LLAMA_ROPE_TYPE_NONE) { - return k; - } - ggml_tensor * p = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 1); - return ggml_rope(ctx, k, p, hparams.n_rot, hparams.rope_type); - }); ggml_context * ctx = ctx_for_buft(buft); if (!ctx) { @@ -3535,17 +3580,30 @@ static bool llama_kv_cache_init( } ggml_backend_buffer_clear(buf, 0); LLAMA_LOG_INFO("%s: %10s KV buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0); - cache.bufs.push_back(buf); + cache.bufs.emplace_back(buf); } return true; } +// a structure holds information about the slot found in llama_kv_cache_find_slot +struct llama_kv_cache_slot_info { + std::pair boundaries; // slot boundaries [begin, end) + bool found = false; // the slot was found + + explicit llama_kv_cache_slot_info(bool found_) : found{found_} {} + llama_kv_cache_slot_info(uint32_t begin, uint32_t end) : boundaries{begin, end}, found{true} {} + + operator bool() const { return found; } +}; +static const llama_kv_cache_slot_info llama_kv_cache_slot_info_failed{false}; + // find an empty slot of size "n_tokens" in the cache // updates the cache head +// returns a structure holding information about the slot found // Note: On success, it's important that cache.head points // to the first cell of the slot. -static bool llama_kv_cache_find_slot( +static struct llama_kv_cache_slot_info llama_kv_cache_find_slot( struct llama_kv_cache & cache, const struct llama_ubatch & batch) { const uint32_t n_tokens = batch.n_tokens; @@ -3573,7 +3631,7 @@ static bool llama_kv_cache_find_slot( // too big seq_id // TODO: would it be possible to resize the cache instead? LLAMA_LOG_ERROR("%s: seq_id=%d >= n_seq_max=%d Try using a bigger --parallel value\n", __func__, seq_id, cache.size); - return false; + return llama_kv_cache_slot_info_failed; } if (j > 0) { llama_kv_cell & seq = cache.cells[seq_id]; @@ -3708,15 +3766,17 @@ static bool llama_kv_cache_find_slot( // allow getting the range of used cells, from head to head + n cache.head = min; cache.n = max - min + 1; + cache.used = std::count_if(cache.cells.begin(), cache.cells.end(), + [](const llama_kv_cell& cell){ return !cell.is_empty(); }); // sanity check - return cache.n >= n_seqs; + return llama_kv_cache_slot_info(cache.n >= n_seqs); } // otherwise, one cell per token. if (n_tokens > cache.size) { LLAMA_LOG_ERROR("%s: n_tokens=%d > cache.size=%d\n", __func__, n_tokens, cache.size); - return false; + return llama_kv_cache_slot_info_failed; } uint32_t n_tested = 0; @@ -3744,7 +3804,7 @@ static bool llama_kv_cache_find_slot( if (n_tested >= cache.size) { //LLAMA_LOG_ERROR("%s: failed to find a slot for %d tokens\n", __func__, n_tokens); - return false; + return llama_kv_cache_slot_info_failed; } } @@ -3761,7 +3821,7 @@ static bool llama_kv_cache_find_slot( cache.used += n_tokens; - return true; + return llama_kv_cache_slot_info(cache.head, cache.head + n_tokens); } // find how many cells are currently in use @@ -3788,7 +3848,7 @@ static void llama_kv_cache_clear(struct llama_kv_cache & cache) { cache.used = 0; for (auto & buf : cache.bufs) { - ggml_backend_buffer_clear(buf, 0); + ggml_backend_buffer_clear(buf.get(), 0); } } @@ -4037,6 +4097,53 @@ static uint32_t llama_kv_cache_get_padding(const struct llama_cparams & cparams) return cparams.flash_attn ? 256u : 32u; } +// saves the kv_cache state for future recovery. +// used to rollback llama_kv_cache_find_slot changes. +struct llama_kv_slot_restorer { + struct llama_kv_cache_state { + uint32_t head = 0; + uint32_t n = 0; + } old_state; + + // for non-recurrent models only + // list of slots to restore + std::vector> slot_boundaries; + + bool do_restore = false; + + explicit llama_kv_slot_restorer(const struct llama_kv_cache & cache) { + old_state.head = cache.head; + old_state.n = cache.n; + } + + // saves a slot information for future restoration + void save(const struct llama_kv_cache_slot_info & slot) { + if (slot) { + do_restore = true; + if (slot.boundaries.first != slot.boundaries.second) { + slot_boundaries.push_back(slot.boundaries); + } + } + } + + // must be explicitly called to restore the kv_cache state + // and rollback changes from all llama_kv_cache_find_slot calls + void restore(struct llama_kv_cache & cache) { + if (do_restore) { + cache.head = old_state.head; + cache.n = old_state.n; + + if (cache.recurrent) { // recurrent models like Mamba or RWKV can't have a state partially erased + llama_kv_cache_seq_rm(cache, -1, -1, -1); + } else { + for (auto & slot : slot_boundaries) { + llama_kv_cache_seq_rm(cache, -1, slot.first, slot.second); + } + } + } + } +}; + // // model loading and saving // @@ -4252,8 +4359,8 @@ struct llama_model_loader { int n_tensors = 0; int n_created = 0; - int64_t n_elements = 0; - size_t n_bytes = 0; + uint64_t n_elements = 0; + size_t n_bytes = 0; bool use_mmap = false; bool check_tensors; @@ -4271,21 +4378,38 @@ struct llama_model_loader { ggml_tensor * tensor; - llama_tensor_weight(const llama_file * file, uint16_t idx, const char * name, const struct gguf_context * gguf_ctx, ggml_tensor * tensor) : idx(idx), tensor(tensor) { - const int tensor_idx = gguf_find_tensor(gguf_ctx, name); - offs = gguf_get_data_offset(gguf_ctx) + gguf_get_tensor_offset(gguf_ctx, tensor_idx); + llama_tensor_weight(const llama_file * file, uint16_t idx, const struct gguf_context * gguf_ctx, ggml_tensor * tensor) : idx(idx), tensor(tensor) { + const int tensor_idx = gguf_find_tensor(gguf_ctx, ggml_get_name(tensor)); + if (tensor_idx < 0) { + throw std::runtime_error(format("tensor '%s' not found in the model", ggml_get_name(tensor))); + } + offs = gguf_get_data_offset(gguf_ctx) + gguf_get_tensor_offset(gguf_ctx, tensor_idx); if (offs + ggml_nbytes(tensor) < offs || offs + ggml_nbytes(tensor) > file->size) { - throw std::runtime_error(format("tensor '%s' data is not within the file bounds, model is corrupted or incomplete", name)); + throw std::runtime_error(format("tensor '%s' data is not within the file bounds, model is corrupted or incomplete", ggml_get_name(tensor))); } } }; - std::vector weights; + // custom comparator to sort weights more nicely by layer + struct weight_name_comparer { + bool operator()(const std::string & a, const std::string & b) const { + int a_layer = -1; + int b_layer = -1; + sscanf(a.c_str(), "blk.%d.", &a_layer); + sscanf(b.c_str(), "blk.%d.", &b_layer); + if (a_layer != b_layer) { + return a_layer < b_layer; + } + return a < b; + } + }; + + std::map weights_map; std::unordered_map kv_overrides; - struct gguf_context * meta = NULL; - std::vector contexts; + gguf_context_ptr meta; + std::vector contexts; std::string arch_name; LLM_KV llm_kv = LLM_KV(LLM_ARCH_UNKNOWN); @@ -4308,7 +4432,7 @@ struct llama_model_loader { /*.ctx = */ &ctx, }; - meta = gguf_init_from_file(fname.c_str(), params); + meta.reset(gguf_init_from_file(fname.c_str(), params)); if (!meta) { throw std::runtime_error(format("%s: failed to load model from %s\n", __func__, fname.c_str())); } @@ -4323,7 +4447,14 @@ struct llama_model_loader { // For subsidiary files, `meta` tensor data offset must not be used, // so we build a unified tensors index for weights. for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) { - weights.emplace_back(files.back().get(), 0, cur->name, meta, cur); + std::string tensor_name = std::string(cur->name); + // make sure there is no duplicated tensor names + if (weights_map.find(tensor_name) != weights_map.end()) { + throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", ggml_get_name(cur))); + } + n_elements += ggml_nelements(cur); + n_bytes += ggml_nbytes(cur); + weights_map.emplace(tensor_name, llama_tensor_weight(files.back().get(), 0, meta.get(), cur)); } uint16_t n_split = 0; get_key(llm_kv(LLM_KV_SPLIT_COUNT), n_split, false); @@ -4353,7 +4484,7 @@ struct llama_model_loader { /*.no_alloc = */ true, /*.ctx = */ &ctx, }; - struct gguf_context * ctx_gguf = gguf_init_from_file(split_path, split_params); + gguf_context_ptr ctx_gguf { gguf_init_from_file(split_path, split_params) }; if (!ctx_gguf) { throw std::runtime_error(format("%s: failed to load GGUF split from %s\n", __func__, split_path)); } @@ -4363,17 +4494,22 @@ struct llama_model_loader { // Save tensors data offset info of the shard. for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) { - weights.emplace_back(files.back().get(), idx, cur->name, ctx_gguf, cur); + std::string tensor_name = std::string(cur->name); + // make sure there is no duplicated tensor names + if (weights_map.find(tensor_name) != weights_map.end()) { + throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", ggml_get_name(cur))); + } + n_elements += ggml_nelements(cur); + n_bytes += ggml_nbytes(cur); + weights_map.emplace(tensor_name, llama_tensor_weight(files.back().get(), idx, ctx_gguf.get(), cur)); } - - gguf_free(ctx_gguf); } get_key(llm_kv(LLM_KV_SPLIT_TENSORS_COUNT), n_tensors); // sanity check { - const int n_tensors_loaded = (int) weights.size(); + const int n_tensors_loaded = (int) weights_map.size(); if (n_tensors != n_tensors_loaded) { throw std::runtime_error(format("corrupted model: %d tensors expected but %d found", n_tensors, n_tensors_loaded)); } @@ -4382,23 +4518,10 @@ struct llama_model_loader { LLAMA_LOG_INFO("%s: additional %d GGUFs metadata loaded.\n", __func__, n_split - 1); } - n_kv = gguf_get_n_kv(meta); - n_tensors = weights.size(); + n_kv = gguf_get_n_kv(meta.get()); + n_tensors = weights_map.size(); - fver = (enum llama_fver) gguf_get_version(meta); - - std::set tensor_names; - for (auto & w : weights) { - n_elements += ggml_nelements(w.tensor); - n_bytes += ggml_nbytes(w.tensor); - // make sure there is no duplicated tensor names - const std::string name(w.tensor->name); - auto found = tensor_names.find(name); - if (found != tensor_names.end()) { - throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", w.tensor->name)); - } - tensor_names.insert(name); - } + fver = (enum llama_fver) gguf_get_version(meta.get()); LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n", __func__, n_kv, n_tensors, fname.c_str(), llama_file_version_name(fver)); @@ -4411,8 +4534,10 @@ struct llama_model_loader { uint32_t n_type_max = 0; enum ggml_type type_max = GGML_TYPE_F32; - for (int i = 0; i < n_tensors; i++) { - const ggml_tensor * tensor = weights.at(i).tensor; + for (const auto & it : weights_map) { + const llama_tensor_weight & w = it.second; + const ggml_tensor * tensor = w.tensor; + enum ggml_type type = tensor->type; n_type[type]++; @@ -4423,8 +4548,8 @@ struct llama_model_loader { } if (trace > 0) { - const uint16_t sid = weights.at(i).idx; - LLAMA_LOG_INFO("%s: - tensor %4d, split %2d: %32s %-8s [ %s ]\n", __func__, i, sid, ggml_get_name(tensor), ggml_type_name(type), llama_format_tensor_shape(tensor).c_str()); + const uint16_t sid = w.idx; + LLAMA_LOG_INFO("%s: - tensor split %2d: %32s %-8s [ %s ]\n", __func__, sid, ggml_get_name(tensor), ggml_type_name(type), llama_format_tensor_shape(tensor).c_str()); } } @@ -4467,23 +4592,23 @@ struct llama_model_loader { ftype = (llama_ftype) (ftype | LLAMA_FTYPE_GUESSED); { - const int kid = gguf_find_key(meta, "general.file_type"); // TODO: use LLM_KV + const int kid = gguf_find_key(meta.get(), "general.file_type"); // TODO: use LLM_KV if (kid >= 0) { - ftype = (llama_ftype) gguf_get_val_u32(meta, kid); + ftype = (llama_ftype) gguf_get_val_u32(meta.get(), kid); } } LLAMA_LOG_INFO("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__); for (int i = 0; i < n_kv; i++) { - const char * name = gguf_get_key(meta, i); - const enum gguf_type type = gguf_get_kv_type(meta, i); + const char * name = gguf_get_key(meta.get(), i); + const enum gguf_type type = gguf_get_kv_type(meta.get(), i); const std::string type_name = type == GGUF_TYPE_ARRAY - ? format("%s[%s,%d]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(meta, i)), gguf_get_arr_n(meta, i)) + ? format("%s[%s,%d]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(meta.get(), i)), gguf_get_arr_n(meta.get(), i)) : gguf_type_name(type); - std::string value = gguf_kv_to_str(meta, i); + std::string value = gguf_kv_to_str(meta.get(), i); const size_t MAX_VALUE_LEN = 40; if (value.size() > MAX_VALUE_LEN) { value = format("%s...", value.substr(0, MAX_VALUE_LEN - 3).c_str()); @@ -4512,19 +4637,10 @@ struct llama_model_loader { this->check_tensors = check_tensors; } - ~llama_model_loader() { - if (meta) { - gguf_free(meta); - } - for (auto * ctx : contexts) { - ggml_free(ctx); - } - } - template typename std::enable_if::value, bool>::type get_arr_n(const std::string & key, T & result, const bool required = true) { - const int kid = gguf_find_key(meta, key.c_str()); + const int kid = gguf_find_key(meta.get(), key.c_str()); if (kid < 0) { if (required) { @@ -4534,7 +4650,7 @@ struct llama_model_loader { } struct GGUFMeta::ArrayInfo arr_info = - GGUFMeta::GKV::get_kv(meta, kid); + GGUFMeta::GKV::get_kv(meta.get(), kid); result = arr_info.length; @@ -4549,9 +4665,9 @@ struct llama_model_loader { template bool get_arr(const std::string & key, std::vector & result, const bool required = true) { - const int kid = gguf_find_key(meta, key.c_str()); + const int kid = gguf_find_key(meta.get(), key.c_str()); - if (kid < 0 || gguf_get_kv_type(meta, kid) != GGUF_TYPE_ARRAY) { + if (kid < 0 || gguf_get_kv_type(meta.get(), kid) != GGUF_TYPE_ARRAY) { if (required) { throw std::runtime_error(format("array key not found in model: %s", key.c_str())); } @@ -4559,7 +4675,7 @@ struct llama_model_loader { } struct GGUFMeta::ArrayInfo arr_info = - GGUFMeta::GKV::get_kv(meta, kid); + GGUFMeta::GKV::get_kv(meta.get(), kid); switch (arr_info.gt) { case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same::value)); break; @@ -4578,9 +4694,9 @@ struct llama_model_loader { template bool get_arr(const std::string & key, std::array & result, const bool required = true) { - const int kid = gguf_find_key(meta, key.c_str()); + const int kid = gguf_find_key(meta.get(), key.c_str()); - if (kid < 0 || gguf_get_kv_type(meta, kid) != GGUF_TYPE_ARRAY) { + if (kid < 0 || gguf_get_kv_type(meta.get(), kid) != GGUF_TYPE_ARRAY) { if (required) { throw std::runtime_error(format("array key not found in model: %s", key.c_str())); } @@ -4588,7 +4704,7 @@ struct llama_model_loader { } struct GGUFMeta::ArrayInfo arr_info = - GGUFMeta::GKV::get_kv(meta, kid); + GGUFMeta::GKV::get_kv(meta.get(), kid); switch (arr_info.gt) { case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same::value)); break; @@ -4620,7 +4736,7 @@ struct llama_model_loader { const struct llama_model_kv_override * override = it != kv_overrides.end() ? &it->second : nullptr; - const bool found = GGUFMeta::GKV::set(meta, key, result, override); + const bool found = GGUFMeta::GKV::set(meta.get(), key, result, override); if (required && !found) { throw std::runtime_error(format("key not found in model: %s", key.c_str())); @@ -4637,7 +4753,7 @@ struct llama_model_loader { // get array of n <= N_MAX elements, or a single element repeated n times template bool get_key_or_arr(const std::string & key, std::array & result, uint32_t n, const bool required = true) { - const int kid = gguf_find_key(meta, key.c_str()); + const int kid = gguf_find_key(meta.get(), key.c_str()); if (kid < 0) { if (required) { @@ -4650,9 +4766,9 @@ struct llama_model_loader { throw std::runtime_error(format("n > N_MAX: %u > %u for key %s", (uint32_t) n, (uint32_t) N_MAX, key.c_str())); } - if (gguf_get_kv_type(meta, kid) == GGUF_TYPE_ARRAY) { + if (gguf_get_kv_type(meta.get(), kid) == GGUF_TYPE_ARRAY) { struct GGUFMeta::ArrayInfo arr_info = - GGUFMeta::GKV::get_kv(meta, kid); + GGUFMeta::GKV::get_kv(meta.get(), kid); if (n != arr_info.length) { throw std::runtime_error(format("key %s has wrong array length; expected %u, got %u", key.c_str(), n, (uint32_t) arr_info.length)); @@ -4688,21 +4804,13 @@ struct llama_model_loader { return llm_kv.arch; } - const char * get_tensor_name(int i) const { - return weights.at(i).tensor->name; - } - const llama_tensor_weight * get_weight(const char * name) const { - for (const auto & weight : weights) { - if (strcmp(name, weight.tensor->name) == 0) { - return &weight; - } + auto pos = weights_map.find(name); + if (pos != weights_map.end()) { + return &pos->second; } - return nullptr; - } - const llama_tensor_weight * get_weight(int i) const { - return get_weight(get_tensor_name(i)); + return nullptr; } const llama_tensor_weight & require_weight(const char * name) const { @@ -4729,10 +4837,6 @@ struct llama_model_loader { return tensor; } - struct ggml_tensor * get_tensor_meta(int i) const { - return get_tensor_meta(get_tensor_name(i)); - } - const struct ggml_tensor * check_tensor_dims(const std::string & name, const std::vector & ne, bool required) const { const struct ggml_tensor * cur = get_tensor_meta(name.c_str()); @@ -4827,7 +4931,9 @@ struct llama_model_loader { mappings.reserve(files.size()); mmaps_used.reserve(files.size()); for (const auto & file : files) { - std::unique_ptr mapping(new llama_mmap(file.get(), prefetch ? -1 : 0, ggml_is_numa())); + auto * reg = ggml_backend_dev_backend_reg(ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU)); + auto * is_numa_fn = (decltype(ggml_is_numa) *) ggml_backend_reg_get_proc_address(reg, "ggml_backend_cpu_is_numa"); + std::unique_ptr mapping(new llama_mmap(file.get(), prefetch ? -1 : 0, is_numa_fn())); mmaps_used.emplace_back(mapping->size, 0); if (mlock_mmaps) { std::unique_ptr mlock_mmap(new llama_mlock()); @@ -4839,8 +4945,8 @@ struct llama_model_loader { } // compute the total size of all tensors for progress reporting - for (auto & w : weights) { - size_data += ggml_nbytes(w.tensor); + for (const auto & it : weights_map) { + size_data += ggml_nbytes(it.second.tensor); } } @@ -4852,19 +4958,12 @@ struct llama_model_loader { *last = 0; *addr = mapping->addr; for (ggml_tensor * tensor = ggml_get_first_tensor(ctx); tensor; tensor = ggml_get_next_tensor(ctx, tensor)) { - try { - const auto * weight = get_weight(ggml_get_name(tensor)); - if (!weight) { - continue; - } - if (weight->idx != idx) { - continue; - } - *first = std::min(*first, weight->offs); - *last = std::max(*last, weight->offs + ggml_nbytes(tensor)); - } catch(...) { - // the tensor is not in the model + const auto * weight = get_weight(ggml_get_name(tensor)); + if (!weight || weight->idx != idx) { + continue; } + *first = std::min(*first, weight->offs); + *last = std::max(*last, weight->offs + ggml_nbytes(tensor)); } } @@ -5041,7 +5140,6 @@ struct llama_model_loader { ggml_backend_tensor_set(cur, data, 0, n_size); } } else { - GGML_ASSERT(weight->idx < files.size()); const auto & file = files.at(weight->idx); if (ggml_backend_buffer_is_host(cur->buffer)) { file->seek(weight->offs, SEEK_SET); @@ -5278,6 +5376,7 @@ static const char * llama_model_type_name(e_model type) { case MODEL_1B: return "1B"; case MODEL_1_3B: return "1.3B"; case MODEL_1_4B: return "1.4B"; + case MODEL_1_5B: return "1.5B"; case MODEL_1_6B: return "1.6B"; case MODEL_2B: return "2B"; case MODEL_2_8B: return "2.8B"; @@ -5296,6 +5395,7 @@ static const char * llama_model_type_name(e_model type) { case MODEL_16B: return "16B"; case MODEL_20B: return "20B"; case MODEL_30B: return "30B"; + case MODEL_32B: return "32B"; case MODEL_34B: return "34B"; case MODEL_35B: return "35B"; case MODEL_40B: return "40B"; @@ -5331,6 +5431,11 @@ static const char * llama_model_vocab_type_name(enum llama_vocab_type type){ } } +static void llm_load_stats(llama_model_loader & ml, llama_model & model) { + model.n_elements = ml.n_elements; + model.n_bytes = ml.n_bytes; +} + static void llm_load_arch(llama_model_loader & ml, llama_model & model) { model.arch = ml.get_arch(); if (model.arch == LLM_ARCH_UNKNOWN) { @@ -5342,7 +5447,7 @@ static void llm_load_hparams( llama_model_loader & ml, llama_model & model) { auto & hparams = model.hparams; - const gguf_context * ctx = ml.meta; + const gguf_context * ctx = ml.meta.get(); // get metadata as string for (int i = 0; i < gguf_get_n_kv(ctx); i++) { @@ -5478,8 +5583,12 @@ static void llm_load_hparams( case LLM_ARCH_MINICPM: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + ml.get_key(LLM_KV_EMBEDDING_SCALE, hparams.f_embedding_scale); + ml.get_key(LLM_KV_RESIDUAL_SCALE, hparams.f_residual_scale); + ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale); switch (hparams.n_layer) { + case 52: model.type = e_model::MODEL_1B; break; case 40: model.type = e_model::MODEL_2B; break; default: model.type = e_model::MODEL_UNKNOWN; } @@ -5649,8 +5758,12 @@ static void llm_load_hparams( ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); switch (hparams.n_layer) { case 24: model.type = hparams.n_embd == 1024 ? e_model::MODEL_0_5B : e_model::MODEL_1B; break; + case 28: model.type = hparams.n_embd == 1536 ? e_model::MODEL_1_5B : e_model::MODEL_7B; break; case 32: model.type = e_model::MODEL_7B; break; + case 36: model.type = e_model::MODEL_3B; break; case 40: model.type = hparams.n_head() == 20 ? e_model::MODEL_4B : e_model::MODEL_13B; break; + case 48: model.type = e_model::MODEL_14B; break; + case 64: model.type = e_model::MODEL_32B; break; case 80: model.type = e_model::MODEL_70B; break; default: model.type = e_model::MODEL_UNKNOWN; } @@ -5860,6 +5973,17 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_OLMO2: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + + switch (hparams.n_layer) { + case 16: model.type = e_model::MODEL_1B; break; + case 32: model.type = e_model::MODEL_7B; break; + case 40: model.type = e_model::MODEL_13B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; case LLM_ARCH_OLMOE: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); @@ -6109,7 +6233,7 @@ static void llm_load_vocab( llama_model & model) { auto & vocab = model.vocab; - struct gguf_context * ctx = ml.meta; + struct gguf_context * ctx = ml.meta.get(); const auto kv = LLM_KV(model.arch); @@ -6355,6 +6479,9 @@ static void llm_load_vocab( vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CHAMELEON; vocab.tokenizer_add_bos = true; vocab.tokenizer_clean_spaces = false; + } else if ( + tokenizer_pre == "minerva-7b") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_MINERVA; } else { throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str())); } @@ -6948,7 +7075,7 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { LLAMA_LOG_INFO("%s: n_ff_shexp = %d\n", __func__, hparams.n_ff_shexp); } - if (model.arch == LLM_ARCH_GRANITE || model.arch == LLM_ARCH_GRANITE_MOE) { + if (model.arch == LLM_ARCH_MINICPM || model.arch == LLM_ARCH_GRANITE || model.arch == LLM_ARCH_GRANITE_MOE) { LLAMA_LOG_INFO("%s: f_embedding_scale = %f\n", __func__, hparams.f_embedding_scale); LLAMA_LOG_INFO("%s: f_residual_scale = %f\n", __func__, hparams.f_residual_scale); LLAMA_LOG_INFO("%s: f_attention_scale = %f\n", __func__, hparams.f_attention_scale); @@ -7062,7 +7189,7 @@ static const std::map llm_tensor_info_mapping = { {LLM_TENSOR_TIME_MIX_LERP_R, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}}, {LLM_TENSOR_TIME_MIX_LERP_G, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}}, {LLM_TENSOR_TIME_MIX_DECAY, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}}, - {LLM_TENSOR_TIME_MIX_FIRST, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_RWKV_WKV}}, + {LLM_TENSOR_TIME_MIX_FIRST, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_RWKV_WKV6}}, {LLM_TENSOR_ATTN_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, {LLM_TENSOR_ATTN_NORM_2, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, {LLM_TENSOR_ATTN_OUT_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}}, @@ -7104,10 +7231,11 @@ static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w /*.mem_buffer =*/ NULL, /*.no_alloc =*/ true, }; - ggml_context * ctx = ggml_init(params); - if (!ctx) { + ggml_context_ptr ctx_ptr { ggml_init(params) }; + if (!ctx_ptr) { throw std::runtime_error(format("failed to create ggml context")); } + ggml_context * ctx = ctx_ptr.get(); ggml_tensor * op_tensor = nullptr; @@ -7119,7 +7247,7 @@ static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w } break; case GGML_OP_MUL_MAT: { - ggml_tensor * b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, w->ne[0], 512); + ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], 512, w->ne[2], w->ne[3]); op_tensor = ggml_mul_mat(ctx, w, b); } break; case GGML_OP_MUL_MAT_ID: @@ -7131,12 +7259,12 @@ static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w } break; case GGML_OP_ADD: { - ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, w->ne[0], 512); + ggml_tensor * a = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], w->ne[1], w->ne[2], w->ne[3]); op_tensor = ggml_add(ctx, a, w); } break; case GGML_OP_MUL: { - ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, w->ne[0], 512); + ggml_tensor * a = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], w->ne[1], w->ne[2], w->ne[3]); op_tensor = ggml_mul(ctx, a, w); } break; case GGML_OP_DIV: @@ -7159,18 +7287,38 @@ static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w } break; case GGML_OP_SSM_CONV: { - // TODO: ggml_ssm_conv(ctx, conv_x, model.layers[il].ssm_conv1d); - op_tensor = ggml_ssm_conv(ctx, nullptr, w); + // FIXME + ggml_tensor * conv_x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 12345, w->ne[1], 6789); + op_tensor = ggml_ssm_conv(ctx, conv_x, w); } break; case GGML_OP_SSM_SCAN: { - // TODO: ggml_ssm_scan(ctx, ssm, x, dt, model.layers[il].ssm_a, B, C); - op_tensor = ggml_ssm_scan(ctx, nullptr, nullptr, nullptr, w, nullptr, nullptr); + // FIXME + const int64_t d_state = w->ne[0]; + const int64_t d_inner = w->ne[1]; + const int64_t n_seq_tokens = 512; + const int64_t n_seqs = 1; + ggml_tensor * s = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, d_inner, n_seqs); + ggml_tensor * x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_inner, n_seq_tokens, n_seqs); + ggml_tensor * dt = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_inner, n_seq_tokens, n_seqs); + ggml_tensor * B = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, n_seq_tokens, n_seqs); + ggml_tensor * C = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, n_seq_tokens, n_seqs); + op_tensor = ggml_ssm_scan(ctx, s, x, dt, w, B, C); } break; - case GGML_OP_RWKV_WKV: + case GGML_OP_RWKV_WKV6: { - // TODO: ggml_rwkv_wkv(ctx, k, v, r, layer->time_mix_first, w, *wkv_state); - op_tensor = ggml_rwkv_wkv(ctx, nullptr, nullptr, nullptr, w, nullptr, nullptr); + // FIXME + const int64_t S = 123; + const int64_t H = 123; + const int64_t n_tokens = 123; + const int64_t n_seqs = 123; + ggml_tensor * k = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, S, 1, H, n_tokens); + ggml_tensor * v = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 1, S, H, n_tokens); + ggml_tensor * r = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 1, S, H, n_tokens); + ggml_tensor * tf = w; + ggml_tensor * td = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 1, S, H, n_tokens); + ggml_tensor * state = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, S, n_seqs, S, H); + op_tensor = ggml_rwkv_wkv6(ctx, k, v, r, tf, td, state); } break; default: GGML_ABORT("%s: missing test for op %s for tensor %s", __func__, ggml_op_name(op), w->name); @@ -7183,8 +7331,6 @@ static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w ggml_backend_buffer_free(w->buffer); w->buffer = nullptr; - ggml_free(ctx); - return op_supported; } @@ -7221,7 +7367,7 @@ static llama_model::buft_list_t make_cpu_buft_list(llama_model & model) { auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU); auto * cpu_reg = ggml_backend_dev_backend_reg(cpu_dev); auto ggml_backend_dev_get_extra_bufts_fn = (ggml_backend_dev_get_extra_bufts_t) - ggml_backend_reg_get_proc_address(cpu_reg, "ggml_backend_cpu_get_extra_bufts"); + ggml_backend_reg_get_proc_address(cpu_reg, "ggml_backend_dev_get_extra_bufts"); if (ggml_backend_dev_get_extra_bufts_fn) { ggml_backend_buffer_type_t * extra_bufts = ggml_backend_dev_get_extra_bufts_fn(cpu_dev); while (extra_bufts && *extra_bufts) { @@ -7375,7 +7521,8 @@ static bool llm_load_tensors( std::map ctx_map; auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * { - if (ctx_map.count(buft) == 0) { + auto it = ctx_map.find(buft); + if (it == ctx_map.end()) { ggml_init_params params = { /*.mem_size =*/ ctx_size, /*.mem_buffer =*/ NULL, @@ -7386,9 +7533,10 @@ static bool llm_load_tensors( throw std::runtime_error(format("failed to create ggml context")); } ctx_map[buft] = ctx; - model.ctxs.push_back(ctx); + model.ctxs.emplace_back(ctx); + return ctx; } - return ctx_map.at(buft); + return it->second; }; // create tensors for the weights @@ -7426,7 +7574,7 @@ static bool llm_load_tensors( if (flags & llama_model_loader::TENSOR_NOT_REQUIRED) { return nullptr; } - throw std::runtime_error(format("missing tensor %s", tn.str().c_str())); + throw std::runtime_error(format("missing tensor '%s'", tn.str().c_str())); } // some models use the token embedding tensor as the output, but since these are used in different layers and with different ops @@ -7445,7 +7593,7 @@ static bool llm_load_tensors( // tensors with "bias" suffix are always used with GGML_OP_ADD ggml_op op; - bool bias = strcmp(tn.suffix, "bias") == 0; + bool bias = tn.suffix != nullptr && strcmp(tn.suffix, "bias") == 0; if (bias) { op = GGML_OP_ADD; } else { @@ -7486,7 +7634,7 @@ static bool llm_load_tensors( // avoid using a host buffer when using mmap auto * buft_dev = ggml_backend_buft_get_device(buft); - if (ml.use_mmap && buft == ggml_backend_dev_host_buffer_type(buft_dev)) { + if (ml.use_mmap && buft_dev && buft == ggml_backend_dev_host_buffer_type(buft_dev)) { auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU); buft = ggml_backend_dev_buffer_type(cpu_dev); } @@ -7552,7 +7700,13 @@ static bool llm_load_tensors( layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); - layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); + if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) { + layer.rope_long = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); + layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); + } + else { + layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); + } if (n_expert == 0) { layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); @@ -8521,6 +8675,31 @@ static bool llm_load_tensors( layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } } break; + case LLM_ARCH_OLMO2: + { + model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + + // output + model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); + + for (int i = 0; i < n_layer; ++i) { + auto & layer = model.layers[i]; + + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, 0); + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, 0); + layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0); + + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0); + } + } break; case LLM_ARCH_OLMOE: { model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); @@ -9093,6 +9272,10 @@ static bool llm_load_tensors( // check if it is possible to use buffer_from_host_ptr with this buffer type ggml_backend_dev_t dev = ggml_backend_buft_get_device(buft); + if (!dev) { + // FIXME: workaround for CPU backend buft having a NULL device + dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU); + } ggml_backend_dev_props props; ggml_backend_dev_get_props(dev, &props); bool buffer_from_host_ptr_supported = props.caps.buffer_from_host_ptr; @@ -9114,7 +9297,7 @@ static bool llm_load_tensors( if (buf == nullptr) { throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft))); } - model.bufs.push_back(buf); + model.bufs.emplace_back(buf); bufs.emplace(idx, buf); } } @@ -9123,7 +9306,7 @@ static bool llm_load_tensors( if (buf == nullptr) { throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft))); } - model.bufs.push_back(buf); + model.bufs.emplace_back(buf); if (use_mlock && ggml_backend_buffer_is_host(buf)) { model.mlock_bufs.emplace_back(new llama_mlock); auto & mlock_buf = model.mlock_bufs.back(); @@ -9163,13 +9346,13 @@ static bool llm_load_tensors( } // print memory requirements per buffer type - for (ggml_backend_buffer_t buf : model.bufs) { - LLAMA_LOG_INFO("%s: %10s model buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf) / 1024.0 / 1024.0); + for (auto & buf : model.bufs) { + LLAMA_LOG_INFO("%s: %12s model buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf.get()), ggml_backend_buffer_get_size(buf.get()) / 1024.0 / 1024.0); } // populate tensors_by_name - for (ggml_context * ctx : model.ctxs) { - for (auto * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) { + for (auto & ctx : model.ctxs) { + for (auto * cur = ggml_get_first_tensor(ctx.get()); cur != NULL; cur = ggml_get_next_tensor(ctx.get(), cur)) { model.tensors_by_name.emplace_back(ggml_get_name(cur), cur); } } @@ -9217,6 +9400,7 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam throw std::runtime_error("error loading model vocabulary: " + std::string(e.what())); } + llm_load_stats(ml, model); llm_load_print_meta(ml, model); if (model.vocab.type != LLAMA_VOCAB_TYPE_NONE && @@ -10113,7 +10297,7 @@ static struct ggml_tensor * llm_build_rwkv6_time_mix( v = ggml_transpose(ctx, v); r = ggml_transpose(ctx, r); - struct ggml_tensor * wkv_output = ggml_rwkv_wkv(ctx, k, v, r, layer->time_mix_first, w, *wkv_state); + struct ggml_tensor * wkv_output = ggml_rwkv_wkv6(ctx, k, v, r, layer->time_mix_first, w, *wkv_state); cur = ggml_view_1d(ctx, wkv_output, n_embd * n_tokens, 0); *wkv_state = ggml_view_1d(ctx, wkv_output, n_embd * head_size * n_seqs, n_embd * n_tokens * sizeof(float)); @@ -10274,10 +10458,8 @@ struct llm_build_context { } void free() { - if (ctx0) { - ggml_free(ctx0); - ctx0 = nullptr; - } + ggml_free(ctx0); + ctx0 = nullptr; } struct ggml_cgraph * build_k_shift() { @@ -10305,10 +10487,10 @@ struct llm_build_context { // dequantize to f32 -> RoPE -> quantize back tmp = ggml_cast(ctx0, k, GGML_TYPE_F32); cb(tmp, "K_f32", il); - for (auto * backend : lctx.backends) { + for (auto & backend : lctx.backends) { // Figure out which backend KV cache belongs to - if (ggml_backend_supports_buft(backend, ggml_backend_buffer_get_type(kv_self.k_l[il]->buffer))) { - ggml_backend_sched_set_tensor_backend(lctx.sched, tmp, backend); + if (ggml_backend_supports_buft(backend.get(), ggml_backend_buffer_get_type(kv_self.k_l[il]->buffer))) { + ggml_backend_sched_set_tensor_backend(lctx.sched.get(), tmp, backend.get()); break; } } @@ -13331,153 +13513,6 @@ struct llm_build_context { return gf; } - // ref: https://arxiv.org/abs/2203.03466 - // https://github.com/ggerganov/llama.cpp/issues/5276#issuecomment-1925774738 - // based on the original build_llama() function - struct ggml_cgraph * build_minicpm() { - struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false); - - const int64_t n_embd_head = hparams.n_embd_head_v; - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); - - const int64_t n_embd = hparams.n_embd; - //TODO: if the model varies, these parameters need to be read from the model - const int64_t n_embd_base = 256; - const float scale_embd = 12.0f; - const float scale_depth = 1.4f; - - struct ggml_tensor * cur; - struct ggml_tensor * inpL; - - inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); - - // scale the input embeddings - inpL = ggml_scale(ctx0, inpL, scale_embd); - cb(inpL, "inp_scaled", -1); - - // inp_pos - contains the positions - struct ggml_tensor * inp_pos = build_inp_pos(); - - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); - - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * inpSA = inpL; - - // norm - cur = llm_build_norm(ctx0, inpL, hparams, - model.layers[il].attn_norm, NULL, - LLM_NORM_RMS, cb, il); - cb(cur, "attn_norm", il); - - // self-attention - { - // compute Q and K and RoPE them - struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur); - cb(Qcur, "Qcur", il); - if (model.layers[il].bq) { - Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); - cb(Qcur, "Qcur", il); - } - - struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur); - cb(Kcur, "Kcur", il); - if (model.layers[il].bk) { - Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); - cb(Kcur, "Kcur", il); - } - - struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur); - cb(Vcur, "Vcur", il); - if (model.layers[il].bv) { - Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); - cb(Vcur, "Vcur", il); - } - - Qcur = ggml_rope_ext( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, - n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, - ext_factor, attn_factor, beta_fast, beta_slow - ); - cb(Qcur, "Qcur", il); - - Kcur = ggml_rope_ext( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, - n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, - ext_factor, attn_factor, beta_fast, beta_slow - ); - cb(Kcur, "Kcur", il); - - cur = llm_build_kv(ctx0, lctx, kv_self, gf, - model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - } - - if (il == n_layer - 1) { - // skip computing output for unused tokens - struct ggml_tensor * inp_out_ids = build_inp_out_ids(); - cur = ggml_get_rows(ctx0, cur, inp_out_ids); - inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); - } - - // scale_res - scale the hidden states for residual connection - const float scale_res = scale_depth/sqrtf(float(n_layer)); - cur = ggml_scale(ctx0, cur, scale_res); - cb(cur, "hidden_scaled", -1); - - struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); - cb(ffn_inp, "ffn_inp", il); - - // feed-forward network - { - cur = llm_build_norm(ctx0, ffn_inp, hparams, - model.layers[il].ffn_norm, NULL, - LLM_NORM_RMS, cb, il); - cb(cur, "ffn_norm", il); - - cur = llm_build_ffn(ctx0, lctx, cur, - model.layers[il].ffn_up, NULL, NULL, - model.layers[il].ffn_gate, NULL, NULL, - model.layers[il].ffn_down, NULL, NULL, - NULL, - LLM_FFN_SILU, LLM_FFN_PAR, cb, il); - cb(cur, "ffn_out", il); - } - - // scale the hidden states for residual connection - cur = ggml_scale(ctx0, cur, scale_res); - cb(cur, "hidden_scaled_ffn", -1); - - cur = ggml_add(ctx0, cur, ffn_inp); - cur = lctx.cvec.apply_to(ctx0, cur, il); - cb(cur, "l_out", il); - - // input for next layer - inpL = cur; - } - - cur = inpL; - - cur = llm_build_norm(ctx0, cur, hparams, - model.output_norm, NULL, - LLM_NORM_RMS, cb, -1); - cb(cur, "result_norm", -1); - - // lm_head scaling - const float scale_lmhead = float(n_embd_base)/float(n_embd); - cur = ggml_scale(ctx0, cur, scale_lmhead); - cb(cur, "lmhead_scaling", -1); - - // lm_head - cur = llm_build_lora_mm(lctx, ctx0, model.output, cur); - cb(cur, "result_output", -1); - - ggml_build_forward_expand(gf, cur); - - return gf; - } - struct ggml_cgraph * build_minicpm3() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false); @@ -14383,6 +14418,130 @@ struct llm_build_context { return gf; } + struct ggml_cgraph * build_olmo2() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false); + + // mutable variable, needed during the last layer of the computation to skip unused tokens + int32_t n_tokens = this->n_tokens; + + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = build_inp_pos(); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + cur = inpL; + + // self_attention + { + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + + struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + + struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + + Qcur = llm_build_norm(ctx0, Qcur, hparams, model.layers[il].attn_q_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(Qcur, "Qcur_normed", il); + + Kcur = llm_build_norm(ctx0, Kcur, hparams, model.layers[il].attn_k_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(Kcur, "Kcur_normed", il); + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur_rope", il); + + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur_rope", il); + + cur = llm_build_kv(ctx0, lctx, kv_self, gf, + model.layers[il].wo, NULL, + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + } + + cur = llm_build_norm(ctx0, cur, hparams, + model.layers[il].attn_post_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_post_norm", il); + + if (il == n_layer - 1) { + // skip computing output for unused tokens + struct ggml_tensor * inp_out_ids = build_inp_out_ids(); + n_tokens = n_outputs; + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + cur = llm_build_ffn(ctx0, lctx, ffn_inp, + model.layers[il].ffn_up, NULL, NULL, + model.layers[il].ffn_gate, NULL, NULL, + model.layers[il].ffn_down, NULL, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + + cur = llm_build_norm(ctx0, cur, hparams, + model.layers[il].ffn_post_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "ffn_post_norm", -1); + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "ffn_out", il); + + cur = lctx.cvec.apply_to(ctx0, cur, il); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = llm_build_lora_mm(lctx, ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } + // based on the build_qwen2moe() function, changes: // * removed shared experts // * removed bias @@ -16423,7 +16582,7 @@ static struct ggml_cgraph * llama_build_graph( if (!lctx.cparams.offload_kqv) { if (strcmp(name, "kqv_merged_cont") == 0) { // all nodes between the KV store and the attention output are run on the CPU - ggml_backend_sched_set_tensor_backend(lctx.sched, cur, lctx.backend_cpu); + ggml_backend_sched_set_tensor_backend(lctx.sched.get(), cur, lctx.backend_cpu); } } @@ -16433,10 +16592,10 @@ static struct ggml_cgraph * llama_build_graph( if (ubatch.n_tokens < 32 || full_offload) { if (il != -1 && strcmp(name, "norm") == 0) { const auto & dev_layer = lctx.model.dev_layer.at(il); - for (auto * backend : lctx.backends) { - if (ggml_backend_get_device(backend) == dev_layer.dev) { - if (ggml_backend_supports_op(backend, cur)) { - ggml_backend_sched_set_tensor_backend(lctx.sched, cur, backend); + for (auto & backend : lctx.backends) { + if (ggml_backend_get_device(backend.get()) == dev_layer.dev) { + if (ggml_backend_supports_op(backend.get(), cur)) { + ggml_backend_sched_set_tensor_backend(lctx.sched.get(), cur, backend.get()); } } } @@ -16452,6 +16611,7 @@ static struct ggml_cgraph * llama_build_graph( switch (model.arch) { case LLM_ARCH_LLAMA: + case LLM_ARCH_MINICPM: case LLM_ARCH_GRANITE: case LLM_ARCH_GRANITE_MOE: { @@ -16535,10 +16695,6 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_internlm2(); } break; - case LLM_ARCH_MINICPM: - { - result = llm.build_minicpm(); - } break; case LLM_ARCH_MINICPM3: { result = llm.build_minicpm3(); @@ -16575,6 +16731,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_olmo(); } break; + case LLM_ARCH_OLMO2: + { + result = llm.build_olmo2(); + } break; case LLM_ARCH_OLMOE: { result = llm.build_olmoe(); @@ -17123,7 +17283,7 @@ static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) { lctx.output_ids.resize(n_batch); } - const size_t prev_size = lctx.buf_output ? ggml_backend_buffer_get_size(lctx.buf_output) : 0; + const size_t prev_size = lctx.buf_output ? ggml_backend_buffer_get_size(lctx.buf_output.get()) : 0; const size_t new_size = (logits_size + embd_size) * sizeof(float); // alloc only when more than the current capacity is required @@ -17134,7 +17294,6 @@ static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) { // This doesn't happen often, but may be annoying in some cases (like the HellaSwag benchmark) LLAMA_LOG_INFO("%s: reallocating output buffer from size %.02f MiB to %.02f MiB\n", __func__, prev_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0); #endif - ggml_backend_buffer_free(lctx.buf_output); lctx.buf_output = nullptr; lctx.logits = nullptr; lctx.embd = nullptr; @@ -17142,27 +17301,19 @@ static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) { auto * buft = ggml_backend_cpu_buffer_type(); // try to use the host buffer of the device where the output tensor is allocated for faster transfer to system memory - ggml_tensor * output_tensor = lctx.model.output; - if (!output_tensor) { - // bert models don't have an output tensor, use the last layer - output_tensor = lctx.model.layers.back().layer_out_norm; + auto * output_dev = lctx.model.dev_output.dev; + auto * output_dev_host_buft = output_dev ? ggml_backend_dev_host_buffer_type(output_dev) : nullptr; + if (output_dev_host_buft) { + buft = output_dev_host_buft; } - if (output_tensor) { - auto * output_buft = ggml_backend_buffer_get_type(output_tensor->buffer); - auto * output_dev = ggml_backend_buft_get_device(output_buft); - auto * output_dev_host_buft = ggml_backend_dev_host_buffer_type(output_dev); - if (output_dev_host_buft) { - buft = output_dev_host_buft; - } - } - lctx.buf_output = ggml_backend_buft_alloc_buffer(buft, new_size); + lctx.buf_output.reset(ggml_backend_buft_alloc_buffer(buft, new_size)); if (lctx.buf_output == nullptr) { LLAMA_LOG_ERROR("%s: failed to allocate output buffer of size %.2f MiB\n", __func__, new_size / (1024.0 * 1024.0)); return 0; } } - float * output_base = (float *) ggml_backend_buffer_get_base(lctx.buf_output); + float * output_base = (float *) ggml_backend_buffer_get_base(lctx.buf_output.get()); lctx.logits = has_logits ? output_base : nullptr; lctx.embd = has_embd ? output_base + logits_size : nullptr; @@ -17174,7 +17325,7 @@ static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) { // set all ids as invalid (negative) std::fill(lctx.output_ids.begin(), lctx.output_ids.end(), -1); - ggml_backend_buffer_clear(lctx.buf_output, 0); + ggml_backend_buffer_clear(lctx.buf_output.get(), 0); lctx.n_outputs = 0; @@ -17219,14 +17370,16 @@ static void llama_output_reorder(struct llama_context * ctx) { } } -static void llama_graph_compute( +// returns the result of ggml_backend_sched_graph_compute_async execution +static enum ggml_status llama_graph_compute( llama_context & lctx, ggml_cgraph * gf, int n_threads, ggml_threadpool * threadpool) { if (lctx.backend_cpu != nullptr) { - ggml_backend_cpu_set_threadpool(lctx.backend_cpu, threadpool); - ggml_backend_cpu_set_abort_callback(lctx.backend_cpu, lctx.abort_callback, lctx.abort_callback_data); + auto * reg = ggml_backend_dev_backend_reg(ggml_backend_get_device(lctx.backend_cpu)); + auto * set_threadpool_fn = (decltype(ggml_backend_cpu_set_threadpool) *) ggml_backend_reg_get_proc_address(reg, "ggml_backend_cpu_set_threadpool"); + set_threadpool_fn(lctx.backend_cpu, threadpool); } // set the number of threads for all the backends @@ -17234,15 +17387,20 @@ static void llama_graph_compute( set_n_threads_fn.second(set_n_threads_fn.first, n_threads); } - auto err = ggml_backend_sched_graph_compute_async(lctx.sched, gf); - if (err != GGML_STATUS_SUCCESS) { - LLAMA_LOG_ERROR("%s: ggml_backend_sched_graph_compute_async failed with error %d\n", __func__, err); + auto status = ggml_backend_sched_graph_compute_async(lctx.sched.get(), gf); + if (status != GGML_STATUS_SUCCESS) { + LLAMA_LOG_ERROR("%s: ggml_backend_sched_graph_compute_async failed with error %d\n", __func__, status); } // fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched)); + + return status; } // decode a batch of tokens by evaluating the transformer +// in case of unsuccessful decoding (error or warning), +// the kv_cache state will be returned to its original state +// (for non-recurrent models) or cleaned (for recurrent models) // // - lctx: llama context // - batch: batch to evaluate @@ -17292,6 +17450,7 @@ static int llama_decode_internal( lctx.n_queued_tokens += n_tokens_all; auto & kv_self = lctx.kv_self; + llama_kv_slot_restorer kv_slot_restorer(kv_self); const int64_t n_embd = hparams.n_embd; const int64_t n_vocab = hparams.n_vocab; @@ -17376,9 +17535,11 @@ static int llama_decode_internal( kv_self.head = 0; } - if (!llama_kv_cache_find_slot(kv_self, ubatch)) { + const auto slot = llama_kv_cache_find_slot(kv_self, ubatch); + if (!slot) { return 1; } + kv_slot_restorer.save(slot); if (!kv_self.recurrent) { // a heuristic, to avoid attending the full cache if it is not yet utilized @@ -17392,8 +17553,8 @@ static int llama_decode_internal( //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head); - ggml_backend_sched_reset(lctx.sched); - ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data); + ggml_backend_sched_reset(lctx.sched.get()); + ggml_backend_sched_set_eval_callback(lctx.sched.get(), lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data); ggml_cgraph * gf = llama_build_graph(lctx, ubatch, false); @@ -17421,11 +17582,23 @@ static int llama_decode_internal( } // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs); - ggml_backend_sched_alloc_graph(lctx.sched, gf); + ggml_backend_sched_alloc_graph(lctx.sched.get(), gf); llama_set_inputs(lctx, ubatch); - llama_graph_compute(lctx, gf, n_threads, threadpool); + const auto compute_status = llama_graph_compute(lctx, gf, n_threads, threadpool); + if (compute_status != GGML_STATUS_SUCCESS) { + kv_slot_restorer.restore(kv_self); + switch (compute_status) { + case GGML_STATUS_ABORTED: + return 2; + case GGML_STATUS_ALLOC_FAILED: + return -2; + case GGML_STATUS_FAILED: + default: + return -3; + } + } // update the kv ring buffer { @@ -17444,7 +17617,7 @@ static int llama_decode_internal( // extract logits if (res) { - ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(lctx.sched, res); + ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(lctx.sched.get(), res); GGML_ASSERT(backend_res != nullptr); GGML_ASSERT(lctx.logits != nullptr); @@ -17460,7 +17633,7 @@ static int llama_decode_internal( // extract embeddings if (embd) { - ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched, embd); + ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched.get(), embd); GGML_ASSERT(backend_embd != nullptr); switch (cparams.pooling_type) { @@ -17555,7 +17728,7 @@ static int llama_decode_internal( // Reset state for the next token before backend sync, to allow the CPU activities in the reset to // overlap with device computation. - ggml_backend_sched_reset(lctx.sched); + ggml_backend_sched_reset(lctx.sched.get()); return 0; } @@ -17633,8 +17806,8 @@ static int llama_encode_internal( GGML_ASSERT(n_threads > 0); - ggml_backend_sched_reset(lctx.sched); - ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data); + ggml_backend_sched_reset(lctx.sched.get()); + ggml_backend_sched_set_eval_callback(lctx.sched.get(), lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data); ggml_cgraph * gf = llama_build_graph(lctx, ubatch, false); @@ -17658,15 +17831,26 @@ static int llama_encode_internal( } } - ggml_backend_sched_alloc_graph(lctx.sched, gf); + ggml_backend_sched_alloc_graph(lctx.sched.get(), gf); llama_set_inputs(lctx, ubatch); - llama_graph_compute(lctx, gf, n_threads, threadpool); + const auto compute_status = llama_graph_compute(lctx, gf, n_threads, threadpool); + switch (compute_status) { + case GGML_STATUS_SUCCESS: + break; + case GGML_STATUS_ABORTED: + return 2; + case GGML_STATUS_ALLOC_FAILED: + return -2; + case GGML_STATUS_FAILED: + default: + return -3; + } // extract embeddings if (embd) { - ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched, embd); + ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched.get(), embd); GGML_ASSERT(backend_embd != nullptr); if (llama_model_has_decoder(&lctx.model)) { @@ -17733,7 +17917,7 @@ static int llama_encode_internal( // Reset state for the next token before backend sync, to allow the CPU activities in the reset to // overlap with device computation. - ggml_backend_sched_reset(lctx.sched); + ggml_backend_sched_reset(lctx.sched.get()); return 0; } @@ -17947,7 +18131,7 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { #else // ggml_graph defrag - ggml_backend_sched_reset(lctx.sched); + ggml_backend_sched_reset(lctx.sched.get()); ggml_cgraph * gf = llama_build_graph_defrag(lctx, ids); @@ -17962,18 +18146,18 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { static void llama_kv_cache_update_internal(struct llama_context & lctx) { bool need_reserve = false; - // apply K-shift if needed - if (lctx.model.hparams.rope_type != LLAMA_ROPE_TYPE_NONE && lctx.kv_self.has_shift) { - if (lctx.model.arch == LLM_ARCH_DEEPSEEK2) { // not supported due to MLA - GGML_ABORT("Deepseek2 does not support K-shift"); + if (lctx.kv_self.has_shift) { + if (!llama_kv_cache_can_shift(&lctx)) { + GGML_ABORT("The current context does not support K-shift"); } - { - ggml_backend_sched_reset(lctx.sched); + // apply K-shift if needed + if (lctx.model.hparams.rope_type != LLAMA_ROPE_TYPE_NONE) { + ggml_backend_sched_reset(lctx.sched.get()); ggml_cgraph * gf = llama_build_graph_k_shift(lctx); - ggml_backend_sched_alloc_graph(lctx.sched, gf); + ggml_backend_sched_alloc_graph(lctx.sched.get(), gf); llama_set_k_shift(lctx); @@ -18013,8 +18197,8 @@ static void llama_kv_cache_update_internal(struct llama_context & lctx) { ggml_cgraph * gf = llama_build_graph(lctx, ubatch, true); // initialize scheduler with the worst-case graph - ggml_backend_sched_reset(lctx.sched); - if (!ggml_backend_sched_reserve(lctx.sched, gf)) { + ggml_backend_sched_reset(lctx.sched.get()); + if (!ggml_backend_sched_reserve(lctx.sched.get(), gf)) { LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__); } } @@ -18541,6 +18725,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s llama_model model; llm_load_arch(ml, model); llm_load_hparams(ml, model); + llm_load_stats(ml, model); struct quantize_state_internal qs(model, params); @@ -18565,40 +18750,57 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s } const size_t align = GGUF_DEFAULT_ALIGNMENT; - struct gguf_context * ctx_out = gguf_init_empty(); + gguf_context_ptr ctx_out { gguf_init_empty() }; // copy the KV pairs from the input file - gguf_set_kv (ctx_out, ml.meta); - gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION); // TODO: use LLM_KV - gguf_set_val_u32(ctx_out, "general.file_type", ftype); // TODO: use LLM_KV + gguf_set_kv (ctx_out.get(), ml.meta.get()); + gguf_set_val_u32(ctx_out.get(), "general.quantization_version", GGML_QNT_VERSION); // TODO: use LLM_KV + gguf_set_val_u32(ctx_out.get(), "general.file_type", ftype); // TODO: use LLM_KV // Remove split metadata - gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_NO).c_str()); - gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str()); - gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str()); + gguf_remove_key(ctx_out.get(), ml.llm_kv(LLM_KV_SPLIT_NO).c_str()); + gguf_remove_key(ctx_out.get(), ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str()); + gguf_remove_key(ctx_out.get(), ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str()); if (params->kv_overrides) { const std::vector & overrides = *(const std::vector *)params->kv_overrides; - for (auto & o : overrides) { + for (const auto & o : overrides) { if (o.key[0] == 0) break; if (o.tag == LLAMA_KV_OVERRIDE_TYPE_FLOAT) { - gguf_set_val_f32(ctx_out, o.key, o.val_f64); + gguf_set_val_f32(ctx_out.get(), o.key, o.val_f64); } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_INT) { - gguf_set_val_i32(ctx_out, o.key, o.val_i64); + gguf_set_val_i32(ctx_out.get(), o.key, o.val_i64); } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_BOOL) { - gguf_set_val_bool(ctx_out, o.key, o.val_bool); + gguf_set_val_bool(ctx_out.get(), o.key, o.val_bool); } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_STR) { - gguf_set_val_str(ctx_out, o.key, o.val_str); + gguf_set_val_str(ctx_out.get(), o.key, o.val_str); } else { LLAMA_LOG_WARN("%s: unknown KV override type for key %s\n", __func__, o.key); } } } - for (int i = 0; i < ml.n_tensors; ++i) { - const struct ggml_tensor * meta = ml.get_tensor_meta(i); + // make a list of weights + std::vector tensors; + tensors.reserve(ml.weights_map.size()); + for (const auto & it : ml.weights_map) { + tensors.push_back(&it.second); + } - const std::string name = ggml_get_name(meta); + // keep_split requires that the weights are sorted by split index + if (params->keep_split) { + std::sort(tensors.begin(), tensors.end(), [](const llama_model_loader::llama_tensor_weight * a, const llama_model_loader::llama_tensor_weight * b) { + if (a->idx == b->idx) { + return a->offs < b->offs; + } + return a->idx < b->idx; + }); + } + + for (const auto * it : tensors) { + const struct ggml_tensor * tensor = it->tensor; + + const std::string name = ggml_get_name(tensor); // TODO: avoid hardcoded tensor names - use the TN_* constants if (name.find("attn_v.weight") != std::string::npos || @@ -18636,32 +18838,32 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s std::vector> f32_conv_buf; uint16_t n_split = 1; + // Assume split index is continuous if (params->keep_split) { - for (int i = 0; i < ml.n_tensors; ++i) { - n_split = std::max(uint16_t(ml.get_weight(i)->idx+1), n_split); + for (const auto * it : tensors) { + n_split = std::max(uint16_t(it->idx + 1), n_split); } } - std::vector ctx_outs(n_split, NULL); - ctx_outs[0] = ctx_out; + std::vector ctx_outs(n_split); + ctx_outs[0] = std::move(ctx_out); // populate the original tensors so we get an initial meta data - for (int i = 0; i < ml.n_tensors; ++i) { - auto weight = ml.get_weight(i); - uint16_t i_split = params->keep_split ? weight->idx : 0; - struct ggml_tensor * tensor = weight->tensor; - if (ctx_outs[i_split] == NULL) { - ctx_outs[i_split] = gguf_init_empty(); + for (const auto * it : tensors) { + uint16_t i_split = params->keep_split ? it->idx : 0; + struct ggml_tensor * tensor = it->tensor; + if (!ctx_outs[i_split]) { + ctx_outs[i_split].reset(gguf_init_empty()); } - gguf_add_tensor(ctx_outs[i_split], tensor); + gguf_add_tensor(ctx_outs[i_split].get(), tensor); } // Set split info if needed if (n_split > 1) { for (size_t i = 0; i < ctx_outs.size(); ++i) { - gguf_set_val_u16(ctx_outs[i], ml.llm_kv(LLM_KV_SPLIT_NO).c_str(), i); - gguf_set_val_u16(ctx_outs[i], ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str(), n_split); - gguf_set_val_i32(ctx_outs[i], ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str(), ml.n_tensors); + gguf_set_val_u16(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_NO).c_str(), i); + gguf_set_val_u16(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str(), n_split); + gguf_set_val_i32(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str(), ml.n_tensors); } } @@ -18671,8 +18873,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s // Write metadata and close file handler if (fout.is_open()) { fout.seekp(0); - std::vector data(gguf_get_meta_size(ctx_outs[cur_split])); - gguf_get_meta_data(ctx_outs[cur_split], data.data()); + std::vector data(gguf_get_meta_size(ctx_outs[cur_split].get())); + gguf_get_meta_data(ctx_outs[cur_split].get(), data.data()); fout.write((const char *) data.data(), data.size()); fout.close(); } @@ -18689,19 +18891,19 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s fout = std::ofstream(fname, std::ios::binary); fout.exceptions(std::ofstream::failbit); // fail fast on write errors - const size_t meta_size = gguf_get_meta_size(ctx_outs[cur_split]); + const size_t meta_size = gguf_get_meta_size(ctx_outs[cur_split].get()); // placeholder for the meta data ::zeros(fout, meta_size); }; const auto tn = LLM_TN(model.arch); new_ofstream(0); - for (int i = 0; i < ml.n_tensors; ++i) { - auto weight = ml.get_weight(i); - struct ggml_tensor * tensor = weight->tensor; - if (weight->idx != cur_split && params->keep_split) { + for (const auto * it : tensors) { + const auto & weight = *it; + struct ggml_tensor * tensor = weight.tensor; + if (weight.idx != cur_split && params->keep_split) { close_ofstream(); - new_ofstream(weight->idx); + new_ofstream(weight.idx); } const std::string name = ggml_get_name(tensor); @@ -18874,17 +19076,14 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s total_size_new += new_size; // update the gguf meta data as we go - gguf_set_tensor_type(ctx_outs[cur_split], name.c_str(), new_type); - gguf_set_tensor_data(ctx_outs[cur_split], name.c_str(), new_data, new_size); + gguf_set_tensor_type(ctx_outs[cur_split].get(), name.c_str(), new_type); + gguf_set_tensor_data(ctx_outs[cur_split].get(), name.c_str(), new_data, new_size); // write tensor data + padding fout.write((const char *) new_data, new_size); zeros(fout, GGML_PAD(new_size, align) - new_size); } close_ofstream(); - for (auto & c:ctx_outs) { - gguf_free(c); - } LLAMA_LOG_INFO("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0); LLAMA_LOG_INFO("%s: quant size = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0); @@ -18898,51 +19097,51 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s static void llama_lora_adapter_init_internal(struct llama_model * model, const char * path_lora, struct llama_lora_adapter & adapter) { LLAMA_LOG_INFO("%s: loading lora adapter from '%s' ...\n", __func__, path_lora); - ggml_context * ctx = nullptr; + ggml_context * ctx_init; struct gguf_init_params meta_gguf_params = { /* .no_alloc = */ true, - /* .ctx = */ &ctx, + /* .ctx = */ &ctx_init, }; - struct gguf_context * ctx_gguf = gguf_init_from_file(path_lora, meta_gguf_params); + + gguf_context_ptr ctx_gguf { gguf_init_from_file(path_lora, meta_gguf_params) }; if (!ctx_gguf) { throw std::runtime_error("failed to load lora adapter file from " + std::string(path_lora)); } + ggml_context_ptr ctx { ctx_init }; + // check metadata { auto get_kv_str = [&](const std::string & key) -> std::string { - int id = gguf_find_key(ctx_gguf, key.c_str()); - return id < 0 ? "" : std::string(gguf_get_val_str(ctx_gguf, id)); + int id = gguf_find_key(ctx_gguf.get(), key.c_str()); + return id < 0 ? "" : std::string(gguf_get_val_str(ctx_gguf.get(), id)); }; auto get_kv_f32 = [&](const std::string & key) -> float { - int id = gguf_find_key(ctx_gguf, key.c_str()); - return id < 0 ? 0.0f : gguf_get_val_f32(ctx_gguf, id); + int id = gguf_find_key(ctx_gguf.get(), key.c_str()); + return id < 0 ? 0.0f : gguf_get_val_f32(ctx_gguf.get(), id); }; LLM_KV llm_kv = LLM_KV(LLM_ARCH_UNKNOWN); auto general_type = get_kv_str(llm_kv(LLM_KV_GENERAL_TYPE)); if (general_type != "adapter") { - gguf_free(ctx_gguf); throw std::runtime_error("expect general.type to be 'adapter', but got: " + general_type); } auto general_arch_str = get_kv_str(llm_kv(LLM_KV_GENERAL_ARCHITECTURE)); auto general_arch = llm_arch_from_string(general_arch_str); if (general_arch != model->arch) { - gguf_free(ctx_gguf); throw std::runtime_error("model arch and LoRA arch mismatch"); } auto adapter_type = get_kv_str(llm_kv(LLM_KV_ADAPTER_TYPE)); if (adapter_type != "lora") { - gguf_free(ctx_gguf); throw std::runtime_error("expect adapter.type to be 'lora', but got: " + adapter_type); } adapter.alpha = get_kv_f32(llm_kv(LLM_KV_ADAPTER_LORA_ALPHA)); } - int n_tensors = gguf_get_n_tensors(ctx_gguf); + int n_tensors = gguf_get_n_tensors(ctx_gguf.get()); // contexts for each buffer type std::map ctx_map; @@ -18956,7 +19155,11 @@ static void llama_lora_adapter_init_internal(struct llama_model * model, const c /*.no_alloc =*/ true, }; ggml_context * buft_ctx = ggml_init(params); + if (!buft_ctx) { + return nullptr; + } ctx_map[buft] = buft_ctx; + adapter.ctxs.emplace_back(buft_ctx); return buft_ctx; }; return it->second; @@ -18967,7 +19170,7 @@ static void llama_lora_adapter_init_internal(struct llama_model * model, const c auto str_endswith = [](const std::string & str, const std::string & suffix) { return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0; }; - for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) { + for (ggml_tensor * cur = ggml_get_first_tensor(ctx.get()); cur; cur = ggml_get_next_tensor(ctx.get(), cur)) { std::string name(cur->name); if (str_endswith(name, ".lora_a")) { replace_all(name, ".lora_a", ""); @@ -18984,8 +19187,6 @@ static void llama_lora_adapter_init_internal(struct llama_model * model, const c ab_map[name].b = cur; } } else { - gguf_free(ctx_gguf); - ggml_free(ctx); throw std::runtime_error("LoRA tensor '" + name + "' has unexpected suffix"); } } @@ -18996,28 +19197,20 @@ static void llama_lora_adapter_init_internal(struct llama_model * model, const c llama_lora_weight & w = it.second; if (!w.a || !w.b) { - gguf_free(ctx_gguf); - ggml_free(ctx); throw std::runtime_error("LoRA tensor pair for '" + name + "' is missing one component"); } // device buft and device ctx auto * model_tensor = llama_get_model_tensor(model, name.c_str()); if (!model_tensor) { - gguf_free(ctx_gguf); - ggml_free(ctx); throw std::runtime_error("LoRA tensor '" + name + "' does not exist in base model"); } struct ggml_context * dev_ctx = ctx_for_buft(ggml_backend_buffer_get_type(model_tensor->buffer)); // validate tensor shape if (model_tensor->ne[0] != w.a->ne[0] || model_tensor->ne[1] != w.b->ne[1]) { - gguf_free(ctx_gguf); - ggml_free(ctx); throw std::runtime_error("tensor '" + name + "' has incorrect shape"); } if (w.a->ne[1] != w.b->ne[0]) { - gguf_free(ctx_gguf); - ggml_free(ctx); throw std::runtime_error("lora_a tensor is not transposed (hint: adapter from \"finetune\" example is no longer supported)"); } // save tensor to adapter @@ -19032,18 +19225,15 @@ static void llama_lora_adapter_init_internal(struct llama_model * model, const c { adapter.ctxs.reserve(ctx_map.size()); adapter.bufs.reserve(ctx_map.size()); - for (auto it : ctx_map) { + for (auto & it : ctx_map) { ggml_backend_buffer_type_t buft = it.first; ggml_context * ctx_dev = it.second; - ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx_dev, buft); + ggml_backend_buffer_ptr buf { ggml_backend_alloc_ctx_tensors_from_buft(ctx_dev, buft) }; if (!buf) { - gguf_free(ctx_gguf); - ggml_free(ctx); throw std::runtime_error("failed to allocate buffer for lora adapter\n"); } - LLAMA_LOG_INFO("%s: %10s LoRA buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0); - adapter.ctxs.push_back(ctx_dev); - adapter.bufs.push_back(buf); + LLAMA_LOG_INFO("%s: %10s LoRA buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf.get()), ggml_backend_buffer_get_size(buf.get())/1024.0/1024.0); + adapter.bufs.emplace_back(std::move(buf)); } } @@ -19052,7 +19242,7 @@ static void llama_lora_adapter_init_internal(struct llama_model * model, const c llama_file gguf_file(path_lora, "rb"); std::vector read_buf; auto set_tensor = [&](struct ggml_tensor * orig, struct ggml_tensor * dev) { - size_t offs = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, gguf_find_tensor(ctx_gguf, orig->name)); + size_t offs = gguf_get_data_offset(ctx_gguf.get()) + gguf_get_tensor_offset(ctx_gguf.get(), gguf_find_tensor(ctx_gguf.get(), orig->name)); size_t size = ggml_nbytes(orig); read_buf.resize(size); gguf_file.seek(offs, SEEK_SET); @@ -19068,10 +19258,6 @@ static void llama_lora_adapter_init_internal(struct llama_model * model, const c } LLAMA_LOG_INFO("%s: loaded %zu tensors from lora file\n", __func__, adapter.ab_map.size()*2); - - // free ctx for reading gguf - gguf_free(ctx_gguf); - ggml_free(ctx); } int32_t llama_lora_adapter_set( @@ -19110,6 +19296,7 @@ void llama_lora_adapter_free(struct llama_lora_adapter * adapter) { // struct llama_model_params llama_model_default_params() { struct llama_model_params result = { + /*.devices =*/ nullptr, /*.n_gpu_layers =*/ 0, /*.split_mode =*/ LLAMA_SPLIT_MODE_LAYER, /*.main_gpu =*/ 0, @@ -19227,7 +19414,11 @@ void llama_backend_init(void) { void llama_numa_init(enum ggml_numa_strategy numa) { if (numa != GGML_NUMA_STRATEGY_DISABLED) { - ggml_numa_init(numa); + auto * dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU); + GGML_ASSERT(dev && "CPU backend is not loaded"); + auto * reg = ggml_backend_dev_backend_reg(dev); + auto * numa_init_fn = (decltype(ggml_numa_init) *) ggml_backend_reg_get_proc_address(reg, "ggml_backend_cpu_numa_init"); + numa_init_fn(numa); } } @@ -19318,19 +19509,24 @@ struct llama_model * llama_load_model_from_file( } // create list of devices to use with this model - // currently, we use all available devices - // TODO: rework API to give user more control over device selection - for (size_t i = 0; i < ggml_backend_dev_count(); ++i) { - ggml_backend_dev_t dev = ggml_backend_dev_get(i); - switch (ggml_backend_dev_type(dev)) { - case GGML_BACKEND_DEVICE_TYPE_CPU: - case GGML_BACKEND_DEVICE_TYPE_ACCEL: - // skip CPU backends since they are handled separately - break; + if (params.devices) { + for (ggml_backend_dev_t * dev = params.devices; *dev; ++dev) { + model->devices.push_back(*dev); + } + } else { + // use all available devices + for (size_t i = 0; i < ggml_backend_dev_count(); ++i) { + ggml_backend_dev_t dev = ggml_backend_dev_get(i); + switch (ggml_backend_dev_type(dev)) { + case GGML_BACKEND_DEVICE_TYPE_CPU: + case GGML_BACKEND_DEVICE_TYPE_ACCEL: + // skip CPU backends since they are handled separately + break; - case GGML_BACKEND_DEVICE_TYPE_GPU: - model->devices.push_back(dev); - break; + case GGML_BACKEND_DEVICE_TYPE_GPU: + model->devices.push_back(dev); + break; + } } } @@ -19480,15 +19676,26 @@ struct llama_context * llama_new_context_with_model( cparams.causal_attn = params.attention_type == LLAMA_ATTENTION_TYPE_CAUSAL; } - LLAMA_LOG_INFO("%s: n_ctx = %u\n", __func__, cparams.n_ctx); - LLAMA_LOG_INFO("%s: n_batch = %u\n", __func__, cparams.n_batch); - LLAMA_LOG_INFO("%s: n_ubatch = %u\n", __func__, cparams.n_ubatch); - LLAMA_LOG_INFO("%s: flash_attn = %d\n", __func__, cparams.flash_attn); - LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, cparams.rope_freq_base); - LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, cparams.rope_freq_scale); + const uint32_t n_ctx_per_seq = cparams.n_ctx / cparams.n_seq_max; - ctx->abort_callback = params.abort_callback; - ctx->abort_callback_data = params.abort_callback_data; + LLAMA_LOG_INFO("%s: n_seq_max = %u\n", __func__, cparams.n_seq_max); + LLAMA_LOG_INFO("%s: n_ctx = %u\n", __func__, cparams.n_ctx); + LLAMA_LOG_INFO("%s: n_ctx_per_seq = %u\n", __func__, n_ctx_per_seq); + LLAMA_LOG_INFO("%s: n_batch = %u\n", __func__, cparams.n_batch); + LLAMA_LOG_INFO("%s: n_ubatch = %u\n", __func__, cparams.n_ubatch); + LLAMA_LOG_INFO("%s: flash_attn = %d\n", __func__, cparams.flash_attn); + LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, cparams.rope_freq_base); + LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, cparams.rope_freq_scale); + + if (n_ctx_per_seq < hparams.n_ctx_train) { + LLAMA_LOG_WARN("%s: n_ctx_per_seq (%u) < n_ctx_train (%u) -- the full capacity of the model will not be utilized\n", + __func__, n_ctx_per_seq, hparams.n_ctx_train); + } + + if (n_ctx_per_seq > hparams.n_ctx_train) { + LLAMA_LOG_WARN("%s: n_ctx_pre_seq (%u) > n_ctx_train (%u) -- possible training context overflow\n", + __func__, n_ctx_per_seq, hparams.n_ctx_train); + } ctx->logits_all = params.logits_all; @@ -19520,7 +19727,7 @@ struct llama_context * llama_new_context_with_model( llama_free(ctx); return nullptr; } - ctx->backends.push_back(backend); + ctx->backends.emplace_back(backend); } // add ACCEL backends (such as BLAS) @@ -19533,31 +19740,33 @@ struct llama_context * llama_new_context_with_model( llama_free(ctx); return nullptr; } - ctx->backends.push_back(backend); + ctx->backends.emplace_back(backend); } } // add CPU backend - ctx->backend_cpu = ggml_backend_cpu_init(); + ctx->backend_cpu = ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_CPU, nullptr); if (ctx->backend_cpu == nullptr) { LLAMA_LOG_ERROR("%s: failed to initialize CPU backend\n", __func__); llama_free(ctx); return nullptr; } - ctx->backends.push_back(ctx->backend_cpu); + ctx->backends.emplace_back(ctx->backend_cpu); // create a list of the set_n_threads functions in the backends - for (auto * backend : ctx->backends) { - ggml_backend_dev_t dev = ggml_backend_get_device(backend); + for (auto & backend : ctx->backends) { + ggml_backend_dev_t dev = ggml_backend_get_device(backend.get()); ggml_backend_reg_t reg = dev ? ggml_backend_dev_backend_reg(dev) : nullptr; if (reg) { auto ggml_backend_set_n_threads_fn = (ggml_backend_set_n_threads_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_set_n_threads"); if (ggml_backend_set_n_threads_fn) { - ctx->set_n_threads_fns.emplace_back(backend, ggml_backend_set_n_threads_fn); + ctx->set_n_threads_fns.emplace_back(backend.get(), ggml_backend_set_n_threads_fn); } } } + llama_set_abort_callback(ctx, params.abort_callback, params.abort_callback_data); + if (!llama_kv_cache_init(ctx->kv_self, ctx, type_k, type_v, kv_size, cparams.offload_kqv)) { LLAMA_LOG_ERROR("%s: llama_kv_cache_init() failed for self-attention cache\n", __func__); llama_free(ctx); @@ -19592,17 +19801,19 @@ struct llama_context * llama_new_context_with_model( } LLAMA_LOG_INFO("%s: %10s output buffer size = %8.2f MiB\n", __func__, - ggml_backend_buffer_name(ctx->buf_output), - ggml_backend_buffer_get_size(ctx->buf_output) / 1024.0 / 1024.0); + ggml_backend_buffer_name(ctx->buf_output.get()), + ggml_backend_buffer_get_size(ctx->buf_output.get()) / 1024.0 / 1024.0); } // scheduler and compute buffers { // buffer types used for the compute buffer of each backend std::vector backend_buft; - for (auto * backend : ctx->backends) { - auto * buft = ggml_backend_get_default_buffer_type(backend); - if (ggml_backend_is_cpu(backend) && !model->devices.empty()) { + std::vector backend_ptrs; + for (auto & backend : ctx->backends) { + auto * buft = ggml_backend_get_default_buffer_type(backend.get()); + auto backend_type = ggml_backend_dev_type(ggml_backend_get_device(backend.get())); + if (backend_type == GGML_BACKEND_DEVICE_TYPE_CPU && !model->devices.empty()) { // use the host buffer of the first device CPU for faster transfer of the intermediate state auto * dev = model->devices[0]; auto * host_buft = ggml_backend_dev_host_buffer_type(dev); @@ -19611,6 +19822,7 @@ struct llama_context * llama_new_context_with_model( } } backend_buft.push_back(buft); + backend_ptrs.push_back(backend.get()); } const size_t max_nodes = llama_model_max_nodes(*model); @@ -19628,12 +19840,13 @@ struct llama_context * llama_new_context_with_model( // pipeline parallelism requires support for async compute and events in all devices if (pipeline_parallel) { - for (auto * backend : ctx->backends) { - if (ggml_backend_is_cpu(backend)) { + for (auto & backend : ctx->backends) { + auto dev_type = ggml_backend_dev_type(ggml_backend_get_device(backend.get())); + if (dev_type == GGML_BACKEND_DEVICE_TYPE_CPU) { // ignore CPU backend continue; } - auto * dev = ggml_backend_get_device(backend); + auto * dev = ggml_backend_get_device(backend.get()); ggml_backend_dev_props props; ggml_backend_dev_get_props(dev, &props); if (!props.caps.async || !props.caps.events) { @@ -19644,10 +19857,10 @@ struct llama_context * llama_new_context_with_model( } } - ctx->sched = ggml_backend_sched_new(ctx->backends.data(), backend_buft.data(), ctx->backends.size(), max_nodes, pipeline_parallel); + ctx->sched.reset(ggml_backend_sched_new(backend_ptrs.data(), backend_buft.data(), backend_ptrs.size(), max_nodes, pipeline_parallel)); if (pipeline_parallel) { - LLAMA_LOG_INFO("%s: pipeline parallelism enabled (n_copies=%d)\n", __func__, ggml_backend_sched_get_n_copies(ctx->sched)); + LLAMA_LOG_INFO("%s: pipeline parallelism enabled (n_copies=%d)\n", __func__, ggml_backend_sched_get_n_copies(ctx->sched.get())); } // initialize scheduler with the worst-case graph @@ -19659,29 +19872,29 @@ struct llama_context * llama_new_context_with_model( ggml_cgraph * gf_pp = llama_build_graph(*ctx, ubatch_pp, true); // reserve pp graph first so that buffers are only allocated once - ggml_backend_sched_reserve(ctx->sched, gf_pp); - int n_splits_pp = ggml_backend_sched_get_n_splits(ctx->sched); + ggml_backend_sched_reserve(ctx->sched.get(), gf_pp); + int n_splits_pp = ggml_backend_sched_get_n_splits(ctx->sched.get()); int n_nodes_pp = ggml_graph_n_nodes(gf_pp); // reserve with tg graph to get the number of splits and nodes llama_ubatch ubatch_tg = { true, 1, 1, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr}; ggml_cgraph * gf_tg = llama_build_graph(*ctx, ubatch_tg, true); - ggml_backend_sched_reserve(ctx->sched, gf_tg); - int n_splits_tg = ggml_backend_sched_get_n_splits(ctx->sched); + ggml_backend_sched_reserve(ctx->sched.get(), gf_tg); + int n_splits_tg = ggml_backend_sched_get_n_splits(ctx->sched.get()); int n_nodes_tg = ggml_graph_n_nodes(gf_tg); // reserve again with pp graph to avoid ggml-alloc reallocations during inference - gf_pp = llama_build_graph(*ctx, ubatch_pp, false); - if (!ggml_backend_sched_reserve(ctx->sched, gf_pp)) { + gf_pp = llama_build_graph(*ctx, ubatch_pp, true); + if (!ggml_backend_sched_reserve(ctx->sched.get(), gf_pp)) { LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__); llama_free(ctx); return nullptr; } - for (size_t i = 0; i < ctx->backends.size(); i++) { - ggml_backend_t backend = ctx->backends[i]; + for (size_t i = 0; i < backend_ptrs.size(); ++i) { + ggml_backend_t backend = backend_ptrs[i]; ggml_backend_buffer_type_t buft = backend_buft[i]; - size_t size = ggml_backend_sched_get_buffer_size(ctx->sched, backend); + size_t size = ggml_backend_sched_get_buffer_size(ctx->sched.get(), backend); if (size > 1) { LLAMA_LOG_INFO("%s: %10s compute buffer size = %8.2f MiB\n", __func__, ggml_backend_buft_name(buft), @@ -19803,6 +20016,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_QWEN: case LLM_ARCH_QWEN2: case LLM_ARCH_QWEN2MOE: + case LLM_ARCH_OLMO2: case LLM_ARCH_OLMOE: case LLM_ARCH_PHI2: case LLM_ARCH_PHI3: @@ -19876,19 +20090,11 @@ int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t bu } uint64_t llama_model_size(const struct llama_model * model) { - uint64_t size = 0; - for (const auto & it : model->tensors_by_name) { - size += ggml_nbytes(it.second); - } - return size; + return model->n_bytes; } uint64_t llama_model_n_params(const struct llama_model * model) { - uint64_t nparams = 0; - for (const auto & it : model->tensors_by_name) { - nparams += ggml_nelements(it.second); - } - return nparams; + return model->n_elements; } struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name) { @@ -19961,7 +20167,8 @@ static bool llama_control_vector_init(struct llama_control_vector & cvec, const // create a context for each buffer type std::map ctx_map; auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * { - if (ctx_map.count(buft) == 0) { + auto it = ctx_map.find(buft); + if (it == ctx_map.end()) { struct ggml_init_params params = { /*.mem_size =*/ model.hparams.n_layer*ggml_tensor_overhead(), /*.mem_buffer =*/ NULL, @@ -19972,12 +20179,12 @@ static bool llama_control_vector_init(struct llama_control_vector & cvec, const return nullptr; } ctx_map[buft] = ctx; - cvec.ctxs.push_back(ctx); + cvec.ctxs.emplace_back(ctx); + return ctx; } - return ctx_map.at(buft); + return it->second; }; - // make tensors cvec.tensors.reserve(model.hparams.n_layer); cvec.tensors.push_back(nullptr); // there's never a tensor for layer 0 @@ -20008,7 +20215,7 @@ static bool llama_control_vector_init(struct llama_control_vector & cvec, const return false; } ggml_backend_buffer_clear(buf, 0); - cvec.bufs.push_back(buf); + cvec.bufs.emplace_back(buf); } return true; @@ -20201,6 +20408,10 @@ void llama_kv_cache_update(struct llama_context * ctx) { llama_kv_cache_update_internal(*ctx); } +bool llama_kv_cache_can_shift(struct llama_context * ctx) { + return !ctx->kv_self.recurrent && ctx->model.arch != LLM_ARCH_DEEPSEEK2; // not supported due to MLA +} + // deprecated size_t llama_get_state_size(struct llama_context * ctx) { return llama_state_get_size(ctx); @@ -21185,6 +21396,14 @@ int32_t llama_n_threads_batch(struct llama_context * ctx) { void llama_set_abort_callback(struct llama_context * ctx, bool (*abort_callback)(void * data), void * abort_callback_data) { ctx->abort_callback = abort_callback; ctx->abort_callback_data = abort_callback_data; + + for (auto & backend : ctx->backends) { + auto * reg = ggml_backend_dev_backend_reg(ggml_backend_get_device(backend.get())); + auto * set_abort_callback_fn = (ggml_backend_set_abort_callback_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_set_abort_callback"); + if (set_abort_callback_fn) { + set_abort_callback_fn(backend.get(), ctx->abort_callback, ctx->abort_callback_data); + } + } } void llama_set_embeddings(struct llama_context * ctx, bool embeddings) { @@ -21276,7 +21495,7 @@ int32_t llama_decode( } void llama_synchronize(struct llama_context * ctx) { - ggml_backend_sched_synchronize(ctx->sched); + ggml_backend_sched_synchronize(ctx->sched.get()); // FIXME: if multiple single tokens are evaluated without a synchronization, // the stats will be added to the prompt evaluation stats @@ -21551,19 +21770,110 @@ int32_t llama_detokenize( // chat templates // +static llm_chat_template llama_chat_detect_template(const std::string & tmpl) { + if (LLM_CHAT_TEMPLATES.find(tmpl) != LLM_CHAT_TEMPLATES.end()) { + return LLM_CHAT_TEMPLATES.at(tmpl); + } + auto tmpl_contains = [&tmpl](const char * haystack) -> bool { + return tmpl.find(haystack) != std::string::npos; + }; + if (tmpl_contains("<|im_start|>")) { + return LLM_CHAT_TEMPLATE_CHATML; + } else if (tmpl.find("mistral") == 0 || tmpl_contains("[INST]")) { + if (tmpl_contains("[SYSTEM_PROMPT]")) { + return LLM_CHAT_TEMPLATE_MISTRAL_V7; + } else if ( + // catches official 'v1' template + tmpl_contains("' [INST] ' + system_message") + // catches official 'v3' and 'v3-tekken' templates + || tmpl_contains("[AVAILABLE_TOOLS]") + ) { + // Official mistral 'v1', 'v3' and 'v3-tekken' templates + // See: https://github.com/mistralai/cookbook/blob/main/concept-deep-dive/tokenization/chat_templates.md + // See: https://github.com/mistralai/cookbook/blob/main/concept-deep-dive/tokenization/templates.md + if (tmpl_contains(" [INST]")) { + return LLM_CHAT_TEMPLATE_MISTRAL_V1; + } else if (tmpl_contains("\"[INST]\"")) { + return LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN; + } + return LLM_CHAT_TEMPLATE_MISTRAL_V3; + } else { + // llama2 template and its variants + // [variant] support system message + // See: https://huggingface.co/blog/llama2#how-to-prompt-llama-2 + bool support_system_message = tmpl_contains("<>"); + bool add_bos_inside_history = tmpl_contains("bos_token + '[INST]"); + bool strip_message = tmpl_contains("content.strip()"); + if (strip_message) { + return LLM_CHAT_TEMPLATE_LLAMA_2_SYS_STRIP; + } else if (add_bos_inside_history) { + return LLM_CHAT_TEMPLATE_LLAMA_2_SYS_BOS; + } else if (support_system_message) { + return LLM_CHAT_TEMPLATE_LLAMA_2_SYS; + } else { + return LLM_CHAT_TEMPLATE_LLAMA_2; + } + } + } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|end|>")) { + return LLM_CHAT_TEMPLATE_PHI_3; + } else if (tmpl_contains("<|user|>") && tmpl_contains("<|endoftext|>")) { + return LLM_CHAT_TEMPLATE_ZEPHYR; + } else if (tmpl_contains("bos_token + message['role']")) { + return LLM_CHAT_TEMPLATE_MONARCH; + } else if (tmpl_contains("")) { + return LLM_CHAT_TEMPLATE_GEMMA; + } else if (tmpl_contains("'\\n\\nAssistant: ' + eos_token")) { + // OrionStarAI/Orion-14B-Chat + return LLM_CHAT_TEMPLATE_ORION; + } else if (tmpl_contains("GPT4 Correct ")) { + // openchat/openchat-3.5-0106 + return LLM_CHAT_TEMPLATE_OPENCHAT; + } else if (tmpl_contains("USER: ") && tmpl_contains("ASSISTANT: ")) { + // eachadea/vicuna-13b-1.1 (and Orca variant) + if (tmpl_contains("SYSTEM: ")) { + return LLM_CHAT_TEMPLATE_VICUNA_ORCA; + } + return LLM_CHAT_TEMPLATE_VICUNA; + } else if (tmpl_contains("### Instruction:") && tmpl_contains("<|EOT|>")) { + // deepseek-ai/deepseek-coder-33b-instruct + return LLM_CHAT_TEMPLATE_DEEPSEEK; + } else if (tmpl_contains("<|START_OF_TURN_TOKEN|>") && tmpl_contains("<|USER_TOKEN|>")) { + // CohereForAI/c4ai-command-r-plus + return LLM_CHAT_TEMPLATE_COMMAND_R; + } else if (tmpl_contains("<|start_header_id|>") && tmpl_contains("<|end_header_id|>")) { + return LLM_CHAT_TEMPLATE_LLAMA_3; + } else if (tmpl_contains("[gMASK]sop")) { + // chatglm3-6b + return LLM_CHAT_TEMPLATE_CHATGML_3; + } else if (tmpl_contains("[gMASK]")) { + return LLM_CHAT_TEMPLATE_CHATGML_4; + } else if (tmpl_contains(LU8("<用户>"))) { + // MiniCPM-3B-OpenHermes-2.5-v2-GGUF + return LLM_CHAT_TEMPLATE_MINICPM; + } else if (tmpl_contains("'Assistant: ' + message['content'] + eos_token")) { + return LLM_CHAT_TEMPLATE_DEEPSEEK_2; + } else if (tmpl_contains("[|system|]") && tmpl_contains("[|assistant|]") && tmpl_contains("[|endofturn|]")) { + // ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/discussions/8#66bae61b1893d14ee8ed85bb + // EXAONE-3.0-7.8B-Instruct + return LLM_CHAT_TEMPLATE_EXAONE_3; + } else if (tmpl_contains("rwkv-world")) { + return LLM_CHAT_TEMPLATE_RWKV_WORLD; + } else if (tmpl_contains("<|start_of_role|>")) { + return LLM_CHAT_TEMPLATE_GRANITE; + } + return LLM_CHAT_TEMPLATE_UNKNOWN; +} + // Simple version of "llama_apply_chat_template" that only works with strings // This function uses heuristic checks to determine commonly used template. It is not a jinja parser. static int32_t llama_chat_apply_template_internal( - const std::string & tmpl, + const llm_chat_template tmpl, const std::vector & chat, std::string & dest, bool add_ass) { // Taken from the research: https://github.com/ggerganov/llama.cpp/issues/5527 std::stringstream ss; - auto tmpl_contains = [&tmpl](std::string haystack) -> bool { - return tmpl.find(haystack) != std::string::npos; - }; - if (tmpl == "chatml" || tmpl_contains("<|im_start|>")) { + if (tmpl == LLM_CHAT_TEMPLATE_CHATML) { // chatml template for (auto message : chat) { ss << "<|im_start|>" << message->role << "\n" << message->content << "<|im_end|>\n"; @@ -21571,16 +21881,59 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "<|im_start|>assistant\n"; } - } else if (tmpl == "llama2" || tmpl == "mistral" || tmpl_contains("[INST]")) { + } else if (tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V7) { + // Official mistral 'v7' template + // See: https://huggingface.co/mistralai/Mistral-Large-Instruct-2411#basic-instruct-template-v7 + for (auto message : chat) { + std::string role(message->role); + std::string content(message->content); + if (role == "system") { + ss << "[SYSTEM_PROMPT] " << content << "[/SYSTEM_PROMPT]"; + } else if (role == "user") { + ss << "[INST] " << content << "[/INST]"; + } + else { + ss << " " << content << ""; + } + } + } else if (tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V1 + || tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V3 + || tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN) { + // See: https://github.com/mistralai/cookbook/blob/main/concept-deep-dive/tokenization/chat_templates.md + // See: https://github.com/mistralai/cookbook/blob/main/concept-deep-dive/tokenization/templates.md + std::string leading_space = tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V1 ? " " : ""; + std::string trailing_space = tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN ? "" : " "; + bool trim_assistant_message = tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V3; + bool is_inside_turn = false; + for (auto message : chat) { + if (!is_inside_turn) { + ss << leading_space << "[INST]" << trailing_space; + is_inside_turn = true; + } + std::string role(message->role); + std::string content(message->content); + if (role == "system") { + ss << content << "\n\n"; + } else if (role == "user") { + ss << content << leading_space << "[/INST]"; + } else { + ss << trailing_space << (trim_assistant_message ? trim(content) : content) << ""; + is_inside_turn = false; + } + } + } else if ( + tmpl == LLM_CHAT_TEMPLATE_LLAMA_2 + || tmpl == LLM_CHAT_TEMPLATE_LLAMA_2_SYS + || tmpl == LLM_CHAT_TEMPLATE_LLAMA_2_SYS_BOS + || tmpl == LLM_CHAT_TEMPLATE_LLAMA_2_SYS_STRIP) { // llama2 template and its variants // [variant] support system message - bool support_system_message = tmpl_contains("<>") || tmpl == "mistral"; - // [variant] space before + after response - bool space_around_response = tmpl_contains("' ' + eos_token"); + // See: https://huggingface.co/blog/llama2#how-to-prompt-llama-2 + bool support_system_message = tmpl != LLM_CHAT_TEMPLATE_LLAMA_2; // [variant] add BOS inside history - bool add_bos_inside_history = tmpl_contains("bos_token + '[INST]"); + bool add_bos_inside_history = tmpl == LLM_CHAT_TEMPLATE_LLAMA_2_SYS_BOS; // [variant] trim spaces from the input message - bool strip_message = tmpl_contains("content.strip()"); + bool strip_message = tmpl == LLM_CHAT_TEMPLATE_LLAMA_2_SYS_STRIP; // construct the prompt bool is_inside_turn = true; // skip BOS at the beginning ss << "[INST] "; @@ -21601,12 +21954,11 @@ static int32_t llama_chat_apply_template_internal( } else if (role == "user") { ss << content << " [/INST]"; } else { - ss << (space_around_response ? " " : "") << content << (space_around_response ? " " : "") << ""; + ss << content << ""; is_inside_turn = false; } } - // llama2 templates seem to not care about "add_generation_prompt" - } else if (tmpl == "phi3" || (tmpl_contains("<|assistant|>") && tmpl_contains("<|end|>"))) { + } else if (tmpl == LLM_CHAT_TEMPLATE_PHI_3) { // Phi 3 for (auto message : chat) { std::string role(message->role); @@ -21615,7 +21967,7 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "<|assistant|>\n"; } - } else if (tmpl == "zephyr" || tmpl_contains("<|user|>")) { + } else if (tmpl == LLM_CHAT_TEMPLATE_ZEPHYR) { // zephyr template for (auto message : chat) { ss << "<|" << message->role << "|>" << "\n" << message->content << "<|endoftext|>\n"; @@ -21623,7 +21975,7 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "<|assistant|>\n"; } - } else if (tmpl == "monarch" || tmpl_contains("bos_token + message['role']")) { + } else if (tmpl == LLM_CHAT_TEMPLATE_MONARCH) { // mlabonne/AlphaMonarch-7B template (the is included inside history) for (auto message : chat) { std::string bos = (message == chat.front()) ? "" : ""; // skip BOS for first message @@ -21632,7 +21984,7 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "assistant\n"; } - } else if (tmpl == "gemma" || tmpl == "gemma2" || tmpl_contains("")) { + } else if (tmpl == LLM_CHAT_TEMPLATE_GEMMA) { // google/gemma-7b-it std::string system_prompt = ""; for (auto message : chat) { @@ -21654,7 +22006,7 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "model\n"; } - } else if (tmpl == "orion" || tmpl_contains("'\\n\\nAssistant: ' + eos_token")) { + } else if (tmpl == LLM_CHAT_TEMPLATE_ORION) { // OrionStarAI/Orion-14B-Chat std::string system_prompt = ""; for (auto message : chat) { @@ -21674,7 +22026,7 @@ static int32_t llama_chat_apply_template_internal( ss << message->content << ""; } } - } else if (tmpl == "openchat" || tmpl_contains("GPT4 Correct ")) { + } else if (tmpl == LLM_CHAT_TEMPLATE_OPENCHAT) { // openchat/openchat-3.5-0106, for (auto message : chat) { std::string role(message->role); @@ -21688,13 +22040,13 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "GPT4 Correct Assistant:"; } - } else if (tmpl == "vicuna" || tmpl == "vicuna-orca" || (tmpl_contains("USER: ") && tmpl_contains("ASSISTANT: "))) { + } else if (tmpl == LLM_CHAT_TEMPLATE_VICUNA || tmpl == LLM_CHAT_TEMPLATE_VICUNA_ORCA) { // eachadea/vicuna-13b-1.1 (and Orca variant) for (auto message : chat) { std::string role(message->role); if (role == "system") { // Orca-Vicuna variant uses a system prefix - if (tmpl == "vicuna-orca" || tmpl_contains("SYSTEM: ")) { + if (tmpl == LLM_CHAT_TEMPLATE_VICUNA_ORCA) { ss << "SYSTEM: " << message->content << "\n"; } else { ss << message->content << "\n\n"; @@ -21708,7 +22060,7 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "ASSISTANT:"; } - } else if (tmpl == "deepseek" || (tmpl_contains("### Instruction:") && tmpl_contains("<|EOT|>"))) { + } else if (tmpl == LLM_CHAT_TEMPLATE_DEEPSEEK) { // deepseek-ai/deepseek-coder-33b-instruct for (auto message : chat) { std::string role(message->role); @@ -21723,7 +22075,7 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "### Response:\n"; } - } else if (tmpl == "command-r" || (tmpl_contains("<|START_OF_TURN_TOKEN|>") && tmpl_contains("<|USER_TOKEN|>"))) { + } else if (tmpl == LLM_CHAT_TEMPLATE_COMMAND_R) { // CohereForAI/c4ai-command-r-plus for (auto message : chat) { std::string role(message->role); @@ -21738,7 +22090,7 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>"; } - } else if (tmpl == "llama3" || (tmpl_contains("<|start_header_id|>") && tmpl_contains("<|end_header_id|>"))) { + } else if (tmpl == LLM_CHAT_TEMPLATE_LLAMA_3) { // Llama 3 for (auto message : chat) { std::string role(message->role); @@ -21747,7 +22099,7 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "<|start_header_id|>assistant<|end_header_id|>\n\n"; } - } else if (tmpl == "chatglm3" || tmpl_contains("[gMASK]sop")) { + } else if (tmpl == LLM_CHAT_TEMPLATE_CHATGML_3) { // chatglm3-6b ss << "[gMASK]" << "sop"; for (auto message : chat) { @@ -21757,7 +22109,7 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "<|assistant|>"; } - } else if (tmpl == "chatglm4" || tmpl_contains("[gMASK]")) { + } else if (tmpl == LLM_CHAT_TEMPLATE_CHATGML_4) { ss << "[gMASK]" << ""; for (auto message : chat) { std::string role(message->role); @@ -21766,7 +22118,7 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "<|assistant|>"; } - } else if (tmpl == "minicpm" || tmpl_contains(LU8("<用户>"))) { + } else if (tmpl == LLM_CHAT_TEMPLATE_MINICPM) { // MiniCPM-3B-OpenHermes-2.5-v2-GGUF for (auto message : chat) { std::string role(message->role); @@ -21778,7 +22130,7 @@ static int32_t llama_chat_apply_template_internal( ss << trim(message->content); } } - } else if (tmpl == "deepseek2" || tmpl_contains("'Assistant: ' + message['content'] + eos_token")) { + } else if (tmpl == LLM_CHAT_TEMPLATE_DEEPSEEK_2) { // DeepSeek-V2 for (auto message : chat) { std::string role(message->role); @@ -21793,7 +22145,7 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "Assistant:"; } - } else if (tmpl == "exaone3" || (tmpl_contains("[|system|]") && tmpl_contains("[|assistant|]") && tmpl_contains("[|endofturn|]"))) { + } else if (tmpl == LLM_CHAT_TEMPLATE_EXAONE_3) { // ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/discussions/8#66bae61b1893d14ee8ed85bb // EXAONE-3.0-7.8B-Instruct for (auto message : chat) { @@ -21809,7 +22161,7 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "[|assistant|]"; } - } else if (tmpl == "rwkv-world" || tmpl_contains("rwkv-world")) { + } else if (tmpl == LLM_CHAT_TEMPLATE_RWKV_WORLD) { // this template requires the model to have "\n\n" as EOT token for (auto message : chat) { std::string role(message->role); @@ -21819,12 +22171,15 @@ static int32_t llama_chat_apply_template_internal( ss << message->content << "\n\n"; } } - } else if (tmpl == "granite" || tmpl_contains("<|start_of_role|>")) { + } else if (tmpl == LLM_CHAT_TEMPLATE_GRANITE) { // IBM Granite template for (const auto & message : chat) { std::string role(message->role); - ss << "<|start_of_role|>" << role << "<|end_of_role|>" - << message->content << "<|end_of_text|>\n"; + ss << "<|start_of_role|>" << role << "<|end_of_role|>"; + if (role == "assistant_tool_call") { + ss << "<|tool_call|>"; + } + ss << message->content << "<|end_of_text|>\n"; } if (add_ass) { ss << "<|start_of_role|>assistant<|end_of_role|>\n"; @@ -21868,7 +22223,11 @@ int32_t llama_chat_apply_template( } std::string formatted_chat; - int32_t res = llama_chat_apply_template_internal(curr_tmpl, chat_vec, formatted_chat, add_ass); + llm_chat_template detected_tmpl = llama_chat_detect_template(curr_tmpl); + if (detected_tmpl == LLM_CHAT_TEMPLATE_UNKNOWN) { + return -1; + } + int32_t res = llama_chat_apply_template_internal(detected_tmpl, chat_vec, formatted_chat, add_ass); if (res < 0) { return res; } @@ -21878,6 +22237,15 @@ int32_t llama_chat_apply_template( return res; } +int32_t llama_chat_builtin_templates(const char ** output, size_t len) { + auto it = LLM_CHAT_TEMPLATES.begin(); + for (size_t i = 0; i < std::min(len, LLM_CHAT_TEMPLATES.size()); i++) { + output[i] = it->first.c_str(); + std::advance(it, 1); + } + return (int32_t) LLM_CHAT_TEMPLATES.size(); +} + // // sampling // @@ -21930,29 +22298,21 @@ int llama_split_prefix(char * dest, size_t maxlen, const char * split_path, int const char * llama_print_system_info(void) { static std::string s; - s = ""; - s += "AVX = " + std::to_string(ggml_cpu_has_avx()) + " | "; - s += "AVX_VNNI = " + std::to_string(ggml_cpu_has_avx_vnni()) + " | "; - s += "AVX2 = " + std::to_string(ggml_cpu_has_avx2()) + " | "; - s += "AVX512 = " + std::to_string(ggml_cpu_has_avx512()) + " | "; - s += "AVX512_VBMI = " + std::to_string(ggml_cpu_has_avx512_vbmi()) + " | "; - s += "AVX512_VNNI = " + std::to_string(ggml_cpu_has_avx512_vnni()) + " | "; - s += "AVX512_BF16 = " + std::to_string(ggml_cpu_has_avx512_bf16()) + " | "; - s += "AMX_INT8 = " + std::to_string(ggml_cpu_has_amx_int8()) + " | "; - s += "FMA = " + std::to_string(ggml_cpu_has_fma()) + " | "; - s += "NEON = " + std::to_string(ggml_cpu_has_neon()) + " | "; - s += "SVE = " + std::to_string(ggml_cpu_has_sve()) + " | "; - s += "ARM_FMA = " + std::to_string(ggml_cpu_has_arm_fma()) + " | "; - s += "F16C = " + std::to_string(ggml_cpu_has_f16c()) + " | "; - s += "FP16_VA = " + std::to_string(ggml_cpu_has_fp16_va()) + " | "; - s += "RISCV_VECT = " + std::to_string(ggml_cpu_has_riscv_v()) + " | "; - s += "WASM_SIMD = " + std::to_string(ggml_cpu_has_wasm_simd()) + " | "; - s += "BLAS = " + std::to_string(ggml_cpu_has_blas()) + " | "; - s += "SSE3 = " + std::to_string(ggml_cpu_has_sse3()) + " | "; - s += "SSSE3 = " + std::to_string(ggml_cpu_has_ssse3()) + " | "; - s += "VSX = " + std::to_string(ggml_cpu_has_vsx()) + " | "; - s += "MATMUL_INT8 = " + std::to_string(ggml_cpu_has_matmul_int8()) + " | "; - s += "LLAMAFILE = " + std::to_string(ggml_cpu_has_llamafile()) + " | "; + for (size_t i = 0; i < ggml_backend_reg_count(); i++) { + auto * reg = ggml_backend_reg_get(i); + auto * get_features_fn = (ggml_backend_get_features_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_get_features"); + if (get_features_fn) { + ggml_backend_feature * features = get_features_fn(reg); + s += ggml_backend_reg_name(reg); + s += " : "; + for (; features->name; features++) { + s += features->name; + s += " = "; + s += features->value; + s += " | "; + } + } + } return s.c_str(); } @@ -21993,28 +22353,6 @@ void llama_perf_context_reset(struct llama_context * ctx) { ctx->t_p_eval_us = ctx->n_p_eval = 0; } -void llama_perf_dump_yaml(FILE * stream, const llama_context * ctx) { - fprintf(stream, "\n"); - fprintf(stream, "###########\n"); - fprintf(stream, "# Timings #\n"); - fprintf(stream, "###########\n"); - fprintf(stream, "\n"); - - fprintf(stream, "mst_eval: %.2f # ms / token during generation\n", - 1.0e-3 * ctx->t_eval_us / ctx->n_eval); - fprintf(stream, "mst_p_eval: %.2f # ms / token during prompt processing\n", - 1.0e-3 * ctx->t_p_eval_us / ctx->n_p_eval); - fprintf(stream, "n_eval: %d # number of tokens generated (excluding the first one)\n", ctx->n_eval); - fprintf(stream, "n_p_eval: %d # number of tokens processed in batches at the beginning\n", ctx->n_p_eval); - fprintf(stream, "t_eval_us: %" PRId64 " # total microseconds spent generating tokens\n", ctx->t_eval_us); - fprintf(stream, "t_load_us: %" PRId64 " # total microseconds spent loading the model\n", ctx->t_load_us); - fprintf(stream, "t_p_eval_us: %" PRId64 " # total microseconds spent prompt processing\n", ctx->t_p_eval_us); - fprintf(stream, "ts_eval: %.2f # tokens / second during generation\n", - 1.0e6 * ctx->n_eval / ctx->t_eval_us); - fprintf(stream, "ts_p_eval: %.2f # tokens / second during prompt processing\n", - 1.0e6 * ctx->n_p_eval / ctx->t_p_eval_us); -} - // For internal test use const std::vector> & llama_internal_get_tensor_map( struct llama_context * ctx diff --git a/src/unicode.cpp b/src/unicode.cpp index 50b35bbbc..3d4592635 100644 --- a/src/unicode.cpp +++ b/src/unicode.cpp @@ -201,7 +201,18 @@ static std::unordered_map unicode_utf8_to_byte_map() { } static inline std::wstring unicode_wstring_from_utf8(const std::string & s) { +#if defined(__clang__) + // disable C++17 deprecation warning for std::codecvt_utf8 +# pragma clang diagnostic push +# pragma clang diagnostic ignored "-Wdeprecated-declarations" +#endif + std::wstring_convert> conv; + +#if defined(__clang__) +# pragma clang diagnostic pop +#endif + return conv.from_bytes(s); } diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index d7ffed8b3..de7fd3956 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -110,27 +110,29 @@ llama_test(test-tokenizer-1-spm NAME test-tokenizer-1-llama-spm ARGS ${CMAKE_CU # llama_target_and_test(test-double-float.cpp) # SLOW llama_target_and_test(test-log.cpp) llama_target_and_test(test-arg-parser.cpp) -llama_target_and_test(test-quantize-fns.cpp) -llama_target_and_test(test-quantize-perf.cpp) llama_target_and_test(test-sampling.cpp) llama_target_and_test(test-chat-template.cpp) llama_target_and_test(test-grammar-parser.cpp) -llama_target_and_test(test-llama-grammar.cpp) llama_target_and_test(test-grammar-integration.cpp) -llama_target_and_test(test-grad0.cpp) -llama_target_and_test(test-barrier.cpp) +llama_target_and_test(test-llama-grammar.cpp) # llama_target_and_test(test-opt.cpp) # SLOW llama_target_and_test(test-backend-ops.cpp) llama_target_and_test(test-antiprompts.cpp) llama_target_and_test(test-minja.cpp) llama_target_and_test(test-tool-call.cpp) -llama_target_and_test(test-rope.cpp) - llama_target_and_test(test-model-load-cancel.cpp LABEL "model") llama_target_and_test(test-autorelease.cpp LABEL "model") +if (NOT GGML_BACKEND_DL) + # these tests use the backends directly and cannot be built with dynamic loading + llama_target_and_test(test-barrier.cpp) + llama_target_and_test(test-quantize-fns.cpp) + llama_target_and_test(test-quantize-perf.cpp) + llama_target_and_test(test-rope.cpp) +endif() + # TODO: disabled on loongarch64 because the ggml-ci node lacks Python 3.8 if (NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64") llama_target_and_test(test-json-schema-to-grammar.cpp WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/..) diff --git a/tests/run-json-schema-to-grammar.mjs b/tests/run-json-schema-to-grammar.mjs index 71bf62ed3..b20ac1d6b 100644 --- a/tests/run-json-schema-to-grammar.mjs +++ b/tests/run-json-schema-to-grammar.mjs @@ -1,5 +1,5 @@ import { readFileSync } from "fs" -import { SchemaConverter } from "../examples/server/public/json-schema-to-grammar.mjs" +import { SchemaConverter } from "../examples/server/public_legacy/json-schema-to-grammar.mjs" const [, , file] = process.argv const url = `file://${file}` diff --git a/tests/test-arg-parser.cpp b/tests/test-arg-parser.cpp index 3665238b5..69604b87c 100644 --- a/tests/test-arg-parser.cpp +++ b/tests/test-arg-parser.cpp @@ -70,7 +70,7 @@ int main(void) { // non-existence arg in specific example (--draft cannot be used outside llama-speculative) argv = {"binary_name", "--draft", "123"}; - assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_SERVER)); + assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_EMBEDDING)); printf("test-arg-parser: test valid usage\n\n"); @@ -96,7 +96,7 @@ int main(void) { // --draft cannot be used outside llama-speculative argv = {"binary_name", "--draft", "123"}; assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_SPECULATIVE)); - assert(params.n_draft == 123); + assert(params.speculative.n_max == 123); // skip this part on windows, because setenv is not supported #ifdef _WIN32 diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 2e3ad79f0..9dd41260a 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -25,7 +25,6 @@ #include #include #include -#include #include #include #include @@ -638,19 +637,20 @@ struct test_case { // determine number of runs int n_runs; + bool is_cpu = ggml_backend_dev_type(ggml_backend_get_device(backend)) == GGML_BACKEND_DEVICE_TYPE_CPU; if (op_flops(out) > 0) { // based on flops const uint64_t GFLOP = 1000 * 1000 * 1000; const uint64_t target_flops_cpu = 8ULL * GFLOP; const uint64_t target_flops_gpu = 100ULL * GFLOP; - uint64_t target_flops = ggml_backend_is_cpu(backend) ? target_flops_cpu : target_flops_gpu; + uint64_t target_flops = is_cpu ? target_flops_cpu : target_flops_gpu; n_runs = std::min(ggml_graph_size(gf) - ggml_graph_n_nodes(gf), target_flops / op_flops(out)) + 1; } else { // based on memory size const size_t GB = 1ULL << 30; const size_t target_size_cpu = 8 * GB; const size_t target_size_gpu = 32 * GB; - size_t target_size = ggml_backend_is_cpu(backend) ? target_size_cpu : target_size_gpu; + size_t target_size = is_cpu ? target_size_cpu : target_size_gpu; n_runs = std::min(ggml_graph_size(gf) - ggml_graph_n_nodes(gf), target_size / op_size(out)) + 1; } @@ -680,6 +680,7 @@ struct test_case { // run int64_t total_time_us = 0; + int64_t total_mem = 0; int total_runs = 0; do { int64_t start_time = ggml_time_us(); @@ -687,6 +688,7 @@ struct test_case { int64_t end_time = ggml_time_us(); total_time_us += end_time - start_time; + total_mem += mem; total_runs += n_runs; } while (total_time_us < 1000*1000); // run for at least 1 second @@ -716,7 +718,7 @@ struct test_case { } else { printf("%8zu kB/run - \033[1;34m%7.2f GB/s\033[0m", op_size(out) / 1024, - mem / (total_time_us / 1e6) / 1024.0 / 1024.0 / 1024.0); + total_mem / (total_time_us / 1e6) / 1024.0 / 1024.0 / 1024.0); } printf("\n"); @@ -808,15 +810,14 @@ struct test_case { ggml_build_forward_expand(gf, out); ggml_graph_cpy(gf, gb); - ggml_build_backward_expand(ctx, gf, gb, false); + ggml_build_backward_expand(ctx, ctx, gb, false); if (expect.size() != 1 || expect[0] != 0.0f) { GGML_ASSERT(ggml_graph_n_nodes(gb) > ggml_graph_n_nodes(gf)); for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { - GGML_ASSERT(!(t->flags & GGML_TENSOR_FLAG_PARAM) || t->grad->op != GGML_OP_NONE); + GGML_ASSERT(!(t->flags & GGML_TENSOR_FLAG_PARAM) || ggml_graph_get_grad(gb, t)->op != GGML_OP_NONE); } } - // TODO: refactor so that this check is only needed once for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { if (!ggml_backend_supports_op(backend, t)) { printf("not supported [%s] ", ggml_backend_name(backend)); @@ -859,7 +860,13 @@ struct test_case { const char * bn = ggml_backend_name(backend); const int64_t ne = ggml_nelements(t); - std::vector ga = tensor_to_float(t->grad); + std::vector ga; + struct ggml_tensor * grad = ggml_graph_get_grad(gb, t); + if (grad) { + ga = tensor_to_float(grad); + } else { + ga.resize(ne); // default value is 0.0f + } for (int64_t i = 0; i < ne; ++i) { // gradient algebraic // check for nans @@ -1146,6 +1153,26 @@ struct test_argmax : public test_case { return out; } + void initialize_tensors(ggml_context * ctx) override { + std::random_device rd; + std::default_random_engine rng(rd()); + for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { + if (t->type == GGML_TYPE_F32) { + // initialize with unique values to avoid ties + for (int64_t r = 0; r < ggml_nrows(t); r++) { + std::vector data(t->ne[0]); + for (int i = 0; i < t->ne[0]; i++) { + data[i] = i; + } + std::shuffle(data.begin(), data.end(), rng); + ggml_backend_tensor_set(t, data.data(), r * t->nb[1], t->ne[0] * sizeof(float)); + } + } else { + init_tensor_uniform(t); + } + } + } + double max_nmse_err() override { return 0.0; } @@ -1613,8 +1640,8 @@ struct test_ssm_scan : public test_case { } }; -// GGML_OP_RWKV_WKV -struct test_rwkv_wkv : public test_case { +// GGML_OP_RWKV_WKV6 +struct test_rwkv_wkv6 : public test_case { const ggml_type type; const int64_t head_count; @@ -1626,7 +1653,7 @@ struct test_rwkv_wkv : public test_case { return VARS_TO_STR5(type, head_count, head_size, n_seq_tokens, n_seqs); } - test_rwkv_wkv(ggml_type type = GGML_TYPE_F32, + test_rwkv_wkv6(ggml_type type = GGML_TYPE_F32, int64_t head_count = 32, int64_t head_size = 64, int64_t n_seq_tokens = 32, int64_t n_seqs = 32) : type(type), head_count(head_count), head_size(head_size), n_seq_tokens(n_seq_tokens), n_seqs(n_seqs) {} @@ -1638,7 +1665,7 @@ struct test_rwkv_wkv : public test_case { ggml_tensor * tf = ggml_new_tensor(ctx, type, 2, std::vector{ head_size, head_count }.data()); ggml_tensor * td = ggml_new_tensor(ctx, type, 4, std::vector{ 1, head_size, head_count, n_tokens }.data()); ggml_tensor * s = ggml_new_tensor(ctx, type, 2, std::vector{ head_size * head_size * head_count, n_seqs }.data()); - ggml_tensor * out = ggml_rwkv_wkv(ctx, k, v, r, tf, td, s); + ggml_tensor * out = ggml_rwkv_wkv6(ctx, k, v, r, tf, td, s); return out; } }; @@ -2497,6 +2524,35 @@ struct test_sum_rows : public test_case { } }; +// GGML_OP_MEAN +struct test_mean : public test_case { + const ggml_type type; + const std::array ne; + + std::string vars() override { + return VARS_TO_STR2(type, ne); + } + + test_mean(ggml_type type = GGML_TYPE_F32, + std::array ne = {10, 5, 4, 3}) + : type(type), ne(ne) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); + ggml_set_param(ctx, a); + ggml_set_name(a, "a"); + + ggml_tensor * out = ggml_mean(ctx, a); + ggml_set_name(out, "out"); + + return out; + } + + float grad_eps() override { + return 0.1f * ne[0]*ne[1]*ne[2]*ne[3]; + } +}; + // GGML_OP_UPSCALE struct test_upscale : public test_case { const ggml_type type; @@ -2641,6 +2697,33 @@ struct test_pad : public test_case { } }; +// GGML_OP_PAD_REFLECT_1D +struct test_pad_reflect_1d : public test_case { + const ggml_type type; + const std::array ne_a; + const int pad_0; + const int pad_1; + + std::string vars() override { + return VARS_TO_STR4(type, ne_a, pad_0, pad_1); + } + + test_pad_reflect_1d(ggml_type type = GGML_TYPE_F32, + std::array ne_a = {512, 34, 2, 1}, + int pad_0 = 10, int pad_1 = 9) + : type(type), ne_a(ne_a), pad_0(pad_0), pad_1(pad_1) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * a = ggml_new_tensor(ctx, type, 2, ne_a.data()); + ggml_set_name(a, "a"); + + ggml_tensor * out = ggml_pad_reflect_1d(ctx, a, pad_0, pad_1); + ggml_set_name(out, "out"); + + return out; + } +}; + // GGML_OP_ARANGE struct test_arange : public test_case { const ggml_type type; @@ -2739,6 +2822,13 @@ struct test_flash_attn_ext : public test_case { return 5e-4; } + uint64_t op_flops(ggml_tensor * t) override { + GGML_UNUSED(t); + // Just counting matmul costs: + // Q*K^T is nb x hs x kv, P*V is nb x kv x hs, per head + return 2 * 2 * nh * nb * hs * kv; + } + test_flash_attn_ext(int64_t hs = 128, int64_t nh = 32, int64_t kv = 96, int64_t nb = 8, bool mask = true, float max_bias = 0.0f, float logit_softcap = 0.0f, ggml_type type_KV = GGML_TYPE_F16) : hs(hs), nh(nh), kv(kv), nb(nb), mask(mask), max_bias(max_bias), logit_softcap(logit_softcap), type_KV(type_KV) {} @@ -2824,24 +2914,14 @@ struct test_cross_entropy_loss : public test_case { struct test_opt_step_adamw : public test_case { const ggml_type type; const std::array ne; - const float alpha; - const float beta1; - const float beta2; - const float eps; - const float wd; std::string vars() override { - return VARS_TO_STR7(type, ne, alpha, beta1, beta2, eps, wd); + return VARS_TO_STR2(type, ne); } test_opt_step_adamw(ggml_type type = GGML_TYPE_F32, - std::array ne = {10, 5, 4, 3}, - float alpha = 1e-3f, - float beta1 = 0.9f, - float beta2 = 0.999f, - float eps = 1e-8f, - float wd = 0.0f) - : type(type), ne(ne), alpha(alpha), beta1(beta1), beta2(beta2), eps(eps), wd(wd) {} + std::array ne = {10, 5, 4, 3}) + : type(type), ne(ne) {} ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]); @@ -2851,7 +2931,16 @@ struct test_opt_step_adamw : public test_case { ggml_tensor * grad = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]); ggml_set_name(grad, "grad"); - ggml_tensor * out = ggml_opt_step_adamw(ctx, a, grad, alpha, beta1, beta2, eps, wd); + ggml_tensor * grad_m = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]); + ggml_set_name(grad_m, "grad_m"); + + ggml_tensor * grad_v = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]); + ggml_set_name(grad_v, "grad_v"); + + ggml_tensor * adamw_params = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 7); + ggml_set_name(adamw_params, "adamw_params"); + + ggml_tensor * out = ggml_opt_step_adamw(ctx, a, grad, grad_m, grad_v, adamw_params); ggml_set_name(out, "out"); return out; @@ -2859,7 +2948,7 @@ struct test_opt_step_adamw : public test_case { void initialize_tensors(ggml_context * ctx) override { for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { - init_tensor_uniform(t, 0.0f, 1.0f); // grad_v needs non-negative values. + init_tensor_uniform(t, 0.0f, 1.0f); // grad_v and adamw_params need non-negative values. } } @@ -3272,7 +3361,9 @@ static const ggml_type all_types[] = { static const ggml_type base_types[] = { GGML_TYPE_F32, GGML_TYPE_F16, + GGML_TYPE_Q8_0, // for I8MM tests GGML_TYPE_Q4_0, + GGML_TYPE_Q4_1, // for I8MM tests GGML_TYPE_Q4_K, GGML_TYPE_IQ2_XXS }; @@ -3396,9 +3487,15 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {3,1,2,1}, 1, 0, 1)); test_cases.emplace_back(new test_conv_transpose_1d({2,1,1,1}, {3,1,1,1}, 1, 0, 1)); - test_cases.emplace_back(new test_argmax()); test_cases.emplace_back(new test_count_equal()); + test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {32, 1, 1, 1})); + test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {100, 10, 1, 1})); + test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {1024, 10, 1, 1})); + test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {1024, 12, 1, 1})); + test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {2000, 10, 1, 1})); + test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {5438, 3, 1, 1})); + for (int ne3 : {1, 3}) { // CUDA backward pass only supports ne3 == 1 test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 5, 4, ne3}, {1, 1, 1, 1})); test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 5, 4, ne3}, {2, 1, 1, 1})); @@ -3424,6 +3521,10 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_set(GGML_TYPE_F32, GGML_TYPE_F32, {6, 5, 4, 3}, dim)); } + for (int dim = 1; dim < GGML_MAX_DIMS; ++dim) { + test_cases.emplace_back(new test_set(GGML_TYPE_I32, GGML_TYPE_I32, {6, 5, 4, 3}, dim)); + } + for (ggml_type type_src : {GGML_TYPE_F16, GGML_TYPE_F32}) { for (ggml_type type_dst : all_types) { test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 4, 4, 4})); @@ -3498,10 +3599,23 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 16, 1024, 32, 4)); - test_cases.emplace_back(new test_rwkv_wkv(GGML_TYPE_F32, 32, 64, 1, 1)); - test_cases.emplace_back(new test_rwkv_wkv(GGML_TYPE_F32, 32, 64, 32, 1)); - test_cases.emplace_back(new test_rwkv_wkv(GGML_TYPE_F32, 32, 64, 32, 4)); - test_cases.emplace_back(new test_rwkv_wkv(GGML_TYPE_F32, 32, 64, 128, 4)); + test_cases.emplace_back(new test_rwkv_wkv6(GGML_TYPE_F32, 32, 64, 1, 1)); + test_cases.emplace_back(new test_rwkv_wkv6(GGML_TYPE_F32, 32, 64, 32, 1)); + test_cases.emplace_back(new test_rwkv_wkv6(GGML_TYPE_F32, 32, 64, 32, 4)); + test_cases.emplace_back(new test_rwkv_wkv6(GGML_TYPE_F32, 32, 64, 128, 4)); + + for (int i = 1; i < 9; ++i) { + test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 16, i, 256, { 1, 1}, {1, 1})); + test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q4_0, GGML_TYPE_F32, 16, i, 256, { 1, 1}, {1, 1})); + test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q4_1, GGML_TYPE_F32, 16, i, 256, { 1, 1}, {1, 1})); + test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q5_0, GGML_TYPE_F32, 16, i, 256, { 1, 1}, {1, 1})); + test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q5_1, GGML_TYPE_F32, 16, i, 256, { 1, 1}, {1, 1})); + test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q8_0, GGML_TYPE_F32, 16, i, 256, { 1, 1}, {1, 1})); + test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q4_K, GGML_TYPE_F32, 16, i, 256, { 1, 1}, {1, 1})); + test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q5_K, GGML_TYPE_F32, 16, i, 256, { 1, 1}, {1, 1})); + test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q6_K, GGML_TYPE_F32, 16, i, 256, { 1, 1}, {1, 1})); + test_cases.emplace_back(new test_mul_mat(GGML_TYPE_IQ4_NL, GGML_TYPE_F32, 16, i, 256, { 1, 1}, {1, 1})); + } #if 1 for (ggml_type type_a : base_types) { @@ -3598,7 +3712,7 @@ static std::vector> make_test_cases_eval() { for (int n_mats : {4}) { for (int n_used : {2}) { for (bool b : {false}) { - for (int n : {1}) { + for (int n : {1, 32}) { int m = 512; int k = 256; test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, n_mats, n_used, b, m, n, k)); @@ -3725,12 +3839,15 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_sum()); test_cases.emplace_back(new test_sum_rows()); + test_cases.emplace_back(new test_mean()); test_cases.emplace_back(new test_upscale()); test_cases.emplace_back(new test_upscale(GGML_TYPE_F32, { 512, 512, 3, 1 }, 2, true)); test_cases.emplace_back(new test_upscale_ext()); - test_cases.emplace_back(new test_group_norm()); + test_cases.emplace_back(new test_group_norm(GGML_TYPE_F32, {64, 64, 320, 1})); + test_cases.emplace_back(new test_group_norm(GGML_TYPE_F32, {9, 9, 1280, 1})); test_cases.emplace_back(new test_acc()); test_cases.emplace_back(new test_pad()); + test_cases.emplace_back(new test_pad_reflect_1d()); test_cases.emplace_back(new test_arange()); test_cases.emplace_back(new test_timestep_embedding()); test_cases.emplace_back(new test_leaky_relu()); @@ -3744,7 +3861,7 @@ static std::vector> make_test_cases_eval() { for (int nh : { 32, }) { for (int kv : { 512, 1024, }) { for (int nb : { 1, 3, 32, 35, }) { - for (ggml_type type_KV : {GGML_TYPE_F16, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0}) { + for (ggml_type type_KV : {GGML_TYPE_F16, GGML_TYPE_BF16, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0}) { test_cases.emplace_back(new test_flash_attn_ext(hs, nh, kv, nb, mask, max_bias, logit_softcap, type_KV)); } } @@ -3756,9 +3873,7 @@ static std::vector> make_test_cases_eval() { } test_cases.emplace_back(new test_cross_entropy_loss()); - for (float wd : {0.0f, 1e-2f}) { - test_cases.emplace_back(new test_opt_step_adamw(GGML_TYPE_F32, {10, 5, 4, 3}, 1.0f, 1e-3f, 0.9f, 0.999f, wd)); - } + test_cases.emplace_back(new test_opt_step_adamw(GGML_TYPE_F32, {10, 5, 4, 3})); // these tests are disabled to save execution time, but they can be handy for debugging #if 0 @@ -3778,6 +3893,22 @@ static std::vector> make_test_cases_perf() { test_cases.emplace_back(new test_bin_bcast(ggml_add, GGML_TYPE_F32, {4096, 1, 1, 1}, {1, 1, 1, 1})); test_cases.emplace_back(new test_bin_bcast(ggml_add, GGML_TYPE_F32, {4096, 1, 1, 1}, {1, 512, 1, 1})); + test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F16, {512, 3072, 1, 1})); + test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {8192, 512, 2, 1}, {0, 2, 1, 3})); + test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {3072, 512, 2, 1}, {0, 2, 1, 3})); + + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {4096, 4096, 5, 1}, false, 1.0f, 0.0f)); + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {77, 4096, 5, 1}, false, 1.0f, 0.0f)); + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {1024, 1024, 10, 1}, false, 1.0f, 0.0f)); + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {77, 1024, 10, 1}, false, 1.0f, 0.0f)); + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {256, 256, 20, 1}, false, 1.0f, 0.0f)); + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {64, 64, 20, 1}, false, 1.0f, 0.0f)); + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {77, 64, 20, 1}, false, 1.0f, 0.0f)); + + test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {32, 10, 1, 1})); + test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {1024, 10, 1, 1})); + test_cases.emplace_back(new test_argmax(GGML_TYPE_F32, {32000, 512, 1, 1})); + for (int bs : {1, 512}) { for (ggml_type type_a : all_types) { for (ggml_type type_b : {GGML_TYPE_F32}) { @@ -3792,7 +3923,11 @@ static std::vector> make_test_cases_perf() { static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op_name) { if (mode == MODE_TEST) { auto test_cases = make_test_cases_eval(); - ggml_backend_t backend_cpu = ggml_backend_cpu_init(); + ggml_backend_t backend_cpu = ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_CPU, NULL); + if (backend_cpu == NULL) { + printf(" Failed to initialize CPU backend\n"); + return false; + } size_t n_ok = 0; for (auto & test : test_cases) { @@ -3872,7 +4007,9 @@ int main(int argc, char ** argv) { } } - // enumerate backends + // load and enumerate backends + ggml_backend_load_all(); + printf("Testing %zu devices\n\n", ggml_backend_dev_count()); size_t n_ok = 0; @@ -3888,16 +4025,15 @@ int main(int argc, char ** argv) { continue; } - ggml_backend_t backend = ggml_backend_dev_init(dev, NULL); - GGML_ASSERT(backend != NULL); - - if (backend_filter == NULL && ggml_backend_is_cpu(backend) && mode != MODE_GRAD) { + if (backend_filter == NULL && ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU && mode != MODE_GRAD) { printf(" Skipping CPU backend\n"); - ggml_backend_free(backend); n_ok++; continue; } + ggml_backend_t backend = ggml_backend_dev_init(dev, NULL); + GGML_ASSERT(backend != NULL); + ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev); auto ggml_backend_set_n_threads_fn = (ggml_backend_set_n_threads_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_set_n_threads"); if (ggml_backend_set_n_threads_fn) { @@ -3926,6 +4062,8 @@ int main(int argc, char ** argv) { ggml_backend_free(backend); } + ggml_quantize_free(); + printf("%zu/%zu backends passed\n", n_ok, ggml_backend_dev_count()); if (n_ok != ggml_backend_dev_count()) { @@ -3933,8 +4071,6 @@ int main(int argc, char ** argv) { return 1; } - ggml_quantize_free(); - printf("\033[1;32mOK\033[0m\n"); return 0; } diff --git a/tests/test-barrier.cpp b/tests/test-barrier.cpp index cf54237db..d85bf912b 100644 --- a/tests/test-barrier.cpp +++ b/tests/test-barrier.cpp @@ -1,4 +1,5 @@ #include "ggml.h" +#include "ggml-cpu.h" #include "ggml-backend.h" #include diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp index 554a8036d..de279f5b3 100644 --- a/tests/test-chat-template.cpp +++ b/tests/test-chat-template.cpp @@ -144,7 +144,7 @@ static void test_legacy_templates() { std::vector templates = { // teknium/OpenHermes-2.5-Mistral-7B "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}", - // mistralai/Mistral-7B-Instruct-v0.2 + // mistralai/Mistral-7B-Instruct-v0.2 (NOTE: Old pre-v1 without a system prompt) "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}", // TheBloke/FusionNet_34Bx2_MoE-AWQ "{%- for idx in range(0, messages|length) -%}\\n{%- if messages[idx]['role'] == 'user' -%}\\n{%- if idx > 1 -%}\\n{{- bos_token + '[INST] ' + messages[idx]['content'] + ' [/INST]' -}}\\n{%- else -%}\\n{{- messages[idx]['content'] + ' [/INST]' -}}\\n{%- endif -%}\\n{% elif messages[idx]['role'] == 'system' %}\\n{{- '[INST] <>\\\\n' + messages[idx]['content'] + '\\\\n<>\\\\n\\\\n' -}}\\n{%- elif messages[idx]['role'] == 'assistant' -%}\\n{{- ' ' + messages[idx]['content'] + ' ' + eos_token -}}\\n{% endif %}\\n{% endfor %}", @@ -190,16 +190,24 @@ static void test_legacy_templates() { "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}", // ibm-granite/granite-3.0-8b-instruct "{%- if tools %}\n {{- '<|start_of_role|>available_tools<|end_of_role|>\n' }}\n {%- for tool in tools %}\n {{- tool | tojson(indent=4) }}\n {%- if not loop.last %}\n {{- '\n\n' }}\n {%- endif %}\n {%- endfor %}\n {{- '<|end_of_text|>\n' }}\n{%- endif %}\n{%- for message in messages %}\n {%- if message['role'] == 'system' %}\n {{- '<|start_of_role|>system<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n {%- elif message['role'] == 'user' %}\n {{- '<|start_of_role|>user<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n {%- elif message['role'] == 'assistant' %}\n {{- '<|start_of_role|>assistant<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n {%- elif message['role'] == 'assistant_tool_call' %}\n {{- '<|start_of_role|>assistant<|end_of_role|><|tool_call|>' + message['content'] + '<|end_of_text|>\n' }}\n {%- elif message['role'] == 'tool_response' %}\n {{- '<|start_of_role|>tool_response<|end_of_role|>' + message['content'] + '<|end_of_text|>\n' }}\n {%- endif %}\n {%- if loop.last and add_generation_prompt %}\n {{- '<|start_of_role|>assistant<|end_of_role|>' }}\n {%- endif %}\n{%- endfor %}", + // mistralai/Mistral-7B-Instruct-v0.2 (mistralai 'v1' template with a system prompt) + "{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content'] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}\n {{- raise_exception('After the optional system message, conversation roles must alternate user/assistant/user/assistant/...') }}\n {%- endif %}\n {%- if message['role'] == 'user' %}\n {%- if loop.first and system_message is defined %}\n {{- ' [INST] ' + system_message + '\\n\\n' + message['content'] + ' [/INST]' }}\n {%- else %}\n {{- ' [INST] ' + message['content'] + ' [/INST]' }}\n {%- endif %}\n {%- elif message['role'] == 'assistant' %}\n {{- ' ' + message['content'] + eos_token}}\n {%- else %}\n {{- raise_exception('Only user and assistant roles are supported, with the exception of an initial optional system message!') }}\n {%- endif %}\n{%- endfor %}\n", + // Mistral-Large-Instruct-2407 (mistralai 'v3' template) + "{%- if messages[0][\"role\"] == \"system\" %}\n {%- set system_message = messages[0][\"content\"] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n {%- endif %}\n {%- set ns.index = ns.index + 1 %}\n {%- endif %}\n{%- endfor %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n {%- if message[\"role\"] == \"user\" %}\n {%- if tools is not none and (message == user_messages[-1]) %}\n {{- \"[AVAILABLE_TOOLS] [\" }}\n {%- for tool in tools %}\n {%- set tool = tool.function %}\n {{- '{\"type\": \"function\", \"function\": {' }}\n {%- for key, val in tool.items() if key != \"return\" %}\n {%- if val is string %}\n {{- '\"' + key + '\": \"' + val + '\"' }}\n {%- else %}\n {{- '\"' + key + '\": ' + val|tojson }}\n {%- endif %}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \"}}\" }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"[/AVAILABLE_TOOLS]\" }}\n {%- endif %}\n {%- if loop.last and system_message is defined %}\n {{- \"[INST] \" + system_message + \"\\n\\n\" + message[\"content\"] + \"[/INST]\" }}\n {%- else %}\n {{- \"[INST] \" + message[\"content\"] + \"[/INST]\" }}\n {%- endif %}\n {%- elif message.tool_calls is defined and message.tool_calls is not none %}\n {{- \"[TOOL_CALLS] [\" }}\n {%- for tool_call in message.tool_calls %}\n {%- set out = tool_call.function|tojson %}\n {{- out[:-1] }}\n {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" + eos_token }}\n {%- endif %}\n {%- endfor %}\n {%- elif message[\"role\"] == \"assistant\" %}\n {{- \" \" + message[\"content\"]|trim + eos_token}}\n {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n {%- if message.content is defined and message.content.content is defined %}\n {%- set content = message.content.content %}\n {%- else %}\n {%- set content = message.content %}\n {%- endif %}\n {{- '[TOOL_RESULTS] {\"content\": ' + content|string + \", \" }}\n {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n {%- else %}\n {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n {%- endif %}\n{%- endfor %}\n", + // Mistral-Nemo-Instruct-2407 (mistralai 'v3-tekken' template) + "{%- if messages[0][\"role\"] == \"system\" %}\n {%- set system_message = messages[0][\"content\"] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n {%- endif %}\n {%- set ns.index = ns.index + 1 %}\n {%- endif %}\n{%- endfor %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n {%- if message[\"role\"] == \"user\" %}\n {%- if tools is not none and (message == user_messages[-1]) %}\n {{- \"[AVAILABLE_TOOLS][\" }}\n {%- for tool in tools %}\n {%- set tool = tool.function %}\n {{- '{\"type\": \"function\", \"function\": {' }}\n {%- for key, val in tool.items() if key != \"return\" %}\n {%- if val is string %}\n {{- '\"' + key + '\": \"' + val + '\"' }}\n {%- else %}\n {{- '\"' + key + '\": ' + val|tojson }}\n {%- endif %}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \"}}\" }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"[/AVAILABLE_TOOLS]\" }}\n {%- endif %}\n {%- if loop.last and system_message is defined %}\n {{- \"[INST]\" + system_message + \"\\n\\n\" + message[\"content\"] + \"[/INST]\" }}\n {%- else %}\n {{- \"[INST]\" + message[\"content\"] + \"[/INST]\" }}\n {%- endif %}\n {%- elif (message.tool_calls is defined and message.tool_calls is not none) %}\n {{- \"[TOOL_CALLS][\" }}\n {%- for tool_call in message.tool_calls %}\n {%- set out = tool_call.function|tojson %}\n {{- out[:-1] }}\n {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" + eos_token }}\n {%- endif %}\n {%- endfor %}\n {%- elif message[\"role\"] == \"assistant\" %}\n {{- message[\"content\"] + eos_token}}\n {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n {%- if message.content is defined and message.content.content is defined %}\n {%- set content = message.content.content %}\n {%- else %}\n {%- set content = message.content %}\n {%- endif %}\n {{- '[TOOL_RESULTS]{\"content\": ' + content|string + \", \" }}\n {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n {%- else %}\n {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n {%- endif %}\n{%- endfor %}\n", + // mistralai/Mistral-Large-Instruct-2411 (mistralai 'v7' template) + "{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + '[/INST]' }}{% elif message['role'] == 'system' %}{{ '[SYSTEM_PROMPT] ' + message['content'] + '[/SYSTEM_PROMPT]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + message['content'] + eos_token }}{% else %}{{ raise_exception('Only user, system and assistant roles are supported!') }}{% endif %}{% endfor %}", }; std::vector expected_output = { // teknium/OpenHermes-2.5-Mistral-7B "<|im_start|>system\nYou are a helpful assistant<|im_end|>\n<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\nHi there<|im_end|>\n<|im_start|>user\nWho are you<|im_end|>\n<|im_start|>assistant\n I am an assistant <|im_end|>\n<|im_start|>user\nAnother question<|im_end|>\n<|im_start|>assistant\n", - // mistralai/Mistral-7B-Instruct-v0.2 + // mistralai/Mistral-7B-Instruct-v0.2 (NOTE: Old pre-v1 without a system prompt) "[INST] You are a helpful assistant\nHello [/INST]Hi there[INST] Who are you [/INST] I am an assistant [INST] Another question [/INST]", // TheBloke/FusionNet_34Bx2_MoE-AWQ - "[INST] <>\nYou are a helpful assistant\n<>\n\nHello [/INST] Hi there [INST] Who are you [/INST] I am an assistant [INST] Another question [/INST]", + "[INST] <>\nYou are a helpful assistant\n<>\n\nHello [/INST]Hi there[INST] Who are you [/INST] I am an assistant [INST] Another question [/INST]", // bofenghuang/vigogne-2-70b-chat - "[INST] <>\nYou are a helpful assistant\n<>\n\nHello [/INST] Hi there [INST] Who are you [/INST] I am an assistant [INST] Another question [/INST]", + "[INST] <>\nYou are a helpful assistant\n<>\n\nHello [/INST]Hi there[INST] Who are you [/INST]I am an assistant[INST] Another question [/INST]", // mlabonne/AlphaMonarch-7B "system\nYou are a helpful assistant\nuser\nHello\nassistant\nHi there\nuser\nWho are you\nassistant\n I am an assistant \nuser\nAnother question\nassistant\n", // google/gemma-7b-it @@ -236,10 +244,29 @@ static void test_legacy_templates() { u8"You are a helpful assistant\n\nUser: Hello\n\nAssistant: Hi there<｜end▁of▁sentence｜>User: Who are you\n\nAssistant: I am an assistant <｜end▁of▁sentence｜>User: Another question\n\nAssistant:", // ibm-granite/granite-3.0-8b-instruct "<|start_of_role|>system<|end_of_role|>You are a helpful assistant<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Hello<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Hi there<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Who are you<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|> I am an assistant <|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Another question<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>\n", + // mistralai/Mistral-7B-Instruct-v0.2 (mistralai 'v1' template with a system prompt) + " [INST] You are a helpful assistant\n\nHello [/INST] Hi there [INST] Who are you [/INST] I am an assistant [INST] Another question [/INST]", + // Mistral-Large-Instruct-2407 (mistralai 'v3' template; modified to have system prompt at start) + "[INST] You are a helpful assistant\n\nHello[/INST] Hi there[INST] Who are you[/INST] I am an assistant[INST] Another question[/INST]", + // Mistral-Nemo-Instruct-2407 (mistralai 'v3-tekken' template; modified to have system prompt at start) + "[INST]You are a helpful assistant\n\nHello[/INST]Hi there[INST]Who are you[/INST] I am an assistant [INST]Another question[/INST]", + // mistralai/Mistral-Large-Instruct-2411 (mistralai 'v7' template) + "[SYSTEM_PROMPT] You are a helpful assistant[/SYSTEM_PROMPT][INST] Hello[/INST] Hi there[INST] Who are you[/INST] I am an assistant [INST] Another question[/INST]", }; std::vector formatted_chat(1024); int32_t res; + // list all supported templates + std::vector supported_tmpl; + res = llama_chat_builtin_templates(nullptr, 0); + assert(res > 0); + supported_tmpl.resize(res); + res = llama_chat_builtin_templates(supported_tmpl.data(), supported_tmpl.size()); + printf("Built-in chat templates:\n"); + for (auto tmpl : supported_tmpl) { + printf(" %s\n", tmpl); + } + // test invalid chat template res = llama_chat_apply_template(nullptr, "INVALID TEMPLATE", conversation, message_count, true, formatted_chat.data(), formatted_chat.size()); assert(res < 0); @@ -277,7 +304,13 @@ static void test_legacy_templates() { return output; }; assert(fmt_sys("chatml") == "<|im_start|>system\nYou are a helpful assistant<|im_end|>\n"); + assert(fmt_sys("mistral-v1") == " [INST] You are a helpful assistant\n\n"); + assert(fmt_sys("mistral-v3") == "[INST] You are a helpful assistant\n\n"); + assert(fmt_sys("mistral-v3-tekken") == "[INST]You are a helpful assistant\n\n"); + assert(fmt_sys("mistral-v7") == "[SYSTEM_PROMPT] You are a helpful assistant[/SYSTEM_PROMPT]"); assert(fmt_sys("llama2") == "[INST] You are a helpful assistant\n"); + assert(fmt_sys("llama2-sys") == "[INST] <>\nYou are a helpful assistant\n<>\n\n"); + assert(fmt_sys("mistral") == "[INST] You are a helpful assistant\n"); // for old pre-v1 templates assert(fmt_sys("gemma") == ""); // for gemma, system message is merged with user message assert(fmt_sys("llama3") == "<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|>"); @@ -296,7 +329,12 @@ static void test_legacy_templates() { return output; }; assert(fmt_single("chatml") == "\n<|im_start|>user\nHow are you<|im_end|>\n<|im_start|>assistant\n"); + assert(fmt_single("mistral-v1") == " [INST] How are you [/INST]"); + assert(fmt_single("mistral-v3") == "[INST] How are you[/INST]"); + assert(fmt_single("mistral-v3-tekken") == "[INST]How are you[/INST]"); + assert(fmt_single("mistral-v7") == "[INST] How are you[/INST]"); assert(fmt_single("llama2") == "[INST] How are you [/INST]"); + assert(fmt_single("mistral") == "[INST] How are you [/INST]"); // for old pre-v1 templates assert(fmt_single("gemma") == "\nuser\nHow are you\nmodel\n"); assert(fmt_single("llama3") == "<|start_header_id|>user<|end_header_id|>\n\nHow are you<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"); } @@ -310,5 +348,7 @@ int main(void) { test_jinja_templates(); } + printf("Test chat templates: OK\n"); + return 0; } diff --git a/tests/test-grad0.cpp b/tests/test-grad0.cpp deleted file mode 100644 index 2200ad93d..000000000 --- a/tests/test-grad0.cpp +++ /dev/null @@ -1,1683 +0,0 @@ -#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnings on Windows -#include "ggml.h" - -#include -#include -#include -#include -#include -#include -#include -#include - -#if defined(_MSC_VER) -#pragma warning(disable: 4244 4267) // possible loss of data -#endif - -#if defined(__GNUC__) -#pragma GCC diagnostic ignored "-Wdouble-promotion" -#endif - -#define MAX_NARGS 3 - -#undef MIN -#undef MAX -#define MIN(a, b) ((a) < (b) ? (a) : (b)) -#define MAX(a, b) ((a) > (b) ? (a) : (b)) - -#define GGML_SILU_FP16 - -// -// logging -// - -#if (GGML_DEBUG >= 1) -#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__) -#else -#define GGML_PRINT_DEBUG(...) -#endif - -#if (GGML_DEBUG >= 5) -#define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__) -#else -#define GGML_PRINT_DEBUG_5(...) -#endif - -#if (GGML_DEBUG >= 10) -#define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__) -#else -#define GGML_PRINT_DEBUG_10(...) -#endif - -#define GGML_PRINT(...) printf(__VA_ARGS__) - -static float frand(void) { - return (float)rand()/(float)RAND_MAX; -} - -static int irand(int n) { - if (n == 0) return 0; - return rand()%n; -} - -static void get_random_dims(int64_t * dims, int ndims) { - dims[0] = dims[1] = dims[2] = dims[3] = 1; - - for (int i = 0; i < ndims; i++) { - dims[i] = 1 + irand(4); - } -} - -static struct ggml_tensor * get_random_tensor_f32( - struct ggml_context * ctx0, - int ndims, - int64_t ne[], - float fmin, - float fmax) { - struct ggml_tensor * result = ggml_new_tensor(ctx0, GGML_TYPE_F32, ndims, ne); - - switch (ndims) { - case 1: - for (int i0 = 0; i0 < ne[0]; i0++) { - ((float *)result->data)[i0] = frand()*(fmax - fmin) + fmin; - } - break; - case 2: - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((float *)result->data)[i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin; - } - } - break; - case 3: - for (int i2 = 0; i2 < ne[2]; i2++) { - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((float *)result->data)[i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin; - } - } - } - break; - case 4: - for (int i3 = 0; i3 < ne[3]; i3++) { - for (int i2 = 0; i2 < ne[2]; i2++) { - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((float *)result->data)[i3*ne[2]*ne[1]*ne[0] + i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin; - } - } - } - } - break; - default: - assert(false); - } - - return result; -} - -static struct ggml_tensor * get_random_tensor_f16( - struct ggml_context * ctx0, - int ndims, - int64_t ne[], - float fmin, - float fmax) { - struct ggml_tensor * result = ggml_new_tensor(ctx0, GGML_TYPE_F16, ndims, ne); - - switch (ndims) { - case 1: - for (int i0 = 0; i0 < ne[0]; i0++) { - ((ggml_fp16_t *)result->data)[i0] = ggml_fp32_to_fp16(frand()*(fmax - fmin) + fmin); - } - break; - case 2: - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((ggml_fp16_t *)result->data)[i1*ne[0] + i0] = ggml_fp32_to_fp16(frand()*(fmax - fmin) + fmin); - } - } - break; - case 3: - for (int i2 = 0; i2 < ne[2]; i2++) { - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((ggml_fp16_t *)result->data)[i2*ne[1]*ne[0] + i1*ne[0] + i0] = ggml_fp32_to_fp16(frand()*(fmax - fmin) + fmin); - } - } - } - break; - case 4: - for (int i3 = 0; i3 < ne[3]; i3++) { - for (int i2 = 0; i2 < ne[2]; i2++) { - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((ggml_fp16_t *)result->data)[i3*ne[2]*ne[1]*ne[0] + i2*ne[1]*ne[0] + i1*ne[0] + i0] = ggml_fp32_to_fp16(frand()*(fmax - fmin) + fmin); - } - } - } - } - break; - default: - assert(false); - } - - return result; -} - -static struct ggml_tensor * get_random_tensor_i32( - struct ggml_context * ctx0, - int ndims, - int64_t ne[], - int32_t imin, - int32_t imax) { - struct ggml_tensor * result = ggml_new_tensor(ctx0, GGML_TYPE_I32, ndims, ne); - - switch (ndims) { - case 1: - for (int i0 = 0; i0 < ne[0]; i0++) { - ((int32_t *)result->data)[i0] = irand(imax - imin) + imin; - } - break; - case 2: - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((int32_t *)result->data)[i1*ne[0] + i0] = irand(imax - imin) + imin; - } - } - break; - case 3: - for (int i2 = 0; i2 < ne[2]; i2++) { - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((int32_t *)result->data)[i2*ne[1]*ne[0] + i1*ne[0] + i0] = irand(imax - imin) + imin; - } - } - } - break; - case 4: - for (int i3 = 0; i3 < ne[3]; i3++) { - for (int i2 = 0; i2 < ne[2]; i2++) { - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((int32_t *)result->data)[i3*ne[2]*ne[1]*ne[0] + i2*ne[1]*ne[0] + i1*ne[0] + i0] = irand(imax - imin) + imin; - } - } - } - } - break; - default: - assert(false); - } - - return result; -} - -static bool check_gradient( - const char * op_name, - struct ggml_context * ctx0, - struct ggml_tensor * x[], - struct ggml_tensor * f, - int ndims, - int nargs, - float eps, - float max_error_abs, - float max_error_rel, - std::vector expected_vals) { - - static int n_threads = -1; - if (n_threads < 0) { - n_threads = GGML_DEFAULT_N_THREADS; - - const char *env = getenv("GGML_N_THREADS"); - if (env) { - n_threads = atoi(env); - } - - printf("GGML_N_THREADS = %d\n", n_threads); - } - - struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, GGML_DEFAULT_GRAPH_SIZE, true); - struct ggml_cgraph * gb = ggml_new_graph_custom(ctx0, GGML_DEFAULT_GRAPH_SIZE, true); - ggml_build_forward_expand(gf, f); - ggml_graph_cpy(gf, gb); - ggml_build_backward_expand(ctx0, gf, gb, false); - - ggml_graph_compute_with_ctx(ctx0, gf, n_threads); - - ggml_graph_reset(gb); - if (f->grad) { - ggml_set_f32(f->grad, 1.0f); - } - - ggml_graph_compute_with_ctx(ctx0, gb, n_threads); - - // ggml_graph_dump_dot(gf, NULL, "test-grad0-forward.dot"); - // ggml_graph_dump_dot(gb, gf, "test-grad0-backward.dot"); - - for (int i = 0; i < nargs; ++i) { - bool all_g0_bad = true; - const int nelements = ggml_nelements(x[i]); - for (int k = 0; k < nelements; ++k) { - // Calculate gradient numerically: - const float x0 = ggml_get_f32_1d(x[i], k); - const float xm = x0 - eps; - const float xp = x0 + eps; - ggml_set_f32_1d(x[i], k, xp); - - ggml_graph_compute_with_ctx(ctx0, gf, n_threads); - - const double f0 = ggml_get_f32_1d(f, 0); - - ggml_set_f32_1d(x[i], k, xm); - - ggml_graph_compute_with_ctx(ctx0, gf, n_threads); - - const double f1 = ggml_get_f32_1d(f, 0); - const double g0 = (f0 - f1)/(2.0*(double) eps); - - // The numerical calculation of the gradient fails around noncontinuities (e.g. 0 for ReLU). - // In such cases, provide a vector of expected values and skip the comparison for failed calculations. - if (!expected_vals.empty()) { - bool matches_any = false; - for (const double & ev : expected_vals) { - const double error_abs = std::fabs(g0 - ev); - if (error_abs > max_error_abs) { - continue; - } - const double error_rel = g0 != 0.0 ? fabs(g0 - ev)/fabs(g0) : 0.0; - if (error_rel > max_error_rel) { - continue; - } - matches_any = true; - break; - } - if (!matches_any) { - continue; - } - } - all_g0_bad = false; - - ggml_set_f32_1d(x[i], k, x0); - - // compute gradient using backward graph - ggml_graph_reset(gb); - if (f->grad) { - ggml_set_f32(f->grad, 1.0f); - } - - ggml_graph_compute_with_ctx(ctx0, gb, n_threads); - - const double g1 = ggml_get_f32_1d(x[i]->grad, k); - - const double error_abs = fabs(g0 - g1); - const double error_rel = g0 != 0.0 ? fabs(g0 - g1)/fabs(g0) : 0.0; - - if (error_abs > max_error_abs || error_rel > max_error_rel) { - printf("%s: ndims=%d, i=%d, k=%d, x0=%f, xm=%f, xp=%f, f0=%f, f1=%f, g0=%f, g1=%f, eps=%f, error_abs=%f, error_rel=%f\n", - op_name, ndims, i, k, x0, xm, xp, f0, f1, g0, g1, eps, error_abs, error_rel); - //assert(false); - return false; - } - } - if (all_g0_bad) { - printf("%s: numerical calculation of the gradient failed for all values\n", op_name); - return false; - } - } - - return true; -} - -// TODO: clean-up this .. -static bool check_mat_mul( - const struct ggml_tensor * y, - const struct ggml_tensor * x0, - const struct ggml_tensor * x1) { - float * dst = (float *) y->data; - float * src0 = (float *) x0->data; - float * src1 = (float *) x1->data; - - const int nc = x0->ne[1]; - const int nr = x1->ne[1]; - const int nk = x0->ne[0]; - - GGML_PRINT_DEBUG("check_mat_mul: nc=%d, nr=%d, nk=%d\n", nc, nr, nk); - - GGML_PRINT_DEBUG("x0:\n"); - for (int j = 0; j < x0->ne[1]; ++j) { - for (int i = 0; i < x0->ne[0]; ++i) { - GGML_PRINT_DEBUG("%6.3f ", src0[j*nk + i]); - } - GGML_PRINT_DEBUG("\n"); - } - GGML_PRINT_DEBUG("\n"); - - GGML_PRINT_DEBUG("x1:\n"); - for (int j = 0; j < x1->ne[1]; ++j) { - for (int i = 0; i < x1->ne[0]; ++i) { - GGML_PRINT_DEBUG("%6.3f ", src1[j*nk + i]); - } - GGML_PRINT_DEBUG("\n"); - } - GGML_PRINT_DEBUG("\n"); - - GGML_PRINT_DEBUG("y: n_dims = %d, (%lld, %lld)\n", y->n_dims, y->ne[0], y->ne[1]); - for (int j = 0; j < y->ne[1]; ++j) { - for (int i = 0; i < y->ne[0]; ++i) { - GGML_PRINT_DEBUG("%6.3f ", dst[j*nr + i]); - } - GGML_PRINT_DEBUG("\n"); - } - - for (int i = 0; i < nr; ++i) { - for (int j = 0; j < nc; ++j) { - float sum = 0.0f; - - for (int k = 0; k < nk; ++k) { - sum += src0[j*nk + k]*src1[i*nk + k]; - } - - if (fabsf(dst[i*nc + j] - sum) > 1e-5f) { - fprintf(stderr, "check_mat_mul: dst[%d] = %f, sum = %f\n", i*nc + j, dst[i*nc + j], sum); - assert(false); - return false; - } - } - } - - return true; -} - -#define NUM_PERMUTATIONS (4*3*2*1) - -int main(int argc, const char ** argv) { - struct ggml_init_params params = { - /* .mem_size = */ 256*1024*1024, - /* .mem_buffer = */ NULL, - /* .no_alloc = */ false, - }; - - int64_t ne[4]; - - int all_permutations[4 * NUM_PERMUTATIONS]; - { - int count = 0; - for (int ax0=0; ax0<4; ++ax0) { - for (int ax1=0; ax1<4; ++ax1) { - if (ax1 == ax0) continue; - for (int ax2=0; ax2<4; ++ax2) { - if (ax2 == ax0) continue; - if (ax2 == ax1) continue; - for (int ax3=0; ax3<4; ++ax3) { - if (ax3 == ax0) continue; - if (ax3 == ax1) continue; - if (ax3 == ax2) continue; - assert(count < NUM_PERMUTATIONS); - all_permutations[count*4+0] = ax0; - all_permutations[count*4+1] = ax1; - all_permutations[count*4+2] = ax2; - all_permutations[count*4+3] = ax3; - ++count; - } - } - } - } - } - - unsigned seed_iter = 1; - - // original loop: 1000 - int niter = 4; - const char *env = getenv("GGML_NLOOP"); - if (env != NULL) { - niter = atoi(env); - } - if (argc > 1) { - niter = atoi(argv[1]); - } - for (int iter = 0; iter < niter; ++iter) { - srand(seed_iter); - seed_iter = rand(); - unsigned seed = rand(); - - printf("test-grad0: iter:%d/%d\n", (iter+1), niter); - struct ggml_context * ctx0 = ggml_init(params); - - get_random_dims(ne, 4); - - struct ggml_tensor * x[MAX_NARGS]; - - // add f32 - { - srand(seed); - const int nargs = 2; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_add(ctx0, x[0], x[1])); - - check_gradient("add f32", ctx0, x, f, ndims, nargs, 1e-3f, 2e-3f, 2e-3f, {}); - } - } - - // add f16 - { - srand(seed); - const int nargs = 2; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f16(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_add(ctx0, x[0], x[1])); - - check_gradient("add f16", ctx0, x, f, ndims, nargs, 1e-1f, 2e-1f, 2e-1f, {}); - } - } - - // sub - { - srand(seed); - const int nargs = 2; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_sub(ctx0, x[0], x[1])); - - check_gradient("sub", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {}); - } - } - - // mul - { - srand(seed); - const int nargs = 2; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_mul(ctx0, x[0], x[1])); - - check_gradient("mul", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // div - { - srand(seed); - const int nargs = 2; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, 0.5f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_div(ctx0, x[0], x[1])); - - check_gradient("div", ctx0, x, f, ndims, nargs, 1e-3f, 1e-1f, 1e-1f, {}); - } - } - - // sqr - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 2; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqr(ctx0, x[0])); - - check_gradient("sqr", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // sqrt - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 2; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, 2.0f*1e-3f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqrt(ctx0, x[0])); - - check_gradient("sqrt", ctx0, x, f, ndims, nargs, 1e-3f, 2e-2f, 1e-1f, {}); - } - } - - // log - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 2; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, 2.0f*1e-3f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_log(ctx0, x[0])); - - check_gradient("log", ctx0, x, f, ndims, nargs, 1e-3f, INFINITY, 1e-1f, {}); - } - } - - // sum - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 2; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, x[0]); - - check_gradient("sum", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {}); - } - } - - - // sum_rows - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqr(ctx0, ggml_sum_rows(ctx0, x[0]))); - - check_gradient("sum_rows", ctx0, x, f, ndims, nargs, 1e-3f, 1e-2f, INFINITY, {}); - } - } - - // mean, not yet fully implemented - if(0) - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_mean(ctx0, x[0])); - - check_gradient("mean", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {}); - } - } - - // argmax - if (0) - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_argmax(ctx0, x[0])); - - check_gradient("argmax", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {}); - } - } - - // repeat - { - srand(seed); - int64_t ne2[4]; - get_random_dims(ne2, 4); - - ne2[0] = ne[0] * ne2[0]; - ne2[1] = ne[1] * ne2[1]; - ne2[2] = 1; - ne2[3] = 1; - - const int nargs = 1; - for (int ndims = 1; ndims <= 2; ++ndims) { - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - x[1] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f); - ggml_set_param(ctx0, x[0]); - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqr(ctx0, ggml_sub(ctx0, x[1], ggml_repeat(ctx0, x[0], x[1])))); - - check_gradient("repeat", ctx0, x, f, ndims, nargs, 1e-3f, 1e-2f, INFINITY, {}); - } - } - - // repeat back - { - srand(seed); - int64_t ne2[4]; - get_random_dims(ne2, 4); - - ne2[0] = ne[0] * ne2[0]; - ne2[1] = ne[1] * ne2[1]; - ne2[2] = 1; - ne2[3] = 1; - - const int nargs = 1; - for (int ndims = 1; ndims <= 2; ++ndims) { - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - x[1] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f); - ggml_set_param(ctx0, x[0]); - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqr(ctx0, ggml_sub(ctx0, x[0], ggml_repeat_back(ctx0, x[1], x[0])))); - - check_gradient("repeat back", ctx0, x, f, ndims, nargs, 1e-3f, 1e-2f, INFINITY, {}); - } - } - - // abs - { - const int nargs = 1; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_abs(ctx0, x[0])); - - check_gradient("abs", ctx0, x, f, ndims, nargs, 1e-3f, INFINITY, 1e-3f, {-1.0, 1.0}); - } - } - - // sgn - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor* f = ggml_sum(ctx0, ggml_sgn(ctx0, x[0])); - - check_gradient("sgn", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {0.0}); - } - } - - // neg - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor* f = ggml_sum(ctx0, ggml_neg(ctx0, x[0])); - - check_gradient("neg", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {}); - } - } - - // step - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor* f = ggml_sum(ctx0, ggml_step(ctx0, x[0])); - - check_gradient("step", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {0.0}); - } - } - - // tanh, not yet fully implemented - if(0) - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor* f = ggml_sum(ctx0, ggml_tanh(ctx0, x[0])); - - check_gradient("tanh", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {}); - } - } - - // mul_mat - { - srand(seed); - const int nargs = 2; - - for (int ndims = 2; ndims <= 4; ++ndims) { - int max_nrep = (ndims >= 3) ? 2 : 1; - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - for (int nrep2 = 1; nrep2 < max_nrep; ++nrep2) { - for (int nrep3 = 1; nrep3 < max_nrep; ++nrep3) { - { - int64_t ne2[4]; - get_random_dims(ne2, 4); - ne2[0] = ne[0]; - ne2[2] = nrep2 * ne[2]; - ne2[3] = nrep3 * ne[3]; - x[1] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f); - } - - ggml_set_param(ctx0, x[0]); - ggml_set_param(ctx0, x[1]); - - struct ggml_tensor * m = ggml_mul_mat(ctx0, x[1], x[0]); - struct ggml_tensor * f = ggml_sum(ctx0, m); - - GGML_PRINT_DEBUG("testing: mul_mat, [%lld, %lld] (%d) * [%lld, %lld] (%d)\n", x[1]->ne[0], x[1]->ne[1], x[1]->n_dims, x[0]->ne[0], x[0]->ne[1], x[0]->n_dims); - - check_gradient("mul_mat", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - if (ndims == 2) { - // check_mat_mul does not support ndims > 2 - check_mat_mul(m, x[1], x[0]); - } - } - } - } - } - - // elu, not yet fully implemented - if(0) - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor* f = ggml_sum(ctx0, ggml_elu(ctx0, x[0])); - - check_gradient("elu", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {}); - } - } - - // relu - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor* f = ggml_sum(ctx0, ggml_relu(ctx0, x[0])); - - check_gradient("relu", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {0.0, 1.0}); - } - } - - // gelu, not yet fully implemented - if(0) - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 4; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor* f = ggml_sum(ctx0, ggml_gelu(ctx0, x[0])); - - check_gradient("gelu", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f, {}); - } - } - - // silu - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 2; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_silu(ctx0, x[0])); - -#ifdef GGML_SILU_FP16 - // due to GGML_SILU_FP16 the finite difference method will be slightly wrong -> increase error bounds. - check_gradient("silu", ctx0, x, f, ndims, nargs, 1e-3f, 0.5, INFINITY, {}); -#else - check_gradient("silu", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); -#endif - } - } - - // rms_norm - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 2; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_rms_norm(ctx0, x[0], 1e-6f)); - - check_gradient("rms_norm", ctx0, x, f, ndims, nargs, 1e-4f, 1.0f, INFINITY, {}); - } - } - - // scale - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 2; ++ndims) { - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - - const float s = -1.0f + 2.0f*frand(); - - ggml_set_param(ctx0, x[0]); - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_scale(ctx0, x[0], s)); - - check_gradient("scale", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // cpy f32 - { - srand(seed); - const int nargs = 2; - - for (int ndims = 1; ndims <= 2; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - // x[1] is overwritten by x[0], so the gradients don't propagate to x[1] - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_cpy(ctx0, x[0], x[1])); - - check_gradient("cpy f32", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // cpy f16 - { - srand(seed); - const int nargs = 2; - - for (int ndims = 1; ndims <= 2; ++ndims) { - for (int i = 0; i < nargs; ++i) { - x[i] = get_random_tensor_f16(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[i]); - } - // x[1] is overwritten by x[0], so the gradients don't propagate to x[1] - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_cpy(ctx0, x[0], x[1])); - - check_gradient("cpy f16", ctx0, x, f, ndims, nargs, 1e-1f, 1e-1f, INFINITY, {}); - } - } - - // reshape (1d->nd) - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 2; ++ndims) { - int64_t ne2[4]; - ne2[0] = 1; - ne2[1] = 1; - ne2[2] = 1; - ne2[3] = 1; - for (int i = 0; i < ndims; ++i) { - ne2[0] *= ne[i]; - } - x[0] = get_random_tensor_f32(ctx0, 1, ne2, -1.0f, 1.0f); - x[1] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[0]); - - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_reshape(ctx0, x[0], x[1])); - check_gradient("reshape", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // reshape (nd->1d) - { - srand(seed); - const int nargs = 1; - - for (int ndims = 1; ndims <= 2; ++ndims) { - int64_t ne2[4]; - ne2[0] = 1; - ne2[1] = 1; - ne2[2] = 1; - ne2[3] = 1; - for (int i = 0; i < ndims; ++i) { - ne2[0] *= ne[i]; - } - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - x[1] = get_random_tensor_f32(ctx0, 1, ne2, -1.0f, 1.0f); - ggml_set_param(ctx0, x[0]); - - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_reshape(ctx0, x[0], x[1])); - check_gradient("reshape", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // acc 1d - { - srand(seed); - int64_t ne2[4] = { 1, 1, 1, 1 }; - - const int nargs = 2; - for (int ndims = 1; ndims <= 4; ++ndims) { - - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[0]); - - get_random_dims(ne2, 1); - while ((ne2[0] > ne[0]) || (ne2[0] > ggml_nelements(x[0]))) { - get_random_dims(ne2, 1); - } - - x[1] = get_random_tensor_f32(ctx0, 1, ne2, -1.0f, 1.0f); - ggml_set_param(ctx0, x[1]); - - const int max_offset = MAX(0, ggml_nelements(x[0]) - ggml_nelements(x[1])); - const int offset = irand(max_offset) * ggml_element_size(x[0]); - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_acc(ctx0, x[0], x[1], x[0]->nb[1], x[0]->nb[2], x[0]->nb[3], offset)); - - check_gradient("acc 1d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // acc 2d - { - srand(seed); - int64_t ne2[4] = { 1, 1, 1, 1 }; - int64_t max_offsets[4] = { 0, 0, 0, 0 }; - int64_t offsets[4] = { 0, 0, 0, 0 }; - - const int nargs = 2; - for (int ndims = 2; ndims <= 4; ++ndims) { - - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[0]); - - get_random_dims(ne2, 2); - while ((ne2[0] > ne[0]) || (ne2[1] > ne[1]) || (ne2[0]*ne2[1] > ggml_nelements(x[0]))) { - get_random_dims(ne2, 2); - } - - x[1] = get_random_tensor_f32(ctx0, 2, ne2, -1.0f, 1.0f); - ggml_set_param(ctx0, x[1]); - - max_offsets[0] = MAX(0, x[0]->ne[0] - x[1]->ne[0]); - max_offsets[1] = MAX(0, x[0]->ne[1] - x[1]->ne[1]); - offsets[0] = irand(max_offsets[0]) * x[0]->nb[0]; - offsets[1] = irand(max_offsets[1]) * x[0]->nb[1]; - const int offset = offsets[0] + offsets[1]; - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_acc(ctx0, x[0], x[1], x[0]->nb[1], x[0]->nb[2], x[0]->nb[3], offset)); - - check_gradient("acc 2d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // acc 3d - { - srand(seed); - int64_t ne2[4] = { 1, 1, 1, 1 }; - int64_t max_offsets[4] = { 0, 0, 0, 0 }; - int64_t offsets[4] = { 0, 0, 0, 0 }; - - const int nargs = 2; - for (int ndims = 3; ndims <= 4; ++ndims) { - - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[0]); - - get_random_dims(ne2, 3); - while ((ne2[0] > ne[0]) || (ne2[1] > ne[1]) || (ne2[2] > ne[2]) || (ne2[0]*ne2[1]*ne2[2] > ggml_nelements(x[0]))) { - get_random_dims(ne2, 3); - } - - x[1] = get_random_tensor_f32(ctx0, 3, ne2, -1.0f, 1.0f); - ggml_set_param(ctx0, x[1]); - - max_offsets[0] = MAX(0, x[0]->ne[0] - x[1]->ne[0]); - max_offsets[1] = MAX(0, x[0]->ne[1] - x[1]->ne[1]); - max_offsets[2] = MAX(0, x[0]->ne[2] - x[1]->ne[2]); - offsets[0] = irand(max_offsets[0]) * x[0]->nb[0]; - offsets[1] = irand(max_offsets[1]) * x[0]->nb[1]; - offsets[2] = irand(max_offsets[2]) * x[0]->nb[2]; - const int offset = offsets[0] + offsets[1] + offsets[2]; - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_acc(ctx0, x[0], x[1], x[0]->nb[1], x[0]->nb[2], x[0]->nb[3], offset)); - - check_gradient("acc 3d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // acc 4d - { - srand(seed); - int64_t ne2[4] = { 1, 1, 1, 1 }; - int64_t max_offsets[4] = { 0, 0, 0, 0 }; - int64_t offsets[4] = { 0, 0, 0, 0 }; - - const int nargs = 2; - for (int ndims = 4; ndims <= 4; ++ndims) { - - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[0]); - - get_random_dims(ne2, 4); - while ((ne2[0] > ne[0]) || (ne2[1] > ne[1]) || (ne2[2] > ne[2]) || (ne2[3] > ne[3]) || (ne2[0]*ne2[1]*ne2[2]*ne2[3] > ggml_nelements(x[0]))) { - get_random_dims(ne2, 4); - } - - x[1] = get_random_tensor_f32(ctx0, 4, ne2, -1.0f, 1.0f); - ggml_set_param(ctx0, x[1]); - - max_offsets[0] = MAX(0, x[0]->ne[0] - x[1]->ne[0]); - max_offsets[1] = MAX(0, x[0]->ne[1] - x[1]->ne[1]); - max_offsets[2] = MAX(0, x[0]->ne[2] - x[1]->ne[2]); - max_offsets[3] = MAX(0, x[0]->ne[3] - x[1]->ne[3]); - offsets[0] = irand(max_offsets[0]) * x[0]->nb[0]; - offsets[1] = irand(max_offsets[1]) * x[0]->nb[1]; - offsets[2] = irand(max_offsets[2]) * x[0]->nb[2]; - offsets[3] = irand(max_offsets[3]) * x[0]->nb[3]; - const int offset = offsets[0] + offsets[1] + offsets[2] + offsets[3]; - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_acc(ctx0, x[0], x[1], x[0]->nb[1], x[0]->nb[2], x[0]->nb[3], offset)); - - check_gradient("acc 4d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // set_1d - { - srand(seed); - int64_t ne2[4]; - - const int nargs = 2; - for (int ndims = 1; ndims <= 4; ++ndims) { - - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[0]); - - get_random_dims(ne2, 1); - while ((ne2[0] > ne[0]) || (ne2[0] > ggml_nelements(x[0]))) { - get_random_dims(ne2, 1); - } - - x[1] = get_random_tensor_f32(ctx0, 1, ne2, -1.0f, 1.0f); - ggml_set_param(ctx0, x[1]); - - const int max_offset = MAX(0, ggml_nelements(x[0]) - ggml_nelements(x[1])); - const int offset = irand(max_offset) * ggml_element_size(x[0]); - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_set_1d(ctx0, x[0], x[1], offset)); - - check_gradient("set_1d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // set_2d - { - srand(seed); - int64_t ne2[4]; - int64_t max_offsets[4] = { 0, 0, 0, 0 }; - int64_t offsets[4] = { 0, 0, 0, 0 }; - - const int nargs = 1; - for (int ndims = 2; ndims <= 4; ++ndims) { - - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - ggml_set_param(ctx0, x[0]); - - get_random_dims(ne2, 2); - while ((ne2[0] > ne[0]) || (ne2[1] > ne[1]) || (ne2[0]*ne2[1] > ggml_nelements(x[0]))) { - get_random_dims(ne2, 2); - } - - x[1] = get_random_tensor_f32(ctx0, 2, ne2, -1.0f, 1.0f); - ggml_set_param(ctx0, x[1]); - - max_offsets[0] = MAX(0, x[0]->ne[0] - x[1]->ne[0]); - max_offsets[1] = MAX(0, x[0]->ne[1] - x[1]->ne[1]); - offsets[0] = irand(max_offsets[0]) * x[0]->nb[0]; - offsets[1] = irand(max_offsets[1]) * x[0]->nb[1]; - const int offset = offsets[0] + offsets[1]; - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_set_2d(ctx0, x[0], x[1], x[1]->nb[1], offset)); - - check_gradient("set_2d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // view_1d - { - srand(seed); - const int nargs = 1; - for (int ndims = 1; ndims <= 4; ++ndims) { - - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - - ggml_set_param(ctx0, x[0]); - - const int k0 = irand(ggml_nelements(x[0])); - const int k1 = irand(ggml_nelements(x[0])); - const int i0 = MIN(k0, k1); - const int i1 = MAX(k0, k1); - - const int offset = i0 * sizeof(float); - const int nelem = i1 - i0; - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_view_1d(ctx0, x[0], nelem, offset)); - - check_gradient("view_1d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // view_2d - { - srand(seed); - int64_t ne2[4]; - int64_t nb2[4]; - - const int nargs = 1; - for (int ndims = 1; ndims <= 4; ++ndims) { - - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - - get_random_dims(ne2, 2); - while (ne2[0]*ne2[1] > ggml_nelements(x[0])) { - get_random_dims(ne2, 2); - } - const int count = ne2[0]*ne2[1]; - - nb2[0] = sizeof(float); - nb2[1] = nb2[0]*ne2[0]; - - ggml_set_param(ctx0, x[0]); - - const int max_offset = ggml_nelements(x[0]) - count; - const int offset = irand(max_offset+1) * sizeof(float); - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_view_2d(ctx0, x[0], ne2[0], ne2[1], nb2[1], offset)); - - check_gradient("view_2d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // view_3d - { - srand(seed); - int64_t ne2[4] = {1,1,1,1}; - int64_t nb2[4] = {0,0,0,0}; - - const int nargs = 1; - for (int ndims = 1; ndims <= 4; ++ndims) { - - x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); - - get_random_dims(ne2, 3); - while (ne2[0]*ne2[1]*ne2[2] > ggml_nelements(x[0])) { - get_random_dims(ne2, 3); - } - const int count = ne2[0]*ne2[1]*ne2[2]; - - nb2[0] = sizeof(float); - nb2[1] = nb2[0]*ne2[0]; - nb2[2] = nb2[1]*ne2[1]; - - ggml_set_param(ctx0, x[0]); - - const int max_offset = ggml_nelements(x[0]) - count; - const int offset = irand(max_offset+1) * sizeof(float); - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_view_3d(ctx0, x[0], ne2[0], ne2[1], ne2[2], nb2[1], nb2[2], offset)); - - check_gradient("view_3d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // permute - { - srand(seed); - int64_t ne2[4]; - - const int nargs = 1; - for (int ndims = 1; ndims <= 4; ++ndims) - { - // ggml_permute will set axes of dimensions below n_dims to 1. - // to make ggml_permute work correctly on all axes, - // the input tensor needs maximal n_dim of 4. - for (int i=0; i finite differences should not work - // instead use sum(log(soft_max()*(1-eps)+eps)); use eps to avoid log(0) - struct ggml_tensor * f = ggml_sum(ctx0, - ggml_log(ctx0, - ggml_add1(ctx0, - ggml_scale(ctx0, - ggml_soft_max(ctx0, x[0]), - 1.0f - eps), - ggml_new_f32(ctx0, eps)))); - - check_gradient("softmax", ctx0, x, f, ndims, nargs, 1e-3f, 2e-1f, INFINITY, {}); - // NOTE: softmax forward is computed using f16 table lookup instead of using actual expf, but backward assumes actual expf. - // this may result in different gradients too finite differences. - // when this test reports errors, first try to replace the table lookup with actual expf and test again to see if just that was the cause. - // if only the table lookup causes gradients to differ this is acceptable. - } - } - - // cross_entropy_loss - { - srand(seed); - const int nargs = 1; - - int64_t ne2[4]; - get_random_dims(ne2, 4); - - for (int ndims = 1; ndims <= 4; ++ndims) { - x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f); - x[1] = get_random_tensor_f32(ctx0, ndims, ne2, 0.0f, 1.0f); - // the second argument to cross_entropy_loss must sum up to 1 for each row - int nr = ggml_nrows(x[1]); - int nc = ggml_nelements(x[1]) / nr; - for (int ir = 0; ir < nr; ++ir) { - float sum = 0; - for (int ic = 0; ic < nc; ++ic) { - sum += ((float *) x[1]->data)[ic + ir*nc]; - } - for (int ic = 0; ic < nc; ++ic) { - ((float *) x[1]->data)[ic + ir*nc] /= sum; - } - } - ggml_set_param(ctx0, x[0]); - - struct ggml_tensor * f = ggml_cross_entropy_loss(ctx0, x[0], x[1]); - - check_gradient("cross_entropy_loss", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {}); - } - } - - // rope f32 - { - srand(seed); - const int nargs = 1; - - int64_t ne2[4]; - get_random_dims(ne2, 4); - ne2[0] += ne2[0] % 2; - int n_rot = ne2[0]; - - for (int ndims = 3; ndims <= 4; ++ndims) { - for (int mode = 0; mode < 4; ++mode) { - for (int n_past = 1; n_past < ne2[2]; ++n_past) { - x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f); - - struct ggml_tensor * p = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne2[2]); - for (int i = 0; i < ne2[2]; ++i) { - ((int32_t *) p->data)[i] = n_past + i; - } - - ggml_set_param(ctx0, x[0]); - - const bool skip_past = (mode & 1); - if (skip_past) { - // we have no past, so this would have to work on uninitialized memory. - // we only test the gradients here; - // skip_past should have no influence on gradient computation. - // so when other modes work, we assume that this does as well. - continue; - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], p, n_rot, mode)); - - GGML_PRINT_DEBUG("rope f32: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode); - check_gradient("rope f32", ctx0, x, f, ndims, nargs, 1e-2f, 1e-3f, INFINITY, {}); - } - } - } - } - - // rope f16 - { - srand(seed); - const int nargs = 1; - - int64_t ne2[4]; - get_random_dims(ne2, 4); - ne2[0] += ne2[0] % 2; - int n_rot = ne2[0]; - - for (int ndims = 3; ndims <= 4; ++ndims) { - for (int mode = 0; mode < 4; ++mode) { - for (int n_past = 1; n_past < ne2[2]; ++n_past) { - x[0] = get_random_tensor_f16(ctx0, ndims, ne2, -1.0f, 1.0f); - - struct ggml_tensor * p = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne2[2]); - for (int i = 0; i < ne2[2]; ++i) { - ((int32_t *) p->data)[i] = n_past + i; - } - - ggml_set_param(ctx0, x[0]); - - const bool skip_past = (mode & 1); - if (skip_past) { - // we have no past, so this would have to work on uninitialized memory. - // we only test the gradients here; - // skip_past should have no influence on gradient computation. - // so when other modes work, we assume that this does as well. - continue; - } - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], p, n_rot, mode)); - - GGML_PRINT_DEBUG("rope f16: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode); - check_gradient("rope f16", ctx0, x, f, ndims, nargs, 1e-1f, 1e-1f, INFINITY, {}); - } - } - } - } - - // im2col f32 - { - srand(seed); - const int nargs = 1; - const int ndims = 4; - - for (const bool is_2D : {false, true}) { - int64_t ne0[ndims]; - int64_t ne1[ndims]; - get_random_dims(ne0, ndims); - get_random_dims(ne1, ndims); - - // // Ensure that the output is not zero-sized: - ne1[0] += 8; - ne1[1] += 8; - - if (is_2D) { - ne1[2] = ne0[2]; - } else { - ne1[1] = ne0[1]; - ne0[3] = 1; - ne1[3] = 1; - } - - // The order of arguments is swapped because the first tensor is only used for its shape. - x[1] = get_random_tensor_f16(ctx0, ndims, ne0, -1.0f, 1.0f); - x[0] = get_random_tensor_f32(ctx0, ndims, ne1, -1.0f, 1.0f); - - ggml_set_param(ctx0, x[0]); - - const int s0 = 1 + irand(2); - const int s1 = is_2D ? 1 + irand(2) : 0; - const int p0 = 0 + irand(2); - const int p1 = is_2D ? 0 + irand(2) : 0; - const int d0 = 1 + irand(2); - const int d1 = is_2D ? 1 + irand(2) : 0; - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_im2col(ctx0, x[1], x[0], s0, s1, p0, p1, d0, d1, is_2D, GGML_TYPE_F32)); - - GGML_PRINT_DEBUG("im2col f32: is_2D=%s, s0=%d, s1=%d, p0=%d, p1=%d, d0=%d, d1=%d\n", is_2D ? "yes" : "no", s0, s1, p0, p1, d0, d1); - check_gradient("im2col f32", ctx0, x, f, ndims, nargs, 1e-2f, 1e-3f, INFINITY, {}); - } - } - - // pool_2d f32 - { - srand(seed); - const int nargs = 1; - const int ndims = 4; - - for (const enum ggml_op_pool op : {GGML_OP_POOL_AVG, GGML_OP_POOL_MAX}) { - int64_t ne0[ndims]; - get_random_dims(ne0, ndims); - - ne0[0] += 8; - ne0[1] += 8; - - x[0] = get_random_tensor_f32(ctx0, ndims, ne0, -1.0f, 1.0f); - - ggml_set_param(ctx0, x[0]); - - const int k0 = 2 + irand(2); - const int k1 = 2 + irand(2); - const int s0 = 2 + irand(2); - const int s1 = 2 + irand(2); - const int p0 = 0 + irand(2); - const int p1 = 0 + irand(2); - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_pool_2d(ctx0, x[0], op, k0, k1, s0, s1, p0, p1)); - - GGML_PRINT_DEBUG("ggml_pool_2d f32: op=%s k0=%d, k1=%d, s0=%d, s1=%d, p0=%d, p1=%d\n", - op == GGML_OP_POOL_MAX ? "max" : "avg", k0, k1, s0, s1, p0, p1); - std::vector expected_vals; - if (op == GGML_OP_POOL_MAX) { - expected_vals.push_back(0.0); - expected_vals.push_back(1.0); - } - check_gradient("ggml_pool_2d f32", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, expected_vals); - } - } - - // flash_attn f32 - // TODO: adapt to ggml_flash_attn_ext() changes - //{ - // srand(seed); - // const int nargs = 3; - - // int64_t ne2[4]; - - // get_random_dims(ne2, 4); - // int64_t D = ne2[0]; - // int64_t N = ne2[1]; - // int64_t M = ne2[2] + N; - // int64_t B = ne2[3]; - - // for (int masked = 0; masked <= 1; ++masked) { - // for (int ndims = 2; ndims <= 4; ++ndims) { - // int max_nrep = (ndims >= 3) ? 2 : 1; - // for (int nrep = 1; nrep < max_nrep; ++nrep) { - // int64_t neq[4] = { D, N, B*nrep, ne[3] }; - // int64_t nek[4] = { D, M, B, ne[3] }; - // int64_t nev[4] = { M, D, B, ne[3] }; - // if (ndims == 2) { - // neq[2] = 1; neq[3] = 1; - // nek[2] = 1; nek[3] = 1; - // nev[2] = 1; nev[3] = 1; - // } else if (ndims == 3) { - // neq[3] = 1; - // nek[3] = 1; - // nev[3] = 1; - // } - // x[0] = get_random_tensor_f32(ctx0, ndims, neq, -0.1250f, 0.1250f); - // x[1] = get_random_tensor_f32(ctx0, ndims, nek, -0.1250f, 0.1250f); - // x[2] = get_random_tensor_f32(ctx0, ndims, nev, -0.1250f, 0.1250f); - // ggml_set_param(ctx0, x[0]); - // ggml_set_param(ctx0, x[1]); - // ggml_set_param(ctx0, x[2]); - - // struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0))); - - // check_gradient("flash_attn f32", ctx0, x, f, ndims, nargs, 1.5e-4f, 1e-3f, INFINITY, {}); - // } - // } - // } - //} - - ggml_free(ctx0); - } - - return 0; -} diff --git a/tests/test-lora-conversion-inference.sh b/tests/test-lora-conversion-inference.sh index fe90ce0d1..fb308a9ff 100755 --- a/tests/test-lora-conversion-inference.sh +++ b/tests/test-lora-conversion-inference.sh @@ -10,11 +10,16 @@ declare -a params=( MODELS_REPO=lora-tests MODELS_REPO_URL=https://huggingface.co/ggml-org/$MODELS_REPO +COMMIT=c26d5fb85b4070a9e9c4e65d132c783b98086890 # Clone the Hugging Face repository if the directory does not exist if [ ! -d "$MODELS_REPO" ]; then echo "Cloning the Hugging Face repository..." git clone $MODELS_REPO_URL --depth 1 + cd $MODELS_REPO + git fetch --depth=1 origin $COMMIT + git reset --hard $COMMIT + cd - else echo "Repository already exists. Skipping clone." fi diff --git a/tests/test-opt.cpp b/tests/test-opt.cpp index 546ca230b..f90c92b4b 100644 --- a/tests/test-opt.cpp +++ b/tests/test-opt.cpp @@ -1,181 +1,892 @@ #include "ggml.h" +#include "ggml-alloc.h" +#include "ggml-backend.h" +#include "ggml-cpu.h" +#include "ggml-opt.h" #include -#include -#include -#include +#include +#include +#include +#include +#include -#define MAX_NARGS 2 - -#if defined(__GNUC__) -#pragma GCC diagnostic ignored "-Wdouble-promotion" -#endif - -// -// logging -// -#define GGML_DEBUG 0 -#if (GGML_DEBUG >= 1) -#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__) -#else -#define GGML_PRINT_DEBUG(...) -#endif - -#if (GGML_DEBUG >= 5) -#define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__) -#else -#define GGML_PRINT_DEBUG_5(...) -#endif - -#if (GGML_DEBUG >= 10) -#define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__) -#else -#define GGML_PRINT_DEBUG_10(...) -#endif - -#define GGML_PRINT(...) printf(__VA_ARGS__) - - -static float frand(void) { - return (float)rand()/(float)RAND_MAX; +static bool almost_equal(const double a, const double b, const double atol) { + return fabs(a - b) < atol; } -static struct ggml_tensor * get_random_tensor( - struct ggml_context * ctx0, int ndims, int64_t ne[], float fmin, float fmax -) { - struct ggml_tensor * result = ggml_new_tensor(ctx0, GGML_TYPE_F32, ndims, ne); +constexpr int64_t ne_datapoint = 2; +constexpr int64_t ne_label = 1; +constexpr int64_t ndata = 6; - switch (ndims) { - case 1: - for (int i0 = 0; i0 < ne[0]; i0++) { - ((float *)result->data)[i0] = frand()*(fmax - fmin) + fmin; +struct helper_ctx_data { + std::vector datasets_supervised; + std::vector data_batch; + std::vector labels_batch; + + ggml_opt_dataset_t dataset_unsupervised; + struct ggml_context * ctx_static; + struct ggml_context * ctx_compute; + struct ggml_opt_params opt_params; + ggml_opt_context_t opt_ctx; + struct ggml_tensor * inputs; + struct ggml_tensor * weights; + struct ggml_tensor * outputs; + ggml_backend_buffer_t buf; + ggml_opt_result_t result; + ggml_opt_result_t result2; +}; + +// These default values make it easier to check optimization results vs. expected values. +static ggml_opt_optimizer_params helper_get_test_opt_pars(void * userdata) { + ggml_opt_optimizer_params result = ggml_opt_get_default_optimizer_params(userdata); + result.adamw.alpha = 1.0f; + result.adamw.beta1 = 0.0f; + result.adamw.beta2 = 0.0f; + result.adamw.eps = 0.0f; + return result; +} + +static helper_ctx_data helper_get_ctx_data( + ggml_backend_sched_t backend_sched, + ggml_backend_t backend, + const bool init_opt_ctx = true, + const bool optimizer_defaults = true, + int64_t nbatch_logical = 1, + int64_t nbatch_physical = 1, + enum ggml_opt_loss_type loss_type = GGML_OPT_LOSS_TYPE_SUM) { + std::vector datasets(ndata); + for (int64_t ndata_shard = 1; ndata_shard <= ndata; ++ndata_shard) { + ggml_opt_dataset_t dataset = ggml_opt_dataset_init(ne_datapoint, ne_label, ndata, ndata_shard); + + float * data = ggml_get_data_f32(ggml_opt_dataset_data( dataset)); + float * labels = ggml_get_data_f32(ggml_opt_dataset_labels(dataset)); + + for (int64_t idata = 0; idata < ndata; ++idata) { + for (int64_t id = 0; id < ne_datapoint; ++id) { + data[ idata*ne_datapoint + id] = 16*idata + id; } - break; - case 2: - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((float *)result->data)[i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin; - } + for (int64_t il = 0; il < ne_label; ++il) { + labels[idata*ne_label + il] = 16*(16*idata + il); } - break; - case 3: - for (int i2 = 0; i2 < ne[2]; i2++) { - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((float *)result->data)[i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin; + } + + datasets[ndata_shard-1] = dataset; + } + + ggml_opt_dataset_t dataset_unsupervised = ggml_opt_dataset_init(1, 0, ndata, /*ndata_shard =*/ 1); + + float * data = ggml_get_data_f32(ggml_opt_dataset_data(dataset_unsupervised)); + + for (int64_t idata = 0; idata < ndata; ++idata) { + data[idata] = idata; + } + + struct ggml_context * ctx_static; + struct ggml_context * ctx_compute; + { + struct ggml_init_params params = { + /*.mem_size =*/ (2*ndata + 2)*ggml_tensor_overhead(), + /*.mem_buffer =*/ nullptr, + /*.no_alloc =*/ true, + }; + ctx_static = ggml_init(params); + } + { + struct ggml_init_params params = { + /*.mem_size =*/ GGML_DEFAULT_GRAPH_SIZE*ggml_tensor_overhead() + 3*ggml_graph_overhead(), + /*.mem_buffer =*/ nullptr, + /*.no_alloc =*/ true, + }; + ctx_compute = ggml_init(params); + } + + std::vector data_batch(ndata); + std::vector labels_batch(ndata); + for (int64_t ndata_batch = 1; ndata_batch <= ndata; ++ndata_batch) { + data_batch[ndata_batch-1] = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, ndata_batch*ne_datapoint); + labels_batch[ndata_batch-1] = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, ndata_batch*ne_label); + } + + struct ggml_tensor * inputs = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, nbatch_physical); + ggml_set_name(inputs, "inputs"); + + struct ggml_tensor * weights = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, 1); + ggml_set_name(weights, "weights"); + ggml_set_param(ctx_static, weights); + + struct ggml_tensor * intermediary = ggml_add(ctx_compute, inputs, weights); + + struct ggml_tensor * outputs = ggml_scale(ctx_compute, intermediary, 1.0f); + ggml_set_name(outputs, "outputs"); + + ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx_static, backend); + const float w0 = float(ndata)/2; + ggml_backend_tensor_set(weights, &w0, 0, sizeof(float)); + + GGML_ASSERT(nbatch_logical % nbatch_physical == 0); + const int32_t opt_period = nbatch_logical / nbatch_physical; + + struct ggml_opt_params opt_params = ggml_opt_default_params(backend_sched, ctx_compute, inputs, outputs, loss_type); + opt_params.opt_period = opt_period; + if (!optimizer_defaults) { + opt_params.get_opt_pars = helper_get_test_opt_pars; + } + ggml_opt_context_t opt_ctx = init_opt_ctx ? ggml_opt_init(opt_params) : nullptr; + + ggml_opt_result_t result = ggml_opt_result_init(); + ggml_opt_result_t result2 = ggml_opt_result_init(); + + return {datasets, data_batch, labels_batch, dataset_unsupervised, ctx_static, ctx_compute, opt_params, opt_ctx, inputs, weights, outputs, buf, result, result2}; +} + +static void helper_free_ctx_data(struct helper_ctx_data ctx_data) { + ggml_opt_result_free(ctx_data.result); + ggml_opt_result_free(ctx_data.result2); + ggml_opt_free(ctx_data.opt_ctx); + ggml_backend_buffer_free(ctx_data.buf); + ggml_free(ctx_data.ctx_static); + ggml_free(ctx_data.ctx_compute); + for (ggml_opt_dataset_t dataset : ctx_data.datasets_supervised) { + ggml_opt_dataset_free(dataset); + } + ggml_opt_dataset_free(ctx_data.dataset_unsupervised); +} + +static void helper_after_test( + const char * func, const bool high_level, const std::string options, + const std::string subtest, const bool subtest_ok, int & ntest, int & npass) { + printf(" %s(high_level=%s%s, subtest=%s): ", + func, high_level ? "yes" : "no", options.c_str(), subtest.c_str()); + if (subtest_ok) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; +} + +static std::pair test_dataset(ggml_backend_sched_t backend_sched, ggml_backend_t backend, const bool shuffle) { + int ntest = 0; + int npass = 0; + + struct helper_ctx_data cd = helper_get_ctx_data(backend_sched, backend); + + for (int64_t ndata_shard = 1; ndata_shard <= ndata; ++ndata_shard) { + ggml_opt_dataset_t dataset = cd.datasets_supervised[ndata_shard-1]; + + if (shuffle) { + ggml_opt_dataset_shuffle(cd.opt_ctx, dataset, -1); + } + + for (int64_t ndata_batch = 1; ndata_batch <= ndata; ++ndata_batch) { + if (ndata_batch % ndata_shard != 0) { + continue; + } + bool subtest_ok = true; + + struct ggml_tensor * data_batch = cd.data_batch[ndata_batch-1]; + struct ggml_tensor * labels_batch = cd.labels_batch[ndata_batch-1]; + + std::vector data(ggml_nelements( data_batch)); + std::vector labels(ggml_nelements(labels_batch)); + + std::vector idata_shuffled; + const int64_t nbatches = ndata / ndata_batch; + for (int64_t ibatch = 0; ibatch < nbatches; ++ibatch) { + ggml_opt_dataset_get_batch(dataset, data_batch, labels_batch, ibatch); + + ggml_backend_tensor_get( data_batch, data.data(), 0, ggml_nbytes( data_batch)); + ggml_backend_tensor_get(labels_batch, labels.data(), 0, ggml_nbytes(labels_batch)); + + for (int64_t idata_batch = 0; idata_batch < ndata_batch; ++idata_batch) { + const int64_t idata = ibatch*ndata_batch + idata_batch; + const int64_t idata_found = data[idata_batch*ne_datapoint] / 16; + subtest_ok = subtest_ok && (shuffle || idata_found == idata); + idata_shuffled.push_back(idata_found); + + for (int64_t id = 0; id < ne_datapoint; ++id) { + if (data[ idata_batch*ne_datapoint + id] != 16*idata_found + id) { + subtest_ok = false; + } } - } - } - break; - case 4: - for (int i3 = 0; i3 < ne[3]; i3++) { - for (int i2 = 0; i2 < ne[2]; i2++) { - for (int i1 = 0; i1 < ne[1]; i1++) { - for (int i0 = 0; i0 < ne[0]; i0++) { - ((float *)result->data)[i3*ne[2]*ne[1]*ne[0] + i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin; + for (int64_t il = 0; il < ne_label; ++il) { + if (labels[idata_batch*ne_label + il] != 16*(16*idata_found + il)) { + subtest_ok = false; } } } } - break; - default: - assert(false); + + if (!shuffle || ndata % ndata_batch == 0) { + const int ndata_max = (ndata / ndata_batch) * ndata_batch; + + for (int64_t idata = 0; subtest_ok && idata < ndata_max; ++idata) { + int ninstances = 0; + for (int64_t id : idata_shuffled) { + ninstances += id == idata; + } + if (ninstances != 1) { + subtest_ok = false; + } + } + } + + printf(" %s(shuffle=%s, ndata_shard=%" PRId64 ", ndata_batch=%" PRId64 "): ", + __func__, shuffle ? "yes" : "no", ndata_shard, ndata_batch); + if (subtest_ok) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + } } + helper_free_ctx_data(cd); + + return std::make_pair(npass, ntest); +} + +static std::pair test_grad(ggml_backend_sched_t backend_sched, ggml_backend_t backend) { + int ntest = 0; + int npass = 0; + + struct helper_ctx_data cd = helper_get_ctx_data(backend_sched, backend, /*init_opt_ctx =*/ true, /*optimizer_defaults =*/ false, + /*nbatch_logical =*/ 999999, /*nbatch_physical =*/ 1); + + std::vector grad_history(ndata); + for (int64_t idata = 0; idata < ndata; ++idata) { + grad_history[idata] = NAN; + } + + for (int idata = 0; idata < ndata; ++idata) { + const float idataf = idata; + ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs)); + ggml_opt_forward_backward(cd.opt_ctx, cd.result); + ggml_backend_tensor_get(ggml_opt_grad_acc(cd.opt_ctx, cd.weights), grad_history.data() + idata, 0, sizeof(float)); + } + + { + bool subtest_ok = true; + for (int idata = 0; idata < ndata; ++idata) { + if (grad_history[idata] != idata + 1) { + subtest_ok = false; + } + } + printf(" %s(): ", __func__); + if (subtest_ok) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + } + + helper_free_ctx_data(cd); + + return std::make_pair(npass, ntest); +} + +static void helper_after_test_forward_backward( + const char * func, const bool high_level, const bool shuffle, + const std::string subtest, const bool subtest_ok, int & ntest, int & npass) { + std::string options = ", shuffle="; + options += shuffle ? "yes" : "no"; + helper_after_test(func, high_level, options, subtest, subtest_ok, ntest, npass); +} + +static std::pair test_forward_backward( + ggml_backend_sched_t backend_sched, ggml_backend_t backend, const bool high_level, const bool shuffle) { + int ntest = 0; + int npass = 0; + + struct helper_ctx_data cd = helper_get_ctx_data(backend_sched, backend, /*init_opt_ctx =*/ true, /*optimizer_defaults =*/ false); + struct ggml_tensor * loss = ggml_opt_loss(cd.opt_ctx); + + std::vector loss_history(ndata); + for (int64_t idata = 0; idata < ndata; ++idata) { + loss_history[idata] = NAN; + } + + { + int64_t ndata; + ggml_opt_result_ndata(cd.result, &ndata); + double loss; + double loss_unc; + ggml_opt_result_loss(cd.result, &loss, &loss_unc); + double accuracy; + double accuracy_unc; + ggml_opt_result_accuracy(cd.result, &accuracy, &accuracy_unc); + const bool subtest_ok = ndata == 0 && loss == 0.0 && std::isnan(loss_unc) && std::isnan(accuracy) && std::isnan(accuracy_unc); + helper_after_test_forward_backward(__func__, high_level, shuffle, "results_initial", subtest_ok, ntest, npass); + } + + if (high_level) { + ggml_opt_dataset_t dataset = cd.dataset_unsupervised; + if (shuffle) { + ggml_opt_dataset_shuffle(cd.opt_ctx, dataset, -1); + } + ggml_opt_epoch(cd.opt_ctx, dataset, nullptr, cd.result, 0, nullptr, nullptr); + } else { + for (int idata = 0; idata < ndata; ++idata) { + const float idataf = idata; + ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs)); + ggml_opt_forward(cd.opt_ctx, cd.result); + ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float)); + } + } + + { + float weights; + ggml_backend_tensor_get(cd.weights, &weights, 0, sizeof(float)); + const bool subtest_ok = weights == ndata/2; + helper_after_test_forward_backward(__func__, high_level, shuffle, "weights_after_forward", subtest_ok, ntest, npass); + } + { + int64_t ndata; + ggml_opt_result_ndata(cd.result, &ndata); + bool subtest_ok = ndata == 6; + + double loss; + double loss_unc; + ggml_opt_result_loss(cd.result, &loss, &loss_unc); + subtest_ok = subtest_ok && loss == 33.0 && almost_equal(loss_unc, sqrt(3.5), 1e-10); + + double accuracy; + double accuracy_unc; + ggml_opt_result_accuracy(cd.result, &accuracy, &accuracy_unc); + subtest_ok = subtest_ok && std::isnan(accuracy) && std::isnan(accuracy_unc); + + helper_after_test_forward_backward(__func__, high_level, shuffle, "results_after_forward", subtest_ok, ntest, npass); + } + + float w0; + ggml_backend_tensor_get(cd.weights, &w0, 0, sizeof(float)); + for (int i = 0; i < 10; ++i) { + ggml_opt_forward_backward(cd.opt_ctx, nullptr); + } + ggml_backend_tensor_set(cd.weights, &w0, 0, sizeof(float)); + + ggml_opt_reset(cd.opt_ctx, /*optimizer =*/ false); + ggml_opt_result_reset(cd.result); + + for (int64_t idata = 0; idata < ndata; ++idata) { + loss_history[idata] = NAN; + } + + if (high_level) { + ggml_opt_dataset_t dataset = cd.dataset_unsupervised; + if (shuffle) { + ggml_opt_dataset_shuffle(cd.opt_ctx, dataset, -1); + } + ggml_opt_epoch(cd.opt_ctx, dataset, cd.result, nullptr, ndata, nullptr, nullptr); + } else { + for (int idata = 0; idata < ndata; ++idata) { + const float idataf = idata; + ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs)); + ggml_opt_forward_backward(cd.opt_ctx, cd.result); + ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float)); + } + } + + { + float weights; + ggml_backend_tensor_get(cd.weights, &weights, 0, sizeof(float)); + const bool subtest_ok = weights == -ndata/2; + helper_after_test_forward_backward(__func__, high_level, shuffle, "weights_after_forward_backward", subtest_ok, ntest, npass); + } + { + int64_t ndata; + ggml_opt_result_ndata(cd.result, &ndata); + bool subtest_ok = ndata == 6; + + double loss; + double loss_unc; + ggml_opt_result_loss(cd.result, &loss, &loss_unc); + subtest_ok = subtest_ok && loss == 18.0 && (shuffle || loss_unc == 0.0); + + double accuracy; + double accuracy_unc; + ggml_opt_result_accuracy(cd.result, &accuracy, &accuracy_unc); + subtest_ok = subtest_ok && std::isnan(accuracy) && std::isnan(accuracy_unc); + + helper_after_test_forward_backward(__func__, high_level, shuffle, "result_after_forward_backward", subtest_ok, ntest, npass); + } + + helper_free_ctx_data(cd); + + return std::make_pair(npass, ntest); +} + +static std::pair test_epoch_vs_fit(ggml_backend_sched_t backend_sched, ggml_backend_t backend) { + int ntest = 0; + int npass = 0; + + float weights_epoch; + float weights_fit; + + { + struct helper_ctx_data cd = helper_get_ctx_data(backend_sched, backend, /*init_opt_ctx =*/ true); + ggml_opt_dataset_t dataset = cd.dataset_unsupervised; + + ggml_opt_dataset_shuffle(cd.opt_ctx, dataset, -1); + ggml_opt_epoch(cd.opt_ctx, dataset, cd.result, nullptr, ndata, nullptr, nullptr); + + ggml_backend_tensor_get(cd.weights, &weights_epoch, 0, ggml_nbytes(cd.weights)); + helper_free_ctx_data(cd); + } + { + struct helper_ctx_data cd = helper_get_ctx_data(backend_sched, backend, /*init_opt_ctx =*/ false); + ggml_opt_dataset_t dataset = cd.dataset_unsupervised; + + ggml_opt_fit(backend_sched, cd.ctx_compute, cd.inputs, cd.outputs, dataset, + GGML_OPT_LOSS_TYPE_SUM, ggml_opt_get_default_optimizer_params, 1, 1, 0.0f, true); + + ggml_backend_tensor_get(cd.weights, &weights_fit, 0, ggml_nbytes(cd.weights)); + helper_free_ctx_data(cd); + } + + const bool subtest_ok = weights_epoch == weights_fit; + + printf(" %s(): ", __func__); + if (subtest_ok) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + + return std::make_pair(npass, ntest); +} + +static void helper_after_test_idata_split( + const char * func, const bool high_level, const int epoch, + const std::string subtest, const bool subtest_ok, int & ntest, int & npass) { + std::string options = ", epoch="; + options += std::to_string(epoch); + helper_after_test(func, high_level, options, subtest, subtest_ok, ntest, npass); +} + +static std::pair test_idata_split(ggml_backend_sched_t backend_sched, ggml_backend_t backend, const bool high_level) { + int ntest = 0; + int npass = 0; + + struct helper_ctx_data cd = helper_get_ctx_data(backend_sched, backend, /*init_opt_ctx =*/ true, /*optimizer_defaults =*/ false); + struct ggml_tensor * loss = ggml_opt_loss(cd.opt_ctx); + const int idata_split = ndata * 2/3; + + std::vector loss_history(ndata); + for (int64_t idata = 0; idata < ndata; ++idata) { + loss_history[idata] = NAN; + } + + for (int epoch = 1; epoch <= 4; ++epoch) { + if (high_level) { + ggml_opt_epoch(cd.opt_ctx, cd.dataset_unsupervised, cd.result, cd.result2, idata_split, nullptr, nullptr); + } else { + int idata = 0; + for (; idata < idata_split; ++idata) { + const float idataf = idata; + ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs)); + ggml_opt_forward_backward(cd.opt_ctx, cd.result); + ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float)); + } + for (; idata < ndata; ++idata) { + const float idataf = idata; + ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs)); + ggml_opt_forward(cd.opt_ctx, cd.result2); + ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float)); + } + } + + { + float weights; + ggml_backend_tensor_get(cd.weights, &weights, 0, sizeof(float)); + const bool subtest_ok = weights == ndata/2 - epoch*idata_split; + helper_after_test_idata_split(__func__, high_level, epoch, "weights", subtest_ok, ntest, npass); + } + { + int64_t ndata_result; + ggml_opt_result_ndata(cd.result, &ndata_result); + bool subtest_ok = ndata_result == idata_split; + + double loss; + double loss_unc; + ggml_opt_result_loss(cd.result, &loss, &loss_unc); + subtest_ok = subtest_ok && loss == 28.0 - epoch*16.0 && loss_unc == 0.0; + + double accuracy; + double accuracy_unc; + ggml_opt_result_accuracy(cd.result, &accuracy, &accuracy_unc); + subtest_ok = subtest_ok && std::isnan(accuracy) && std::isnan(accuracy_unc); + + helper_after_test_idata_split(__func__, high_level, epoch, "results_backward", subtest_ok, ntest, npass); + } + { + int64_t ndata_result; + ggml_opt_result_ndata(cd.result2, &ndata_result); + bool subtest_ok = ndata_result == ndata - idata_split; + + double loss; + double loss_unc; + ggml_opt_result_loss(cd.result2, &loss, &loss_unc); + subtest_ok = subtest_ok && loss == 15.0 - epoch*8 && almost_equal(loss_unc, sqrt(0.5), 1e-10); + + double accuracy; + double accuracy_unc; + ggml_opt_result_accuracy(cd.result2, &accuracy, &accuracy_unc); + subtest_ok = subtest_ok && std::isnan(accuracy) && std::isnan(accuracy_unc); + + helper_after_test_idata_split(__func__, high_level, epoch, "results_forward", subtest_ok, ntest, npass); + } + + ggml_opt_result_reset(cd.result); + ggml_opt_result_reset(cd.result2); + } + + helper_free_ctx_data(cd); + + return std::make_pair(npass, ntest); +} + +static void helper_after_test_gradient_accumulation( + const char * func, const int nbatch_physical, const enum ggml_opt_loss_type loss_type, const int epoch, + const std::string subtest, const bool subtest_ok, int & ntest, int & npass) { + std::string options = ", nbatch_physical="; + options += std::to_string(nbatch_physical); + options += ", loss_type="; + options += loss_type == GGML_OPT_LOSS_TYPE_MEAN ? "mean" : "sum"; + options += ", epoch="; + options += std::to_string(epoch); + helper_after_test(func, false, options, subtest, subtest_ok, ntest, npass); +} + +static std::pair test_gradient_accumulation( + ggml_backend_sched_t backend_sched, ggml_backend_t backend, const int32_t nbatch_physical, const enum ggml_opt_loss_type loss_type) { + int ntest = 0; + int npass = 0; + + struct helper_ctx_data cd = helper_get_ctx_data( + backend_sched, backend, /*init_opt_ctx =*/ true, /*optimizer_defaults =*/ false, /*nbatch_logical =*/ 6, nbatch_physical, loss_type); + struct ggml_tensor * loss = ggml_opt_loss(cd.opt_ctx); + + std::vector grad_history(ndata); + for (int64_t idata = 0; idata < ndata; ++idata) { + grad_history[idata] = NAN; + } + + for (int epoch = 1; epoch <= 4; ++epoch) { + if (nbatch_physical == 1) { + for (int idata = 0; idata < ndata; ++idata) { + const float idataf = idata; + ggml_backend_tensor_set(cd.inputs, &idataf, 0, 1*sizeof(float)); + ggml_opt_forward_backward(cd.opt_ctx, cd.result); + ggml_backend_tensor_get(ggml_opt_grad_acc(cd.opt_ctx, cd.weights), grad_history.data() + idata, 0, 1*sizeof(float)); + } + } else if (nbatch_physical == 2) { + for (int idata = 0; idata < ndata; idata += 2) { + const float idataf[2] = {float(idata + 0), float(idata + 1)}; + ggml_backend_tensor_set(cd.inputs, idataf, 0, 2*sizeof(float)); + ggml_opt_forward_backward(cd.opt_ctx, cd.result); + + grad_history[idata + 0] = 0.0f; + ggml_backend_tensor_get(ggml_opt_grad_acc(cd.opt_ctx, cd.weights), grad_history.data() + idata + 1, 0, 1*sizeof(float)); + } + } else { + GGML_ASSERT(false); + } + + { + GGML_ASSERT(ndata == 6); + constexpr double atol = 1e-6; + bool subtest_ok = true; + if (loss_type == GGML_OPT_LOSS_TYPE_SUM) { + if (nbatch_physical == 1) { + subtest_ok = subtest_ok && almost_equal(grad_history[0], 1.0, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[2], 3.0, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[4], 5.0, atol); + } else { + subtest_ok = subtest_ok && almost_equal(grad_history[0], 0.0, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[2], 0.0, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[4], 0.0, atol); + } + subtest_ok = subtest_ok && almost_equal(grad_history[1], 2.0, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[3], 4.0, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[5], 0.0, atol); + } else if (loss_type == GGML_OPT_LOSS_TYPE_MEAN) { + if (nbatch_physical == 1) { + subtest_ok = subtest_ok && almost_equal(grad_history[0], 1.0/ndata, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[2], 3.0/ndata, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[4], 5.0/ndata, atol); + } else { + subtest_ok = subtest_ok && almost_equal(grad_history[0], 0.0/ndata, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[2], 0.0/ndata, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[4], 0.0/ndata, atol); + } + subtest_ok = subtest_ok && almost_equal(grad_history[1], 2.0/ndata, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[3], 4.0/ndata, atol); + subtest_ok = subtest_ok && almost_equal(grad_history[5], 0.0/ndata, atol); + } else { + GGML_ASSERT(false); + } + helper_after_test_gradient_accumulation(__func__, nbatch_physical, loss_type, epoch, "grads", subtest_ok, ntest, npass); + } + { + float weights; + ggml_backend_tensor_get(cd.weights, &weights, 0, sizeof(float)); + const bool subtest_ok = weights == (ndata/2) - epoch; + helper_after_test_gradient_accumulation(__func__, nbatch_physical, loss_type, epoch, "weights", subtest_ok, ntest, npass); + } + { + int64_t ndata_result; + ggml_opt_result_ndata(cd.result, &ndata_result); + bool subtest_ok = ndata_result == ndata/nbatch_physical; + + double loss; + ggml_opt_result_loss(cd.result, &loss, /*loss_unc =*/ nullptr); + if (loss_type == GGML_OPT_LOSS_TYPE_SUM) { + subtest_ok = subtest_ok && loss == (39.0 - epoch*6.0); + } else if (loss_type == GGML_OPT_LOSS_TYPE_MEAN) { + subtest_ok = subtest_ok && almost_equal(loss, (39.0 - epoch*6.0) / ndata, 1e-6); + } else { + GGML_ASSERT(false); + } + + double accuracy; + double accuracy_unc; + ggml_opt_result_accuracy(cd.result, &accuracy, &accuracy_unc); + subtest_ok = subtest_ok && std::isnan(accuracy) && std::isnan(accuracy_unc); + + helper_after_test_gradient_accumulation(__func__, nbatch_physical, loss_type, epoch, "results", subtest_ok, ntest, npass); + } + + ggml_opt_result_reset(cd.result); + } + + helper_free_ctx_data(cd); + + return std::make_pair(npass, ntest); +} + +static ggml_opt_optimizer_params helper_get_regression_opt_pars(void * userdata) { + ggml_opt_optimizer_params result = ggml_opt_get_default_optimizer_params(userdata); + result.adamw.alpha = 0.1f; return result; } -int main(void) { - struct ggml_init_params params = { - /* .mem_size = */ 1024*1024*1024, - /* .mem_buffer = */ NULL, - /* .no_alloc = */ false, - }; +static std::pair test_regression(ggml_backend_sched_t backend_sched, ggml_backend_t backend) { + int ntest = 0; + int npass = 0; - struct ggml_context * ctx = ggml_init(params); + // Test for simple regression with f(x) = a*x + b - int64_t ne1[4] = {4, 128, 1, 1}; - int64_t ne2[4] = {4, 256, 1, 1}; - int64_t ne3[4] = {128, 256, 1, 1}; + constexpr int64_t ndata_regression = 201; + constexpr float a_true = 1.2f; + constexpr float b_true = 3.4f; - struct ggml_tensor * a = get_random_tensor(ctx, 2, ne1, -1, +1); - struct ggml_tensor * b = get_random_tensor(ctx, 2, ne2, -1, +1); - ggml_set_param(ctx, a); - ggml_set_param(ctx, b); + std::mt19937 gen(12345); + std::normal_distribution nd{0.0f, 0.1f}; - struct ggml_tensor * c = get_random_tensor(ctx, 2, ne3, -1, +1); + ggml_opt_dataset_t dataset = ggml_opt_dataset_init(1, 1, ndata_regression, ndata_regression); - struct ggml_tensor * ab = ggml_mul_mat(ctx, a, b); - struct ggml_tensor * d = ggml_sub(ctx, c, ab); - struct ggml_tensor * e = ggml_sum(ctx, ggml_sqr(ctx, d)); + float * data = ggml_get_data_f32(ggml_opt_dataset_data( dataset)); + float * labels = ggml_get_data_f32(ggml_opt_dataset_labels(dataset)); - struct ggml_cgraph * ge = ggml_new_graph_custom(ctx, GGML_DEFAULT_GRAPH_SIZE, true); - ggml_build_forward_expand(ge, e); - ggml_graph_reset(ge); + constexpr float x_min = -100.0f; + constexpr float x_max = 100.0f; - ggml_graph_compute_with_ctx(ctx, ge, /*n_threads*/ 1); + for (int64_t idata = 0; idata < ndata_regression; ++idata) { + const float x = x_min + (x_max - x_min) * idata/(ndata_regression-1); + const float y = a_true*x + b_true + nd(gen); - const float fe = ggml_get_f32_1d(e, 0); - printf("%s: e = %.4f\n", __func__, fe); + data[idata] = x; + labels[idata] = y; + } - struct ggml_opt_params opt_params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM); + struct ggml_context * ctx_static; + struct ggml_context * ctx_compute; + { + struct ggml_init_params params = { + /*.mem_size =*/ 3*ggml_tensor_overhead(), + /*.mem_buffer =*/ nullptr, + /*.no_alloc =*/ true, + }; + ctx_static = ggml_init(params); + } + { + struct ggml_init_params params = { + /*.mem_size =*/ GGML_DEFAULT_GRAPH_SIZE*ggml_tensor_overhead() + 3*ggml_graph_overhead(), + /*.mem_buffer =*/ nullptr, + /*.no_alloc =*/ true, + }; + ctx_compute = ggml_init(params); + } - ggml_opt(ctx, opt_params, e); + // The first dimension is the dimension of the datapoints, the second dimension is the number of datapoints. + struct ggml_tensor * x = ggml_new_tensor_2d(ctx_static, GGML_TYPE_F32, 1, ndata_regression); + ggml_set_name(x, "x"); - ggml_graph_reset(ge); + struct ggml_tensor * a = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, 1); + ggml_set_name(a, "a"); + ggml_set_param(ctx_static, a); - ggml_graph_compute_with_ctx(ctx, ge, /*n_threads*/ 1); + struct ggml_tensor * b = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, 1); + ggml_set_name(b, "b"); + ggml_set_param(ctx_static, b); - const float fe_opt = ggml_get_f32_1d(e, 0); - printf("%s: original e = %.4f\n", __func__, fe); - printf("%s: optimized e = %.4f\n", __func__, fe_opt); + struct ggml_tensor * f = ggml_add(ctx_compute, ggml_mul(ctx_compute, x, a), b); + ggml_set_name(f, "f"); + ggml_set_param(ctx_static, f); - const bool success = (fe_opt <= fe); - assert(success); + ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx_static, backend); + const float a0 = 1.0f; + const float b0 = 3.0f; + ggml_backend_tensor_set(a, &a0, 0, sizeof(float)); + ggml_backend_tensor_set(b, &b0, 0, sizeof(float)); - ggml_free(ctx); - return success ? 0 : -1; + ggml_opt_fit(backend_sched, ctx_compute, x, f, dataset, GGML_OPT_LOSS_TYPE_MEAN_SQUARED_ERROR, + helper_get_regression_opt_pars, 100, ndata_regression, 0.0f, true); + + { + float a_fit; + ggml_backend_tensor_get(a, &a_fit, 0, sizeof(float)); + float b_fit; + ggml_backend_tensor_get(b, &b_fit, 0, sizeof(float)); + const bool subtest_ok = almost_equal(a_fit, a_true, 1e-2) && almost_equal(b_fit, b_true, 1e-2); + printf(" %s(subtest=weights): ", __func__); + if (subtest_ok) { + printf("\033[1;32mOK\033[0m\n"); + npass++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + ntest++; + } + + ggml_backend_buffer_free(buf); + ggml_free(ctx_static); + ggml_opt_dataset_free(dataset); + + return std::make_pair(npass, ntest); } -// int64_t ne1[4] = {4, 128, 1, 1}; -// int64_t ne2[4] = {4, 256, 1, 1};; -// int64_t ne3[4] = {128, 256, 1, 1}; -// main: original e = 25890.9375 -// main: optimized e = 10094.7031 -// int64_t ne1[4] = {8, 128, 1, 1}; -// int64_t ne2[4] = {8, 256, 1, 1};; -// int64_t ne3[4] = {128, 256, 1, 1}; -// main: original e = 39429.5078 -// main: optimized e = 9275.8936 +static std::pair test_backend(ggml_backend_sched_t backend_sched, ggml_backend_t backend) { + int npass = 0; + int ntest = 0; -// int64_t ne1[4] = {16, 128, 1, 1}; -// int64_t ne2[4] = {16, 256, 1, 1};; -// int64_t ne3[4] = {128, 256, 1, 1}; -// main: original e = 68371.1328 -// main: optimized e = 7854.4502 + for (bool shuffle : {false, true}) { + std::pair partial = test_dataset(backend_sched, backend, shuffle); + npass += partial.first; + ntest += partial.second; + } + { + std::pair partial = test_grad(backend_sched, backend); + npass += partial.first; + ntest += partial.second; + } + for (bool high_level : {false, true}){ + for (bool shuffle : {false, true}) { + if (!high_level && shuffle) { + continue; + } + std::pair partial = test_forward_backward(backend_sched, backend, high_level, shuffle); + npass += partial.first; + ntest += partial.second; + } + } + { + std::pair partial = test_epoch_vs_fit(backend_sched, backend); + npass += partial.first; + ntest += partial.second; + } + for (bool high_level : {false, true}){ + std::pair partial = test_idata_split(backend_sched, backend, high_level); + npass += partial.first; + ntest += partial.second; + } + for (int32_t nbatch_physical : {2, 1}) { + for (enum ggml_opt_loss_type loss_type : {GGML_OPT_LOSS_TYPE_SUM, GGML_OPT_LOSS_TYPE_MEAN}) { + std::pair partial = test_gradient_accumulation(backend_sched, backend, nbatch_physical, loss_type); + npass += partial.first; + ntest += partial.second; + } + } + { + std::pair partial = test_regression(backend_sched, backend); + npass += partial.first; + ntest += partial.second; + } -// int64_t ne1[4] = {32, 128, 1, 1}; -// int64_t ne2[4] = {32, 256, 1, 1};; -// int64_t ne3[4] = {128, 256, 1, 1}; -// main: original e = 126061.1953 -// main: optimized e = 5451.0166 + return std::make_pair(npass, ntest); +} -// int64_t ne1[4] = {4, 1024, 1, 1}; -// int64_t ne2[4] = {4, 2048, 1, 1};; -// int64_t ne3[4] = {1024, 2048, 1, 1}; -// main: original e = 1620817.8750 -// main: optimized e = 698387.6875 +int main(void) { + const size_t dev_count = ggml_backend_dev_count(); + printf("Testing %zu devices\n\n", dev_count); + size_t n_ok = 0; -// another run on M1 -// int64_t ne1[4] = {4, 1024, 1, 1}; -// int64_t ne2[4] = {4, 2048, 1, 1};; -// int64_t ne3[4] = {1024, 2048, 1, 1}; -// main: original e = 1629595.6250 -// main: optimized e = 698169.1250 + std::vector devs; + std::vector backends; -// int64_t ne1[4] = {32, 1024, 1, 1}; -// int64_t ne2[4] = {32, 2048, 1, 1};; -// int64_t ne3[4] = {1024, 2048, 1, 1}; -// main: original e = 8146770.5000 -// main: optimized e = 651119.1250 + for (size_t i = 0; i < dev_count; ++i) { + devs.push_back(ggml_backend_dev_get(i)); + + ggml_backend_t backend = ggml_backend_dev_init(devs[i], NULL); + GGML_ASSERT(backend != NULL); + + if (ggml_backend_is_cpu(backend)) { + ggml_backend_cpu_set_n_threads(backend, std::thread::hardware_concurrency() / 2); + } + + backends.push_back(backend); + } + + for (size_t i = 0; i < dev_count; ++i) { + // Put the backend to be tested in front so that it's prioritized: + std::vector backends_modded = {backends[i]}; + backends_modded.insert(backends_modded.end(), backends.begin(), backends.end()); + + ggml_backend_sched_t backend_sched = ggml_backend_sched_new( + backends_modded.data(), nullptr, backends_modded.size(), GGML_DEFAULT_GRAPH_SIZE, false); + + printf("Backend %zu/%zu: %s\n", i + 1, dev_count, ggml_backend_dev_name(devs[i])); + printf(" Device description: %s\n", ggml_backend_dev_description(devs[i])); + size_t free, total; // NOLINT + ggml_backend_dev_memory(devs[i], &free, &total); + printf(" Device memory: %zu MB (%zu MB free)\n", total / 1024 / 1024, free / 1024 / 1024); + printf("\n"); + + std::pair result = test_backend(backend_sched, backends[i]); + + printf(" %d/%d tests passed\n", result.first, result.second); + printf(" Backend %s: ", ggml_backend_name(backends[i])); + if (result.first == result.second) { + printf("\033[1;32mOK\033[0m\n"); + n_ok++; + } else { + printf("\033[1;31mFAIL\033[0m\n"); + } + + printf("\n"); + + ggml_backend_sched_free(backend_sched); + } + + for (ggml_backend_t backend : backends) { + ggml_backend_free(backend); + } + + printf("%zu/%zu backends passed\n", n_ok, dev_count); + if (n_ok != dev_count) { + printf("\033[1;31mFAIL\033[0m\n"); + return 1; + } + printf("\033[1;32mOK\033[0m\n"); + return 0; +} diff --git a/tests/test-quantize-fns.cpp b/tests/test-quantize-fns.cpp index d50417ba0..c77c8ed13 100644 --- a/tests/test-quantize-fns.cpp +++ b/tests/test-quantize-fns.cpp @@ -1,6 +1,7 @@ // Unit tests for quantization specific functions - quantize, dequantize and dot product #include "ggml.h" +#include "ggml-cpu.h" #undef NDEBUG #include @@ -44,22 +45,23 @@ static float array_rmse(const float * a1, const float * a2, size_t n) { } // Total quantization error on test data -static float total_quantization_error(const ggml_type_traits * qfns, size_t test_size, const float * test_data) { +static float total_quantization_error(const ggml_type_traits * qfns, const ggml_type_traits_cpu * qfns_cpu, size_t test_size, const float * test_data) { std::vector tmp_q(2*test_size); std::vector tmp_out(test_size); - qfns->from_float(test_data, tmp_q.data(), test_size); + qfns_cpu->from_float(test_data, tmp_q.data(), test_size); qfns->to_float(tmp_q.data(), tmp_out.data(), test_size); return array_rmse(test_data, tmp_out.data(), test_size); } // Total quantization error on test data -static float reference_quantization_error(const ggml_type_traits * qfns, size_t test_size, const float * test_data) { +static float reference_quantization_error(const ggml_type_traits * qfns, const ggml_type_traits_cpu * qfns_cpu, size_t test_size, const float * test_data) { std::vector tmp_q(2*test_size); std::vector tmp_out(test_size); std::vector tmp_out_ref(test_size); - qfns->from_float(test_data, tmp_q.data(), test_size); + // FIXME: why is done twice? + qfns_cpu->from_float(test_data, tmp_q.data(), test_size); qfns->to_float(tmp_q.data(), tmp_out.data(), test_size); qfns->from_float_ref(test_data, tmp_q.data(), test_size); @@ -77,19 +79,19 @@ static float dot_product(const float * a1, const float * a2, size_t test_size) { } // Total dot product error -static float dot_product_error( - const ggml_type_traits * qfns, size_t test_size, const float * test_data1, const float *test_data2 -) { +static float dot_product_error(const ggml_type_traits * qfns, const ggml_type_traits_cpu * qfns_cpu, size_t test_size, const float * test_data1, const float * test_data2) { + GGML_UNUSED(qfns); + std::vector tmp_q1(2*test_size); std::vector tmp_q2(2*test_size); - const auto * vdot = ggml_get_type_traits(qfns->vec_dot_type); + const auto * vdot = ggml_get_type_traits_cpu(qfns_cpu->vec_dot_type); - qfns->from_float(test_data1, tmp_q1.data(), test_size); + qfns_cpu->from_float(test_data1, tmp_q1.data(), test_size); vdot->from_float(test_data2, tmp_q2.data(), test_size); float result = INFINITY; - qfns->vec_dot(test_size, &result, 0, tmp_q1.data(), 0, tmp_q2.data(), 0, 1); + qfns_cpu->vec_dot(test_size, &result, 0, tmp_q1.data(), 0, tmp_q2.data(), 0, 1); const float dot_ref = dot_product(test_data1, test_data2, test_size); @@ -132,6 +134,7 @@ int main(int argc, char * argv[]) { for (int i = 0; i < GGML_TYPE_COUNT; i++) { ggml_type type = (ggml_type) i; const auto * qfns = ggml_get_type_traits(type); + const auto * qfns_cpu = ggml_get_type_traits_cpu(type); // deprecated - skip if (qfns->blck_size == 0) { @@ -143,8 +146,8 @@ int main(int argc, char * argv[]) { printf("Testing %s\n", ggml_type_name((ggml_type) i)); ggml_quantize_init(ei); - if (qfns->from_float && qfns->to_float) { - const float total_error = total_quantization_error(qfns, test_size, test_data.data()); + if (qfns_cpu->from_float && qfns->to_float) { + const float total_error = total_quantization_error(qfns, qfns_cpu, test_size, test_data.data()); const float max_quantization_error = type == GGML_TYPE_TQ1_0 ? MAX_QUANTIZATION_TOTAL_ERROR_TERNARY : type == GGML_TYPE_TQ2_0 ? MAX_QUANTIZATION_TOTAL_ERROR_TERNARY : @@ -159,14 +162,14 @@ int main(int argc, char * argv[]) { printf("%5s absolute quantization error: %s (%f)\n", ggml_type_name(type), RESULT_STR[failed], total_error); } - const float reference_error = reference_quantization_error(qfns, test_size, test_data.data()); + const float reference_error = reference_quantization_error(qfns, qfns_cpu, test_size, test_data.data()); failed = !(reference_error < MAX_QUANTIZATION_REFERENCE_ERROR); num_failed += failed; if (failed || verbose) { printf("%5s reference implementation error: %s (%f)\n", ggml_type_name(type), RESULT_STR[failed], reference_error); } - const float vec_dot_error = dot_product_error(qfns, test_size, test_data.data(), test_data2.data()); + const float vec_dot_error = dot_product_error(qfns, qfns_cpu, test_size, test_data.data(), test_data2.data()); const float max_allowed_error = type == GGML_TYPE_Q2_K || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ2_XXS || type == GGML_TYPE_IQ3_XXS || type == GGML_TYPE_IQ3_S || type == GGML_TYPE_IQ2_S ? MAX_DOT_PRODUCT_ERROR_LOWBIT diff --git a/tests/test-quantize-perf.cpp b/tests/test-quantize-perf.cpp index bdbdd90a8..288288493 100644 --- a/tests/test-quantize-perf.cpp +++ b/tests/test-quantize-perf.cpp @@ -1,12 +1,12 @@ // Benchmark quantization specific functions on synthetic data #include "ggml.h" +#include "ggml-cpu.h" #undef NDEBUG #include #include #include -#include #include #include #include @@ -122,9 +122,10 @@ static void usage(char * argv[]) { printf(" --type TYPE set test type as"); for (int i = 0; i < GGML_TYPE_COUNT; i++) { ggml_type type = (ggml_type) i; - const auto * qfns = ggml_get_type_traits(type); + const auto * qfns = ggml_get_type_traits(type); + const auto * qfns_cpu = ggml_get_type_traits_cpu(type); if (ggml_type_name(type) != NULL) { - if (qfns->from_float && qfns->to_float) { + if (qfns_cpu->from_float && qfns->to_float) { printf(" %s", ggml_type_name(type)); } } @@ -271,11 +272,12 @@ int main(int argc, char * argv[]) { for (int i = 0; i < GGML_TYPE_COUNT; i++) { ggml_type type = (ggml_type) i; const auto * qfns = ggml_get_type_traits(type); + const auto * qfns_cpu = ggml_get_type_traits_cpu(type); if (!params.include_types.empty() && ggml_type_name(type) && std::find(params.include_types.begin(), params.include_types.end(), ggml_type_name(type)) == params.include_types.end()) { continue; } - if (qfns->from_float && qfns->to_float) { + if (qfns_cpu->from_float && qfns->to_float) { printf("%s\n", ggml_type_name(type)); ggml_quantize_init(type); @@ -299,7 +301,7 @@ int main(int argc, char * argv[]) { for (size_t size : params.test_sizes) { printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024)); auto quantize_fn = [&](void) -> float { - qfns->from_float(test_data1, test_q1, size); + qfns_cpu->from_float(test_data1, test_q1, size); return test_q1[0]; }; size_t quantized_size = ggml_row_size(type, size); @@ -310,7 +312,7 @@ int main(int argc, char * argv[]) { if (params.op_dequantize_row_q) { printf(" dequantize_row_q\n"); - qfns->from_float(test_data1, test_q1, largest); + qfns_cpu->from_float(test_data1, test_q1, largest); for (size_t size : params.test_sizes) { printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024)); auto quantize_fn = [&](void) -> float { @@ -328,7 +330,7 @@ int main(int argc, char * argv[]) { for (size_t size : params.test_sizes) { printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024)); auto quantize_fn = [&](void) -> float { - const auto * vdot = ggml_get_type_traits(qfns->vec_dot_type); + const auto * vdot = ggml_get_type_traits_cpu(qfns_cpu->vec_dot_type); vdot->from_float(test_data1, test_q1, size); return test_q1[0]; }; @@ -340,13 +342,13 @@ int main(int argc, char * argv[]) { if (params.op_vec_dot_q) { printf(" vec_dot_q\n"); - qfns->from_float(test_data1, test_q1, largest); - qfns->from_float(test_data2, test_q2, largest); + qfns_cpu->from_float(test_data1, test_q1, largest); + qfns_cpu->from_float(test_data2, test_q2, largest); for (size_t size : params.test_sizes) { printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024)); auto quantize_fn = [&](void) -> float { float result; - qfns->vec_dot(size, &result, 0, test_q1, 0, test_q2, 0, 1); + qfns_cpu->vec_dot(size, &result, 0, test_q1, 0, test_q2, 0, 1); return result; }; size_t quantized_size = ggml_row_size(type, size); diff --git a/tests/test-rope.cpp b/tests/test-rope.cpp index 246bb227d..4656b30f0 100644 --- a/tests/test-rope.cpp +++ b/tests/test-rope.cpp @@ -1,4 +1,5 @@ #include "ggml.h" +#include "ggml-cpu.h" #include #include diff --git a/tests/test-sampling.cpp b/tests/test-sampling.cpp index be370044d..e5c9e75e4 100644 --- a/tests/test-sampling.cpp +++ b/tests/test-sampling.cpp @@ -284,7 +284,7 @@ static void test_perf() { data.reserve(n_vocab); for (int i = 0; i < n_vocab; i++) { - const float logit = 2.0f*((float)(rand())/RAND_MAX - 0.5f); + const float logit = 2.0f*((double)(rand())/RAND_MAX - 0.5); data.emplace_back(llama_token_data{i, logit, 0.0f}); }